Futility Analysis and Conditional Power

Adaptive design has been used to drug development programs more efficient. According to FDA's guidance Adaptive Designs for Clinical Trials of Drugs and BiologicsGuidance for Industry, an adaptive design is defined as a clinical trial design that allows for prospectively planned modifications to one or more aspects of the design based on accumulating data from subjects in the trial. The modifications to the design based on the accumulating data from an ongoing study are through 'interim analysis'. An interim analysis is any examination of data obtained from subjects in a trial while that trial is ongoing and is not restricted to cases in which there are formal between-group comparisons. The observed data used in the interim analysis can include one or more types, such as baseline data, safety outcome data, pharmacokinetic, pharmacodynamic, biomarker data, or efficacy outcome data.

when an adaptive design is proposed, which aspect(s) of the trial to be adapted will need to be pre-specified and agreed upon by the regulatory agencies such as FDA. In the list of adaptations, the most common type of adaptive design is 'group sequential design'. 

• Group sequential design
• Adaptations to the sample size
• Adaptations to the patient population (e.e., adaptive enrichment)
• Adaptations to treatment arm selection
• Adaptations to patient allocation 
• Adaptations to endpoint selection
• Adaptations to multiple design features

Group sequential design is probably the most commonly used adaptive design (even before the adaptive design concept came out). Group sequential design was once categorized as 'well-understood' adaptive design. Ironically, many studies with group sequential design may not be called 'adaptive design' and the term 'group sequential design' may not be used in the study protocol at all. 

According to FDA's Adaptive Designs for Clinical Trials of Drugs and Biologics Guidance for Industry

"Group sequential designs may include rules for stopping the trial when there is sufficient evidence of efficacy to support regulatory decision-making or when there is evidence that the trial is unlikely to demonstrate efficacy, which is often called stopping for futility."

"There are a number of additional considerations for ensuring the appropriate design, conduct, and analysis of a group sequential trial. First, for group sequential methods to be valid, it is important to adhere to the prospective analytic plan and terminate the trial for efficacy only if the stopping criteria are met. Second, guidelines for stopping the trial early for futility should be implemented appropriately. Trial designs often employ nonbinding futility rules, in that the futility stopping criteria are guidelines that may or may not be followed, depending on the totality of the available interim results. The addition of such nonbinding futility guidelines to a fixed sample trial, or to a trial with appropriate group sequential stopping rules for efficacy, does not increase the Type I error probability and is often appropriate. Alternatively, a group sequential design may include binding futility rules, in that the trial should always stop if the futility criteria are met. Binding futility rules can provide some advantages in efficacy analyses (e.g., a relaxed threshold for a determination of efficacy), but the Type I error probability is controlled only if the stopping rules are followed. Therefore, if a trial continues despite meeting prespecified binding futility rules, the Agency will likely consider that trial to have failed to provide evidence of efficacy, regardless of the outcome at the final analysis. Note also that some DMCs might prefer the flexibility of nonbinding futility guidelines."

With group sequential design, interim analyses will be performed during the study to evaluate early evidence of efficacy or early evidence of futility. To stop the study for efficacy, the most common approach is so-called 'repeat significance testing'. to stop the study for futility, the most common approach is through calculating the conditional power.

Group sequential design:

• The interim analysis for efficacy: To see if the new treatment is overwhelmingly better than control - then stop the trial for efficacy
• Repeat significance testing
• Pocock
• O'Brien-Fleming
• Alpha-spending by Lan and DeMets 

• The interim analysis for futility (futility analysis): To see if the new treatment is unlikely to be superior to the control – then stop the trial for futility - this is called ‘futility analysis’.
• Repeat significance testing
• Stochastic curtailment approach with three families of stochastic curtailment tests
• Conditional power tests (frequentist approach)
• Predictive power tests (mixed Bayesian-frequentist approach).
• Predictive probability tests (Bayesian approach)

The most common futility analysis requires the calculation of the conditional power (CP) that is the probability that the study will demonstrate statistical significance at the end of the study (i.e. final analysis to claim superiority), conditioning on the data observed in the study thus far, and an assumption about the trend of the data to be observed in the remainder of the study. 

According to the paper by Lachin "A review of methods for futility stopping based on conditional power":

“Conditional power (CP) is the probability that the final study result will be statistically significant, given the data observed thus far and a specific assumption about the pattern of the data to be observed in the remainder of the study, such as assuming the original design effect, or the effect estimated from the current data, or under the null hypothesis.”

In conditional power calculation, assumptions about the trend of the data in the remainder of the study can be as the following and the assumption of the remainder data following the observed data is probably more reasonable. The assumption of the remainder data following the alternative hypothesis can overestimate the overall treatment effect (especially if the alternative hypothesis was based on the aggressive, over-optimistic assumptions) resulting in inflated conditional power. On the other hand, the assumption of the remainder data following the null hypothesis can underestimate the overall treatment effect resulting in deflated conditional power.  

• Observed data - the effect estimated from the current data so far
• The alternative hypothesis - assuming the original design effect
• The null hypothesis - assuming no effect in the remainder of the study 

To summarize, the futility analysis is through interim analysis to determine if the trial data indicates the inability of a clinical trial to achieve its objectives. Futility analysis usually requires the calculation of the conditional power (CP) that is defined as the probability that the final study result will be statistically significant, given the data observed thus far at the time of the interim data cut and a specific assumption about the pattern of the data to be observed in the remainder of the study, such as assuming the original design effect (alternative hypothesis) or the effect estimated from the current data. It is pretty common that the threshold for futility is defined as CP less than 20% - suggesting that the probability of the final result to be statistically significant is less than 20% given the data observed at the time of interim analysis. If the CP is less than 20% at the time of the interim analysis, the Data Monitoring Committee may recommend the sponsor stop the trial (stop the trial for futility).  

in a book chapter by Tin, Ming T "Conditional Power in Clinical Trial Monitoring", the pros and cons of conditional power were discussed. 

