The term 'globalization' is nothing new. According to Wikipedia, "globalization describes an ongoing process by which regional economies, societies, and cultures have become integrated through a globe-spanning network of communication and trade. The term is sometimes used to refer specifically to economic globalization: the integration of national economies into the international economy through trade, foreign direct investment, capital flows, migration, and the spread of technology." Globalization certainly has its benefits, but it has its victims too, and the results can be deadly. As the global economy knits countries closer together, it becomes easier for diseases to spread through states, over borders and across oceans.
Globalization has impact on the medicines we take (many of them are manufactured outside of a specific country) and the conduct of the clinical trials (the clinical trial data are cross borders from multiple nations). Last year, when I attended the FDA/Industry Statistical Workshop, the theme of the workshop is 'global harmonization' - another way to say 'globalization'.
Recently I attended a conference in Duke, the focus again was 'globalization' with emphasis on Asia. One session discussed the tourism medicine and 'patients without borders'. It will be trend that with globalization, patients can cross border to choose the health care that will better service them (with cost and quality of care in mind). One day, we could share the health care resources much like the sharing of the technologies.
I also understand that the sharing of the health care resource will not be an easy task. Several days ago, one of my American colleagues asked me if it is possible for foreigners to have renal (kidney) transplantation in China (for obvious reason of the shortage in kidney donors). When I posted the question to my alumni email list, I immediately got some response such as the one below "I believe that all Chinese with renal failure have the absolute right for having kidney transplant in China. As a Chinese, I strongly against any give away of basic human right..." I sort of agree with this. The world is not ready to share the health care resource (at least the organs for transplant).
Within the country, there may or may not be any policy or procedure to ensure the fairness between the rich and the poor, not to mention the fairness across countries.
China actually has its policies on organ transplantation including renal
(kidney) transplantation.The policies basically prohibit the tourism medical treatment in China for organ transplantation.
卫生部办公厅关于境外人员申请人体器官移植有关问题的通知 (General Office of the Ministry of Health personnel for human organ transplants outside the Issues)
人体器官移植条例 (Human Organ Transplant Ordinance)
Tuesday, March 30, 2010
Thursday, March 18, 2010
Dealer's choice
According to dictionary.com, the term "dealer's choice" is defined as "
a card game, as poker, in which the dealer decides what particular game is to be played, often depending on the number of players, and designates any special variations or unusual rules, including setting the stakes."
In contrary to the randomized clinical trial, an approach called "dealer's choice" has now been used in clinical trials or in clinical setting (more often in oncology clinical trials) where the treating physician decides which treatment regimen will be given to the patients/subjects. Typically, this happens when there are several treatment regimens, but there is no sufficient information to decide one is better than others.
While this is ok for treating the patients in clinical setting, it is not ideal when this approach is used in clinical trial. # of subjects who receive on specific treatment regimen will depend on the dealer's choice and may be imbalanced in terms of the sample size and the subject characteristics across different regimens. At the end of the study, if there is any difference among treatment regimens, there may be due to the 'unknown' confounding factor rather than the treatment itself.
Here are some of the examples using "dealer's approach":
An intergroup rectal cancer trial supported by NCI employed the dealer's choice approach in which treatment would be chosen by the physician and patient together.
Interventional Management of Stroke (IMS) III Trial (IMSIII) did not explicitly use the term "dealer's approach". However, in the investigational treatment arm, after initial tPA treatment fails, the doctor will choose--based on the location and extent of the blood clot--one of 4 possible IA treatments given directly in the brain artery that will be most effective in reopening the blocked artery. There is no randomization. It is dealer's choice in choosing one of the four treatment options.
There may be legitimate reason in using "dealer's choice" approach, it certainly compromise the quality of the trial and make the interpretation of the results more difficult.
Saturday, March 13, 2010
Ghost writer, ghost surgeon,...
"Ghost writing" practice has been criticized recently due to the reveal of details targeting several big pharmaceutical companies. With medical ghostwriting, pharmaceutical companies pay both professional writers to produce papers and then pay other scientists or physicans to attach their names to these papers before they are published in a medical or scientific journals."Ghost writer" may never be acknowledged in the publication. Wyeth was actually sued over the ghost writing practice (see reports from NPR and New York Times). Other companies had similar practices: for example, Merck's case and GSK. New York Times recently reported that Drug Maker Wrote Entire Book Under 2 Doctors’ Names.
“To ghostwrite an entire textbook is a new level of chutzpah,” said Dr. David A. Kessler, former commissioner of the Food and Drug Administration, after reviewing the documents. “I’ve never heard of that before. It takes your breath away.”
Actually, "ghost writing" practice has been out there for many years and it is not just in pharmaceutical industry. Celebrities, executives, and political leaders often hire ghostwriters to draft or edit autobiographies, magazine articles, or other written material.No wonder they can publish nice books and articles.
Perhaps, there is nothing wrong in terms of the business model. Medical writer and freelance writer get pays for their services and whether or not being acknowledged is not important to them.
Recently, I heard a new term "ghost surgery": a practice of performing surgery on another phycian's patient by arrangement with the physician but unknown to the patient.A famous surgeon could make an arrangement to have a substitute (perhaps a resident) to perform the surgery without patient's knowledge (patient could be unconscious). What can you do about this practice? can you sue? Not necessarily.
Ghost writer and ghost surgeon are certainly not all 'ghost' out there. There are far more 'ghost' in our daily life and in business practice.
“To ghostwrite an entire textbook is a new level of chutzpah,” said Dr. David A. Kessler, former commissioner of the Food and Drug Administration, after reviewing the documents. “I’ve never heard of that before. It takes your breath away.”
Actually, "ghost writing" practice has been out there for many years and it is not just in pharmaceutical industry. Celebrities, executives, and political leaders often hire ghostwriters to draft or edit autobiographies, magazine articles, or other written material.No wonder they can publish nice books and articles.
Perhaps, there is nothing wrong in terms of the business model. Medical writer and freelance writer get pays for their services and whether or not being acknowledged is not important to them.
Recently, I heard a new term "ghost surgery": a practice of performing surgery on another phycian's patient by arrangement with the physician but unknown to the patient.A famous surgeon could make an arrangement to have a substitute (perhaps a resident) to perform the surgery without patient's knowledge (patient could be unconscious). What can you do about this practice? can you sue? Not necessarily.
Ghost writer and ghost surgeon are certainly not all 'ghost' out there. There are far more 'ghost' in our daily life and in business practice.
Saturday, March 06, 2010
Non-inferiority clinical trials - now comes FDA's draft guidance
In contrary to adaptive design clinical trials, non-inferiority (NI) design has been there for many years (15-20 years) and many studies have been conducted using non-inferiority design. Many products have been approved by regulatory agencies with based on the pivotal studies in non-inferiority design.
Even though there have been tons of presentations, workshops, and text books about the non-inferiority clinical trials, there is no formal guidance from FDA regarding the inferiority clinical trials (until this week).
This week, FDA issues its draft guidance for industry: non-inferiority clinical trials. The guidance gives advice on when NI studies can be interpretable, on how to choose the NI margin, and how to analyze the results.
