Assessing potential unblinding due to imbalance in side effects through exit questionnaires

The critical features of the RCTs include '(concurrent) control', 'randomization', and 'blinding'. These clinical trial features can minimize the conscious or unconscious biases in the endpoint assessments including both the efficacy and safety assessments.

 
Blinding is a procedure in which one or more parties in a trial are kept unaware of which treatment arms participants have been assigned to, i.e. which treatment was received. On the contrary, unblinding is the process by which the treatment allocation is broken so that one or more parties in a trial become aware of the treatment arms the trial participants are on. To maintain the integrity of the RCT trial, the unblinding should occur only after the completion of the study and after the clinical database has been locked (no further modifications to the clinical data) or only in some special situations (unblinding for data monitoring committee, unblinding of the individual trial participants for SUSAR reporting,...). A couple of previous posts discussed exactly the same issue. 

• Potential Unblinding due to Imbalance in Adverse Event Profiles
• Is blinded study really blinded? - assessment of blinding /unblinding in clinical trials

Blinding is an important aspect of any trial. How a trial was blinded should be accurately recorded in order to allow readers to interpret the results of a study. If blinding is ever broken during a trial on individual participants, it needs to be justified and explained.

To implement and maintain the blinding (or treatment concealment), the clinical trial materials need to be manufactured and packaged in the same way. The control group (typically placebo) will have the same specifications as the testing product in shape, size, color texture, weight, taste, and smell. 

The most difficult part of maintaining the blinding is that the testing product and the control product (usually placebo) may have different side effects. The clinical trial participants and the investigators may be able to guess which treatment group the participants are on. It is quite challenging to defend the integrity of the blinding if two treatment groups do have different side effects or adverse event profiles. 

Amylyx recently ran into this issue and successfully defended the integrity of the blinding for their pivotal study (CENTAUR trial) that was published in NEJM. CENTAUR trial results were the basis for Amylyx's NDA submission. In a rare occurrence, FDA organized two external committee meetings by the Peripheral and Central Nervous System Drugs Advisory Committee (PCNS). 

In the first PCNS adcom meeting on March 30, 2022, the integrity of maintaining the blinding was raised:

"The potential for diarrhea and bitter taste were described in the informed consent, which may have alerted the patients to these symptoms and could have led to functional unblinding during the study. These are potential review issues we have identified which contribute to the uncertainty of the results."

In the second PCNS adcom meeting on September 7, 2022, the sponsor addressed the potential unblinding issue in their briefing book:

The sponsor concluded:

"Based on an exit questionnaire performed at the end of the randomized phase, it asked investigators and participants what treatment arm they were assigned to. Neither study investigators nor participants were able to guess the treatment assignment. The active group was not able to guess their treatment assignment any better than chance, indicating that taste and GI adverse events were not leading to unblinding."

Assessing the blinding through exit questionnaires can be risky though. As described in my previous post "Is blinded study really blinded? - assessment of blinding /unblinding in clinical trials":

"Ideally, in a double-blind trial, it is a good practice to evaluate for both the subjects and investigators whether or not blinding / masking has been preserved. However, in the real world, it is rare in double-blinded clinical trials to include a formal assessment of how well the blinding has been preserved. If the assessment of blinding becomes a routine, I think that many studies will show that subjects/investigators guessed correctly more frequently than they should have done by chance alone. Part of the reason this assessment has not been done often is perhaps the difficulty to explain the study results if the blinding is found to be compromised. It will be extremely difficult to assess the magnitude of the impact on the safety and efficacy evaluation if the blinding/treatment assignment concealment is compromised."

Sample size re-estimation or sample size increase?

Recently, a press release from a biotech company caught my eye. This seems to be an example of the adaptive design with sample size re-estimation, however, it is unusual that the sample size is decreased as usually the sample size re-estimation results in an increase in sample size. 

Bellerophon Therapeutics, Inc. (Nasdaq: BLPH) (“Bellerophon” or the “Company”), a clinical-stage biotherapeutics company focused on developing treatments for cardiopulmonary diseases, announced today that the U.S. Food and Drug Administration (FDA) has accepted the Company’s proposal to reduce the study size for its ongoing registrational REBUILD Phase 3 trial of INOpulse® for the treatment of fibrotic Interstitial Lung Disease (fILD). The new study size of 140 subjects does not impact the trial’s principal objective or endpoints and maintains power of >90% (p-value < 0.01) for the primary endpoint of Moderate to Vigorous Physical Activity (MVPA) based on the effect size observed in Phase 2.

Following the evaluation of baseline MVPA characteristics, as measured by actigraphy, compliance to treatment and review of safety data of the randomized subjects in the ongoing Phase 3 REBUILD study, the trial’s independent Data Monitoring Committee (DMC) supported reducing the target study size from 300 to 140 subjects.

Sample size re-estimation is one type of adaptive design where the sample size can be adjusted during the study based on a prespecified rule. Sample size re-estimation has its special features:  

Group Sequential Design (GSD) and Sample Size Re-estimation

Clinical trials with adaptive design can be in different forms depending on what the adaptations are. Two commonly utilized adaptive designs are group sequential design (GSD) and sample size re-estimation (SSR). Implementation of both GSD and SSR is through the interim analyses conducted by the independent data monitoring committee. In GSD studies, we set a large sample size and hope to stop the trial early due to the overwhelming efficacy, futility, or safety at the interim analyses. In adaptive design with SSR, we start with a small study and possibly increase the sample size post an interim analysis. Both GSD and SSR can achieve the same benefits of reduced sample size and potentially an earlier conclusion. 