To put things in perspective, the conditional power approach attempts to assess whether evidence for efficacy or the lack of it based on the interim data is consistent with that at the planned end of the trial by projecting forward or using conditional likelihood given the eventuality. Thus it substantially alleviates the major inconsistency in all other group sequential tests where different sequential procedures applied to the same data yield different answers. ...

The advantage of the conditional power approach for trial monitoring is its flexibility. It can be used for unplanned analysis and even analysis whose timing depends on previous data. For example, it allows inferences from overrunning or underrunning (namely, more data come in after the sequential boundary is crossed, or the trial is stopped before the stopping boundary is reached. Conditional power can be used to aid the decision for early termination of a clinical trial to complement the use of other methods or when other methods are not applicable. 

The caveat is that the conditional power can be calculated with different assumptions about the remaining data. Depending on the assumptions about the remaining data following the observed data, the alternative hypothesis (original design effect), or others, the conditional power can sometimes be quite different resulting in different conclusions about the futility assessment. 

Some examples: 

in the SAP for "Randomized, Open-Label Study of Abiraterone Acetate (JNJ-212082) plus Prednisone with or without Exemestane in Postmenopausal Women with ER+ Metastatic Breast Cancer Progressing after Letrozole or Anastrozole Therapy", conditional power was described to be calculated with both the assumption of the remaining data following the original hazard ratio (alternative hypothesis) and the assumption of the remaining data following the observed hazard ratio at the interim.

3.1.2 Conditional Power

Conditional power is the probability that the study will demonstrate statistical significance at the end of the study (i.e. final analysis to claim superiority on PFS), conditioning on the data observed in the study thus far, and an assumption about the trend of the data to be observed in the remainder of the study. Two assumptions about the trend of the data were presented below: The futility boundary corresponds to a conditional power of approximately 39% if the original hazard ratio assumption is true, while only 4% conditional power will be achieved if the observed hazard ratio at interim is true for the remainder of the study. The efficacy boundary corresponds to a conditional power of approximately 90% if the original hazard ratio assumption is true, and 92% conditional power will be achieved if the observed hazard ratio at interim is true for the remainder of the study. The conditional power of stopping boundaries was computed using method of Lan (2009).

In Gilead's trial "A Multicenter, Adaptive, Randomized Blinded Controlled Trial of the Safety and Efficacy of Investigational Therapeutics for the Treatment of COVID-19 in Hospitalized Adults", the repeat significant test procedure (the alpha spending function) was used to evaluate the potential stop for overwhelming efficacy and the stochastic curtailment approach (conditional power) was used to evaluate the potential stop for futility. 

In a trial by Incyte "GRAVITAS-301: A Randomized, Double-Blind, Placebo-Controlled Phase 3 Study of Itacitinib or Placebo in Combination With Corticosteroids for the Treatment of First-Line Acute Graft-Versus-Host Disease", interim data monitoring for the potential stop for efficacy or futility is assessed and conditional power of 20% is used as the threshold for declaring the futility: 

Further reading: 

• Lachin 2005 A review of methods for futility stopping based on conditional power
• Paul Gallo (2019) Futility Analyses in Confirmatory Clinical Trials –Methods and Procedures
• Dmitrienko and Yang Yuan (2017) Interim Data Monitoring from the book "Analysis of Clinical Trials Using SAS - a practical guide"

Interventional Study (Clinical Trial), Non-interventional Study (Observational Study), and Registry Study

 FDA recently issued two separate guidance documents for industry: 

• Considerations for the use of real-world data and real-world evidence to support regulatory decision-making for drug and biological products” 
• Real-World Data: Assessing Registries to Support Regulatory Decision-Making for Drug and Biological Products

While these two guidance documents are focused on real-world data (RWD) and real-world evidence (RWE), they also provided the definitions for distinctions for interventional study, non-interventional study, and registry study. 

Some of the terms are confusing and non-distinguishable, for example, we use clinical study and clinical trial interchangeably and we use registry and non-interventional study interchangeably, Based on FDA guidance documents, these different terms are for describing different types of studies. 

The term clinical study means research that evaluates human health outcomes associated with taking a drug of interest. Clinical studies include interventional (clinical trial) designs and non-interventional (observational) designs. 

Interventional Study (also referred to as a Clinical Trial): 

the term interventional study (also referred to as a clinical trial) is a study in which participants, either healthy volunteers or volunteers with the disease being studied, are assigned to one or more interventions, according to a study protocol, to evaluate the effects of those interventions on subsequent health-related biomedical or behavioral outcomes. One example of an interventional study is a traditional randomized controlled trial, in which some participants are randomly assigned to receive a drug of interest (test article), whereas others receive an active comparator drug or placebo. Clinical trials with pragmatic elements (e.g., broad eligibility criteria, recruitment of participants in usual care settings) and single-arm trials are other types of interventional study designs.

Non-interventional study (also referred to as an observational study)

a non-interventional study (also referred to as an observational study) is a type of study in which patients received the marketed drug of interest during routine medical practice and are not assigned to an intervention according to a protocol. Examples of  non-interventional study designs include (1) observational cohort studies, in which patients are identified as belonging to a study group according to the drug or drugs received or not received during routine medical practice, and subsequent biomedical or health outcomes are identified and (2) case-control studies, in which patients are identified as belonging to a study group based on having or not having a health-related biomedical or behavioral outcome, and antecedent treatments received are identified.

