Power of words and Safe Communication

Words that end up in court
Altered records, defect, falsification, hazardous, illegal, incompetence, inferior, negligent, reckless, wrongdoing

Fighting words
Allege, argument, complaint, careless, dispute, idiot, inefficient, liable, misinform, problem, shoddy, shortsighted

Loaded questions and negative words
"I am not a cook"
"I am not a bimbo"
How would you respond?
Did the clinical trials go badly?
Do you always drive like a maniac?

Phrases to make people defensive
Apparently you are not aware...
Contrary to your inference...
I don't agree with you...
Let me make this perfectly clear...
You obviously overlooked...
And just for your information...


Phrases that will get you more attention than you want
delete this email
do not distribute
shred this memo
do not tell (insert name here)
can we get away with it?
They'll never find out
I have serious concerns
I don't care what you do
This might not be legal
let's meet to talk about the thing I mentioned last night

Avoiding commenting on potential
Potential liability issues:
Risky way: if we do not conduct the tests I propose, the company could face serious FDA regulatory problems and product liability suits
Safer way: I believe this protocol employs sound research methodologies that will yield scientifically valid data and should be favorably reviewed by the FDA

Be specific and detailed about information sources:
Risky way:Managers send and receive an average of 178 messages a day
Safer way: A recent Gallup poll showed that managers send and receive an average of 178 messages per day

Risky way:Previous studies show the drug is safe
Safer way: According to the 2005 viral transmission study done by Dr. Jackson...

Risky way: He says he won't use our product because the rate of viral transmission of hepatitis C is too high
Safer way: Dr. Bowers had concerns about the possibility of viral transmission of hepatitis C. I explained to him about our...

Close 'open loops'
An open loop

• The investigator again questioned the safety of the new protocol

Closing the loop

• In response to the investigator's query about the safety of the protocol...

Know when not to respond

• when you don't know
• when it falls outside your expertise area
• potential hot button issues
• challenging editors and auditors

Source data in EDC trial

One of my friends asked me what would be the source data and how to verify if the data was directly entered into EDC. A recent article in clinicaltrialsonline.com answered this question. it looks like this is the topic covered under FDA's new guidance "Computerized Systems Used in Clinical Invesitgations" http://www.fda.gov/cder/guidance/7359fnl.htm.

Scenario 1: Data are first captured on paper and then manually entered into a computerized system. Source data are the paper documents. Examples: Data collected at clinical sites, IRBs, and medical practices.

Scenario 2: Data are first captured electronically into a computerized system and then manually entered into another database. Source data are the electronic records in the computerized system. Examples: Data collected at clinical sites, diagnosis after test evaluation in medical institutions.

Scenario: 3: Data are first captured electronically into a computerized system. Source data are the electronic records in the computerized system. Examples: Data collected at clinical sites, clinical laboratories, analytical laboratories, etc.

To read the full article, please visit "http://appliedclinicaltrialsonline.findpharma.com/appliedclinicaltrials/article/articleDetail.jsp?id=546112&sk=&date=&pageID=3"

New Data Management acronyms

The recent effort to standardize the clinical data acquisition results in a lot of new acronyms. Some of them are listed below. A lot of these terms are in CDISC standards http://www.cdisc.org/standards/index.html

CDASH - Clinical Data Acquisition Standards Harmonization – Data acquisition (CRF) standards
CDISC - Clinical Data Interchange Standards Consortium – http://www.cdisc.org
ODM - Operational Data Modeling – CDISC trasnpport standard for acquisition, exchange, submission (define.xml) - http://www.cdisc.org/models/odm/v1.1/odm1-1-0.html
SDTM - Study Data Tabulation Model - http://www.cdisc.org/models/sds/v3.1/index.html
SDS - Submission Data Standards - http://www.cdisc.org/models/sds/v2.0/
SEND - Standard for Exchange of Nonclinical Data
ADaM - Analysis Dataset Model
EDC- Electronic Data Capture
RDC - Remote Data Capture
CTD - Common Technical Document
eCTD - electronic Common Technical Document

Adobe Acrobat Resource Center - weblinks

Training & Resources:
Acrobat for life sciences blog - http://blogs.adobe.com/acrobatforlifesciences/
Acrobat legal links online - https://www.regonline.com/builder/site/default.aspx?eventid=136003
Adobe solutions for life sciences website - http://www.adobe.com/lifesciences/
Adobe and the SAFE-Biopharma Association - http://www.adobe.com/lifesciences/safe.html
SAFE white paper - http://www.adobe.com/lifesciences/pdfs/safe_wp.pdf
Adobe solutions for electronic submissions solution brief - http://www.adobe.com/lifesciences/pdfs/electronic_submissions_sb.pdf

Support & Development
Adobe PDF for Developers Blog - http://blogs.adobe.com/pdfdevjunkie/
Acrobat 8 deployment techniques eSeminar - https://admin.adobe.acrobat.com/_a227210/p92416557
Adobe Support Knowledgebase, Join the acrobat forum and the acrobat user group - http://adobe.com/support
Free acrobat tutorials, user groups, and blogs - http://www.adobe.com/cfusion/designcenter/search.cfm?product=acrobat&term=acrobat&topic

Free Download & Trials
Adobe 8.0 Professional Free Trial Download- http://www.adobe.com/products/acrobatpro/tryout.html
Adobe document center free trial - http://www.adobe.com/products/onlineservices/documentcenter/features.html
ISItoolbox pharma edition free trial - http://www.isitoolbox.com/ProductInformatoin/PharmaEdition/tabid/724/Default.aspx

Introduction to pharmacokinetics and pharmacodynamics

The book by Drs Tozer and Rowland is pretty good.
About dose "To paraphrase Paracelsus, who lived some 500 years ago, "all drugs are poisons, it is just a matter of dose." A dose of 25 mg of aspirin does little to alleviate a headache; a dose closer to 300-600 mg is needed, with little ill effect. However, 10 g taken all at once can be fatal, especially in youn children.

About the definition of PK and PD: In simple terms pharmacokinetics may be viewed as what the body does to the drug, and pharmacodynamics as what the drug does to the body.

On genetic variability in drug response: If we were all alike, there would be only one dose strength and regimen of a drug meeded fro the entire patient population. But we are not alike; we often exhibit great interindividual variability in repsonse to drugs. In rare caes, the "one-dose-for-all" idea, suffices.

About Plasma: In practice, plasma is preferred over whole blood primarily because blood causes interference in many assay techniques. Plasma and serum yield essentially equivalent drug concentrations, but plasma is considered easier to prepare because blood must be allowed to clot to obtain serum. During this process, hemolysis can occur, producing a concentration that is neither that of plasma nor blood, or causing an inference in teh assay.

About the differences among Plasma, Serum, and Whold Blood:
Plasma: Whole blood is centrifuged after adding an anticoagulant, such as heparin or citric acid. Cells are precipitated. The supernate, plasma, contains plasma proteins that often bind drugs. The plasma drug concentration includes drug-bound and unbound to plasma proteins.
Serum: Whole blood is centrifuged after the blood has been clotted. Cells and material forming the clot, including fibrinogen and its clotted form, fibrin, are removed. Binding of drugs to fibrinogen and fibrin is insignificant. Although the protein composition of serum is slightly different from that of plasma, the drug concentrations in serum and plasma are virtually identical.
Whole blood: whoe blood contains red blood cells, white blood cells, platelets and various plasma proteins. An anticoagulant is commonly added and drug is extracted into an organic phase often after denaturing the plasma proteins. The blood drug concentration represents an average over the total sample. Concentrations in the various cell fractions and in plasma may be very different.

On site of administration:
Intravascular: refers to the placement of a drug directly into the blood - either intravenously or intra-arterially.
Extravascular: include the intradermal, intramuscular, oral, pulmonary (inhalation), subcutaneous (into fat under skin), rectal, and sublingual (under the tongue) routes.
Parenteral administration: refers to administration aprat from the intestines. Parenteral administration includes intramuscular, intravascular, and subcutaneous routs. Today, the term is generally restricted to those routes of administration in which drug is injected through a needle. Thus, although the use of skin patches or nasal sprays (for systemic delivery) are strictly forms of parenteral administration, this term is not used for them.