NI is considered a special case of equivalence trial with NI comparing to only lower or upper bound of the confidence interval (but not both). While there are many other issues to be considered when design a NI trial, the selection of the NI margin continue to be a key issue in NI design. For a confirmatory trial for licensure, NI margin must be discussed with regulatory agencies and must be agreed by them. Generally, the wider the margin, the easier the NI trial to be successful. the smaller the margin, the larger the sample size.
It is good to see that the guidance includes a question/answer on "in the situation where a placebo-controlled trial would be considered unethical, but a non-inferiority study cannot be performed, what are the options?" Unfortunately, the answer to this question is somehow not clear.
Further readings:
Even though there have been tons of presentations, workshops, and text books about the non-inferiority clinical trials, there is no formal guidance from FDA regarding the inferiority clinical trials (until this week).
This week, FDA issues its draft guidance for industry: non-inferiority clinical trials. The guidance gives advice on when NI studies can be interpretable, on how to choose the NI margin, and how to analyze the results.
NI is considered a special case of equivalence trial with NI comparing to only lower or upper bound of the confidence interval (but not both). While there are many other issues to be considered when design a NI trial, the selection of the NI margin continue to be a key issue in NI design. For a confirmatory trial for licensure, NI margin must be discussed with regulatory agencies and must be agreed by them. Generally, the wider the margin, the easier the NI trial to be successful. the smaller the margin, the larger the sample size.
It is good to see that the guidance includes a question/answer on "in the situation where a placebo-controlled trial would be considered unethical, but a non-inferiority study cannot be performed, what are the options?" Unfortunately, the answer to this question is somehow not clear.
Further readings:
- Slide presentation by Bob Temple "FDA experience and perspective on non-inferiority trials"
- EMEA "Guideline on the choice of the non-inferiority margin"
- EMEA "Points to consider on switching between superiority and non-inferiority"
- Slide presentation by Bob Temple "Active control non-inferiority studies theory, assay sensitivity, and choice of margin"
- Mary Foulkes "non-inferiority specification of delta"
- Lin et al "Statistical issues in specification of delta"
Thursday, February 25, 2010
Adaptive design clinical trials - now comes the FDA's draft guidance
For last several years, 'adaptive design' and 'adaptive clinical trials' are hot topics in biostatistics and clinical trial fields. The industry seems to think that 'adaptive design' is the solution for lengthy and costly drug development program. There are workshops, symposiums, many publications and books about the adaptive design in last several years. However, in reality, many examples, case studies are post hoc and based on the simulation from the historical non-adaptive clinical trial data ("had we implemented the adaptive design, we would have saved xxx time/cost/sample size..."). Recently we do see some implementation of adaptive designs in real clinical trials, but
these are mainly in early stage studies.
This month, FDA issues its guidance for industry on "adaptive design clinical trials for drugs and biologicals". This lengthy guidance contains plenty of references (perhaps with most references for a FDA guidance). Overall tone of this guidance seems to advise the sponsors be cautious in adopting the adaptive design especially those not well established designs. If you don't want to read the entire guidance, slides by two of the FDA working group members summarize the key points in the guidance.
Ahead of FDA, EU has already issued its guidance on adaptive design (or flexible design) . Their opinions have been laid out in EMEA’s REFLECTION PAPER ON METHODOLOGICAL ISSUES IN CONFIRMATORY CLINICAL TRIALS WITH FLEXIBLE DESIGN AND ANALYSIS PLAN issued in 2006.
There was also a well publicized workshop on adaptive design in 2006 in US. However, we have to wait for four years to see FDA's draft guidance. For documentation, all materials from 2006 workshop are now on the web.
Some of the topics are on technical sides, but there are also talks about the perspectives from FDA regarding the adaptive designs.
- Dr Robert Temple from FDA regarding “Myth Busting – Clinical”
- Dr Robert O’Neill from FDA regarding “Myth Busting – Statistical”
Friday, February 19, 2010
SAS procedures for group sequential design
Group sequential design has been proposed for a while perhaps since Pocock's paper in 1977. At least for last two decades, the group sequential design and interim analysis have been used in may major clinical trials.
Several years ago, I tried to purchase Cytel's EAST program. But I gave up due to the price and the infrequent use in day-to-day practices.
Now I am pretty happy to know that SAS version 9.2 includes two new procedures for group sequential design. Proc SEQDESIGN allows to calculate the boundaries and Proc SEQTEST allows to perform the tests during the interim analysis whether or not the boundaries have been reached. Proc SEQTEST can also be used to calculate the conditional power (Probability of observing a significant result at full information, given the current data and the specified alternative under the statistical design ) and predictive power.
Reference readings:
Several years ago, I tried to purchase Cytel's EAST program. But I gave up due to the price and the infrequent use in day-to-day practices.
Now I am pretty happy to know that SAS version 9.2 includes two new procedures for group sequential design. Proc SEQDESIGN allows to calculate the boundaries and Proc SEQTEST allows to perform the tests during the interim analysis whether or not the boundaries have been reached. Proc SEQTEST can also be used to calculate the conditional power (Probability of observing a significant result at full information, given the current data and the specified alternative under the statistical design ) and predictive power.
Reference readings:
Sunday, February 14, 2010
Cholesterol Drug Approved for People Without High Cholesterol
Are you willing to take Statin medication if you have normal or mild elevation of LDL-Cholesterol level, but with elevated CRP?
Last week, FDA approved new indication for Crestor - one of the Statin class cholesterol lowering drug. This new indication has nothing to do with cholesterol level, but it is based on the CRP (c-reaction protein). Wall Street Jounal Health Blog had an article titled "Cholesterol drug approved for people without high cholesterol". It seems to me that we have now invented another disease - perhpas hyperCRP (instead of hypercholesterolemia). American are now perhaps inventing more diseases/indications than ever before. In the end, both cholesterol level and CRP level are surrogate endpoint. While the relationship between high cholesterol level and the major cardiovascular events (mortality, MI, stroke,...) has been generally recognized, the relationship between high CRP and the major cardiovascular events mainly relies on one study - JUPITER study - a study stopped early for efficacy.When the conflict of interest issue is considered, the purpose of the study is questioned and skeptical. According to the web blog, there are two conflict of interest issues in this study: "first, the study was funded by AstraZeneca, maker of the study drug, rosuvastatin (Crestor); second, the first author, Paul Ridker of Harvard, owns a patent on the high-sensitivity test for C-reactive protein, the test that would be widely used if the study results are accepted."
It will be inevitable that the next step for pharmaceutical companies like AstraZeneca is to push for routine testing of CRP (CRP screening) in clinical practice to identify the patients with normal or mild elevated LDL-Cholesterol level, but with elevated CRP. A cut point (or normal range) for CRP will be established. This is just how it works in capitalism world.
To find out how convincing of the evidences from JUPITER trial, you can read the publications by yourself. While the evidences seem to be convincing, I just don't want to take medications just for the elevated CRP. Perhaps, instead of the benefit from taking long-term treatment of Crestor, the treatment effect is really due to the detrimental effect of long-term treatment of Placebo. Perhaps, the treatment effect will be gone if we compare the Crestor with no-treatment (instead of Placebo).