Blinded Sample Size Re-estimation and Unblinded Sample Size Re-estimation

In FDA guidance "Adaptive Designs for Clinical Trials of Drugs and Biologics", sample size re-estimation was described in section B "adaptations to the sample size". Blinded sample size re-estimation is based on interim estimates of nuisance parameters such as the standard deviation for continuous outcome measure and overall event rate for discreet outcome measure. The unblinded sample size re-estimation is a type of adaptive design where adaptation is to prospectively plan modifications to the sample size based on comparative interim results. Blinded sample size re-estimation may be conducted by the sponsor statistician while unblinded sample size re-estimation must be through an independent data monitoring committee.  

Sample Size Re-estimation and Sample Size Increase

In clinical trials with prospectively planned sample size re-estimation, the sample size is usually increased. It is very rare that the sample size is decreased after the interim analysis. For adaptive clinical trials with adaptation on sample size (i.e., sample size re-estimation), the initial sample size estimation can be based on more aggressive assumptions that result in a smaller sample size. In the middle of the study, interim analyses are performed and the decision can be made (by independent DMC and through a prespecified rule) whether or not the sample size should be increased. 

In FDA's guidance discussing the Adaptations to Sample Sizes, while the terms 'sample size re-estimation' and 'sample size adaptation' are used, the sample size increase is really implied. 

Sample Size Adaptation and Sample Size Increase by a Fixed Number

The sample size re-estimation or sample size adaptation is really a binary decision. If the decision is to increase the sample size (after the interim analysis), the sample size will be increased by a pre-specified, fixed number, not increased by a number that is based on the observed treatment effect at the interim analysis. 

If the sample size is increased by a very exact level calculated from the observed treatment effect at the interim analysis to bring the conditional power up to a target level, there is a potential to reverse calculate the effect size or to at least make an educated guess about what the effect size is from the interim analysis

This potential for an educated guess about the effect size is a huge issue from the regulatory point of view. This specific concern is discussed in FDA's guidance ""Adaptive Designs for Clinical Trials of Drugs and Biologics".

Finally, there are additional challenges in maintaining trial integrity in the presence of sample size adaptations. For example, sample size modification rules are often based on maintaining the conditional probability of a statistically significant treatment effect at the end of the trial (often called the conditional power) at or near some desired level. In this scenario, knowledge of the adaptation rule and the adaptively chosen sample size allows a relatively straightforward back-calculation of the interim estimate of treatment effect. Therefore, additional steps should be taken to limit personnel with this detailed knowledge so that trial integrity can be maintained.

Randomized withdrawal design in action - Accord trial in Alzheimer's agitation

The biotech company, Axsome Therapeutics, announced the positive results from one of their pivotal phase 3 studies (Accord study). 

Axsome's approved depression drug clears Alzheimer's agitation trial months after Lundbeck-Otsuka duo

The unique side of the Accord study is the use of a randomized withdrawal design. The study was registered on clinicaltrials.gov as "A Double-blind, Placebo-controlled, Randomized Withdrawal Trial to Assess the Efficacy and Safety of AXS-05 for the Treatment of Agitation in Subjects With Dementia of the Alzheimer's Type"

With the randomized withdrawal design, all participants were given the active drug (AXS-05) in a run-in phase in an open-label manner. Then, those patients who responded to treatment during the run-in phase were randomly assigned, in a double-blind manner, to either continue treatment with AXS-05 or switch to a placebo.

According to the sponsor, the basic idea behind this randomized withdrawal study design is to see whether those who initially experience a benefit stop doing so when moved to a placebo, indicating that the therapy itself is effective — as opposed to results being due to a placebo effect. The randomized-withdrawal design of this phase 3 trial simultaneously improved signal detection and mitigated placebo response. 

With the randomized withdrawal design, the sample size was reduced. Total 178 patients with Alzheimer's disease agitation were enrolled into the study run-in phase. 108 patients who achieved a sustained clinical response were then included in the randomized withdrawal period. 

"The ACCORD study was a double-blind, placebo-controlled, multi-center, randomized withdrawal, U.S. trial which treated 178 patients with Alzheimer’s disease agitation. Patients achieving a sustained clinical response after open-label treatment with AXS-05 were randomized (n=108) in a 1:1 ratio to continue treatment with AXS-05 or to discontinue AXS-05 and switch to placebo."

According to an article on evaluate.com:

"When Axsome decided to stop the Accord study of AXS-05 in Alzheimer’s disease agitation early, hopes for that trial took a nosedive. So it was clearly a pleasant surprise today when the company announced that the study had hit. Axsome’s stock opened up 33%, and some investors might be hoping for an earlier-than-expected filing, despite the fact that results from the pivotal Advance-2 study are not due until 2025. Accord had initially been intended as a second pivotal, alongside the previously completed Advance-1, but when the number of agitation events turned out lower than expected, management decided to switch focus to Advance-2. Despite this, Accord met its primary endpoint, time to relapse of agitation, and a key secondary, relapse prevention. One potential fly in the ointment could be Accord’s randomised withdrawal design; it comprised an open-label lead-in phase in which all 178 patients were given AXS-05, and those that had a sustained clinical response to the agent were randomised to either continue treatment or switch to placebo. AXS-05, a combination of dextromethorphan and bupropion, is approved in depression as Auvelity and moving into Alzheimer’s agitation would be an important expansion."

Given that Alzheimer's agitation is a common disease (70% of Alzheimer's disease patients may have agitation), more than one adequate and well-controlled study (or pivotal, confirmatory studies) are needed to demonstrate substantial evidence for effectiveness. Besides the Accord study (with randomized withdrawal design), two additional studies were conducted by the sponsor: ADVANCE-1 trial was a Phase 2/3 study with an active control arm. ADVANCE-2 trial is a phase 3 confirmatory study with the largest sample size (350 patients in a 1:1 randomization ratio). ADVANCE-1 study results had already been announced. ADVANCE-2 study has just started the enrollment. Both ADVANCE-1 and ADVANCE-2 studies were designed as traditional RCT design - randomized, double-blind, placebo-controlled, parallel groups. 