Registry study

a registry is defined as an organized system that collects clinical and other data in a standardized format for a population defined by a particular disease, condition, or exposure. Establishing registries involves enrolling a predefined population and collecting prespecified health-related data for each patient in that population (patient-level data). Data about this population can be entered directly into the registry (e.g., clinician-reported outcomes) and can also include additional data linked from other sources that characterize registry participants. Such external data sources can include data from medical claims, from pharmacy and/or laboratory databases, and from EHRs, blood banks, and/or medical device outputs. Trained staff should follow standard operating procedures to aggregate data for a registry and carry out data curation.

Registries range in complexity regarding the extent and detail of the data captured and how the data are curated. For example, registries used for quality assurance purposes related to the delivery of care for a particular health care institution or health care system tend to collect limited data related to the provision of care. Registries designed to address specific research questions tend to systematically collect longitudinal data in a defined population, on factors characterizing patients’ clinical status, treatments received, and subsequent clinical events. The data collected in a given registry and the procedures for data collection are relevant when considering how registry data can be used. 

Registries have the potential to support medical product development, and registry data can ultimately be used, when appropriate, to inform the design and support the conduct of either interventional studies (clinical trials) or non-interventional (observational) studies. Examples of such uses include, but are not limited to: 

• Characterizing the natural history of a disease
• Providing information that can help determine sample size, selection criteria, and study endpoints when planning an interventional study 
• Selecting suitable study participants—based on factors such as demographic characteristics, disease duration or severity, and past history or response to prior  therapy—to include in an interventional study (e.g., randomized trial) that will assign a drug to assess that drug’s safety or effectiveness 
• Identifying biomarkers or clinical characteristics that are associated with important  clinical outcomes of relevance to the planning of interventional and non-interventional studies
• Supporting, in appropriate clinical circumstances, inferences about safety and  effectiveness in the context of: 
• A non-interventional study evaluating a drug received during routine medical practice  and captured by the registry 
• - An externally controlled trial including registry data as an external control arm

An existing registry can be used to collect data for purposes other than those originally intended, and reusing a registry’s infrastructure to support multiple interventional and non-interventional studies can generate efficiencies. Before designing and initiating an interventional or non-interventional study using registry data for regulatory decisions, sponsors should consult with the appropriate FDA review division regarding the appropriateness of using a specific registry as a real-world data source. 

Registries can generally be categorized as

(1) disease registries that use the state of a particular disease or condition as the inclusion criterion,

(2) health services registries where the patient is exposed to a specific health care service, or

(3) product registries where the patient is exposed to a specific health care product. 

The guidance documents also provided definitions for other types of studies: 

Natural history study

a natural history study is a non-interventional (observational) study intended to track the course of the disease for purposes such as identifying demographic, genetic, environmental, and other (e.g., treatment) variables that correlate with disease development and outcomes. Natural history studies are likely to include patients receiving the current standard of care and/or emergent care, which may alter some manifestations of the disease. Disease registries are common platforms to acquire the data for natural history studies.

Externally controlled trial

An externally controlled trial, as one type of clinical trial, compares outcomes in a group of participants receiving the test treatment with outcomes in a group external to the trial, rather than to an internal control group from the same trial population assigned to a different treatment. The external control arm can be a group, treated or untreated, from an earlier time (historical control) or a group, treated or untreated, during the same time period (concurrent control) but in another setting.

Further Reading: 

• FDA guidance for industry "Rare Diseases: Natural History Studies for Drug Development"
• FDA guidance for industry "Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products"
• EMA "Guideline on registry-based studies"
• AHRP "Registries for Evaluating PatientOutcomes:A User’s Guide"
• PCORI "Standards in the Conduct of RegistryStudies for Patient-Centered OutcomesResearch" 
• CTTI: Registry Trials
• Clinical Trials vs Registries

Clinical Trial Design: Double-Blind Fixed Duration Trial with Long-term Double-Blind Various Treatment Duration

The randomized, double-blind, parallel-group design is the most common type of design for clinical trials (especially the confirmatory clinical trials). This type of design can be further classified into clinical trials with a fixed treatment duration or with various treatment durations. For clinical trials with a fixed duration, all patients are treated with study drugs for a fixed duration (for example, 16 weeks, 24 weeks, 52 weeks,...) and the primary efficacy endpoint will be estimated at the end of the fixed duration (for example, change from baseline in xxx measure at week 16, week 24, or week 52,...). For clinical trials with various durations such as event-driven study design, patients are treated with study drugs for various durations - the early enrolled patients may receive the study drugs for a much longer time than those later enrolled patients, and patients will stay in the study and receive the study drugs if no protocol-defined event occurs until the required number of events for the study has occurred and the entire study is closed. The event can be Clinical Worsening Event, MACE, Exacerbation, Hospitalization, Progression-free survival, Death,...

In the informed consent form, patients who participate in the clinical trial will be informed how long they may be treated with the experimental drug or placebo. The ethical issue arises if patients are randomly assigned to the placebo group and treated with placebo for a prolonged period of time.     

Lately, we saw several clinical trials with a hybrid approach containing a double-blind fixed duration and then followed by a double-blind various duration. The primary efficacy endpoint was measured at the end of the fixed duration (i.e., week 52 for INBUILD and ISABELLA trials and week 24 for STELLAR trial). The double-blind various duration was added to the trial to collect the information for secondary and exploratory endpoints that need a longer exposure time. The double-blind various duration depends on the enrollment speed and the timing of patients entering into the study. Early-enrolled patients will stay in the study much longer than the later-enrolled patients. The slower the enrollment speed is, the longer the double-blind various duration takes. 

INBUILD study: Nintedanib in Progressive Fibrosing Interstitial Lung Diseases 

For each patient, the trial consisted of two parts: Part A, which was conducted during the first 52 weeks, and Part B, which was a variable treatment period beyond week 52 during which patients continued to receive either nintedanib or placebo until all the patients had completed Part A. 