Last week, FDA approved new indication for Crestor - one of the Statin class cholesterol lowering drug. This new indication has nothing to do with cholesterol level, but it is based on the CRP (c-reaction protein). Wall Street Jounal Health Blog had an article titled "Cholesterol drug approved for people without high cholesterol". It seems to me that we have now invented another disease - perhpas hyperCRP (instead of hypercholesterolemia). American are now perhaps inventing more diseases/indications than ever before. In the end, both cholesterol level and CRP level are surrogate endpoint. While the relationship between high cholesterol level and the major cardiovascular events (mortality, MI, stroke,...) has been generally recognized, the relationship between high CRP and the major cardiovascular events mainly relies on one study - JUPITER study - a study stopped early for efficacy.When the conflict of interest issue is considered, the purpose of the study is questioned and skeptical. According to the web blog, there are two conflict of interest issues in this study: "first, the study was funded by AstraZeneca, maker of the study drug, rosuvastatin (Crestor); second, the first author, Paul Ridker of Harvard, owns a patent on the high-sensitivity test for C-reactive protein, the test that would be widely used if the study results are accepted."
It will be inevitable that the next step for pharmaceutical companies like AstraZeneca is to push for routine testing of CRP (CRP screening) in clinical practice to identify the patients with normal or mild elevated LDL-Cholesterol level, but with elevated CRP. A cut point (or normal range) for CRP will be established. This is just how it works in capitalism world.
To find out how convincing of the evidences from JUPITER trial, you can read the publications by yourself. While the evidences seem to be convincing, I just don't want to take medications just for the elevated CRP. Perhaps, instead of the benefit from taking long-term treatment of Crestor, the treatment effect is really due to the detrimental effect of long-term treatment of Placebo. Perhaps, the treatment effect will be gone if we compare the Crestor with no-treatment (instead of Placebo).
- JUPITER hits New Orleans: landmark study shows statins benefit healthy individuals with high CRP levels
- Rosuvastatin to prevent vascular events in men and women with elevated c-reaction protein
- JUPITER study slides
- The Jupiter Study, CRP Screening, and Aggressive Statin Therapy-implications for the Primary Prevention of Cardiovascular Disease
Monday, February 01, 2010
Cost-benefit analysis: put a dollar value on human life?
Putting a Price Tag on Life: Today, companies and governments often use Jeremy Bentham’s utilitarian logic under the name of “cost-benefit analysis.” In Michael Sandel's lecture at Harvard, he presents some contemporary cases in which cost-benefit analysis was used to put a dollar value on human life. The cases give rise to several objections to the utilitarian logic of seeking “the greatest good for the greatest number.” Should we always give more weight to the happiness of a majority, even if the majority is cruel or ignoble? Is it possible to sum up and compare all values using a common measure like money?
Ford Pinto Case
One of the examples he used is the cost-benefit analysis in Ford Pinto case. According to Wikipedia,the Ford Pinto model became a focus of a major scandal when it was alleged that the car's design allowed its fuel tank to be easily damaged in the event of a rear-end collision which sometimes resulted in deadly fires and explosions. Critics argued that the vehicle's lack of a true rear bumper as well as any reinforcing structure between the rear panel and the tank meant that in certain collisions, the tank would be thrust forward into the differential, which had a number of protruding bolts that could puncture the tank. This, and the fact that the doors could potentially jam during an accident (due to poor reinforcement)allegedly made the car less safe than its contemporaries.
Ford allegedly was aware of this design flaw but refused to pay for a redesign. Instead, it was argued, Ford decided it would be cheaper to pay off possible lawsuits for resulting deaths. Mother Jones Magazine obtained the cost-benefit analysis that it said Ford had used to compare the cost of an $11 repair against the monetary value of a human life, in what became known as the Ford Pinto memo. The characterization of Ford's design decision as gross disregard for human lives in favor of profits led to significant lawsuits. While Ford was acquitted of criminal charges, it lost several million dollars and gained a reputation for manufacturing "the barbecue that seats four."
Repairing the Ford Pinto
Cost of Repairing Cost of Not Repairing
$ 11 per part 180 deaths x $200,000
x 12.5 million cars + 180 injuries x $67,000
+ 2000 vehicles x $700
=====================================================================
$137 million = $49.5 million
(to improve safety) (to let it go)
Philip Morris Czech Republic Cost-Benefit Analysis of Smoking
This is rather more recent case. The detail report can be found at this website.
Cost Benefits
Increased Health Care costs Tax revenue from cigarette sales
(due to lung cancer) Health care savings
(from early deaths)
Pension savings
Savings in housing costs
Net gain if citizens smoke is $147 million
Saving from premature deaths is $1227.00 per person
The fundamental issue in both cases is whether or not we can put a dollar value on human life (in the first example, a $200,000 tag for each death; in the second example, early death from smoking to benefit the government or other people).
We can easily see what is wrong and what is right in both of these examples. However, in our real life, it is not easy to distinguish the right and the wrong.
In UK, a famous government agency is called The National Institute for Health and Clinical Excellence (NICE). NICE's main function is to provide the appraisals that are based primarily on evaluations of efficacy and cost-effectiveness. They actually put a monetory tag on human life (not for each death, but for a good-quality year). The cost-effectiveness limit (or threshold) for NICE is £30,000 per good-quality year of life gained. In many occasions, the novice drug could be denied if the drug is too expensive (over the limit of the price tag). NICE is not nice to the pharmaceutical companies.
If we can not put a price tag on human life, can we put a price tag on 3 months or 6 months of human life? If we can not put a price tag on 3 months or 6 months of human life (saved) and curb the use of extremely expensive drug, how the medical cost will be controlled? I don't think there is an easy solution.
Further readings:
One of the examples he used is the cost-benefit analysis in Ford Pinto case. According to Wikipedia,the Ford Pinto model became a focus of a major scandal when it was alleged that the car's design allowed its fuel tank to be easily damaged in the event of a rear-end collision which sometimes resulted in deadly fires and explosions. Critics argued that the vehicle's lack of a true rear bumper as well as any reinforcing structure between the rear panel and the tank meant that in certain collisions, the tank would be thrust forward into the differential, which had a number of protruding bolts that could puncture the tank. This, and the fact that the doors could potentially jam during an accident (due to poor reinforcement)allegedly made the car less safe than its contemporaries.
Ford allegedly was aware of this design flaw but refused to pay for a redesign. Instead, it was argued, Ford decided it would be cheaper to pay off possible lawsuits for resulting deaths. Mother Jones Magazine obtained the cost-benefit analysis that it said Ford had used to compare the cost of an $11 repair against the monetary value of a human life, in what became known as the Ford Pinto memo. The characterization of Ford's design decision as gross disregard for human lives in favor of profits led to significant lawsuits. While Ford was acquitted of criminal charges, it lost several million dollars and gained a reputation for manufacturing "the barbecue that seats four."