In a clinical program containing multiple pivotal clinical trials, it is appropriate to select different clinical trial designs. In Axsome's Alzheimer's agitation clinical program, a randomized withdrawal design was used in one of the three pivotal trials, and a traditional RCT design was used in the other two pivotal trials. If all these three trials are successful, the evidence for effectiveness will be more substantial and stronger than three studies with the same study design. 

Treatment Emergent AEs (TEAEs), On-Study AEs, On-Treatment AEs, Non-TEAEs

During the clinical trial, the adverse events (AEs) are collected from the signing of the informed consent to the last dose of the study drug plus some follow-time. For statistical analyses of adverse event data, the treatment-emergent AEs (TEAEs) are usually defined. AEs with an onset date at or after the first dose of the study drug will be defined as TEAEs. AEs with an onset date prior to the first dose of the study drug will then be defined as Non-TEAEs. 

For example, the TEAE can be defined as:

"TEAEs are defined as events that start within the day of the first dose of trial treatment until 28 days after the last dose of treatment" in an SAP for an EMD Serono study.

"Treatment-emergent AEs (TEAEs) are defined as AEs that are not present at baseline or represent an exacerbation of a preexisting condition during the treatment period. Therefore, referencing the protocol, TEAEs will be defined programmatically as any AE record with a start date/time on or after the first study treatment administration (greater than or equal to study day 1), inclusive to the end of the study (specifically the EOS visit or ET visit)." in an SAP for a Regeneron's study

The TEAEs can be further defined based on the comparison of the AE onset date with a cut-off date where the cut-off date may be the last dose date or 28 days after the last dose date. 

In FDA's clinical review document for AstraZeneca's asthma drug, the on-study AE and on-treatment AE were defined:

• On-study AE: events with onset between the first-day dosing and the scheduled follow-up visit. 
• On-treatment AE: events with onset between the first day of treatment and the scheduled end of treatment (EOT) or investigational product discontinuation (IPD) visit. 
• Post-treatment AE: events with onset after the on-treatment period defined above

 On-study AEs include all TEAEs - all AEs recorded on or after the first dose date. 

On-treatment AEs are a subset of all TEAEs or on-study AEs. The AEs with an onset date after the cut-off date will be excluded from on-treatment AEs. 

There are some clinical trials with on-treatment AEs defined as the same as traditional TEAEs (i.e., any AEs with an onset date on or after the first dose of the study drug regardless of the cut-off date).

In a recent workshop "Advancing Premarket Safety Analysis" organized by FDA and Duke Margolis Center for Health Policy, the on-study and on-treatment AEs were specifically discussed. Here are the presentation slides for this topic:

The concept of on-treatment AEs has already been implemented in some clinical trials. For example, in a GSK-sponsored trial "A Phase 3a, Repeat Dose, Open-label, Long-term Safety Study of Mepolizumab in Asthmatic Subjects", the primary outcome measure is "Number of Participants With Any On-treatment Adverse Event (AE) or On-treatment Serious AE (SAE)" where On-treatment AEs and on-treatment SAEs are the events occurring on/after the first dose of open-label mepolizumab date and before/on last dose+28 days.

In a BMS trial "A randomized, open-label, phase 3 study of  BMS-936558 vs. Everolimus in Subjects with advanced or metastatic clear-cell renal cell carcinoma who have received prior anti-angiogenic therapy", the on-treatment AEs were defined as the following with a cut-off date of 100 days of the last dose of study treatment.

"On-treatment AEs will be defined as AEs with an onset date-time on or after the DateTime of the first dose of study treatment (or with an onset date on or after the day of first dose of study treatment if time is not collected or is missing). For subjects who are off study treatment, AEs will be counted as on-treatment if event occurred within 100 days of the last dose of study treatment. No “subtracting rule” will be applied when an AE occurs both pre-treatment and post-treatment with the same preferred term and grade."

Defining on-treatment AEs will require specifying a cut-off date and the cut-off date may be different depending on the potential impact of the study drug after the drug discontinuation and the half-life of the investigational products. 

Defining on-treatment AEs may be necessary for studies with the treatment policy estimand where the efficacy data and AE/SAEs are continued to be collected after the study participants have discontinued the study treatment. 

Previous discussions: 

• Adverse events (AE),treatment-emergent adverse events (TEAE), and adverse drug reaction (ADR)
• Adverse Event Collection: When to Start and When to Stop?

Rolling Review, Real time oncology review (RTOR), and Split real time application review (STAR) Program

For New Drug Application (NDA) and Biological License Application (BLA), the usual process is to submit the entire package with different modules at the same time. The submission package will include the quality, CMC, non-clinical study reports, and clinical study reports,... However, there are processes by which the sponsor can submit the submission package piece by piece: rolling review, real-time oncology review, and split real-time application review (STAR) program.