The primary assessment of benefit-risk of nintedanib in patients with PF-ILD will be based on efficacy and safety data over 52 weeks.

The primary analysis of this study will therefore be performed once the last randomized patient reaches the Week 52 Visit (Visit 9 at the end of Part A). At that time, a database lock will occur and all the data will be unblinded. Efficacy and safety analyses will be performed on the data from Part A of the trial to assess the benefit-risk of nintedanib over 52 weeks. In addition, data collected in Part B of the trial (after 52 weeks) and available at the time of data cut-off for the primary analysis will be reported together with data from Part A (i.e. over the whole trial).

Once the benefit-risk assessment of nintedanib over 52 weeks is confirmed to be positive, all patients receiving trial medication in Part B will be offered open-label treatment with nintedanib in a separate study.

Trial 1199.247 i.e. Part B will continue until all patients have been switched to open-label nintedanib or completed the Follow-up Visit. A final database lock will then occur and Part B data collected between the data cut off for the primary analysis and the final database lock will be reported together with data from Part A i.e. over the whole trial.

ISABELLA Studies: GLPG1690, a novel autotaxin inhibitor, in idiopathic pulmonary fibrosis

See the paper: Rationale, design and objectives of two phase III, randomised, placebo controlled studies of GLPG1690, a novel autotaxin inhibitor, in idiopathic pulmonary fibrosis (ISABELA 1 and 2)

In each study, approximately 750 subjects will be randomized 1:1:1 to receive oral GLPG1690 600 mg, GLPG1690 200 mg or matching placebo, once daily, in addition to local SOC. SOC is defined as either pirfenidone or nintedanib, or neither pirfenidone nor nintedanib (for any reason). Treatment will continue for at least 52 weeks (subjects will continue to receive randomized treatment until the last patient reaches 52 weeks in the study). A follow-up visit will be conducted 4 weeks after the end-of-study visit (figure 1 below).

STELLAR Study: Sotatercept in Pulmonary Arterial Hypertension

According to Acceleron's ATS 2021 INTERNATIONAL CONFERENCE ACCELERON INVESTOR AND ANALYST CALL, the STELLAR study was designed as the following:

The double-blind fixed duration is 24 weeks and the primary efficacy endpoint (6MWD) is measured at week 24. The double-blind various duration had a cap at 72 weeks, i.e., the maximum duration for the period is 72 weeks). Patients can be in the long-term double-blind treatment period for 0 (the last enrolled patient) to 72 weeks (early enrolled patients). 

Venn Diagram to Display the Distribution of the Adverse Events

Visualizing the clinical trial data is becoming more common and various plots can be drawn to visualize the data. In previous posts, we discussed various types of plots that can be used in describing the clinical trial data. 

• Graphic Presentation: Kaplan-Meier Plot, Q-Q plot, Box Plot, Funnel Plot, Swimmer Plot, Forest Plot, Spaghetti Plot, Bubble Plot
• Waterfall plot(s) to display the results in oncology and non-oncology clinical trial

Recently, we are discussing the use of the 'Venn diagram' for displaying the distribution of the adverse events - the number and percentage of overlapping AEs. See an example of a four-way Venn diagram below:

Wikipedia introduced the Venn diagram as the following: 

A Venn diagram is a widely-used diagram style that shows the logical relation between sets, popularized by John Venn in the 1880s. The diagrams are used to teach elementary set theory, and to illustrate simple set relationships in probability, logic, statistics, linguistics and computer science. A Venn diagram uses simple closed curves drawn on a plane to represent sets. Very often, these curves are circles or ellipses.

According to the paper "V is for Venn Diagrams!": 

Venn diagrams where introduced in 1883 by John Venn (1834-1923), the Hull born philosopher and mathematician. They are a great way to visualize elements that are unique to only one group and simultaneously visualize elements that intersect with other groups. They are symmetrical by nature and the number of groups in a Venn diagram = 2n (including the group outside the diagram).

In clinical trials or the pharmacovigilance field, Venn Diagram can be used to virtualize the distribution of the adverse events, especially to display the distribution and relationships of the frequent adverse events. 

In NIH's "Guidance on Reviewing and Reporting Unanticipated ProblemsInvolving Risks to Subjects or Others and Adverse Events", the Venn diagram was mentioned for summarizing the general relationship between adverse events and unanticipated problems. 

In a paper by Gattepaille et al"Prospective Evaluation of Adverse Event Recognition Systems in Twitter: Results from the Web‑RADR Project", the Venn diagram was used to summarize the relationship of the recall performance results of the first two components, the relevance filter, and the NER module. 

In a paper by Xie et al "Differential Adverse Event Profiles Associated with BCG as a Preventive Tuberculosis Vaccine or Therapeutic Bladder Cancer Vaccine Identified by Comparative Ontology-Based VAERS and Literature Meta-Analysis", Venn diagram was used to compare four groups of the AEs associated with BCG TB vaccine or bladder cancer vaccine using VAERS and literature resources.

Additional References:

• Driving Clinical Safety Reviews with Data Standards
• SAS JMP can create the Venn diagram
• Cardiovascular Events Associated with Chimeric Antigen Receptor T Cell Therapy: Cross-Sectional FDA Adverse Events Reporting System Analysis
• Major Adverse Kidney Events in Pediatric Sepsis

Are data listings for clinical study reports still needed in the era of CDISC standard data sets (SDTM)?

Traditionally, all data fields that are collected in clinical trials, will be listed in so-called 'data listings'. The data listings are the basis for the summary and analysis tables or figures, and all together, tables, listings, and figures (TLFs) form the basis for the clinical study report (CSR). According to the ICH E3 "Structure and Content of Clinical Study Reports", these data listings are included in the CSR section 16.2 "Patient data listings". 