Cost of Repairing Cost of Not Repairing
$ 11 per part 180 deaths x $200,000
x 12.5 million cars + 180 injuries x $67,000
+ 2000 vehicles x $700
=====================================================================
$137 million = $49.5 million
(to improve safety) (to let it go)
Cost Benefits
Increased Health Care costs Tax revenue from cigarette sales
(due to lung cancer) Health care savings
(from early deaths)
Pension savings
Savings in housing costs
Saving from premature deaths is $1227.00 per person
If we can not put a price tag on human life, can we put a price tag on 3 months or 6 months of human life? If we can not put a price tag on 3 months or 6 months of human life (saved) and curb the use of extremely expensive drug, how the medical cost will be controlled? I don't think there is an easy solution.
Further readings:
- Smoking is cost-effective, says report
- Doctor's Fury as NICE bans heart drug that could help 40,000 patients
- U.K. Says Tykerb Isn’t Worth Cost, Even With 12 Free Weeks
- UK Says Kidney Cancer Drugs Aren’t Worth the Cost
Saturday, January 23, 2010
Significant level of 0.00125
Recently, I found some materials related to a lawsuit against the Nuvelo Pharmaceuticals – a company who used to develop a clot-busting product in the indication of occluded central venous catheter. It is very interesting to see quite some arguments about the study design and the significance of a p-value. Since Nuvelo planned to conduct only one pivotal trial (instead of two), they had agreed with FDA to use an extraordinarily high threshold (0.00125). This stringent p-value required for a single pivotal trial, less than 0.00125, was not met and the development program was eventually terminated. The issue is that such a stringent p-value was not communicated to the investment community upfront – one of the reasons for the lawsuit.
There are scientific basis for this extraordinarily high significant level (0.00125). Here are the contexts extracted from one of the FDA’s NDA Statistical Review for United Therapeutics Corporation Drug: UniprostTM (treprostinol sodium) for pulmonary arterial hypertension.
There are scientific basis for this extraordinarily high significant level (0.00125). Here are the contexts extracted from one of the FDA’s NDA Statistical Review for United Therapeutics Corporation Drug: UniprostTM (treprostinol sodium) for pulmonary arterial hypertension.
“...A more important issue is the overall Type I error rate for the proposed analysis in this submission. First, consider the traditional standard for approval at the FDA based on two confirmatory trials. Even if the efficacy of a treatment is shown convincingly in one study, the agency likes to see replication in a second study because we will then be in a better position to infer that the results generalize to the entire population of patients with the disease. The overall Type I error rate (or false positive rate) is the chance that both studies will have a p-value less than 0.05 and the results of both studies are in the same direction. If the treatment effects in the two studies are identically 0, then the chance that both p-values will be less than 0.05 and both treatment effects are in the same direction is 0.001251. For this reason, the Division of Cardio-Renal Drugs has often advised sponsors that one study with a p-value less than 0.00125 may be sufficient for approval…”
While not very common, we do see quite some drug development programs with one-single pivotal trial. One such example is the trial called PROTECT where the sample size and power for primary composite endpoint was based on “90% power at two-sided 0.00125 significance level todetect a difference between a distribution of 33% failure, 35% unchanged and 32% success (placebo group) and 25 failure, 34% unchanged, and 42% success (rolofylline group), using the van Elteren extension of the Wilcoxon test”
Designing one single pivotal trial with a significant level of 0.00125 may not be a good strategy in comparing to the conventional two pivotal trials with a significant level of 0.05. A significant level of 0.00125 is forty times more stringent than a significant level of 0.05. Employing such a small significant level will typically require a large sample size and may be difficult to be successful.
FDA’s perspectives for clinical development of tropical microbicides indicated the followings for a single trial:
- No single site provides unusually large fraction of participants
- No single investigator or site provides a disproportionate favorable effect
- Consistency across study subset
- Statistically persuasive
Single Multi-Center Trial Level of Evidence (p value, 2-sided)
· P < 0.001 : persuasive, robust 2*[0.025^2]=0.00125
· 0.05 > P > 0.01: inadequate
· 0.01> p > 0.001: acceptable, if:
- good internal consistency
- low drop-out rates
- Other supportive data
In the end, the evidence of efficacy should not purely rely on the p-values. There are other considerations in assessing the evidence of efficacy. This has been spelled out in FDA’s guidance for Industry: Providing Clinical Evidence of Effectiveness for Human Drug and Biological Products:
Tthe evidence of effectiveness could come from one single study with the following:
- Large multicenter study
- Consistency across study subsets
- Multiple studies in a single study
- Multiple endpoints involving different events
- Statistically very persuasive finding
Here "statistically very persuasive finding" means a very small p-value even though the guidance does not specifically specify how small the p-value should be. It may depend on the negotiation with the corresponding branches in FDA.
Additional reading:
- Lloyd Fisher (1999) ONE LARGE, WELL-DESIGNED, MULTICENTER STUDY AS AN
ALTERNATIVE TO THE USUAL FDA PARADIGM. Drug information journal Vol. 33, pp. 265–271 - Boguang Zhen (2007) Consideration of Operational á Level With Different Approval Strategies. Drug Information Journal, Vol. 41, pp. 23–29, 2007 • 0092-8615
FDA guidance - Guidance for Industry: Evidence-Based Review System for the Scientific Evaluation of Health Claims – Final, 2009
- Shun Z, Chi E, Durrleman S, Fisher L. (2005) Statistical consideration of the strategy for demonstrating clinical evidence of effectiveness—one larger versus two smaller pivotal studies. Stat Med. 24:1619–1637.
Thursday, January 14, 2010
Logistic regression: complete or quasi-complete separation of data points
When we perform the logistic regression, sometimes, we may run into an issue so called ‘complete or quasi-complete separation of data points’. In this situation, the maximum likelihood estimate does not exist. If we use SAS Proc Logistic, SAS log will give a warning message "WARNING: There is possibly a quasi-complete separation of data points. The maximum likelihood estimate may not exist. WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are based on the last maximum likelihood iteration. Validity of the model fit is questionable." SAS will continue to report the Wald test results and odds ratios, however, these tests are no longer valid and results are not reliable (actually not accurate at all).
Complete separation data is something like below:
Y X
0 1
0 2
0 4
1 5
1 6
1 9
There is complete separation because all of the cases in which Y is 0 have X values equal to or less than 4, and the cases in which Y is 1 have X values equal to or greater than 5. In other words, Maximal value in one group is less than the minimal value in another group. When maximal value in one group is equal to the minimal value in another group, quasi-complete separation data may occur.
If the explanatory variable is categorical, complete separation of data points could be something like this:
Response Failure Success
Predictor
0 25 0
1 0 21
Where There are no successes when the value of the predictor variable is 0, and there are no failures when the value of the predictor variable is 1.
For maximum likelihood estimates to exist, there must be some overlaps in the two distributions. Since logistic regression models uses maximum likelihood estimates, when there is no overlaps of data points between two groups, the results from logistic regression models are unreliable and should not be credited.
Starting from SAS version 9.2, Proc Logistic provides Firth estimation for dealing with the issue of quasi or complete separation of data points.
proc logistic;
model y = x /firth;
run;
However, even after Firth estimation, the results should still be interpreted with extreme caution. Complete separation and quasi-complete separation of the data points may occur when the sample size is small and number of data points is not large or in the situation the samples are determined by the outcome (i.e., response) rather than explanatory variables – we see many publications where the analysis is based on the responders vs. non-responders.