Target Product Profile

Target Product Profile (TPP) is a new concept to me. However, it may not be a new term at all. Recently, I was asked to review a TPP and I searched the website to find out what TPP means and what are included in TPP. The description of the clinical development programs has to include some information related to biostatistics such as what to measure (primary or secondary endpoint), what the expected treatment difference, how many subjects we need...... Below is a description about TPP cited from a white paper (http://www.diahome.org/content/community/TPP_White_Paper.doc). While TPP is typically used for better communication with regulatory authorities, it may also be used in other purpose. For example, WHO had a TPP for the Advance Market Commitment (AMC) for Pneumococcal Conjugate Vaccines (http://www.who.int/immunization/sage/target_product_profile.pdf).

"A Target Product Profile (TPP) is a document, prepared by the sponsor, to facilitate clinical drug development and enhance constructive dialogue between the sponsor and reviewing Division at the Food and Drug Administration (FDA). A TPP is written and updated during the investigational phases of development in order to capture the goals of clinical drug development as statements of proposed claims for a prescription drug or biologic product. In the TPP, each proposed claim should be annotated to the specific study or other source of data that is intended to support the key sections and statements in the TPP. In essence, the TPP embodies the notion of "beginning drug development with the end in mind"; that is, the sponsor specifies the proposed claims that are the goals of drug development, documents the specific studies that are intended to support the key claims, and then uses the TPP to facilitate a constructive dialogue with FDA."

FDA actually had a draft guidance on TPP (http://www.fda.gov/CDER/GUIDANCE/6910dft.htm)

The garget product profile, if utilized during the development phases, should facilitate labeling discussions during the review cycle. Labeling discussions will be further enhanced if the applicant and FDA provide their rationale for key elements or proposed changes in drfat labeling. To pursue this principle, the applicant and FDA will provide verbal or written rationale for revisions to draft labeling.

adverse events temporally associated with infusion

In biological field, it is pretty common to collect and analyze the adverse events that are temporally associated with an infusion. For example, this is required in a recent FDA guidance about IGIV (http://www.fda.gov/cber/gdlns/igivimmuno.pdf). The guidance stated “Your protocol should define criteria for establishing an AE as an infusional AE (i.e., an AE temporally associated with an infusion). We recommend you list AEs individually by body system with subject identification numbers and report the overall incidences of all AEs that occur during or within: (a) 1 hour, (b) 24 hours, and (c) 72 hours following an infusion of test product, regardless of other factors that may impact a possible causal association with product administration.”

While this is understandable, we need to be clear about the exact time frame for collecting these AEs when coming to the programming. I had a couple of studies where the definition (therefore the calculation) of the infusion related adverse events are defined differently “Infusion related adverse events are defined as any event that occurs within 1 hour, 24 hours, or 72 hours of initiation of the study drug infusion.” While this is not wrong, the time frame is shorter for counting the infusion related adverse events.

To be crystal clear about the time period where the infusion related AEs are collected, the following sentence seems to be better.

“The incidence of adverse events considered potentially related to Flebogamma 5% DIF during or within 72 hour after completing an infusion…” (refer to http://www.ncbi.nlm.nih.gov/pubmed/17909953?ordinalpos=1&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum”

Quantile regression

Typically, the physicians will listen to statisticians for statistical analysis approaches. In some cases, the physicians can be very smart and knowledgable in statistics. In a recent discussion about exploring the correlation between change in INCAT (a scale for measuring the functional disability) and change in amplitude (a neurophysiology measure), I performed the simple correlation and calculated the Pearson's correlation coefficient. The scatter plot with fitted linear regression line were shown on the right side figure. One of the investigators pointed out that the correlation line drawn in the figure is not the maximum we can get out of it. He think that a curvilinear fitting may be better.

Through his reminder, I refit the data using spline fitting by adding quadratic term and/or cubic terms to the linear regression, the result is no better. I also learned the new regression approach, quantile regression. It turned out that the quantile regression is similar to the linear regression, but is trying to minimize the distance between the observed value and the quantile (median, 75th quantile,...) instead of minimizing the distance between the observed value and the mean. The SAS actually provided an experimental version of Quanreg procedure. The procedure can be downloaded from SAS website for free. http://support.sas.com/rnd/app/da/quantreg.html.

http://www2.sas.com/proceedings/sugi30/213-30.pdf

For my question, the quantile regression is not better than the linear regression or simply correlation.

Some words about adaptive design

Four rules to adapt by:

• Allocation rule: how subjects will be allocated to the avaialble arms
• Sampling rule: how many subjects will be sampled in subsequent stages
• Stopping rule: when to drop an arm or stop the trial (for efficacy, harm or futility)
• Decision rule: the final decision and interim decisions pertaining to design changes not covered in the previous three rules. Examples of the modifications that can result from these decision-making rules include modifying the sample size, dropping a treatment arm, stopping a study early for success or failure, combining phases, and/or adaptive randomization

Adaptive trials can involve any one of these rules, or a combination of them

Top three misconceptions of adaptive trials:

• There are certain areas of confirmatory clinical research where adaptive designs are more applicable and other areas where adaptive designs are less or not applicable
• Adaptive trial designs are characterized by unmanageable complexity and less careful planning
• Adaptive designs require smaller sample sizes than traditional designs

Human beings lean toward wishful thinking. On average, drug effects are overestimated and the variability of drug effects is underestimated. As yet, trials have either been unknowingly underpowered or intentionally overpowered. In the latter case, an adaptive design is more or less unnecessary (aside from the questionable ethics of overpowered trials). The unknowingly underpowered trials, however, is where adaptive designs come into plan. By using an adaptive design, a potentially underpowered trial can be rescued. Overall, adaptive designs make better use of the patient as a resource. Trials no long need to be overpowered, and the number of underpowered trials is rescued.

The heavy burden of the modern clinical trial protocols

A recent article by Mr Getz in Applied Clinical Trials elaborated the heavy burden of protocol design in modern clinical trials. The clinical trial design become more and more complicated, requiring more study procedures, collecting more items. More complex and demanding protocols are hurting clinical trial performance and success.

I absolutely agree with his assessment. I can even add that the protocol design may also require more blood draws from the participants for hematology, chemistry, viral testing, biomarkers, pharmacokinetics, pharmacogenetics,… In some studies, patients may be exposed to more radio material exposures than ever. I even heard that a sponsor provided a comprehensive protocol with adaptive study design with many pages of appendices to describe how Bayesian algorithms are applied. We can imagine the reaction from investigator, CRA, even CRO statisticians. I don’t know how the investigator can understand these Bayesian algorithms. One question that needs to be answered is the target of the study protocol: is it for investigators? For regulatory authorities? For IRBs? For CRAs? Or for all of them?

Everything has a balance. Eventually we will get to a point that a too complex and too demanding protocol may actual hurt the study from every aspect in terms of the cost, resource, patient enrollment, data quality, generalization of the study results,…

Who to blame? I can think of the followings:
Regulatory requirement is getting tighter and tighter.
The sponsor is getting more conservative
The sponsor is trying to collect as much information as they can with no distinguishing of the items that are really necessary and the items that is merely nice to have.
Key opinion leaders are often asking for additional items to be added to the protocol for their own interest.

Communication with non-statisticians

My fellow colleague expressed his frustration about exlaining a statistical concept to our clinical operation colleagues. I fully undertood his feelings. Sometimes, it is not easy to communicate with non-statisticians. The problem could be on both sides: stastician did not use the plain English or non-statisticians lacked the understanding of very basic statistics.

Considering my medical background, I feel a little lucky when communicating with physicians or non-statisticians. Perhaps also because of my teaching experience, I knew how to explain the complicated statistical issues in plain language to the non-statisticians. So I have one area I am proud of myself.

Below I picked up an example to demonstrate how differently the statistical terms can be explained.

Regarding three types of missing data mechnisms, here are the definition from a recent article in Drug Information Journal:

• Data are considered missing completely at random (MCAR) if, conditional upon the independent variables in the analytic model, the missingness does not depend on either the observed or unobserved outcomes of the variable being analyzed (Y)
• Data are missing at random (MAR) if, conditional upon the independent variables in the analytic model, the missingness depends on the observed outcomes of the variable being analyzed (Yobs) but does not depend on the unobserved outcomes of the variable being analyzed (Ymiss).
• Data are missing not at random (MNAR) if, conditional upon the independent variables in the analytic model, the missingness depends on the unobserved outcomes of the variable being analyzed.