Now that the CDISC standard has become a mandate for regulatory submission of the clinical trial data, the data sets submitted to the regulatory agencies will follow the same standards (data structure, data set name, variable names,...). This will enable the regulatory reviewers to use the software (the data visualization software such as JMP-Clinical) to visualize and review the data and perform the data mining activities. Logical thinking is that in the era of CDISC standard data sets (SDTM - study data tabulation model), the data listings become obsolete and redundant. Some sponsors argue that there is no need to generate the data listings if the submitted data sets are in SDTM format. 

In a presentation "Alternatives to Static Data Listings for Clinical Study Reports" in a CDISC virtual conference, the presenters argued:

"Sponsors often create voluminous static listings for Clinical Study Reports (CSRs) and submissions, and possibly for internal use to review safety information.  This is likely due to the perception that they are required and/or lack of knowledge of various alternatives.   However, there are other ways of viewing clinical study safety data which can provide an improved user experience, and are made possible by standard structures for such data, such as the Study Data Tabulation Model (SDTM). The purpose of this paper is to explore some alternatives to providing a complete set of static listings and make a case for sponsors to begin considering these alternatives."

On Pinnacle 21's website, there was a blog article "FDA Final Guidance Webinar Q&A":

28. With the advent of a new "misc folder" instead of listings do you think that FDA is getting away from generating listings for each study? It seems that the data sets (SEND, SDTM, and ADaM) would stand alone to support any listings.

Answer: Once the standards requirements are in effect, the idea would be that listings would be replaced by the SDTM tabulations data, so yes.

It seems that FDA is ok not to receive the data listings since the CDISC-compliant data sets are submitted. However, it is still the sponsor's risk to take if data listings are not generated and not included in the CSR. The vast majority of the sponsors are still generating the data listings for the CSR and including the data listings in the submission even in the era of CDISC standard data sets. 

In deciding if the data listings need to be generated for CSR and submission, the sponsor needs to consider: 

• The industry trend. Is it still the industry standard to generate the data listings even after CDISC compliant data sets are submitted?
• Is there an official guideline indicating that the data listings should not be generated? 
• How to handle CSR section 16.2 according to ICH E3 regarding patient data listings if data listings are not generated? 
• Data listings may be used for internal reviews (medical review, medical writing, Quality Control review,…) even though FDA reviewers may not need the data listings because they have specific tools to review the SDTM data sets
• Data listings may be needed if the submissions are needed for other regulatory agencies beyond the FDA.
• Not all reviewers have the tools or can effectively use the tools to review the electronic data sets. Some reviewers still rely on the data listings to review the individual subject-level data. 

In the era of CDISC standards, more effort was spent on SDTM and ADaM data set programming. Once the SDTM and ADaM data sets are generated and the raw data sets are mapped to the standardized SDTM data sets, generating data listings is not so complicated. 

Protocol Deviations - How is the Protocol Deviation Data Analyzed and Used?

For any clinical trial, the study protocol is the blueprint of how the clinical trial should be conducted. Clinical study protocols must be conducted according to the International Council for Harmonization (ICH) guidance on good clinical practice (GCP), which, among other things, helps safeguard the rights, safety, and well-being of study participants. If conducted as designed, the associated data should be reliable and reproducible and support clear interpretation of the results, while maintaining the participants’ protection. In light of this, one might reasonably assume that deviations from this protocol could be harmful to the participant or the accuracy of the data and should therefore be avoided.

However, the reality is that within clinical trials, protocol deviations do happen, despite best efforts in designing and conducting the clinical trial. According to the ICH E3 Q&A R1, the protocol deviation is defined as: 

"A protocol deviation is any change, divergence, or departure from the study design or procedures defined in the protocol.

Important protocol deviations are a subset of protocol deviations that may significantly impact the completeness, accuracy, and/or reliability of the study data or that may significantly affect a subject's rights, safety, or well-being."

In the early days, the term 'protocol violation' may be used and is still used in some clinical trial documents. According to the ICH E3 Q&A R1, the term 'protocol violation' should be replaced with 'protocol deviation'

"To avoid confusion over terminology, sponsors are encouraged to replace the phrase “protocol violation” in Annex IVa with “protocol deviation”,

There is considerable variability regarding the interpretation, and classification of what an important protocol deviation is, which creates challenges in the identification, collection, and reporting of deviations - resulting in over-reporting PDs that could potentially delay the identification of important patient safety information by increasing the noise in the system or under-reporting PDs that could influence the reliability of the study results and patient safety signals.

In order to reduce the variability in what, when, how the PDs should be collected, it might be helpful to take a look at how the data about the PDs are analyzed and used. 

After the protocol deviation data is collected, the data will be mapped into the CDISC data sets (DV data set in SDTM and ADDV data set in ADaM). Then a data listing will be generated to list all protocol deviations by subjects including the date the protocol deviation occurred, the description of the protocol deviation, the category of the protocol deviation (out of visit window, informed consent issue, SAE safety reporting issue, ......), and the classification of the protocol deviations (minor, major, critical, or important).  A statistical summary table will then be generated for all protocol deviations and for major protocol deviations. 

The protocol deviation data will then be used in the following aspects: 

For the clinical study report (CSR):

CSR must include a section to describe the protocol deviations that occurred during the conduct of the study. According to  ICH E3 (Structure and Content of Clinical Study Reports), the protocol deviations section is an essential part of the CSR. 

10.2 Protocol Deviations 

All important deviations related to study inclusion or exclusion criteria, conduct of the trial, patient managements or patient assessment should be described. 

In the body of the text, protocol deviations should be appropriately summarized by center and grouped into different categories, such as: 

• Those who entered the study even though they did not satisfy the entry criteria. 
• Those who developed withdrawal criteria during the study but were not withdrawn. 
• Those who received the wrong treatment or incorrect dose. 
• Those who received an excluded concomitant treatment. 

In Appendix 16.2.2, individual patients with these protocol deviations should be listed, broken down by center for multicenter studies. 

For FDA BIMO of NDA/BLA submission under Center for Drug Evaluation and Research (CDER):

The FDA Office of Scientific Investigations (OSI) requests that the by-site data and listing are provided for Bioresearch Monitoring (BIMO). The by-site data is used by OSI to select the investigational sites for inspections for CDER submissions.

The FDA Office of Scientific Investigations (OSI) requests that the items described in the draft guidance for industry Standardized Format for Electronic Submission of NDA and BLA Content for the Planning of Bioresearch Monitoring (BIMO) Inspections for CDER Submissions (February 2018) and the associated Bioresearch Monitoring Technical Conformance Guide Containing Technical Specifications be provided to facilitate development of clinical investigator and sponsor/monitor/CRO inspection assignments, and the background packages that are sent with those assignments to the FDA ORA investigators who conduct those inspections. This information is requested for all major
trials used to support safety and efficacy in the application (i.e., phase 2/3 pivotal trials).

One critical piece of the BIMO data is the protocol deviation data by site. The number of protocol deviations or important protocol deviations can be an indicator of protocol compliance and the study quality for specific investigational sites. According to BIORESEARCH MONITORINGTECHNICAL CONFORMANCE GUIDE Technical Specifications Document", the following protocol deviation data should be provided:

"Subject-level data line listings, by clinical site, should include consented subjects, treatment assignment, discontinuations, study population, inclusion and exclusion criteria, adverse events, important protocol deviations, efficacy endpoints, concomitant medications, and safety monitoring, as further described below"

7. Important Protocol Deviations Contains Nonbinding Recommendations 

This by-subject, by-clinical site listing should include all protocol deviations. The listing should include a description of the deviation and identify whether the sponsor considered the deviation to be an important or non-important protocol deviation.  

Clinical Site Data Elements Summary Listing

"Total number of important protocol deviations at a given site by treatment arm for subjects in the SAFPOP. A protocol deviation is any change, divergence, or departure from the study design or procedures defined in the protocol or associated investigational plans that is not implemented or intended as a systematic change. This value should include multiple deviations per subject and all major deviation types. Important deviations are those deviations that might significantly affect the completeness, accuracy, and/or reliability of the study data or that might significantly affect a subject's rights, safety, or well-being"

"Total number of protocol deviations, excluding important protocol deviations, at a given site by treatment arm for subjects in the SAFPOP. A protocol deviation is any change, divergence, or departure from the study design or procedures defined in the protocol or associated investigational plans that is not implemented or intended as a systematic change."

FDA's Good Review Practice: Clinical Review Template listed the protocol violation information as part of the review items for assessing the GCP compliance and the impact of the protocol violations on the data quality and the safety and efficacy results. 

3.2 Compliance With Good Clinical Practices

This section should include comments on compliance with good clinical practices, including informed consent, protocol violations, site-specific issues, and whether the clinical trials were conducted in accordance with acceptable ethical standards. 

If a DSI audit process and report is not requested, provide a brief summary on the quality and nature of other methods used to audit or check the applicant’s data and/or analyses. 

For DSI-requested audits, include a brief summary of the rationale for DSI audits and site selection such as: 

• A specific safety concern at a particular site based on review of adverse events, serious adverse events, deaths, or discontinuation 

• A specific efficacy concern based on review of site-specific efficacy data

 • A specific concern for scientific misconduct at one or more particular sites based on review of financial disclosures, protocol violations, clinical trial discontinuations, or safety and efficacy results  

For defining the per-protocol population for Sensitivity or Supplemental Analyses

Efficacy analyses are usually performed in full analysis set or intention-to-treat population. However, to test the robustness of the statistical results and to assess the impact of the protocol deviations on the statistical results, additional sensitivity or supplemental analyses are usually performed on the per-protocol population. 

According to ICH E9 (Statistical Principles for Clinical Trials), the per-protocol population is defined and sensitivity analyses based on per-protocol population are suggested:  

Per protocol set (valid cases, efficacy sample, evaluable subjects sample): The set of data generated by the subset of subjects who complied with the protocol sufficiently to ensure that these data would be likely to exhibit the effects of treatment according to the underlying scientific model. Compliance covers such considerations as exposure to treatment, availability of measurements, and absence of major protocol violations.

In practice, the per-protocol population is usually defined as "all randomized subjects who have no major protocol deviations" or "all randomized subjects who have no major protocol deviations that may have an impact on the efficacy assessment". The decision on the inclusion of subjects in per-protocol population needs to be decided before the database lock and study unblinding. The protocol deviation data may be reviewed at the blinded data review meeting. Subjects who are excluded from the per-protocol population need to be documented in the blinded data review report.

ICH E9 Addendum also discussed the per-protocol set (PPS) and analyses based on PPS are included as supplemental analysis: 

The meaning and role of an analysis of the per protocol set is also re-visited in this addendum; in particular whether the need to explore the impact of protocol violations and deviations can be addressed in a way that is less biased and more interpretable than naïve analysis of the per protocol set

Section 5.2.3. indicates that it is usually appropriate to plan for analyses based on both the FAS and the Per Protocol Set (PPS) so that differences between them can be the subject of explicit discussion and interpretation.

Additional Reference: Transcelerate Protocol Deviations

Recommendations for DMC: How Many Statisticians Need to be Involved in Successful DMC Process?

Independent data monitoring committee (iDMC) is more commonly used in clinical trials than before for various reasons. For innovative and complex trials, an iDMC is usually a must-to-have external committee. For clinical trials containing the adaptations, interim analyses, iDMC can provide a way to review the unblinded data to recommend some critical suggestions about the ongoing clinical trial while maintaining the study integrity. In some cases, the sponsor just wants to have an independent body to oversee the study for extra credibility. 

There are plenty of regulatory guidelines for the general principles for establishing and operating a DMC. There are also recommendations from the industry/academic for successful DMC implementation. 

• FDA guidance: Establishment and Operation of Clinical Trial Data Monitoring Committees Guidance for Clinical Trial Sponsors
• EMA: GUIDELINE ON DATA MONITORING COMMITTEES
• EMA: Questions and answers on Data Monitoring Committees issues
• RAPs (2020): EMA offers new Q&A on data monitoring committees
• CTTI: Recommendations for data monitoring committees from the Clinical Trials Transformation Initiative
• Annals of Internal Medicine: Presenting Risks and Benefits: Helping the Data Monitoring Committee Do Its Job
• Tips For A Successful Organizational DMC Meeting (wcgclinical.com)

DMC is also mentioned in the recent guidance on Conduct of Clinical Trials of Medical Products During the COVID-19 Public Health Emergency (updated August 2021), COVID-19: Developing Drugs and Biological Products for Treatment or Prevention, and Development and Licensure of Vaccines to Prevent COVID-19. 

"What are some of the key factors that a sponsor should consider when deciding whether to suspend or continue an ongoing study or to initiate a new study during the COVID-19 public health emergency?

Involvement of a study’s DMC, if one has been established, can provide support for the assessments discussed above. Since a primary responsibility of the DMC is assuring the safety of participating trial participants, the DMC’s assessment of the impact of modifications of trial conduct due to COVID-19 on patient safety is important to consider."

When planning for a DMC, multiple statisticians need to be involved - at least three statisticians (blinded and unblinded statisticians) need to play a role in the successful conduct of a DMC. The diagram below lists three different statisticians that are needed for a successful DMC.  

 

DMC Statistician: 

• One of the DMC members
• the voting member
• with biostatistical expertise, applying the statistical methods in the monitoring process. 
• understands the complex clinical trial designs (group sequential design, adaptive design, ...), familiar with the application of the stopping rules, Type I error issue, multiplicity issue. 
• According to FDA's guidance on DMC: "Most DMCs are composed of clinicians with expertise in relevant clinical specialties and at least one biostatistician knowledgeable about statistical methods for clinical trials and sequential analysis of trial data."
• usually unblinded and have access to the unblinded data 

Trial Statistician: 

• clinical trial study team member and can not be the DMC member
• can attend the open session of the DMC meetings; can't attend the closed session of the DMC meetings
• only have access to the blinded or pooled study data during the study
• responsible for the statistical section of the study protocol including the statistical methods for group sequential design, adaptive design, stopping rules... 
• responsible for simulations (if needed) to address questions about the alpha I error spending, multiplicity issue
• maybe the author to develop the statistical analysis plan for DMC and for interim analysis
• provide the blinded data sets to the SDAC (reporting) statistician
• may work with SDAC statistician and review the blinded statistical outputs from the SDAC statistician to ensure the correctness of the statistical package (blinded version)

SDAC Statistician:

• statistician on the Statistical Data Analysis Center (SDAC) - in industry, this is called reporting statistician or independent statistician. 
• non-voting member
• primary responsibility is to prepare the statistical outputs (tables, listings, and figures) for DMC. perform the analyses on unblinded interim data and provide the results of comparative interim analyses.
• Reporting statistician will receive the data sets from the data management group and receive the randomization information (unblinded information) directly from the IRT (interactive response technology) group
• Reporting statistician sends the statistical package (unblinded) to DMC for review. 
• Reporting statistician is usually unblinded.
• reporting statistician may have a programming team to help with generating the data sets, tables, listings, and figures. Anybody who is working with the reporting statistician needs to follow the same rules that are applicable to the reporting statistician. 
• attend the DMC meeting (open and close sessions) and provide explanations for any issues raised by DMC members related to the DMC statistical outputs  

As indicated in the diagram above, the trial statistician and reporting statistician may have close discussions and interactions while working on the blinded data. The reporting statistician and the DMC statistician may also have close interactions while working on the unblinded data. There should be no direct interaction or very little interaction between the trial statistician and the DMC statistician (usually the interaction may be limited to the initial administrative meeting and the open session at the scheduled DMC meetings).

FDA guidance on DMC contains a lengthy section to discuss the reporting statistician's roles in the DMC process. FDA is very concerned about the potential risks that statisticians who conduct the interim analyses may comprise the integrity of the clinical trial. The primary trial statistician should not be the one who conduct the interim analysis. The statistician who conducts the interim analysis should not be in the sponsor's organization.  

Serial Blood Sample Timepoints for Comparing Pharmacokinetics Profiles Between Two Different Dose Frequencies

It is very common in the drug development process that the dosing frequency needs to be studied. The dosing frequency is usually based on the pharmacokinetic profiles. In multiple-dose studies, the dose frequency decides the dosing interval: QD for once daily, BID for twice daily, and TID for three times daily. 

Usually, to compare the pharmacokinetic profiles, serial blood samples will be taken over the period of the dosing interval (between the previous dose and the next dose).  The area under the curve (AUC) will then be calculated over the dosing interval (commonly denoted as tau).

• For QD dose, Tau = 24 hours, AUCtau is AUC[0-24 hours]
• For BID dose, Tau = 12 hours, AUCtau is AUC[0-12 hours]
• For TID dose, Tau = 8 hours, AUCtau is AUC[0-8 hours]

It will be straightforward to select the Pharmacokinetics (PK) sampling time points if two drugs/formulations to be compared have the same dose interval. However, in practice, we often need to compare the PK profiles for two drugs/formulations with different dose intervals, for example, between QD versus BID, or between QD versus TID.  

For comparison of PK profiles between QD dose, BID dose, and TID dose, one will need to compare AUC[0-24 hours] with 2 x AUC[0-12 hours] for BID and with 3 x AUC[0-8 hours] for TID.

For QD dosing, serial PK samples will be collected over 24 hours. For BID dosing and TID dosing, there are two different ways to decide the serial PK samples:

For BID dosing,   

1. serial PK samples can also be collected over 24 hours, the calculated AUC[0-24 hours] can directly be compared with AUC[0-24 hours] from QD dosing even though there will be an extra dose at the middle of the 24 hours period. 
2. serial PK samples can be collected over 12 hours, then the calculated AUC[0-12 hours] needs to be multiplied by 2 in order to be compared with AUC[0-24 hours]

For TID dosing, 

1. serial PK samples can also be collected over 24 hours, the calculated AUC[0-24 hours] can directly be compared with AUC[0-24 hours] from QD dosing even though there will be two extra doses during the 24 hours period. 
2. serial PK samples can be collected over 8 hours, then the calculated AUC[0-8 hours] needs to be multiplied by 3 in order to be compared with AUC[0-24 hours]

In a study by Dawra et al, "A PK/PD study comparing twice-daily to once-daily dosing regimens of ertugliflozin in healthy subjects", the blood samples were collected for QD and BID as the followings:

For each period, blood samples for PK analysis were collected for QD dosing as follows: on days 4, 5, and 6 before administration of the morning dose, and at 0.5, 1, 2, 3, 4, 8, 12, and 24 hours after the morning dose on day 6. For BID dosing, blood samples were collected at 0.5, 1, 2, 3, 4, 8, 12 (preevening dose), 12.5, 13, 14, 15, 16, 20, and 24 hours after the morning dose on day 6.            

Notice that the blood samples for BID were collected at 0.5, 1, 2, 3, 4, 8, 12 post the morning dose and then 0.5, 1, 2, 3, 4, 8, 12 post the evening dose. 

The PK profiles for OD and BID doses are displayed in the figures below: 

This approach of the PK blood sampling schema will require an extensive number of blood draws. For the BID dose, two serial PK samples need to be drawn; for the TID dose, three serial PK samples need to be drawn. The advantage is to capture the potential impact of the circadian and diurnal cycles. 

In some situations, too many blood sample draws may not be practical, and an alternative approach needs to be taken. For example, in pediatric PK studies, the number of blood sample draws may be limited due to the restriction in the total blood volume. 

One of the alternative approaches is to draw the serial PK samples only for one dose interval, not draw additional serial PK samples after the next dose. For example, for the BID dose, the serial PK samples are drawn over 12 hours period assuming that the PK profiles after the evening dose will be the same as the PK profiles over 12 hours post the morning dose. The AUC[0-12 hours] needs to be multiplied by 2 before comparing it to the AUC[0-24 hours] for QD dose. 

In a study to compare the IVIG (every four weeks dosing schedule) and SCIG (weekly dose schedule), the serial PK samples for IVIG were drawn over 4 week period, and the serial PK samples for SCIG were drawn over 1 week period (instead of 4 week period). To compare the AUCs between IVIG and SCIG, the AUC[0-7 days] was multiplied by 4 before comparing it to the AUC[0-28 days]. With this approach, the next three weekly intervals are assumed to have the same PK profiles as the first weekly interval. We can also say that the PK profiles for the next three intervals (dotted lines) are projected or simulated.  This approach was accepted by the FDA and PK results were included in the product label. The PK profiles for IVIG and SCIG are displayed below (the dotted portion for SCIG was projected). 

Advancing the Development of Pediatric Therapeutics (ADEPT) - FDA Workshops on Pediatric Drug Developments

Each year, the FDA's Division of Pediatrics and Maternal Health in collaboration with the University of Maryland CERSI organized an annual public workshop to discuss the issue related to pediatric drug development. Starting in 2014, it has organized 7 public workshops (no event for 2020 due to Covid-19). 

           Advancing the Development of Pediatric Therapeutics (ADEPT)

ADEPT 7	2021	Complex Innovative Trial Design	"Advancing the Development of Pediatric Therapeutics Innovative Trail Design"
ADEPT 6	2019	Pediatric Patient Perspectives	Pediatric Clinical Trial Endpoints for Rare Diseases With a Focus on Pediatric Patient Perspectives NOVEMBER 12, 2019
ADEPT 5	2018	Pediatric Pharmacovigilance	Advancing Pediatric Pharmacovigilance Public Workshop AUGUST 13, 2018
ADEPT 4	2017	Big data	Application of "Big Data" to Pediatric Safety Studies
ADEPT 3	2016	Long-Term Pediatric Safety Studies	Successes and Challenges of Performing Long-Term Pediatric Safety Studies: Pediatric Drug Development Regulatory Considerations International Collaborations in Pediatrics: FDA and EMA growing together International Considerations for Pediatric Master Protocols
ADEPT 2	2015	Long-term Neurocognitive Development in Pediatrics	Evaluation of Long-term Neurocognitive Development in Pediatrics
ADEPT 1	2014	Pediatric Bone Health

For this year (2021), the 2-day public workshop was titled “Advancing the Development of Pediatric Therapeutics Complex Innovative Trial Design”. The purpose of the workshop was to discuss opportunities for leveraging complex and innovative trial designs, understand the challenges with their applications, and develop solutions on how challenges in the designs can be overcome. 

The workshop specifically focused on two topics of interest: 

• bridging biomarkers in pediatric extrapolation 
• Bayesian techniques in pediatric studies. 

In addition, the workshop allowed for an open dialogue around the use of these approaches among regulators, industry, academia, and patient organizations.

The presentation slides & webcast video links from the workshop are now all available.

Advancing the Development of Pediatric Therapeutics Complex Innovative Trial Design Public Workshop

SEPTEMBER 1 - 2, 2021