Complete separation data is something like below:
Y X
0 1
0 2
0 4
1 5
1 6
1 9
There is complete separation because all of the cases in which Y is 0 have X values equal to or less than 4, and the cases in which Y is 1 have X values equal to or greater than 5. In other words, Maximal value in one group is less than the minimal value in another group. When maximal value in one group is equal to the minimal value in another group, quasi-complete separation data may occur.
If the explanatory variable is categorical, complete separation of data points could be something like this:
Response Failure Success
Predictor
0 25 0
1 0 21
Where There are no successes when the value of the predictor variable is 0, and there are no failures when the value of the predictor variable is 1.
For maximum likelihood estimates to exist, there must be some overlaps in the two distributions. Since logistic regression models uses maximum likelihood estimates, when there is no overlaps of data points between two groups, the results from logistic regression models are unreliable and should not be credited.
Starting from SAS version 9.2, Proc Logistic provides Firth estimation for dealing with the issue of quasi or complete separation of data points.
proc logistic;
model y = x /firth;
run;
However, even after Firth estimation, the results should still be interpreted with extreme caution. Complete separation and quasi-complete separation of the data points may occur when the sample size is small and number of data points is not large or in the situation the samples are determined by the outcome (i.e., response) rather than explanatory variables – we see many publications where the analysis is based on the responders vs. non-responders.
When complete separation or quasi-complete separation occurs, for multivariate regression, the explanatory variable causing this situation should be identified and preferably excluded from the model. For univariate regression, other alternative statistical tests (for example group t-test) should be used.
Further reading:
- Computation of the Odds Ratio with Small or Zero Cell Counts by Dr Robin High
- Convergence Failures in Logistic Regression by Paul Allison
- A tutorial on logistic regression by Ying So
- What is new in SAS 9.2?
Sunday, January 03, 2010
Rasch Analysis
I recently noticed a new approach so called 'Rasch Analysis' when I worked on a paper in dealing with the MCID (minimal clinically important difference). I have not got chance to do any Rasch analysis on my own, but I have collected some information here for the future use.
Rasch analysis start to be used in education, survey area. In clinical trial, it is mostly used in psycometric, neurology areas where the outcome assessment relies on the instrument which typically contains certain number of items. These instruments are frequently used in CNS and neurology disease such as stroke, alzheimer, dementia. Traditionally, a scale or instrument will need to be validated through the reliability and validity tests. Recently, in addition to the reliability and validity tests, the Rasch measurement model has set new quality standards for outcome measures by appraising a broad range of measurement properties. You will not be surprised to see many papers if you user the search keyword "Rasch analysis" or "Rasch model" in Pubmed.gov.
There is no existing procedure within SAS to perform the Rasch analysis. However, there are some SAS macros for Rasch analysis on the internet developed by Karl Bang Christensen. The most popular software for Rasch analysis is Winsteps which provide a free download of a Ministep with capability of performing Rasch analysis for less items and less records.
Rasch analysis start to be used in education, survey area. In clinical trial, it is mostly used in psycometric, neurology areas where the outcome assessment relies on the instrument which typically contains certain number of items. These instruments are frequently used in CNS and neurology disease such as stroke, alzheimer, dementia. Traditionally, a scale or instrument will need to be validated through the reliability and validity tests. Recently, in addition to the reliability and validity tests, the Rasch measurement model has set new quality standards for outcome measures by appraising a broad range of measurement properties. You will not be surprised to see many papers if you user the search keyword "Rasch analysis" or "Rasch model" in Pubmed.gov.
There is no existing procedure within SAS to perform the Rasch analysis. However, there are some SAS macros for Rasch analysis on the internet developed by Karl Bang Christensen. The most popular software for Rasch analysis is Winsteps which provide a free download of a Ministep with capability of performing Rasch analysis for less items and less records.
Saturday, January 02, 2010
Winning the holiday gift
During the holiday season, it is very typical for a corporate to hold a party for its employees. During the party, one activity is to win the prizes with the raffle tickets.
Say each employee is distributed with 20 tickets in a raffle with 80 prizes. Which gives you a better chance of winning: putting all of your tickets in one of 80 baskets (your favorite item) or spreading them among 20 baskets with each ticket in one basket?
This seems to be a probability issue. There is an answer from AskMarilyn for the similar issue:
If you can see the baskets and tickets, you should wait until the last minute and then put all of your tickets in the basket that appears to contain the fewest tickets. If you can't see the tickets, put all your tickets in the basket for the least-desirable prize. But if you can't see the tickets and the prizes are equal, it doesn't matter what you do.
In previous year, I won nothing because I put all my raffles in a couple of hot items (there are thousands tickets in the boxes for these hot items). This year, I changed the strategy and put my tickets in the basket with the fewest tickets. I won a digital photo cube. Digital photo cube is not my favorite item, but I demonstrated how changing the strategy could increase the possibility of winning.
Say each employee is distributed with 20 tickets in a raffle with 80 prizes. Which gives you a better chance of winning: putting all of your tickets in one of 80 baskets (your favorite item) or spreading them among 20 baskets with each ticket in one basket?
This seems to be a probability issue. There is an answer from AskMarilyn for the similar issue:
If you can see the baskets and tickets, you should wait until the last minute and then put all of your tickets in the basket that appears to contain the fewest tickets. If you can't see the tickets, put all your tickets in the basket for the least-desirable prize. But if you can't see the tickets and the prizes are equal, it doesn't matter what you do.
In previous year, I won nothing because I put all my raffles in a couple of hot items (there are thousands tickets in the boxes for these hot items). This year, I changed the strategy and put my tickets in the basket with the fewest tickets. I won a digital photo cube. Digital photo cube is not my favorite item, but I demonstrated how changing the strategy could increase the possibility of winning.
Saturday, December 12, 2009
SAS SGPLOT for creating statistical graphs
For a long time, we have been using SAS GPLOT for creating graphs. Beginning from SAS version 9.2, there is a new procedure called SAS SGLPOT, which could be a good tool for statisticians.
The detail about this procedure is described in SAS onlind document. There are several white papers about using this procedure.
The detail about this procedure is described in SAS onlind document. There are several white papers about using this procedure.
- SAS/GRAPH® Procedures for Creating Statistical Graphics
in Data Analysis - SAS blog talking about SGPLOT procedure
- Using PROC SGPLOT for Quick High Quality Graphs
Saturday, December 05, 2009
Subject Diaries in Clinical Trials
Subject Diary, often called Patient Diary, is a tool used in the clinical trials. There could be three types of diary technologies. The traditional approach has been to use paper cards or booklets configured to help the subject follow directions from the clinical protocol. More recently, electronic means have been used, such as dial-in phone numbers with computer-driven questions to answer (interactive voice response systems, IVRS) and handheld devices with alarms and menu-driven prompts to guide the subject through the protocol requirements - e-diaries.
In 2006, FDA issued a draft Guidance for Industry "Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims" This guidance describes how the FDA evaluates patient-reported outcome (PRO) instruments used as effectiveness endpoints in clinical trials. Here the patient-reported outcome is typically collected through subject diary. However, the use of subject diary to collect the information on efficacy is just one of many usages. Subject diary can also be used to collect the information about:
In 2006, FDA issued a draft Guidance for Industry "Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims" This guidance describes how the FDA evaluates patient-reported outcome (PRO) instruments used as effectiveness endpoints in clinical trials. Here the patient-reported outcome is typically collected through subject diary. However, the use of subject diary to collect the information on efficacy is just one of many usages. Subject diary can also be used to collect the information about:
- Daily symptoms, dialy activities
- Safety assessment (such as adverse events, exacerbations)
- Usage of the study medication to measure the compliance
- Usage of the concomitant medication
- Disease episodes on daily basis
Diary does not have to be filled out on daily basis. There are studies using diary and the subjects write down in a diary each time they take the medication,
FDA guidance listed three reasons to collect the data reported by the subject:
- Some treatment effects are known only to the patient;
- There is a desire to know the patient perspective about the effectiveness of a treatment;
- Systematic assessment of the patient’s perspective may provide valuable information that can be lost when that perspective is filtered through a clinician’s evaluation of the patient’s response to clinical interview questions.
The drawback of the diary data is the reliability of the data or the quality of the data. Subject could lose the diaries, forget to complete them or forget to complete the diaries in real time, and in worse situation, falsify information. To ensure that subject diaries comply with GCP standards, the followings are critical (adapted from Good Clinical Practice: A Question & Anwer reference)
"At the beginning of a study, site staff should explain to each subject (or parent) the importance of the diary and how the subject should record data within it. Site staff should review the diary at each visit; deficiencies and attempts to correct these deficiencies should be noted in source records. Site staff must ensure that the diaries are returned at the time designated in the trial protocol. If a patient diary is not returned, the site should make several attempts to retrieve it. These attempts should be documented in the subject’s medical record.
Although clinical auditors and FDA inspectors recognize that diaries often pose a source documentation problem, they expect to see documented efforts to minimize these problems. Diaries that are too neat, all look the same, or have been rewritten by the study coordinator are sure to raise suspicious. "
Subject diary data is often subjected to the audit findings. For example, in one of FDA's warning letters, it cited the violation about subject diary data.
"Protocol (b)(4) specified that subjects were to be distributed patient daily diary cards every 4 weeks, beginning with Week 0, and ending on Week 53. Per the protocol, patients should only have received one set of diary cards for each four-week period. However, during its inspection, FDA discovered that your records contained multiple diary entries for the same subjects on the same dates. Furthermore, the information you reported to the CRF excluded some of the data from one or more sets of patient diaries. This is a violation of 21 CFR 312 .70.
Protocol (b)(4) specified that the study coordinator was to review the patient diary cards with the patient during each scheduled visit and, if possible, query the patient to obtain any missing information at the scheduled visit. Thus, patient diaries should have only contained dated entries for dates that occurred between two scheduled visits.
The daily diary cards included information concerning whether the subject took the doses of medication in the morning and the evening (diary question #1), whether any concomitant medications were taken (diary question #2), the name of the concomitant medication taken (diary question #3), usual daily activity interruptions due to (b)(4 ) pain (diary question #4), whether any medical facility was visited due to ( b) (4) pain (diary question #5), the name of the medical facility visited (diary question 7T6), and the daily pain level experienced on a scale of 0 to 10 (diary question #7). Per the protocol, information in patient diary cards was to be recorded onto the appropriate case report form (CRF).
FDA's audit identified that 2 of 12 subjects enrolled in the study, Subjects #003 and #0 10, had two sets of patient diaries which contained different information for the same dates . From our review of the two sets of patient diaries, we were unable to determine which diary provided the correct information. In addition, we note that the information reported to the CRF was either (1) obtained from one diary but not the other, or (2) could not be verified in our review of either version of the duplicate diaries' entries for specified dates. The discrepancies we observed included, but were not limited to, the following: ...."
In terms of the handling of the subject or patient diary data, there are many different ways depending on the diary technology and the purpose of the diary. Subject diary data could be directly transmitted to the data management group without review by the site investigator or study coordinator. The data clarification (query) process is omitted. For example, in a study with Irritable Bowel Symdrome, diary data collected through IVRS was directly transferred to teh data management group and biostatistics group for analysis. Subject diary data could also be collected by the site and reviewed by the site staff (investigator or study coordinator). In this situation, the subject diary is a tool to assist the site staff in evaluating the subject about the disease status, any significant event, drug compliance,... For example, the subject diary could used to collect the COPD exacerbation (subjects record the dialy symptoms, antibiotic use, steroid use,...). in this case, the subject diary should be periodically reviewed by the site staff.
Tuesday, November 24, 2009
Box-Cox Transformaton
In statistical / biostatistical analysis, it is pretty common to apply the data transformation technique. The reason is to achieve the normality assumption. data transformation refers to the application of a deterministic mathematical function to each point in a data set — that is, each data point zi is replaced with the transformed value yi = f(zi), where f is a function.
The typical data transformations include logarithm, square root, Arcsine transformation. Log transformation is suitable for variables with log-normal distributions. The square-root transformation is commonly used when the variable is a count of something. For arcsin transformation, the numbers to be transformed must be in the range −1 to 1. This is commonly used for proportions, which range from 0 to 1.
Another popular data transformation technique Box-Cox transformation, which we may not use frequently in clinical trials. Box-Cox transformation belongs to the so-called 'power transform'. The Box-Cox family of transformations has two useful features: first, it includes linear and logarithmic transformations as special cases; and, second, it possesses strong scale equivariance properties, including the property that the transformation parameter is unaffected by the rescaling. Application of the Box-Cox transformation algorithm reduced the heterogeneity of error and permitted the assumption of equal variance to be met. Its main disadvantage is that both the domain and the range of the transformation are, in general, bounded.
The typical data transformations include logarithm, square root, Arcsine transformation. Log transformation is suitable for variables with log-normal distributions. The square-root transformation is commonly used when the variable is a count of something. For arcsin transformation, the numbers to be transformed must be in the range −1 to 1. This is commonly used for proportions, which range from 0 to 1.
Another popular data transformation technique Box-Cox transformation, which we may not use frequently in clinical trials. Box-Cox transformation belongs to the so-called 'power transform'. The Box-Cox family of transformations has two useful features: first, it includes linear and logarithmic transformations as special cases; and, second, it possesses strong scale equivariance properties, including the property that the transformation parameter is unaffected by the rescaling. Application of the Box-Cox transformation algorithm reduced the heterogeneity of error and permitted the assumption of equal variance to be met. Its main disadvantage is that both the domain and the range of the transformation are, in general, bounded.
Box-Cox transformation can be easily implemented with SAS Proc Transreg.
Further readings:
Wednesday, November 18, 2009
Dealing with the paired data
There are many practical examples of paring. In clinical trial, crossover design is a special case of the pairing where the same subject receive more than one treatment. If all subjects receive treatment A, then treatment B, it can still be called crossover design (single sequence cross over design). In Epidemiology field, the case-control study is typically paring. There are terms 1:1 matched case-control, and 1:m matched case-control. In education, we can do the paring to compare the scores before and after the training;......
When outcome measures are continuous variable (such as drug concentration), without considering the covariates, analysis of paired data can be implemented by using paired t-test which can be easily performed using SAS PROC UNIVARIATE (calculate the difference for each pair, then run PROC UNIVARIATE) or SAS PROC TTEST (without calculating the difference first). Suppose x1 and x2 are paired variables,
proc ttest;If the normality assumption is questionable, the non-parametric tests (sign test and Wilcoxon signed rank sum test) can be used. UCLA's Statistical Consulting Services web site provided examples for these tests.
paired x1*x2;
run;
In more complicated situation (such as crossover design) or if we have to do the modeling to include the covariates, mixed model needs to be used. SAS PROC MIXED can implement the mixed model easily. See SAS/Stat User's Manual for PROC MIXED. In a research paper titled "Detection of emphysema progression in alpha 1-antitrypsin deficiency using CT densitometry; Methodological advances", I actually dealt with the paired data using so called 'random coefficient model'.
When outcome variable is discrete data, the easiest example is McNemar test. McNemar's test is performed if we are interested in the marginal frequencies of two binary outcomes. These binary outcomes may be the same outcome variable on matched pairs (like a case-control study) or two outcome variables from a single group.
In more complicated situation or if the covarites need to be included in the model, 'conditional logistic regression' needs to be employed. 'Conditional logistical regression' can be implemented using SAS Proc Logistic or SAS Proc PHREG. See following links for detail descriptions.
- A Tutorial on Logistic Regression by Ying So in SAS
- Condition Logistic Regression using SAS Proc PHREG procedure by David Brown
Sunday, November 08, 2009
Pediatric use and geriatric use of drug and biological products
In the United States, every marketed drug or biological product needs to have its product label or package insert. The product label contains the use in special populations including pediatric and geriatric population. Here is a paragraph from FDA guidance on "Labeling for Human Prescription Drug and Biological Products — Implementing the New Content and Format Requirements"
Use in Specific Populations (§ 201.57(a)(13))
Information under the Use in Specific Populations heading includes a concise summary
of any clinically important differences in response or recommendations for use of the
drug in specific populations (e.g., differences between adult and pediatric responses, need
for specific monitoring in patients with hepatic impairment, need for dosing adjustments
in patients with renal impairment). Typically, information under this heading includes
limitations or precautions for specific populations or established differences in response.
Absence of the clinical study data in pediatric and geriatric population could sometimes cause problems in product label or in the drug approval process. During the drug development process, it is prudent to consider the inclusion/exclusion of patient population in terms of the age limit. In the study protocol, the inclusion criteria pertinent to the age limits (upper and lower limits) should be carefully considered. In the statistical analysis, when data for pediatric and/or geriatric population is available, subgroup analysis should always be performed.
In regulatory environment, the classification of the pediatric and geriatric population are defined as:
Pediatric population: according to ICH guidance E11 "Clinical Investigation of Medicinal Products in the Pediatric Population", the pediatric population contains several sub-categories:
for Drugs and Biological Products", the age classification is a little bit different. I am assuming that the ICH guidance E11 should be the correct reference.
Geriatric population:
Geriatric population is defined as persons 65 years of age and older. There is no upper limit of age defined. The Food and Drug Administration has regulations governing the content and format of labelling for human prescription drug products, including biological products, to include information pertinent to the appropriate use of drugs in the elderly and to facilitate access to this information by establishing a “Geriatric use” subsection in the labelling.
Further readings:
Use in Specific Populations (§ 201.57(a)(13))
Information under the Use in Specific Populations heading includes a concise summary
of any clinically important differences in response or recommendations for use of the
drug in specific populations (e.g., differences between adult and pediatric responses, need
for specific monitoring in patients with hepatic impairment, need for dosing adjustments
in patients with renal impairment). Typically, information under this heading includes
limitations or precautions for specific populations or established differences in response.
Absence of the clinical study data in pediatric and geriatric population could sometimes cause problems in product label or in the drug approval process. During the drug development process, it is prudent to consider the inclusion/exclusion of patient population in terms of the age limit. In the study protocol, the inclusion criteria pertinent to the age limits (upper and lower limits) should be carefully considered. In the statistical analysis, when data for pediatric and/or geriatric population is available, subgroup analysis should always be performed.
In regulatory environment, the classification of the pediatric and geriatric population are defined as:
Pediatric population: according to ICH guidance E11 "Clinical Investigation of Medicinal Products in the Pediatric Population", the pediatric population contains several sub-categories:
- preterm newborn infants
- term newborn infants (0 to 27 days)
- infants and toddlers (28 days to 23 months)
- children (2 to 11 years)
- adolescents (12 to 16-18 years (dependent on region))
for Drugs and Biological Products", the age classification is a little bit different. I am assuming that the ICH guidance E11 should be the correct reference.
Geriatric population:
Geriatric population is defined as persons 65 years of age and older. There is no upper limit of age defined. The Food and Drug Administration has regulations governing the content and format of labelling for human prescription drug products, including biological products, to include information pertinent to the appropriate use of drugs in the elderly and to facilitate access to this information by establishing a “Geriatric use” subsection in the labelling.
Further readings:
- Regulatory requirements for the development of medicinal products for pediatric use by Dr. Tom Sam
- FDA good review process "Labeling for Human Prescription Drug and Biological Products — Determining Established Pharmacologic Class for Use in the Highlights of Prescribing Information"
- FDA's Pediatric Drug Development
- Pediatric Trials: a Worldview
Sunday, October 25, 2009
GxP: a collection of quality guidelines in clinical trial
GxP is now used to represent a collection of quality guidelines in clinical trial. The titles of these good practice guidelines usually begin with "Good" and end in "Practice", with the specific practice descriptor in between. A "c" or "C" (stands for 'current') is sometimes added to the front of the acroynm to form cGxP. For example, cGMP is an acronym for "current Good Manufacturing Practices."
GCP: Good Clinical Practices is an international ethical and scientific quality standard for designing, conducting, recording and reporting trials that involve the participation of human subjects. The GCP is governed by ICG guideline E6
http://www.fda.gov/Training/CDRHLearn/ucm176411.htm
GLP: Good Laboratory Practice. Refer to Wikipedia for detail. GLP is the guidance for laboratory tests, pre-clinical tests, bioanalytical assays/measures, toxicology tests,...
GRP: Good Reprint Practices. In January 2009, FDA issued its final version of the guidance "Good Reprint Practices for the Distribution of Medical Journal Articles and Medical or Scientific Reference Publications on Unapproved New Uses of Approved Drugs and Approved or Cleared Medical Devices"
GPP: Good Pharmacovigilance Practices. In 2005, FDA issued its guidance on "Good Pharmacovigilance
Practices and Pharmacoepidemiologic Assessment" to provide guidance on (1) safety signal identification, (2) pharmacoepidemiologic assessment and safety signal interpretation, and (3) pharmacovigilance plan development.
While GRP and GPP are proposed by the regulatory agencies, there is no officially issued guidance on GSP (Good Statistical Practices) and GCDMP (Good Clinical Data Management Practices). However, the principles of these two good practices have been largely covered in ICH guidances, specifically, E9 (Statistical Principles for Clinical Trials) and E6 (Good Clinical Practice).
PSI Professional Standards Working Party developed a GUIDELINES FOR STANDARD OPERATING PROCEDURES for Good Statistical Practice in Clinical Research.
In several DIA presentations, Good Statistical Practices
Science:
- Protocol – Minimize bias – Maximize precision
- Analysis plan
- Presentation of results
- Leadership
Operational Processes
- Controlled statistical environment
- SOPs • Productivity tools
- Data standards
- Training
Credibility Results
- Reproducible research
- Transparent and efficient processes
- Validated analysis
- Data integrity assurance
The Good Clinical Data Management Practices (GCDMP) is developed by the SCDM (Society of Clinical Data Management). It provides assistance to clinical data managers in their implementation of high quality clincal data management processes and is used as a reference tool for clinical data managers when preparing for CDM training and education.
Sunday, October 18, 2009
Biostatistics conferences/workshops
When I started my career in biostatistics, I joined the American Statistical Association (ASA) and attended its annual meeting (Joint Statistical Meeting) rotated in different large cities in North America (US and Canada). I have enjoyed the atmoshpere of the conference and networked with friends and professors in the statistical field.
For two consective years, I have skipped the meeting. Instead, I attended the FDA/Industry Statistical Workshop. JSM may be good to the students, but may not be good for professions (especially for statisticians who are working in drug development area). JSM has a lot of sessions/presentions that are unfiltered and too theoretic. A lot of stuff may never have the value in application. Even though it may be applicable one day, it may not be acceptable to the regulatory agencies.
The statistical conferences, symposiums, workshops with focus on clinical trial and drug development have thrived in recent years. Twice a year, FDA holds its workshops: one with Drug InformationAssociation "FDA/industry statistical forum" and one with ASA "FDA/industry statistical workhop". These conferences are more specific to the biopharmaceutical field and the topics are more relevant to the daily work of biostatisticians.
There are also several societies with focus on biostatics, for example, the International Society for Biopharmaceutical Statistics (ISBS) and the International Society for Clinical Statistics (ISCS). The International Chinese Statistical Association (ICSA) is also adjusting its focus to the biopharmaceutical field. Within ASA, biopharmaceutical network has been formed.
To get a flavor of the topics in these meetings, see the following links:
For two consective years, I have skipped the meeting. Instead, I attended the FDA/Industry Statistical Workshop. JSM may be good to the students, but may not be good for professions (especially for statisticians who are working in drug development area). JSM has a lot of sessions/presentions that are unfiltered and too theoretic. A lot of stuff may never have the value in application. Even though it may be applicable one day, it may not be acceptable to the regulatory agencies.
The statistical conferences, symposiums, workshops with focus on clinical trial and drug development have thrived in recent years. Twice a year, FDA holds its workshops: one with Drug InformationAssociation "FDA/industry statistical forum" and one with ASA "FDA/industry statistical workhop". These conferences are more specific to the biopharmaceutical field and the topics are more relevant to the daily work of biostatisticians.
There are also several societies with focus on biostatics, for example, the International Society for Biopharmaceutical Statistics (ISBS) and the International Society for Clinical Statistics (ISCS). The International Chinese Statistical Association (ICSA) is also adjusting its focus to the biopharmaceutical field. Within ASA, biopharmaceutical network has been formed.
To get a flavor of the topics in these meetings, see the following links:
- 2009 FDA/Industry Statistical Workshop online program and presentations (The password to view presentations is Capitol
- Biopharmaceutical network's webinar topics and handouts
Sunday, October 04, 2009
Positive Psychology - science to find happiness
When I see a news headline about "what is the most popular course in the Harvard University?", my curiosity drives me to find out what the course is. This leads me to the concept of "Positive Psychology". The most popular class is the Psychology 1504 (ie, positive psychology) taught by Dr. Ben-Shahar.
As mentioned in NPR news, "almost every semester for the past ten years, the most popular class at Harvard has been Intro to Economics, or as Tal Ben-Shahar likes to call it, how to get rich, but today there's an even bigger class on campus. It's Ben-Shahar's course on what he calls, how to get happy."
According to Wikipedia, Positive psychology is a recent branch of psychology that "studies the strengths and virtues that enable individuals and communities to thrive". Positive psychologists seek "to find and nurture genius and talent", and "to make normal life more fulfilling", not simply to treat mental illness. In other words, the positive psychology deals with love, happiness, job satisfaction, ...
In contrary to the Positive psychology, there should be a concept of negative psychology. However, even though the current psychology is so focused on the negative side (depression, fear, anxiety, mental illness,...), there is no formal definition of negative psychology.
Further readings about the negative psychology:
However, there is also negative side about the positive psychology. See Dr. BARBARA S.HELD's argument.
In practice, Positive psychology encompass a variety of techniques that encourage people to identify and further develop their own positive emotions, experiences, and character traits. In many ways, positive psychology builds on key tenets of humanistic psychology. Whether or not the positive psychology techniques work will eventually rely on the evidence from the clinical trials. Since the psychology measures are typically intangible, how to design a trial or intervention, what to measure, how long to measure, what instrument to use,... could be challenging even more than the typically psychology measures (with the focus on disease or negative psychology). The following paper discussed this issue.
As mentioned in NPR news, "almost every semester for the past ten years, the most popular class at Harvard has been Intro to Economics, or as Tal Ben-Shahar likes to call it, how to get rich, but today there's an even bigger class on campus. It's Ben-Shahar's course on what he calls, how to get happy."
According to Wikipedia, Positive psychology is a recent branch of psychology that "studies the strengths and virtues that enable individuals and communities to thrive". Positive psychologists seek "to find and nurture genius and talent", and "to make normal life more fulfilling", not simply to treat mental illness. In other words, the positive psychology deals with love, happiness, job satisfaction, ...
In contrary to the Positive psychology, there should be a concept of negative psychology. However, even though the current psychology is so focused on the negative side (depression, fear, anxiety, mental illness,...), there is no formal definition of negative psychology.
Further readings about the negative psychology:
- The website of Tal Ben-Shahar
- Tal Ben-Shahar's video on Youtube
- Dr Seligman's vedio on Youtube
- NPR report on finding happiness
- Harvard psychology course information (including course slides)
- UPenn's Positive Psychology Center
- Good Life: Happiness on Youtube
However, there is also negative side about the positive psychology. See Dr. BARBARA S.HELD's argument.
In practice, Positive psychology encompass a variety of techniques that encourage people to identify and further develop their own positive emotions, experiences, and character traits. In many ways, positive psychology builds on key tenets of humanistic psychology. Whether or not the positive psychology techniques work will eventually rely on the evidence from the clinical trials. Since the psychology measures are typically intangible, how to design a trial or intervention, what to measure, how long to measure, what instrument to use,... could be challenging even more than the typically psychology measures (with the focus on disease or negative psychology). The following paper discussed this issue.
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