Now, for the same concept, the following definitions seem to be better understandable.

• MCAR (data are missing completely at random): A "missing" value does not depend on the variable itself or on the values of other variables in the database.
• MAR (data are missing at random): The probability of missing data on any variable is not related to its particular value. The pattern of missing data is traceable or predictable from other variables in the database.
• NMAR (not missing at random): Missing data are not random and depend on the values that are missing.

Rule of three

Rule of three states that consider a Bernoulli random variable with unknown probability p, if in n independent trials no events occur, a quick-and-ready approximation to the upper 95% confidence bound for p is 3/n.
This rule has particularly been used in pre-licensure clinical trials where the adverse event rate is very rare. Sample sizes of pivotal trials for licensure are set for an efficacy endpoint, and vary according to the indication. Therefore, pivotal confirmatory studies provide adequate denominators for determining adverse events that occur at a frequency higher than or similar to the clinical efficacy outcome. However, sample sizes are not sufficient for detecting the rare events. Only reliable post-marketing surveillance systems will allow detection of a rare adverse event or a small increase in adverse event rate.
Rule of three provides a quick calculation of the upper confidence interval of the observed rate (observed rate is zero when there is no event occurred). It is based on the estimated upper limit of the 95% confidence interval when this particular event has not occurred during a clinical trial or during the clinical development program. As an example in vaccine development program, if no event has been observed with a sample size of 100, the upper limit of the 95% CI of the rate of this event is 3%. A sample size in the range of 10 000 subjects can be considered adequate for establishing the protective efficacy of a new vaccine. If no event of any particular sort has been observed during a pre-licensure clinical program involving 10 000 individuals, it can be estimated that this event rate has an upper limit of 3 per 10 000. Rarer adverse events, those occurring at a lower frequency than the vaccine-targeted disease, or an increase in rare adverse events, are unlikely to be detected before licensure their assessment must rely on post-marketing studies (phase IV).
However, if the rare event does occur during the clinical trial (the event rate is not zero), the rule of three should not be used. Instead, the confidence interval should be calculated according to exact or permutation approach (not the formula that is based on the normal approximation).

Also, we should not attempt to calculate the sample size based on the rule of three. The sample size should still be based on the efficacy endpoint instead of the comparison of the rare events – otherwise, the sample size could be huge.

References:
1. Ernst Eypasch et al. Probability of adverse events that have not yet occurred: a statistical reminder. BMJ 1995
2. Steve Simon's web blog. Stistical confidence interval with zero event

Comparing treatment difference in slopes

In regulatory setting, can we showing the treatment difference by comparing the slopes between two treatment groups?
In a COPD study (e.g., a two arm, parallel group with primary efficacy variable measured at baseline and every 6 months thereafter), one can fit the random coefficient model and compare the treatment difference between two slopes. Also we can compare the treatment difference in terms of change from baseline to the endpoint (the last measure).
To test the difference in slopes, we would need to test whether or not the treatment*time interaction term is statistically significant. The assumption is that at the beginning of the trial, the intercept for both groups are the same - both groups started at the time level. Then if the treatment can slow the disease progression, the treatment group should show a smaller slope comparing with the placebo group.
If all patients are followed up to the end of the study, if the slopes are different, the endpoint (change from baseline) analysis should also be statistically different. However, with a smaller sample size, the results could be inconsistent by using slope comparison approach vs. endpoint analysis approach. For a given study, the decision has to be made which approach is considered as the primary endpoint. Why don't we analyze the data using both approaches? then we have to deal with the adjustment for multiplicity issue.
I used to make a comment and say "some regulatory authorities such as FDA recommend the simpler endpoint analysis"; then I was asked to provide the references to suport my statement. I did quite extensive search, but I could not find any real relevant reference. However, by reviewing 'statistical reviews' in the BLA and NDA in US, it is very rare to see any product approval based on the comparison of the slopes. Many product approvals are based on the comparison of 'change from baseline'.
So this is really a regulatory question. Every indication has their accepted endpoints so tradition takes precedence. According to my colleague, there is a movement in the Alzheimer's arena to look at differences in slopes, but this is basedon trying to claim disease modification. If this is the case, we may also apply this to the COPD area since for certain type of COPD, we can claim the disease modification by showing the differences in slopes.been used in COPD before?
On the other hand, It seems that that the slope model (random coefficient model) may be preferred in academic setting, but endpoint approach - change from baseline (with last value carried forward) may be more practical in the industry setting.
From statistical point of view, the slope approach makes a lot of sense, however, we need to be cautioned about some potential issues: 1. In some endpoint measure, there may be some type of plateau. If you reach that plateau prior to the end of the study there will be a loss of power comparing slopes as compared to some comparison of just the endpoint results or some type of general repeated measures assessment of the average treatment difference.2. If the slope comparison is used as the primary efficacy measure, the # of measurements per year on the primary efficacy variable is relevant. One may think that the more frequent measures will increase the power to show the treatmetn difference in slopes. The question arise when designing the study: are you choose a shorter trial with more frequent measures? or are you choose a longer trial with less frequent measures?

Geometric Statistics, geometric CV, intra-subject variation

In bioavailability and bioequivalence studies, the pharmacokinetic parameters (AUC, Cmax) are often assumed to follow the log normal distribution. Further about log-normal distribution.

The common technique is to calculate the geometric statistics (geometric mean, geometric CV and geometric SD). Notice that the geometric CV is independent of the geometric mean (unlike the arithmetic CV which is dependent on the arithmetic mean) and the geometric CV is used in the sample size calculation. When calculating the geometric statistics, the data in original scale is log-transformed, then anti-log to transform back.

In crossover design, the geometric CV can be estimated from the mixed model and is used to gauge the intra-subject variation. Geometric CV = sqrt(exp(std^2)-1) or CV=sqrt(exp(variance)-1) where the std^2 is estimated by the MSE. Variance is from ODS ‘CovParms’ table of SAS PROC MIXEd. Another variation is inter-subject CV and the std^2 is estimated by the variance estimate for the random subject effect from the proc mixed procedure.

It should be cautioned that Geometric CV sometimes is just being called CV or intra-subject variability. I heard that some large pharmaceutical companies include 'intra-subject variability' in the standard data presentation for pharmacokinetic parameters.

The topic about the CV, geometric CV was discussed in Boomer.org (http://www.boomer.org/pkin/PK06/PK2006512.html), a discussion mailing list on bioavailability and bioequivalences. Boomer.org used to be a great resource for PK-related discussion. However, recently the discussion group was dominated by a lot of the junkies posted by Indian guys. I guess it is because of the booming generic drug development industry in India.

Good Clinical Practice: A question & answer reference guide

I recently find the book edited by Parexel is extremely useful.

Good Clinical Practice: A Question & Answer Reference Guide
Edited by Mark P. Mathieu, Parexel Internationl Corporation
http://www.barnettinternational.com/ or
http://www.barnettinternational.com/EducationalServices_Publication.aspx?p=6544

Due to the fact that I work side by side with study managers and medical directors and due to my responsibility of overseeing the data management activities (outside of my responsibilities for biostatistics), I am involved in a lot of discussions the data collection, data quality. In a lot of situations, the decision has to be made on whether or not an event should be collected as an adverse events or how an event should be collected, ......

The Good Clinical Practice is just like the law. A lot of guidances really depends on how to interpret. The book of "A Question & Answer Reference Guide" is the one attempting to provide the interpretation of the GCPs with practical questions.

Here are two exmamples extracted from this book:

Q. Assuming that it is a study exclusion criterion, is a pregnancy while on study considered an AE? Is it considered an SAE?
A. In and of itself, a pregnancy is not considered an AE or SAE. However, abortion, whether accidental, therapeutic, or spontaneous, should always be classified as a SAE and expeditiously reported to the sponsor. Similarly, any congenital anomaly/birth defect in a child born to a female subject exposed to the investigational product should be recorded and reported as an SAE.
…..

Q. should expected clinical outcomes of the disease under study, which are efficacy endpoints, be reported as AEs/SAEs?
A. Some protocols instruct investigators to record and report all untoward events that occur during a study as AEs/SAEs, which could include common symptoms of the disease under study and/or other expected clinical outcomes. This approach enables frequency comparisons of all events between treatment groups, but can make event recording in the CRF burdensome, result in more expedited reports from investigators to sponsors, and fill safety database with many untoward events that most likely have no relationship to study treatment and that could obscure signal identification.
In some clinical trials, disease symptoms and/or other expected clinical outcomes associated with the disease under study, which might technically meet the ICH definition of an AE or SAE, are collected and assessed as efficacy parameters rather than safety parameters.
.....

==========
Recently, We have a study where several subjects had elective procedures (breast augmentation, mole removal,...). To show the diligence, we might be tempted to consider them as adverse events (even though they are not drug related), however, the elective procedures should not be considered as adverse events. These elective procedures can be collected on a separate CRF page, but should not be reported in AE page.

The best way is to specify the detail either in the study protocol or in the initial training provided to the investigational sites prior to the study start so that the same criteria are followed and all investigators are clear what should be reported and what should not be reported.

Are we become slaves of the Intent to Treat Principle?

The intent to treat (or intention to treat) principle was invented by the statistician about 30 years ago. It took a while for the clinical trial community to accept the this concept. Nowadays, the intent to treat principle has been well accepted by the people well beyond the statisticians. However, I don't think everybody really understand the concept even though he (or she) may mention the intent to treat pricinple every time he (or she) can. I have been really bothered by the comments from regulatory reviewers to suggest us to define an intent to treatment population for studies without randomization and without placebo or active control (for example, a dose escalation study). We seem to become slaves of the intent-to-treat.

In a lot of situations, the intent to treat principle is misunderstood. The intent-to-treat concept is tied with randomization for treatment allocation. No randomization, no Intent-to-treat.
The intent-to-treat concept is really for the large scale, confirmatory, pivitol studies. For very ealier stage studies (for example, the dose escalation studies) with very few subjects, there is no need to follow the intent-to-treat principle.

Intent to treat population includes all randomized patients in the groups to which they were randomly assigned, regardless of their adherence with the entry criteria, regardless of the treatment they actually received, and regardless of subsequent withdrawal from treatment or deviation from the protocol. Stricly according to the intent to treat principle, if a subject is randomized, but never receive study medication, the subject would be included in the statistical analysis; if a subject is randomized to drug A, but wrontly takes the drug B, the subject would be analyzed in treatment group A, not B (so called as randomized, not as treated); if a subject is randomized, but with no outcome measures, the subject would be included in the analysis with subject considered as treatment failure.

Intention to treat analyses are done to avoid the effects of crossover and drop-out, which may break the randomization to the treatment groups in a study. Intention to treat analysis provides information about the potential effects of treatment policy rather than on the potential effects of specific treatment.

To apply the intent to treat principle, an appropriate method for handling the missing data needs to be specified. A popular practical approach (not idea approach from statistical standpoint) is last value carried forward.

Intent to treat principle is not needed for all clinical trials and should not be interpreted as "include all enrolled subjects" or "all subjects who signed informed consent". The intent to treat is from the randomization standpoint, it has nothing to do with "study subject has intention to be treated in the clinical trial".

References:
1. ICH guidance E9 http://www.fda.gov/cder/guidance/ICH_E9-fnl.pdf
2. My presentation on ITT vs mITT http://webspace.webring.com/people/eu/um_3826/ITT_mITT_JSM2004.ppt
3. Wikipedian http://en.wikipedia.org/wiki/Intention_to_treat_analysis

Calculation of length of the study drug exposure

We have been using the following formula in calculating the length of the study drug exposure in many studies:

# of days of drug exposure = the last dose date - the first dose date + 1

However, this seems to be correct only if the subject receive daily dose of the study medication. We have many studies where the subject receive weekly infusion or every three weeks infusion of the study medication. In this situation, the above formula will underestimate the length of the study drug exposure.

The correct formula should be tied up with the dose interval.

If a subject receive weekly dose, the formula would be:
# of days of drug exposure = the last infusion date - the first infusion date + 7
or
# of weeks of drug exposure = (the last infusion date - the first infusion date + 7)/7

If a subject receive study drug every three weeks, the formula would be:
# of days of drug exposure = the last infusion date - the first infusion date + 21
or
# of weeks of drug exposure = (the last infusion date - the first infusion date + 21)/7

An early clinical trial with N=2

In the late 18th century, King Gustav III of Sweden decided that coffee was poison and ordered a clinical trial.

• The King condemned a convicted murderer to drink coffee every day.
• The control was another murderer who was condemned to drink tea daily.
• The outcome measure is 'death'.
• Two physicians were appointed to determine the outcome.

Results:

• The two doctors died first
• The king was murdered
• Both convicts enjoyed long life until the tea drinker died at age 83. (no age was given for the coffee drinker)

J Int Med. Oct 1991:289 - introduction to editorial from Nordic School of Public Health, Goteborg Sweden

Reprinted in Annal of Internal Medicine 1992: 117:30

Lack of clinical equipoise

Clinical equipoise provides the ethical basis for conduct of randomized clinical trials. This principal states that a clinical trial is acceptable only insofar as there is professional disagreement between researchers concerning uncertainty regarding the outcome of the study.1and thus even if a clinician prefers one arm over another, randomization is still sound when there are others who believe the other way around.

However, there are often different opinions between regulatory and investigator regarding the interpretation of equipoise and biases generated from publications suggestive of the efficacy of one product over another may cause lack of equipoise. Lack of clinical equipoise causes unwillingness at the investigator level to enroll patients because of strong belief by the majority of physicians in one treatment being superior over another. This results in difficultly in designing studies to support licensing a product such that physicians are unwilling to participate in a study that is necessary to satisfy regulatory agency(s) requirements for trial data demonstrating efficacy and safety.


References:
Freedman B: Equipoise and the ethics of clinical research. N Engl J Med 317: 141-145, 1987
Lilford RJ Declaratio of Helsinki should be strengthened. BMJ 322(7281) 299
Ashcroft R: Equipoise, knowledge and ethics in clinical research and practice. Bioethics 13 (3/4):314-326, 1999
Royall RM: Ethic and Statistics in Randomized Clinical trials. Statistical science 6(1): 52-66 1991

Phase 0 clinical trial

Traditionally, we have been talking about the phase I to phase IV clinical trials in drug development. We start from the small trials in healthy volunteers (phase I) to dose finding or proof of concept (POC) trials (phase II) to pivotal trials (Phase III), the post-marketing trial (Phase IV). Or we start from trials for MTD (maximal tolerable dose) - Phase I to trials for MED (minimal effective dose) - Phase II. Now there comes a new phase of clinical trial - Phase 0.
According to wikipedia, "Phase 0 is a recent designation for exploratory, first-in-human trials conducted in accordance with the U.S. Food and Drug Administration’s (FDA) 2006 Guidance on Exploratory Investigational New Drug (IND) Studies.[7] Phase 0 trials are also known as human microdosing studies and are designed to speed up the development of promising drugs or imaging agents by establishing very early on whether the drug or agent behaves in human subjects as was anticipated from preclinical studies. Distinctive features of Phase 0 trials include the administration of single subtherapeutic doses of the study drug to a small number of subjects (10 to 15) to gather preliminary data on the agent's pharmacokinetics (how the body processes the drug) and pharmacodynamics (how the drug works in the body).
A Phase 0 study gives no data on safety or efficacy, being by definition a dose too low to cause any therapeutic effect. Drug development companies carry out Phase 0 studies to rank drug candidates in order to decide which has the best PK parameters in humans to take forward into further development. They enable base go/no go decisions to be based on relevant human models instead of relying on animal data, which can be unpredictive and vary between species."

While the term 'phase 0' is fancy and novice, the usefulness of phase 0 trials needs to be proved in the future. At this point, I guess it is just a concept from governmental agencies such NCI (national cancer institute). I doubt that the industry will really be interested in this Phase 0 trial.