Rolling Review

Rolling review was one of the benefits for drug products with Fast Track Designation. According to FDA's website "Fast Track" 

A drug that receives Fast Track designation is eligible for some or all of the following:

More frequent meetings with the  FDA to discuss the drug's development plan and ensure the collection of appropriate data needed to support drug approval

More frequent written communication from FDA about such things as the design of the proposed clinical trials and use of biomarkers

Eligibility for Accelerated Approval and Priority Review, if relevant criteria are met

Rolling Review, which means that a drug company can submit completed sections of its Biologic License Application (BLA) or New Drug Application (NDA) for review by FDA, rather than waiting until every section of the NDA is completed before the entire application can be reviewed. BLA or NDA review usually does not begin until the drug company has submitted the entire application to the FDA

Fast Track Designation was one of the expedited programs described in FDA's guidance "Expedited Programs for Serious Conditions – Drugs and Biologics". Other expedited programs are breakthrough therapy designation, accelerated approval, and priority review designation. In FDA's guidance, the Fast Track Designation contains the benefit of submission of portions of an application (Rolling Review): 

Here are rolling review examples: earlier this month, Iveric Bio Announces Submission of First Part of NDA for Rolling Review of Avacincaptad Pegol for the Treatment of Geographic Atrophy; in September, 2022, Vertex and CRISPR Therapeutics Announce Global exa-cel Regulatory Submissions for Sickle Cell Disease and Beta Thalassemia

Real-Time Oncology Review (RTOR) Program

For oncology products, FDA's The Oncology Center of Excellence has a program called "real-time oncology review (RTOR)". RTOR facilitates earlier submission of topline efficacy and safety results, prior to the submission of the complete application, to support an earlier start to the FDA’s evaluation of the application. FDA's website "Real-Time Oncology Review" described the details about RTOR program. 

Some companies have utilized this program in hope of expediting their submission/review process. 

In a press release "SpringWorks Therapeutics Announces Data from Phase 3 DeFi Trial Evaluating Nirogacestat in Adult Patients with Progressing Desmoid Tumors at the European Society for Medical Oncology (ESMO) Congress 2022", it stated that their Nirogacestat for R/R desmoid tumors will be filed through real-time oncology review (RTOR) program.

Nirogacestat has received Orphan Drug Designation from the U.S. Food and Drug Administration (FDA) for the treatment of desmoid tumors and from the European Commission for the treatment of soft tissue sarcoma. The FDA also granted Fast Track and Breakthrough Therapy Designations for the treatment of adult patients with progressive, unresectable, recurrent or refractory desmoid tumors or deep fibromatosis. SpringWorks plans to submit a New Drug Application (NDA) to the FDA in the second half of 2022, which will be submitted for review under the FDA’s Real-Time Oncology Review (RTOR) program.

Amgen's Sotorasib was approved by FDA for the first and only targeted treatment for patients with KRAS G12C-mutated locally advanced or metastatic non-small cell lung cancer. The Sotorasib's BLA submission was through RTOR:

"In the U.S., LUMAKRAS was reviewed by the FDA under its Real-Time Oncology Review (RTOR), a pilot program that aims to explore a more efficient review process that ensures safe and effective treatments are made available to patients as early as possible."

 An article in Life Science Leader magazine "FDA's RTOR Program: Draft Guidance & Insights" provides a good summary of the RTOR program.

Split Real-Time Application Review (STAR)

STAR program builds off the Oncology Center of Excellence’s Real-Time Oncology Review (RTOR) program, In the newly passed PDUFA VII for the years 2023 through 2027, a new program called Split real-time application review (STAR) was proposed. According to PDUFA reauthorization performance goal and procedures fiscal years 2023 through 2027, the STAR program was described as the following: 

D. SPLIT REAL TIME APPLICATION REVIEW (STAR) PILOT PROGRAM

FDA will establish a STAR pilot program, which has the goal of shortening the time from the date of complete submission to the action date, in order to allow earlier patient access to therapies that address an unmet medical need. The STAR pilot program will apply to efficacy supplements across all therapeutic areas and review disciplines that meet specific criteria. Accepted STAR applications will be submitted in a “split” fashion, specifically in two parts (with the components submitted approximately 2 months apart).

1. Scope: The STAR program will seek to expedite patient access to novel uses for existing therapies by supporting initiation of review earlier than would otherwise occur and therefore allowing earlier approval for qualified efficacy supplements. This program will apply across all therapeutic areas and review disciplines for applications that meet specific criteria. An application will be considered eligible for STAR if each of the following criteria are met: a. Clinical evidence from adequate and well-controlled investigation(s) indicates that the drug may demonstrate substantial improvement on a clinically relevant endpoint(s) over available therapies. Breakthrough Therapy Designation (BTD) or Regenerative Medicine Advanced Therapy Designation (RMAT) is not required, but above criteria must be met. b. The application is for a drug intended to treat a serious condition with an unmet medical need. c. No aspect of the submission is likely to require a longer review time (e.g., requirement for new REMS, etc.). d. There is no chemistry, manufacturing, or control information that would require a foreign manufacturing site inspection (i.e., domestic site inspections may be allowed if it does not affect the expedited timeframe).

FDA's website "Split Real-Time Application Review (STAR)" described how the STAR program should be operated.

Multiple Endpoints in Clinical Trials (Final) - FDA Guidance for Industry

Today, the FDA finalized its guidance for industry "Multiple Endpoints in Clinical Trials". The draft version of this guidance was issued in 2017. The guidance is intended to help sponsors better understand FDA's current thinking about the issues related to the multiple endpoints and multiplicity issues for multiple endpoints, and different approaches in handling multiplicity issues. The guidance also discussed composite endpoints and multi-component endpoints. 

Typically, an adequate and well-controlled study will include only one primary efficacy endpoint and then multiple secondary efficacy endpoints, and additional exploratory endpoints. Exploratory endpoints are those endpoints for research purposes or for new hypotheses generation and not for the purpose of the product label. Primary and secondary efficacy endpoints can potentially be included on the product label. However, the testing hierarchy and sound approach for multiplicity adjustment must be pre-specified. The Fixed-Sequence Method in the appendix of this guidance seems to be commonly used. With Fixed-Sequence Method, the secondary efficacy endpoints will be tested only if the primary efficacy endpoint is statistically significant. The secondary efficacy endpoints are ranked or ordered based on the importance of the endpoints and the likelihood of getting statistically significant results. The next secondary efficacy endpoint will be tested only if the previous secondary endpoint is statistically significant. The testing hierarchy will stop once the hypothesis test for one of the secondary endpoints is not statistically significant.  

If the sponsor wants to include secondary endpoints in the product label, multiplicity adjustment for secondary endpoints must be included in the statistical analysis plan. 

In the section discussing the co-primary endpoints, "When Demonstration of Treatment Effects on Two or More Distinct Endpoints Is Recommended to Establish Clinical Benefit (Co-Primary Endpoints)", the examples of clinical trials with co-primary endpoints included in the draft version of this guidance were removed from the final guidance. For example, the draft guidance mentioned the following example and the final guidance did not:

Presumably, this is due to the revised FDA guidance "Early Alzheimer's Disease: Developing Drugs for Treatment" and the availability of the integrated scale - Clinical Dementia Rating Sum of Boxes (CDR-SB) Score:

"An integrated scale that adequately and meaningfully assesses both daily function and cognitive effects in early AD patients is acceptable as a single primary efficacy outcome measure. " 

"Common Statistical Methods for Addressing Multiple Endpoint-Related Multiplicity Problems" was included in the body of the guidance in the draft guidance and is now moved to the Appendix: Statistical Methods. The list of methods remains the same and includes the Bonferroni method; the Holm procedure; the Hochberg procedure; prospective alpha allocation scheme; the fixed-sequence method; resampling-based, multiple-testing procedures; gatekeeping testing strategies; and graphical approaches based on sequentially rejective tests. 

As a regulatory agency, FDA is conservative and tries to avoid false conclusions. Without adequate adjustment for multiplicity, the alpha level (type I error rate) can be inflated, and statistically, significant differences may be wrongly declared for an ineffective drug. In the summary of this guidance, FDA concludes: 

Previous Posts:

• Weighted Bonferroni Method (or partition of alpha) in Clinical Trials with Multiple Endpoints
• Co-primary endpoints and multiple primary endpoints

Risk difference and confidence interval for Analyses of AEs and Clinical Laboratory Data

Several years ago, I posted an article "Should hypothesis tests be performed and p-values be provided for safety variables in efficacy evaluation clinical trials?". There are some new development on this topic. 

Recently, FDA in collaboration with the Duke-Margolis Center for Health Policy hosted a one-day virtual meeting focused on advancing pre-market safety analytics. At this workshop, it was revealed that FDA Biomedical Informatics and Regulatory Review Science (BIRRS) Team was working on a document called "Standard Safety Tables and Figures: Integrated Guide". The document is currently posted on regulations.gov for public comments. The integrated guide proposed the mockup shells how the safety data analyses (adverse events and clinical laboratory data) should be displayed. Throughout all the proposed shells, we can see that a column for "'Risk Difference (%) (95% CI)" are included. Here are a couple of examples. 

If this integrated guide become official and is implemented, the future analyses for safety data (adverse events and clinical laboratory parameters) will be shifted from the pure summary statistics to summary statistics + point estimate and 95% confidence interval for risk differences. p-values and hypothesis testing should not be provided. 

Risk difference and its 95% confidence interval are provided for the descriptive purpose, not for inferential purpose. As stated in the integrated guide "These safety analyses are exploratory in nature and confidence intervals (CIs) for the risk difference presented here are not adjusted for multiplicity."

In AE tables, the sort order will be by the risk difference (from the highest to the lowest). In this way, the reviewers can easily identify the AEs with largest risk difference between two treatment groups. 

There are several ways in calculating the confidence interval for risk difference. The commonly used approach is Wilson score method - a method of estimating the population probability from a sample probability when the probability follows the binomial distribution.

There seems to be some differences between the regulatory requirement and the requirement by the medical journals. We continue to see the requests from journals like New England Journal of Medicine for providing the p-values for AE summary tables. In our published article, "Inhaled Treprostinil in Pulmonary Hypertension Due to Interstitial Lung Disease", we had to provide the p values for AEs and other safety endpoints for treatment group comparison per NEJM's editor's request. 

PDUFA, GDUFA, BSUFA, and MDUFA - FDA's User Fee Programs

For professionals who are working in drug development areas, the term PDUFA should be a very familiar term. As a matter of fact, FDA's action date or decision date to approve a new drug application (NDA) or biological license application (BLA) is called 'PDUFA date'. There are several trackers to track FDA's calendar for NDA/BLA approvals based on the PSUFA dates. 

PDUFA stands for 'Prescription Drug User Fee Amendments' and it is a program allowing FDA to collect the application fees from the sponsor. The PDUFA was created by Congress in 1992 and authorizes FDA to collect fees from companies that produce certain human drug and biological products. Since the passage of PDUFA, user fees have played an important role in expediting the drug approval process. In "Regulatory Education for Industry (REdI) Annual Conference 2022", Dr. Kevin Bugin from FDA presented "PDUFA Overview and Reauthorization".

"Timely review of the safety and effectiveness of the new drug applications and biologics license applications is essential to FDA mission to protect and promote public health. PDUFA is essential to these efforts. In fact, before PSDUFA is enacted in 1992, American's access to innovative new medicines lacked behind other countries. FDA’s premarket review process was unstaffed and unpredictable, and frankly slow. Agency lacks sufficient staff to perform a timely review and lacks in procedures and standards to ensure a rigorous, consistent, and predictable process.

So to tackle these challenges Congress passed PDUFA and this authorized FDA to collect industry user fees to hire additional staff and upgrade its information technology systems, processes, and so on, in return, it committed the agency to timelines for the application review process for new drugs without compromising its high standards for new drug safety efficacy and quality and, over the years as the PDUFA program has been reauthorized now six times going into its seventh time here hopefully there have been additional enhancements."

PDUFA must be reauthorized or renewed by congress every five years. Since its inception in 1992, it has been amended six times and we are now waiting for congress to authorize the 7th amendment (PDUFA VII). Notice that each PDUFA amendment may have its own name associated with it. 

PDUFA I - Original 

PDUFA II - FDAMA (Food and Drug Administration Modernization Act) 

PDUFA III - Public Health Security and Bioterrorism Preparedness and Response Act of 2002

PDUFA IV - FDAAA (Food and Drug Administration Amendments Act of 2007)

PDUFA V - FDASIA (Food and Drug Administration Safety and Innovation Act)

PDUFA VI - FDARA (FDA Reauthorization Act of 2017)

There are several sister programs designed with the same purpose as PDUFA, but for different approval pathways: GDUFA (Generic Drug User Fee Amendment), BSUFA (Biosimilar User Fee Amendment), and MDUFA (Medical Device User Fee Amendment). Recently, PDUFA VII was authorized by congress, and so were these sister programs. 

Along with the approval of PDUFA, GDUFA, BSUFA, and MDUFA, the user fee rates for various types of applications are updated and released by the FDA:

PDUFA: Prescription Drug User Fee Amendments

Prescription Drug User Fee Rates for Fiscal Year 2023

GDUFA: Generic Drug User Fee Amendment

Generic Drug User Fee Rates for Fiscal Year 2023

BSUFA: Biosimilar User Fee Amendment

Biosimilar User Fee Rates for Fiscal Year 2023

MDUFA: Medical Device User Fee Amendment

Medical Device User Fee Rates for Fiscal Year 2023

For companies that are well-funded and have steady revenues, these user fees are affordable. However, these user fees could be a burden for small companies. 

For drug development in diseases with orphan drug designation, the application fees for NDAs or BLAs are waived - this is an approach to encourage the companies to develop the drugs in orphan diseases. 

FDA's guidance "User Fee Waivers, Reductions,and Refunds for Drug andBiological Products" listed the situations where the user fees can be waived.  

FMQ (FDA Medical Query) and SMQ (Standardized MedDRA Query)

For clinical trials, the safety analyses are mainly based on the analyses of the adverse events including serious adverse events. The adverse events are recorded in CRFs/eCRFs with investigators' verbatim terms (text field). Before the adverse event data can be summarized and analyzed, the verbatim terms need to be coded and standardized. The common practice is to code the adverse events based on the MedDRA dictionary and the coded terms are then summarized and analyzed by system organ class and preferred term. For a while, this approach seems to work very well. However, there are issues with this approach, mainly because different preferred terms may point to the same disease/condition. 

Last Wednesday, The FDA in collaboration with the Duke-Margolis Center for Health Policy hosted a one-day virtual meeting focused on advancing premarket safety analytics. In the morning session, FDA officers discussed the FMQ (FDA Medical Query). In the afternoon session, FDA discussed the standardized presentation of the safety data including the tables and figures for adverse event data and laboratory data. 

The FDA Medical Queries (FMQs) is a standardized approach to group preferred terms. Recognizing the limitation of the current analysis of adverse event data by preferred term, using FMQs can consolidate a medical condition with scattered preferred terms and be more likely to identify any signal of safety issues. The rationales for FDA's efforts in developing various FMQs are described in the slide below: 

FMQs were defined as the following: 

FMQs can be used to identify the safety signals that may be missed by using the conventional preferred term approach. When FMQs are adopted by the regulatory agency and the industry, FMQs (grouped term information) can be included in the ADVERSE REACTIONS Section of the Prescribing Information (or product label). The slide below illustrates a fictitious example of using FMQ term in the ADVERSE REACTIONS section of the product label. The AE table in the product label will include the mixture of the FMQ term (grouped term) and the MedDRA preferred terms. 

A real example of FMQs in the product label can be the drug called Injectafer. The tables for adverse reactions included the mixture of the preferred terms and the grouped terms (based on FDA's FMQs). 

FMQ is exactly the same concept as the SMQ (standardized MedDRA query). The SMQ was defined as the following: 

With each version of the MedDRA dictionary, a set of available SMQs will be included. Included is also the document "Introductory Guide for Standardised MedDRAQueries (SMQs)". 

Both FMQ and SMQs included narrow terms and broad terms. However, the narrow terms are more commonly used in practice. 

One natural question is: what is the difference between FMQ and SMQ? why can't we just use the SMQ? Here are a couple of slides indicating the differences between FMQ and SMQ. There are almost equal numbers of FMQs (104 FMQs for now) to SMQs (110 SMQs).

It is true that SMQs have primarily been used in pharmacovigilance, not in premarket safety assessment. I have previously written a couple of articles about the SMQs: 

• Adverse Event of Special Interest (AESI), Standardized MedDRA Query (SMQs), Customer Queries (CQs), and SAS Programming
• Standardized MedDRA Queries (SMQs) and Standardized Drug Groupings (SDGs)

One drawback of FMQ is that it is developed by the US FDA and its use and application may be limited for market authorization applications in countries outside the US. 

One thing for sure is that we will hear the term FMQ more often in the future and may see the request from FDA to present the grouped terms according to FMQs in the summary and analysis tables for AEs. 

Further reading:

Biomedical Informatics and Safety Analytics in the Office of New Drugs, CDER/FDA – A 2021 Update

Communicating with FDA: Type A, B, C, D meetings, and INTERACT meeting,

For any drug development program, the early and sometimes frequent communications are critical. However, the formal communications between the sponsor and the FDA are a cumbersome process. The sponsor representatives can not directly reach out to FDA reviewers (such as medical reviewer, statistical reviewer, clinical pharmacology reviewer, CMC reviewer,...). On the sponsor side, the communications with FDA is always through the regulatory affairs group. On the FDA side, the communication with the sponsor is through the regulatory project manager (RPM) - each review division at FDA has its own RPM. Direct communications between the sponsor representatives and the FDA reviewers/officers are not good practice and can cause the trouble down the road. We all knew how badly the situation was with Biogen's Aduhelm approval where Biogen executives met with FDA officials outside the normal communication channel. See "FDA chief asks for independent investigation into approval of Biogen's Alzheimer's drug Aduhelm".

Best Communications Practices with FDA is as the following:  

The FDA RPM: The review division regulatory project manager (RPM) is the primary point of contact for communications between IND sponsors and FDA during the life cycle of drug development, and has comprehensive knowledge of the drug and its regulatory history. The RPM is also the primary contact for facilitating the timely resolution of technical, scientific, and regulatory questions, conflicts, or communication challenges between the sponsor and the review team. If sponsors encounter challenges in obtaining timely feedback to inquiries to the review division RPM, they should contact the RPM’s next level supervisor for timely resolution of the issue. 

Communications with FDA are usually through the formal meetings. There are different type of meetings for different purpose. The processes for requesting the formal meetings are described in FDA's guidance below:

• Formal Meetings Between the FDA and Sponsors or Applicants of PDUFA Products Guidance for Industry
• Best Practices for Communication Between IND Sponsors and FDA During Drug Development Guidance for Industry and Review Staff Good Review Practice

In Regulatory Education for Industry (REdI) Annual Conference 2022 - Day 1, Dr Kevin Bugin gave a PSUFA overview where additional type of meetings with FDA were discussed and FDA officer, Jeannie Roule presented "Guidance for Industry: Formal Meetings Between the FDA and Sponsors or Applicants of PDUFA Products".

Different type of Meetings with FDA and the Comparisons:

The Prescription Drug User Fee Act (PDUFA) was created by Congress in 1992 and authorizes FDA to collect fees from companies that produce certain human drug and biological products. Since the passage of PDUFA, user fees have played an important role in expediting the drug approval process. PDUFA needs to be reauthorized by Congress every five years. PDUFA VII for fiscal years 2023 through 2027 is being discussed by Congress and is expected to be reauthorized before the end of the current fiscal year. 

To-be-reauthorized PDUFA VII will create some additional meetings with FDA, specifically, Type D meeting. Once the PDUFA is reauthorized by Congress, the sponsor can request for Type D meeting. 

According to PDUFA REAUTHORIZATION PERFORMANCE GOALS AND PROCEDURES FISCAL YEARS 2023 THROUGH 2027, the Type D meeting is described as the following: 

FDA also had a meeting called INTERACT. INTERACT stands for INitial Targeted Engagement for Regulatory Advice on CBER/CDER ProducTs. It is like the pre-pre-IND meeting and is especially useful in CAR-T, Gene therapy, xenotransplantation development programs. 

Story of BrainStorm's Stem Cell Treatment for ALS - Criticality of the Statistical Analyses

This past week, the biotech company BrainStorm announced the decision to submit a BLA to the FDA for NurOwn® (a stem cell treatment) for the treatment of ALS (Amyotrophic Lateral Sclerosis). The news stirred quite some discussions. The decision to submit the BLA is driven by the reanalysis or the corrected analysis of the previously announced negative results from their pivotal study. In their news announcement, they stated the following: 

New clinical analyses strengthen the conclusions from NurOwn's® Phase 3 clinical trial

A correction was made to the Muscle and Nerve publication from December 2021 describing the results of NurOwn's® Phase 3 clinical trial in ALS following new clinical analyses which strengthen the Company's original conclusions from the trial. The correction results in a statistically significant treatment difference (p=0.050) of more than 2 points for an important secondary endpoint, average change from baseline in ALSFRS-R, in the pre-specified efficacy subgroup of participants with a baseline score of at least 35. Analyses reported in the original publication utilized an efficacy model that unintentionally deviated from the trial's pre-specified statistical analysis plan by erroneously incorporating interaction terms between the subgroup and treatment. The newly published results, which includes supporting information to the publication, employ the efficacy model as pre-specified in the trial's statistical analysis plan, correcting the analyses. The correction also relates to the other subgroup analyses published for this endpoint, demonstrating that all subgroups with ALSFRS-R baseline scores of at least 26 to 35 showed a statistically significant benefit following treatment with NurOwn® (p≤0.050) on this secondary endpoint.

The reanalysis (or as they called it 'correction') was only on the pre-specified subgroup analyses for the secondary endpoint of ALSFRS-R total score (as highlighted in yellow below from the original publication). 

An erratum was issued to present the 'corrected' results for this endpoint: 

The original publication reported results for ALSFRS-R total score subgroup endpoint using a model that unintentionally deviated from the pre-specified statistical analysis plan by erroneously incorporating interaction terms between the subgroup and treatment. The error was made by the CRO who performed the statistical analyses. Applying the correct statistical model for that outcome resulted in the average difference between NurOwn- and placebo-treated patients going from 2.01 points to 2.09 points, but importantly this difference became statistically significant with a P-value of 0.05 (from a p-value of 0.20 in the original analysis). 

While the trial did not reach statistical significance on the primary or secondary endpoints, the company believes these corrected analyses support the conclusion that NurOwn has a positive treatment effect for patients with ALS. 

A year and a half ago, FDA put out a statement (unusual) to advise the BrainStorm not to file the BLA based on the announced results after unblinding of their phase 3 study. FDA specifically stated the following: 

With the recent completion of a randomized phase 3 controlled clinical trial comparing NurOwn to placebo, it has become clear that data do not support the proposed clinical benefit of this therapy. Data indicated that none of the primary or secondary endpoints were met in the group of patients who were randomized. For the main (primary) endpoint, 27.7% of people given the placebo were scored as responding compared to 32.6% of people given NurOwn. The 4.9% absolute difference in responders was not at all statistically significant, and the small difference between the two groups was most likely due to chance. In addition, there was a modest excess in deaths in those treated with NurOwn, the significance of which is unclear at this time. If BrainStorm plans further studies of NurOwn to determine if the product can provide clinical benefit to individuals with ALS, FDA will continue to provide advice to the company on their development program.

Now, A year after FDA slammed on the breaks, BrainStorm is hitting the gas with updated data, approval plans, we will see how the FDA will react to BrainStorm's plan and if FDA will accept the BLA filing by BrainStorm. 

No matter what the fate is for BrainStorm's BLA, one thing is clear: the statistical analyses are critical to the clinical trials and to the overall drug development. It is so important to avoid errors/mistakes in the statistical analyses. This important point has been discussed in previous posts such as "Statistician's nightmare - mistakes in statistical analyses of clinical trials" and "Futility Analysis and Conditional Power When Two Phase 3 Studies are Simultaneously Conducted" where the inappropriate method for futility analysis was implemented. 

It is surprising that the p-value and the statistical significance are still playing a critical role in regulatory decision-making after all of these discussions about retiring statistical significance and p-value. 

Mediation analysis and SAS CAUSALMED procedure

In a recent publication (Benza et al "Contemporary Risk Scores Predict Clinical Worsening in Pulmonary Arterial Hypertension - An Analysis of FREEDOM-EV"), we conducted an analysis called 'Mediation analysis'. In the statistical analysis section, the 'mediation analysis' was stated as the following: 

"To determine whether the change in Week 12 REVEAL Lite 2 risk score ‘mediated’ the treatment effect in delaying clinical worsening, we used SAS (v14.3) CAUSALMED procedure which operationalizes the work of Valeri and VanderWeele.

This analysis attempts to determine what fraction of the total treatment effect appears to be attributable to the treatment effect on the REVEAL Lite 2 score. The analysis was adjusted for baseline REVEAL Lite 2 score; we did the analysis both with and without assuming that there is a treatment and mediator (REVEAL Lite 2 score) interaction on the outcome model (clinical worsening). The definition ‘net clinical benefit’ has been previously proposed as the achievement of all three French non-invasive low risk factors without a clinical worsening event; we retrospectively used the present database to model the performance of this definition."

According to Wikipedia, the mediation model and mediation analysis are defined as the following: 

In statistics, a mediation model seeks to identify and explain the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third hypothetical variable, known as a mediator variable (also a mediating variable, intermediary variable, or intervening variable). Rather than a direct causal relationship between the independent variable and the dependent variable, a mediation model proposes that the independent variable influences the mediator variable, which in turn influences the dependent variable. Thus, the mediator variable serves to clarify the nature of the relationship between the independent and dependent variables.

Mediation analyses are employed to understand a known relationship by exploring the underlying mechanism or process by which one variable influences another variable through a mediator variable. In particular, mediation analysis can contribute to better understanding the relationship between an independent variable and a dependent variable when these variables do not have an obvious direct connection.

A mediator variable can either account for all or some of the observed relationship between two variables. 

Full Mediation

Maximum evidence for mediation, also called full mediation, would occur if the inclusion of the mediation variable drops the relationship between the independent variable and dependent variable to zero. In other words, the effect of the independent variable on the dependent variable is all through the mediator variable. 

Partial mediation

Partial mediation maintains that the mediating variable accounts for some, but not all, of the relationship between the independent variable and dependent variable. Partial mediation implies that there is no only a significant relationship between the mediator and the dependent variable, but also some direct relationship between the independent and dependent variable - the line from independent variable to dependent variable is solid and c is not equal to zero. 

 

The mediation analysis has been used in the data analysis for observational data, clinical trial data, survey data, and epidemiology study data. 

In an article by Eyre et al "Effect of Covid-19 Vaccination on Transmission of Alpha and Delta Variants", the mediation analysis was used to assess whether the effect of the vaccination status of the index patient was explained by Ct values at diagnosis.  Ct values are cycle-threshold values (indicative of viral load21) in the index patient.

In an article by Reaven et al "Intensive Glucose Control in Patients with Type 2 Diabetes — 15-Year Follow-up", mediation analyses were performed:

In prespecified mediation analyses, Cox proportional-hazards models were used to examine the effects of the glycated hemoglobin level on the primary cardiovascular disease outcome and on the observed treatment effects. Specifically, the log-linear association of the cumulative glycated hemoglobin level (modeled as a time-varying covariate) with the primary cardiovascular disease outcome was assessed during the period of separation of the glycated hemoglobin curves and after convergence. Models examined the effect of treatment group (intensive therapy or standard therapy) on the primary outcome in an unadjusted analysis (model 1) or while accounting for baseline, most recent, or cumulative mean glycated hemoglobin level (models 2, 3, and 4, respectively).

A paper by Vo et al summarized "the conduct and reporting of mediation analysis in recently published randomized controlled trials: results from a methodological systematic review"

Mediation analysis can be performed using SAS procedure CAUSALMED. CAUSALMED procedure was developed for estimating causal mediation effects from observational data, but can definitely be used for estimating mediation effects from the randomized controlled clinical trial data. Please see the references below:

• SAS documentation about CAUSALMED procedure including the application examples
• Yung et al Causal Mediation Analysis with the CAUSALMED Procedure
• Yung Introducing the CAUSALMED procedure for causal mediation analysis - Youtube video 
• Youtube video: Modeling causal effect using SAS/Stat

Mediation analysis can be performed using other software. This is very well summarized in a paper by Valente et al "Causal Mediation Programs in R, