COPD as a Syndrome

Typically, in medical journals, we don’t like to include a lot of formula or mathematical expressions in articles. However I recently read a paper where a mathematical expression is used to describe the COPD as a syndrome. It is a good use of mathematical expression in this case.

John J. Reilly “COPD and declining FEV1 – time to divide and conquer?” (NEJM 359:15: 1616-1618 OCT 2009)

“In fact, COPD in the singular is probably a misnomer. It is more appropriate to view COPD as a syndrome that encompasses a variety of obstructive diseases that share a common exposure but differ in terms of mechanism of disease and response to therapy. This concept is expressed in the mathematical notation

 

In which COPD_n represents subgroups of COPD. As a reflection of this recognized heterogeneity, investigator have developed new classification systems, such as the BODE index, which evaluates the body-mass index, the degree of airflow obstruction and dyspnea, and exercise capacity to create a 10-point scale in which higher scores indicate a higher risk of death. In addition, investigators have attempted to define other homogeneous subgroups of patients with COPD. “

For many complicated diseases, when we have a better understanding about the disease, we will see that one specific disease may have many different manifestations or phenotypes – so called ‘heterogeneity’. When designing a clinical trial for a disease with heterogeneity, it may be difficult to show the treatment effect on the patient population as a whole. The drug may only be effective on one of many specific sub-populations. The difficult is usually in finding this specific sub-population. The newly issued FDA guidance “Enrichment Strategies for Clinical Trials to Support Approval of Human Drugs and

Biological Products” is attempting to address this issue. 

Current Topics in Bioethics - A Free Seminar

Free Seminar on Current Topics in Bioethics By Sheila Mikhail, founder of Life Sciences Law (LSL)

The field of bioethics merges various aspects of several disciplines, including biology, medicine, politics, and law. The role of human subjects in the investigation of therapeutics gives rise to several concerns within the realm of bioethics, with such concerns increasing over time.

This presentation will provide an overview of several areas with respect to bioethics, including:

(1) ensuring the safety of study participants in human clinical trials as it relates to the process of obtaining and ensuring informed consent,

(2) the use of drugs that have not yet been approved by the FDA for patients who have no other treatment options available under compassion use principles,

(3) the ownership of biological samples isolated from patients, and

(4) the patentability of isolated gene sequences.

References:

• Tuskegee syphilis experiment
• Nazi Human Experiments
• Unethical Human Experiment in the United States
• Henrietta Lacks
• Hela Cell
• University of Washington vs Dr Catalona
• Havasupai Tribe vs. Arizona State University
• Human Specimen Collection, Biobanking and Genetic Research: PredictER Law and Policy Update
• Compassionate use of unapproved drugs
• Guatemala syphilis experiment
• Results of Death-Camp Experiments: Should They Be Used?
• Nazi Medical Experimentation: Should the Data Obtained be Used?

Should Expected Clinical Outcomes of the Disease under Study, which are study endpoints, be reported as AEs/SAEs?

Generally not. The new IND safety reporting regulation and associated guidance state that study endpoints that are Serious Adverse Events (SAEs) (i.e., the study is evaluating whether the Investigational Medicinal Products reduces the rate of the SAE) do not require immediate reporting to the sponsor unless there is evidence suggesting a causal relationship between the drug and the event. For example, a death ordinarily would not require immediate reporting to the sponsor as an SAE under a trial designed to compare all-cause mortality in subjects receiving either drug treatment or a placebo. On the other hand, in the same trial with an all-cause mortality endpoint, if the death occurred as a result of an anaphylactic reaction that coincided with initial exposure to the drug, or as a result of fatal hepatic necrosis, the death must be immediately reported to the sponsor as a SAE

Study endpoint information should be collected, tracked, and monitored, usually by a data monitoring committee, during the course of the study. The sponsor must report study endpoints to the FDA according to the protocol and not as IND safety reports, except in unusual cases as described above.

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 that are not study endpoints. 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 sponsor, and fill safety databases with many untoward events that most likely have no relationship to study treatment and that could obscure signal identification.

In practice, some companies take the conservative approach and report the underlying disease symptoms / outcomes as the adverse events or serious adverse events. To report all  underlying disease symptoms / outcomes as adverse events is overkill and the symptom worsening could just be the lack of efficacy for the study drug. It may be reasonable not to report the underlying disease symptoms / outcomes as adverse events, but if the underlying disease symptoms / outcomes result in the consequences that meet the criteria for serious adverse event (see the SAE criteria in ICH E2D or EMA document ICH topic E2 A), the underlying disease symptoms / outcomes should be reported as AE and SAE. This seems to be the approach that a lot of pharmaceutical companies are taking.

In a clinical trial with tiotropoim in COPD patients, Boehringer-Ingelheim reported the most commonly serious adverse events as the following:

“COPD exacerbations were the most commonly reported serious adverse events. The incidence of COPD exacerbations reported as SAEs was lower in the Tiotropium group compared to placebo group (4.2% vs. 6.7% of patients, respectively).”

COPD exacerbation is underlying disease symptom and efficacy measure of the COPD, but if COPD exacerbation requires the hospitalization (also meet the severe exacerbation definition), the COPD exacerbation would need to be reported as SAE even though the mild / moderate COPD exacerbation may not be reported as adverse event.

For a clinical trial with thrombolytic agents such as tPA, any bleeding event should be reported as AE. However, in trials in Hemophilia patients, the bleeding event is the manifestation for the underlying disease (hemophilia A). The bleed event should not be reported as AE unless it meet the SAE criteria in which case, the bleeding event should be reported as SAE.  Bleeding would also be included in the efficacy assessment.  It would be appropriate to specify in the study protocol what should be and what should not be reported. The following language would be adequate in Hemophilia trials.

“Spontaneous and trauma-related bleeding episodes are expected as usual events in subjects with severe Hemophilia A. Thus, bleeding episodes need not be reported as adverse events unless severe enough to be classified as a serious adverse events. “

 In the summary of approval document for ADVATE, the bleeding event (Hematoma)  is reported as serious and drug related adverse event.

The worst situation is that the rule for reporting is not explicitly specified in the study protocol and different investigational sites take different approaches (at investigator’s discretion) in reporting the AE/SAE for underline disease symptoms / outcomes. In this situation, data are inconsistent across different sites. 

Non-interventional Clinical Studies

For interventional clinical trials, there are clear regulatory guidelines to govern the development of the study protocol, the conduct of the study, and the layout of the clinical study report. The requirements for clinical study protocol are specified in ICH E6 (Guideline for Good Clinical Practice) and for clinical study report are specified in ICH E3 (Structure and Contents of Clinical Study Reports).

However, there are also non-interventional clinical studies such as Post-Marketing Surveillance (PMS), Post Authorization Safety Study (PASS), Pharmacovigilence study, Pharmacoepidemiologic Study, Drug utilization study, Retrospective and Prospective registry studies. They are clinical studies because the patient information is collected from various clinical sites. They are non-interventional because the sponsor does not provide the study drug and provide no specific requirements how the patients should be treated. The regulatory guidelines for this type of non-interventional clinical studies can sometimes be vague and not as clear as those for clinical trials. The ICH guidelines that were developed for clinical trials may not all be applicable to this type of non-interventional clinical studies.

Post-Marketing Surveillance (PMS) studies are non-interventional, observational, post-authorization studies where the medicinal product(s) is (are) prescribed in the usual manner in accordance with the terms of the marketing authorization (i.e. no study medication). The assignment of the patient to a particular therapeutic strategy is not decided in advance by the trial protocol but falls within current practice and the prescription of the medicine is clearly separated from the decision to include the patient in the study. No additional diagnostic or monitoring procedures shall be applied to the patients and epidemiological methods shall be used for the analysis of collected data.

Post Authorization Safety Study (PASS) in EU: A post-authorization safety study (PASS) is defined in Directive 2001/83/EC (DIR) Art 1(15) as any study relating to an authorized medicinal product conducted with the aim of identifying, characterizing or quantifying a safety hazard, confirming the safety profile of the medicinal product, or of measuring the effectiveness of risk management measures.

Pharmacoepidemiology study: A pharmacoepidemiology study applies epidemiological methods to studies of the clinical use of drugs in populations. A modern definition of pharmacoepidemiology is: «the study of the use and effects/side-effects of drugs in large numbers of people with the purpose of supporting the rational and cost-effective use of drugs in the population thereby improving health outcomes».

Drug utilization research: drug utilization study was defined by WHO in 1977 as «the marketing, distribution, prescription, and use of drugs in a society, with special emphasis on the resulting medical, social and economic consequences». Since then, a number of other terms have come into use and it is important to understand the interrelationships of the different domains.

Pharmacovigilance are terms used to refer to the monitoring of drug safety, for example, by means of spontaneous adverse-effect reporting systems, case-control and cohort studies.

Retrospective Registry: A retrospective study uses information on events that have taken place in the past. In most cases some or most of the data has already been gathered and stored in the registry. However and also in most cases, new data, but always on past events, may have to be requested. The data collection is typically through the medical charter review

Non interventional prospective studies: Non interventional prospective studies are prospective studies which are set up to investigate events that will take place after the study has been initiated. Post-marketing surveillance study may fall into this category. The main and very important difference between a clinical trial and a non interventional prospective study is that the data collection or patient-participation in the non interventional study does not interfere with the choice of treatment, sample collection, procedures, and the treatment itself, which should entirely follow standard hospital practices. Typically a non interventional prospective study behaves like a “fly on the wall” and will aim to collect information on how different centres deal with the same problem or type of patient. A non interventional prospective protocol cannot in any way set up conditions modifying either the treatment of the patient or the number or type of investigations the patients needs to be subjected to. It excludes the possibility of determining which treatment protocol should be used, randomiation or other types of patient allocation to a specific treatment, specified sample collection schedules, or collection of additional samples not included in the center’s routine procedures.

In terms of the elements required for the study protocol and the study report, there are different guidelines than the clinical trials. For PASS study, EMA: Guideline on good pharmacovigilance practices (GVP) Module VIII – Post-authorisation safety studies  should be followed for developing the study protocol and the study report. There are also recommendations on data collection, data quality, data analysis.

For general pharmacoepidemilogy studies, International Society of Pharmacoepidemiology: Guidelines for Good Pharmacoepidemiology Practices (GPP)  should be followed.

In the United States, the most relevant guidance is Good Pharmacovigilance Practices and Pharmacoepidemiologic Assessment and E2E Pharmacovigilance Planning which is mainly focusing on the post-marketing data collection on adverse events or adverse drug reactions.

The recent passed PDUFA V also emphasize the importance of pharmacovigilance:

 C. Conduct and support activities designed to modernize the process ofpharmacovigilance
1. Continued use of expanded database resources: A critical part of the
transformation of the drug safety program is maximizing the usefulness of
tools used for adverse event signal detection and risk assessment. Use of data
other than passive spontaneous reports, including population-based
epidemiological data and other types of observational data resources will
continue to enhance FDA’s capability to conduct targeted post-marketing
surveillance, evaluate class effects of drugs, and potentially conduct signal
detection using data resources other than reports from the Adverse Event
Reporting System (AERS).  FDA will continue training and development of
existing staff on the use of these resources, and develop the information
technology infrastructure needed to support access and analysis of data from
these resources.  

Randomized Withdrawal Design and Randomized Discontinuation Trial

FDA recently issued its new guidance to industry “EnrichmentStrategies for Clinical Trials to Support Approval of Human Drugs andBiological Products”. The section D of the guidance detailed the Randomized Withdrawal Design as one of the enrichment strategies.

Randomized Withdrawal study design is not new and has been practically used in many drug trials and in NDA or BLA applications. It may be called 'randomized discontinuation trial' or 'randomized discontinuation design' in some literature. Randomized Withdrawal study design is especially popular in CNS and neurology areas.

According to ICH ICH Topic E 10 “Choice of Control Group in Clinical Trials”, the Randomized Withdrawal design is defined as:

 “In a randomized withdrawal trial, subjects receiving a test  treatment for a specified time are randomly assigned to continued treatment with the test treatment or to placebo (i.e., withdrawal of active therapy).  Subjects for such a trial could be derived from an organized open single-arm study, from an existing clinical cohort (but usually with a protocol-specified "wash-in" phase to establish the initial on-therapy baseline), from the active arm of a controlled trial, or from one or both arms of  an active control trial.  Any difference that emerges between the group receiving continued treatment and the group randomized to placebo would demonstrate the effect of the active treatment.  The pre-randomization observation period on treatment can be of any length; this approach can therefore be used to study long-term persistence of effectiveness when long-term placebo treatment would not be acceptable.  The post-withdrawal observation period could be of fixed duration or could use early escape or time to event (e.g., relapse  of depression) approaches.  As with the early escape design, careful attention should be paid to procedures for monitoring patients and assessing study endpoints to ensure that patients failing on an assigned treatment are identified rapidly. 

Randomized Withdrawal design is recommended in ICH E12A “PRINCIPLES FOR CLINICAL EVALUATION OF NEW ANTIHYPERTENSIVE DRUGS “

In 2001, Nation Academies published a report “Small Clinical Trials: Issues and Challenges”. It stated the advantages and disadvantages of the Randomized Withdrawal Design:

 “The advantages of this study design are that individuals receiving the experimental intervention continue to do so only if they respond, whereas individuals receiving the placebo do so only until their symptoms return. Disadvantages include carryover effects, difficulties assessing whether the underlying disease process is still active, and long lag times to adverse events if the disease is in remission. This design is more appropriate in phase I and II trials involving healthy volunteers because it is less likely that effective treatments are being withdrawn from those who need it. In some studies, however, measurement of the placebo effect is essential (e.g., studies of drugs for the treatment of depression), and such studies might require the use of a randomized withdrawal design. In those cases, voluntary, informed consent is essential, as is the provision of care during the withdrawal period.”

Additional advantage of this design is to study the long-term efficacy or safety (withdrawal effect) and additional disadvantage is the longer overall study period (additional period is needed to identify responders).

Some of the practical cases of using the Randomized Withdrawal Design in clinical trial are:

• Randomized withdrawal design used in the clinical trials for ADHD
• Cystic Fibrosis-Dependent Exocrine Pancreatic Insufficiency trial used an open label period to establish the baseline for the randomized withdrawal period. The endpoint is the change from the baseline after the open label period to the end of the randomized withdrawal period
• Flibanserin (a so-called Female Viagra drug) for the treatment of hypoactive sexual desire disorder (PSDD) in premenopausal women. One of the pivotal trials began with a 24-week open label phase, which then enrolled only “responders” into the randomized withdrawal phase
• IGIV-C for the treatment of chronic inflammatory demyelinating polyradiculoneuropathy (ICE study): the trial had two randomization. The second randomization was only for responders (to IGIV-C or Placebo) from initial (the first) randomized period. In extension period, the randomized withdrawal design was employed. The responders were re-randomized to IGIV-C or Placebo to study the long-term efficacy by comparing the relapse rates and the time to relapse. Notice that the responders from Placebo group in initial (first) randomized period were also included in the randomized withdrawal period. This is purely for maintaining the blinding.

Additional Reference:     

• Enrichment Strategies Robert Temple, MD
• Kopec (1993) Randomized Discontinuation Trial: Utility and Efficiency
• Strdler (2007) The Randomized Discontinuation trial: a phase II design to assess growth-inhibitory agents
• 
  

Adverse Reaction and Drug-Related Adverse Event

In all clinical trials, it is important to collect the adverse event information accurately. It is very common to collect the adverse events and the characteristics of these adverse events (including seriousness, severity, causality to the study drug, and so on). The causality is usually assessed by the investigator using the categories of ‘not related, unlikely related, possibly related, and related’. These categories are used in ICH E2B examples and the CDASH standards. It is also very common to define the ‘drug-related adverse events’ as any adverse events assessed by the investigators as ‘possibly related’ or ‘related’. The statistical summaries will usually include the tables for drug-related adverse events.

When it comes to the product labels or package inserts, instead of using the term ‘drug-related adverse events’,  ‘adverse drug reaction (ADR)’ or simply “adverse reaction” is used.

Is the term ‘adverse reaction’ equivalent to the term ‘drug-related adverse event’? There is no formal guidance anywhere to indicate that the ‘adverse reaction’ and ‘drug-related adverse event’ are the same thing. In Statement about “Drug-related Adverse Events” of Dr Janet Woodcock before the Senate Committee on Health, Education, Labor, and Pensions in 2000, the terms of ‘drug-related adverse events” and “adverse drug reactions” were used interchangeably.

However, if we look at the formal definition of ‘adverse reactions’ in regulatory guidelines, there seems to be some differences between these two terms and ‘adverse reactions’ seem to enable the sponsor to make the judgment whether or not an adverse event is considered as ‘adverse reaction’, especially for the product labeling purpose. If this is the case, the term ‘adverse reaction’ will be different from the ‘drug-related adverse event’ since the drug-related adverse event is based on the investigator’s assessment.

In the United States, all product labels are required to follow the new formats according to FDA’s guidance “Labeling for Human Prescription Drug and Biological Products — Implementing the New Content and Format Requirements” issued in 2006. The new format requires the reporting of adverse reactions in section 6 of the package insert.

The guidance requires the following:

“• Most frequently occurring adverse reactions
 Information under the Adverse Reactions heading must include a listing of the most frequently occurring adverse reactions, even if they are included elsewhere in Highlights, and the criteria used to determine inclusion (e.g., incidence rate).  The listing should be concise, not lengthy or comprehensive.  This listing may include adverse reactions that are important for reasons other than frequency (e.g., leading to discontinuation or dosage adjustments) unless they are included elsewhere in Highlights (e.g., under Warnings and Precautions or Contraindications).

The adverse reactions listed as most frequently occurring or most common should be selected from the table of adverse reactions from clinical trials in the FPI.  Rates of most common adverse reactions vary, but should be appropriate to the nature of a drug’s adverse reactions profile and the size and composition of the safety database.  The criteria for determining inclusion must be identified along with the listing (e.g., >2%).  If adverse reaction profiles vary significantly for different indications, list the most common adverse reactions by indication.  Also note if different criteria for determining inclusion are used for different indications.”

FDA also issued a subsequent guidance on “Adverse Reactions Section of Labeling for Human Prescription Drug and Biological Products — Content and Format” that attempted to provide clarifications about the adverse reactions section of labeling. This guidance provided the definition for ‘adverse reaction’ and ‘adverse event’ as following:

“Adverse Reaction (21 CFR 201.57(c)(7)): For purposes of prescription drug labeling and this guidance, an adverse reaction is an undesirable effect, reasonably associated with the use of a drug, that may occur as part of the pharmacological action of the drug or may be unpredictable in its occurrence. This definition does not include all adverse events observed during use of a drug, only those for which there is some basis to believe there is a causal relationship between the drug and the occurrence of the adverse event. Adverse reactions may include signs and symptoms, changes in laboratory parameters, and changes in other measures of critical body function, such as vital signs and ECG.
 Adverse Event (or adverse experience): For the purposes of this guidance, the term adverse event refers to any untoward medical event associated with the use of a drug in humans, whether or not considered drug-related.”

Unfortunately, the definition of ‘Adverse reaction’ did not specify who (investigator or sponsor) to make the judgment about ‘reasonably associated with the use of a drug’ and what will be considered as ‘reasonably associated with the use of a drug’. Some people think that ‘unlikely related’ adverse events should also be considered as ‘adverse reactions’ since the association with the drug can not be totally eliminated.

In EMA’s document “CLINICAL SAFETY DATA MANAGEMENT: DEFINITIONS AND STANDARDS FOR EXPEDITED REPORTING” ICH Harmonised Tripartite Guideline, the definition of ‘adverse reaction’ is also discussed. The document provided the definition of ‘adverse reaction’ in two different situation: pre-approval  and the marketed product. The same definition is also echoed in ICH GCP Glossary.

“A. Basic Terms
Definitions for the terms adverse event (or experience), adverse reaction, and unexpected adverse reaction have previously been agreed to by consensus of the more than 30 Collaborating Centres of the WHO International Drug Monitoring Centre (Uppsala, Sweden). [Edwards, I.R., et al, Harmonisation in Pharmacovigilance. Drug Safety 10(2): 93-102, 1994.] Although those definitions can pertain to situations involving clinical investigations, some minor modifications are necessary, especially to accommodate the pre-approval,
development environment.
 The following definitions, with input from the WHO Collaborative Centre, have been agreed:
1. Adverse Event (or Adverse Experience)
Any untoward medical occurrence in a patient or clinical investigation subject
administered a pharmaceutical product and which does not necessarily have to have a causal relationship with this treatment. An adverse event (AE) can therefore be any unfavourable and unintended sign (including an abnormal laboratory finding, for example), symptom, or disease temporally associated with
the use of a medicinal product, whether or not considered related to the medicinal product.
 2. Adverse Drug Reaction (ADR)
In the pre-approval clinical experience with a new medicinal product or its new usages, particularly as the therapeutic dose(s) may not be established:
all noxious and unintended responses to a medicinal product related to any dose
should be considered adverse drug reactions. The phrase "responses to a medicinal products" means that a causal relationship between a medicinal product and an adverse event is at least a reasonable possibility, i.e., the relationship cannot be ruled out.
 Regarding marketed medicinal products, a well-accepted definition of an adverse drug reaction in the post-marketing setting is found in WHO Technical Report 498 [1972] and reads as follows:
 A response to a drug which is noxious and unintended and which occurs at doses normally used in man for prophylaxis, diagnosis, or therapy of disease or for modification of physiological function.
 The old term "side effect" has been used in various ways in the past, usually to describe negative (unfavourable) effects, but also positive (favourable) effects. It is recommended that this term no longer be used and particularly should not be regarded as synonymous with adverse event or adverse reaction.
 3. Unexpected Adverse Drug Reaction
An adverse reaction, the nature or severity of which is not consistent with the
applicable product information (e.g., Investigator's Brochure for an unapproved
investigational medicinal product). (See section III.C.)
 B. Serious Adverse Event or Adverse Drug Reaction
During clinical investigations, adverse events may occur which, if suspected to be medicinal product-related (adverse drug reactions), might be significant enough to lead to important changes in the way the medicinal product is developed (e.g., change in dose, population, needed monitoring, consent forms). This is particularly true for reactions which, in their most severe forms, threaten life or function. Such reactions should be reported promptly to regulators.
Therefore, special medical or administrative criteria are needed to define reactions that, either due to their nature ("serious") or due to the significant, unexpected information they provide, justify expedited reporting.”

In 2006, EC issued “Detailed guidance on the collection, verification and presentation of adverse reaction reports arising from clinical trials on medicinal products for human use”. It specified that the causality should be assessed by both the investigator and the sponsor.

 “All adverse events judged by either the investigator or the sponsor as having a reasonable suspected causal relationship to an investigational medicinal product qualify as adverse reactions. The causality assessment given by the investigator should not be downgraded by the sponsor. If the sponsor disagrees with the investigator’s causality assessment, both, the opinion of the investigator and the sponsor should be provided with the report.”

For now, at least we know that the ‘adverse reaction’ and ‘drug-related adverse event’ are not the same. 'Adverse reaction' concept may have different meaning in pre-marketing clinical trials and in post-marketing studies.

In the product label for ADVATE, the 'adverse reaction' has the following definition:

"ADRs are defined as any Adverse Event that occurred within 24 hours after being infused with investigational product OR all Adverse Events assessed related or possibly related to  investigational product OR Adverse Events for which the investigator's or sponsor's opinion of causality was missing or indeterminate."

where Adverse event that occurred within 24 hours belongs to the concept of 'temporally related adverse events'. The time frame of 24 hours, 48 hours, or 72 hours,...should be determined based on the nature of the drug (for example, the half life).

In a FDA's medical review memo, the reasoning for using above ADR definition was discussed:

"...That most recent information request asked the sponsor to use the following definition of adverse reaction (ADR) for the pooled analysis of ADRs in Table 3 in the ADVERSE REACTIONS section of the PI:

Any Adverse Event that began during an infusion or <= 24 hours after the end of an infusion with the investigational product OR all Adverse Events assessed by the investigator or sponsor as related, probably related, or possibly related to investigational product OR Adverse Events for which the investigator's or sponsor's opinion of causality was missing or indeterminate.

I recommend the sponsor be asked to re-title Table 3 and to use a footnote to provide the operational definition of ADR used for this pooled analysis. OBRR commonly uses temporal association to help define ADRs in clinical trials lacking a randomized parallel placebo group, since there is no assurance in such cases that the sponsor’s or investigator’s assessments of causality of AEs is necessarily accurate. Limiting ADRs in such trials to the ADRs identified by the investigator may lead to an underestimation of the true ADR incidence, but no causality assessment method is wholly satisfactory in the absence of a gold standard. Given that many of the pooled Advate trials involved routine prophylaxis given up to 4 times weekly, the 24 hour time frame for temporal association of ADRs is considered to be more useful in helping to define possibly causally related ADRs, rather than the 72 hour time frame which OBRR has often employed for parenteral biologic products given at less frequent intervals. The sponsor submitted at FDA request the results of ADR analyses as tables using timeframes of temporal association of 24, 48, and 72 hours to help define ADRs (in addition to ADRs consisting of AEs already identified as at least possibly related according to the investigator or the sponsor)."

Reference: 

• FDA (2006) Guidance for Industry Adverse Reactions Section of Labeling for Human Prescription Drug and Biological Products — Content and Format

Patient-focused Drug Development and Qualitative Methods in Clinical Trial

Almost 20 years ago, we introduced the qualitative methods in public health research in China. We organized the workshops and published the papers. We actually implemented the qualitative methods when we assessed the health care quality, KABP (Knowledge, Attitudes, Beliefs, and Practices) survey, and breast feeding research.

I have never thought that I would use any qualitative method since I am now a statistician and in the drug development field.  Last month, I attended a one-day interactive workshop “Clinical trial endpoints: methods and practice in developing measurements” with special emphasis on rare diseases. We spent majority of our time in discussing the qualitative methods (mainly the patient interview) to establish the content validity for patient oriented clinical trial endpoints (for example, the patient reported outcome (PRO)). For many orphan (rare) diseases, the characteristics of the disease may not be well defined and patient population in study may not be homogeneous. For the purpose of the clinical trials, it is challenging to identify a valid clinical endpoint to measure the efficacy. One approach to tackle this is to start with the patient. To interact with patients, the qualitative methods (for example the interview with patients) can be used to solicit the issues/items that are most concerned by the patients and hopefully we can based this information to derive a clinical endpoint for the clinical trials.

With patient-focused approach, the clinical endpoint should reflect the impact on:

• Patient’s mortality
• Patient’s function
• Patient’s feeling

This will be much different from the biomarkers which may not have any direct impact on any of these (at least in the short term). It is probably true that the non-disease specific tool for health related quality of life measure may be less sensitive and less relevant comparing to any tool / instrument / scale derived with the inputs directly from patients.

One thing has become clear that the clinical trial endpoint derived with the patient-focused approach need qualification (not the formal ‘validation’). Qualification process is supposed to be not as time consuming as the formal validation.  In 2010, FDA issued its guidance on “qualification process for drug development tools”.

In the newly passed PDUFA V (prescription drug user fee act), FDA proposed patient-focused drug development and a systematic effort in PDUFA V:

•  PDUFA V provides resources to support additional program staff to expand activities dedicated to providing review divisions with patient input
• FDA will convene meetings with participation from review divisions, the relevant patient advocacy community, and other interested parties
• FDA will hold four public workshops per year – a total of 20 meetings over 5 years. Each meeting will focus on a different disease area, reviewing the armamentarium for that indication, and identifying areas of unmet need.

FDA had already put this in action and organized its first meeting last Month to discuss the list of diseases to be included. The background and the plan about the patient-focused drug development program are described in the presentation by Dr Mullin.

A Statistician or Fortune Teller

The election is over. Other than President Obama, a statistician is shining. See the Yahoo article "He called it, and now Silver's a pop-culture star"  to see if Nate Silver is a statistician or fortune teller.

 

Would the simulation have any value in president election? Read the article "Flip this White House: Could random chance have made Al Gore president? What about Bob Dole?" to see what happens if the simulation is applied.

Using SAS ODS Graphics with Example for Generating Kaplan-Meier Curves

Recently, I spent some time on exploring the feature of SAS ODS Graphics. ODS Graphics offers an easy way to generate high-quality statistical graphics without extensive programming using SAS/Graph.  ODS Graphics has been included in almost all of SAS/Stat Procedure.

In the discussions below, the example from SAS Proc Lifetest is used to illustrate the generation of Kaplan-Meier curve using ODS Graphics.

On the basis of SAS Example 49.2 Enhanced Survival Plot and Multiple-Comparison Adjustments, we can run the following program.

proc format;

      value risk 1='ALL' 2='AML-Low Risk' 3='AML-High Risk';

   

   data BMT;

      input Group T Status @@;

      format Group risk.;

      label T='Disease Free Time';

      datalines;

   1 2081 0 1 1602 0 1 1496 0 1 1462 0 1 1433 0

   1 1377 0 1 1330 0 1  996 0 1  226 0 1 1199 0

   1 1111 0 1  530 0 1 1182 0 1 1167 0 1  418 1

   1  383 1 1  276 1 1  104 1 1  609 1 1  172 1

   1  487 1 1  662 1 1  194 1 1  230 1 1  526 1

   1  122 1 1  129 1 1   74 1 1  122 1 1   86 1

   1  466 1 1  192 1 1  109 1 1   55 1 1    1 1

   1  107 1 1  110 1 1  332 1 2 2569 0 2 2506 0

   2 2409 0 2 2218 0 2 1857 0 2 1829 0 2 1562 0

   2 1470 0 2 1363 0 2 1030 0 2  860 0 2 1258 0

   2 2246 0 2 1870 0 2 1799 0 2 1709 0 2 1674 0

   2 1568 0 2 1527 0 2 1324 0 2  957 0 2  932 0

   2  847 0 2  848 0 2 1850 0 2 1843 0 2 1535 0

   2 1447 0 2 1384 0 2  414 1 2 2204 1 2 1063 1

   2  481 1 2  105 1 2  641 1 2  390 1 2  288 1

   2  421 1 2   79 1 2  748 1 2  486 1 2   48 1

   2  272 1 2 1074 1 2  381 1 2   10 1 2   53 1

   2   80 1 2   35 1 2  248 1 2  704 1 2  211 1

   2  219 1 2  606 1 3 2640 0 3 2430 0 3 2252 0

   3 2140 0 3 2133 0 3 1238 0 3 1631 0 3 2024 0

   3 1345 0 3 1136 0 3  845 0 3  422 1 3  162 1

   3   84 1 3  100 1 3    2 1 3   47 1 3  242 1

   3  456 1 3  268 1 3  318 1 3   32 1 3  467 1

   3   47 1 3  390 1 3  183 1 3  105 1 3  115 1

   3  164 1 3   93 1 3  120 1 3   80 1 3  677 1

   3   64 1 3  168 1 3   74 1 3   16 1 3  157 1

   3  625 1 3   48 1 3  273 1 3   63 1 3   76 1

   3  113 1 3  363 1

   ;

ods rtf file="c:\temp\test.doc" style=journal;

ods graphics on;

ods trace on;

proc lifetest data=BMT plots=survival(atrisk=0 to 2500 by 500);

      ods select SurvivalPlot;

      time T * Status(0);

      strata Group / test=logrank adjust=sidak;

      run;

ods trace off;

ods graphics off;

ods rtf close;

In the above statements,

ods rtf file="c:\temp\test.doc" specifies the ODS output as RTF file and the file location and file name. style=journal specifies the use of output in publication quality.

"ods graphics on" to invoke the ODS Graphics

"ods trace on" is not really necessary, but it is useful when we need to know the name/location of the SAS template. If we check the SAS Log Window, we will be able to see that the SAS template for the corresponding graph is Stat.Lifetest.Graphics.ProductLimitSurvival

Output Added:

-------------

Name:       SurvivalPlot

Label:      Survival Curves

Template:   Stat.Lifetest.Graphics.ProductLimitSurvival

Path:       Lifetest.SurvivalPlot

-------------

PROC LIFETEST is invoked to compute the product-limit estimate of the survivor function for each of three risk categories (1='ALL' 2='AML-Low Risk' 3='AML-High Risk'). Using ODS Graphics, you can display the number of subjects at risk in the survival plot. The PLOTS= option requests that the survival curves be plotted, and the ATRISK= suboption specifies the time points at which the at-risk numbers are displayed. In the STRATA statement, the ADJUST=SIDAK option requests the idák multiple-comparison adjustment, and by default, all paired comparisons are carried out.

After the run, we will need to use the following statements to close ODS.

ods trace off;

ods graphics off;

ods rtf close;

The above program will create a high quality figure like below:

Suppose we would like to change the title and labels of the figure above, we can use PROC Template. First thing we need to do is to identify which Template we need to modify. The SAS Log window from ods trace on statement indicates the following template.

Stat.Lifetest.Graphics.ProductLimitSurvival

To open this template, we would need to the pull down manu in SAS window, then choose View -> Results -> View -> Template

From Template window, we will locate the following folder:

SAShelp.tmplmst -> stat -> lifetest -> graphics

We will then see the template called ProductLimitSurvival. Double click this file, we will be able to see the contents in this template file.

The template file can be copied and pasted into the program window. We can then edit the template file to modify the title, labels, legends,…

Suppose we run the following program to generate the Kaplan-Meier curve (upward curve instead of the original downward curve). The upward curve is sometimes easier to be understood. For example, it is useful in the time to event variable where the event is a better outcome (for example, time to recovery, time to release from the hospital, time to immune tolerance (in Hemophilia). By changing the layout of the figure, we would also need to modify the label.

proc lifetest data=BMT plots=survival(f test);

      ods select failurePlot;

      time T * Status(0);

      strata Group / test=logrank adjust=sidak;

run;

plots=survival(f test) requests the failure Plot (upward) instead of the survival plot (downward). The ‘test’ indicates the display of the logrank test results.

From the LOG window trigged by ODS Trace On statement, we will be able to know that the template is Stat.Lifetest.Graphics.ProductLimitFailure which is located in the same folder as Stat.Lifetest.Graphics.ProductLimitSurvival discussed before.

Output Added:

-------------

Name:       FailurePlot

Label:      Failure Curves

Template:   Stat.Lifetest.Graphics.ProductLimitFailure

Path:       Lifetest.FailurePlot 

We can copy and paste the template statements and run as part of the program.

ods rtf file="c:\temp\test.doc" style=journal;

ods graphics on;

ods trace on;

proc template;                                                               

   define statgraph Stat.Lifetest.Graphics.ProductLimitFailure;              

      dynamic NStrata xName maxTime plotAtRisk plotCensored plotCL plotHW    

         plotEP labelCL labelHW labelEP yMin StratumID classAtRisk plotTest  

         GroupName Transparency SecondTitle TestName pValue;                 

      BeginGraph;                                                             

         if (NSTRATA=1)                                                      

            if (EXISTS(STRATUMID))                                           

            entrytitle "Kaplan-Meier Curve" " for " STRATUMID;       /*Revised the Figure title*/

         else                                                                

            entrytitle "Kaplan-Meier Curve";                        

         endif;                                                               

         if (PLOTATRISK)                                                     

            entrytitle "with Number of Subjects at Risk" / textattrs=        

            GRAPHVALUETEXT;                                                   

         endif;                                                              

         layout overlay / xaxisopts=(label="Time to Recovery" offsetmin=.05 linearopts /*xaxisopts=(label=" ") to change the x-axis label*/

            =(viewmax=MAXTIME)) yaxisopts=(label="Probability of Achieving Tolerance"       

            shortlabel="Failure" linearopts=(viewmin=0 viewmax=1 tickvaluelist

            =(0 .2 .4 .6 .8 1.0)));                                          

            if (PLOTHW=1 AND PLOTEP=0)                                       

               bandplot LimitUpper=eval (1-HW_LCL) LimitLower=eval (1-HW_UCL)

               x=TIME / modelname="Failure" fillattrs=GRAPHCONFIDENCE name=  

               "HW" legendlabel=LABELHW;                                      

            endif;                                                           

            if (PLOTHW=0 AND PLOTEP=1)                                       

               bandplot LimitUpper=eval (1-EP_LCL) LimitLower=eval (1-EP_UCL)

               x=TIME / modelname="Failure" fillattrs=GRAPHCONFIDENCE name=  

               "EP" legendlabel=LABELEP;                                     

            endif;                                                           

            if (PLOTHW=1 AND PLOTEP=1)                                       

               bandplot LimitUpper=eval (1-HW_LCL) LimitLower=eval (1-HW_UCL)

               x=TIME / modelname="Failure" fillattrs=GRAPHDATA1             

               datatransparency=.55 name="HW" legendlabel=LABELHW;           

            bandplot LimitUpper=eval (1-EP_LCL) LimitLower=eval (1-EP_UCL) x=

               TIME / modelname="Failure" fillattrs=GRAPHDATA2               

               datatransparency=.55 name="EP" legendlabel=LABELEP;           

            endif;                                                           

            if (PLOTCL=1)                                                    

               if (PLOTHW=1 OR PLOTEP=1)                                      

               bandplot LimitUpper=eval (1-SDF_LCL) LimitLower=eval (1-SDF_UCL

               ) x=TIME / modelname="Failure" display=(outline) outlineattrs=

               GRAPHPREDICTIONLIMITS name="CL" legendlabel=LABELCL;           

            else                                                             

               bandplot LimitUpper=eval (1-SDF_LCL) LimitLower=eval (1-SDF_UCL

               ) x=TIME / modelname="Failure" fillattrs=GRAPHCONFIDENCE name=

               "CL" legendlabel=LABELCL;                                     

            endif;                                                           

            endif;                                                           

            stepplot y=eval (1-SURVIVAL) x=TIME / name="Failure" rolename=(  

               _tip1=ATRISK _tip2=EVENT) tip=(y x Time _tip1 _tip2)          

               legendlabel="Failure";                                        

            if (PLOTCENSORED)                                                 

               scatterplot y=eval (1-CENSORED) x=TIME / markerattrs=(symbol= 

               plus) name="Censored" legendlabel="Censored";                 

            endif;                                                           

            if (PLOTCL=1 OR PLOTHW=1 OR PLOTEP=1)                            

               discretelegend "Censored" "CL" "HW" "EP" / location=outside   

               halign=center;                                                 

            else                                                             

               if (PLOTCENSORED=1)                                           

               discretelegend "Censored" / location=inside autoalign=(topleft

               bottomright);                                                 

            endif;                                                           

            endif;                                                           

            if (PLOTATRISK=1)                                                

               innermargin / align=bottom;                                   

               blockplot x=TATRISK block=ATRISK / repeatedvalues=true display=

                  (values) valuehalign=start valuefitpolicy=truncate         

                  labelposition=left labelattrs=GRAPHVALUETEXT valueattrs=   

                  GRAPHDATATEXT (size=7pt) includemissingclass=false;        

            endinnermargin;                                                  

            endif;                                                           

         endlayout;                                                           

         else                                                                

            entrytitle "Kaplan-Meier Curve";                       

         if (EXISTS(SECONDTITLE))                                            

            entrytitle SECONDTITLE / textattrs=GRAPHVALUETEXT;               

         endif;                                                              

         layout overlay / xaxisopts=(label="Time to Recovery" offsetmin=.05 linearopts

            =(viewmax=MAXTIME)) yaxisopts=(label="Probability of Recovery"     /*y-axis label*/  

            shortlabel="Failure" linearopts=(viewmin=0 viewmax=1 tickvaluelist

            =(0 .2 .4 .6 .8 1.0)));                                          

            if (PLOTHW=1)                                                    

               bandplot LimitUpper=eval (1-HW_LCL) LimitLower=eval (1-HW_UCL)

               x=TIME / group=STRATUM index=STRATUMNUM modelname="Failure"   

               datatransparency=Transparency;                                

            endif;                                                           

            if (PLOTEP=1)                                                    

               bandplot LimitUpper=eval (1-EP_LCL) LimitLower=eval (1-EP_UCL)

               x=TIME / group=STRATUM index=STRATUMNUM modelname="Failure"   

               datatransparency=Transparency;                                

            endif;                                                           

            if (PLOTCL=1)                                                    

               if (PLOTHW=1 OR PLOTEP=1)                                     

               bandplot LimitUpper=eval (1-SDF_LCL) LimitLower=eval (1-SDF_UCL

               ) x=TIME / group=STRATUM index=STRATUMNUM modelname="Failure" 

               display=(outline);                                            

            else                                                             

               bandplot LimitUpper=eval (1-SDF_UCL) LimitLower=eval (1-SDF_LCL

               ) x=TIME / group=STRATUM index=STRATUMNUM modelname="Failure" 

               datatransparency=Transparency;                                

            endif;                                                            

            endif;                                                           

            stepplot y=eval (1-SURVIVAL) x=TIME / group=STRATUM index=       

               STRATUMNUM name="Failure" rolename=(_tip1=ATRISK _tip2=EVENT) 

               tip=(y x Time _tip1 _tip2);                                   

            if (PLOTCENSORED)                                                

               scatterplot y=eval (1-CENSORED) x=TIME / group=STRATUM index= 

               STRATUMNUM markerattrs=(symbol=plus);                         

            endif;                                                           

            if (PLOTATRISK)                                                  

               innermargin / align=bottom;                                   

               blockplot x=TATRISK block=ATRISK / class=CLASSATRISK          

                  repeatedvalues=true display=(label values) valuehalign=start

                  valuefitpolicy=truncate labelposition=left labelattrs=     

                  GRAPHVALUETEXT valueattrs=GRAPHDATATEXT (size=7pt)         

                  includemissingclass=false;                                 

            endinnermargin;                                                   

            endif;                                                           

            DiscreteLegend "Failure" / title="Risk Group" location=inside autoalign=(bottomright); /*Revise the legend*/   

            if (PLOTCENSORED)                                                

               if (PLOTTEST)                                                 

               layout gridded / rows=2 autoalign=(TOPLEFT BOTTOMRIGHT BOTTOM 

               TOP) border=true BackgroundColor=GraphWalls:Color Opaque=true;

               entry "+ Censored";                                           

               if (PVALUE < .0001)                                           

                  entry TESTNAME " p " eval (PUT(PVALUE, PVALUE6.4));        

               else                                                          

                  entry TESTNAME " p=" eval (PUT(PVALUE, PVALUE6.4));        

               endif;                                                        

            endlayout;                                                       

            else                                                             

               layout gridded / rows=1 autoalign=(TOPLEFT BOTTOMRIGHT BOTTOM 

               TOP) border=true BackgroundColor=GraphWalls:Color Opaque=true;

               entry "+ Censored";                                           

            endlayout;                                                        

            endif;                                                           

            else                                                             

               if (PLOTTEST)                                                  

               layout gridded / rows=1 autoalign=(TOPLEFT BOTTOMRIGHT BOTTOM 

               TOP) border=true BackgroundColor=GraphWalls:Color Opaque=true;

               if (PVALUE < .0001)                                           

                  entry TESTNAME " p " eval (PUT(PVALUE, PVALUE6.4));        

               else                                                          

                  entry TESTNAME " p=" eval (PUT(PVALUE, PVALUE6.4));        

               endif;                                                        

            endlayout;                                                       

            endif;                                                           

            endif;                                                            

         endlayout;                                                          

         endif;                                                              

      EndGraph;                                                              

   end;                                                                      

run; 

proc lifetest data=BMT plots=survival(f test);

      ods select quartiles FailurePlot;

      time T * Status(0);

      strata Group / test=logrank adjust=sidak;

      run;

ods trace off;

ods graphics off;

ods rtf close;

At the end of the program, we use the following program to delete the user defined Template.

proc template;

   delete Stat.Lifetest.Graphics.ProductLimitFailure;

run;

Further reading:

• Resource for ODS Statistics Graphics
• Getting Started with ODS Statistical Graphics in SAS® 9.2 presents the fundamental information you need to get started with ODS Statistical Graphics in SAS 9.2. (Revised 2009)
• An Overview of ODS Statistical Graphics in SAS 9.3
• Summary of ODS Graphics Capabilities provides a concise overview of the capabilities of ODS Graphics.
• The Graph Template Language and the Statistical Graphics Procedures:
• An Example-Driven Introduction
• Customizing ODS Statistical Graphs
• Customizing ODS Statistical Graphs Step-by-Step
• Modifying Graph Titles and Axis Labels
• Graph Templates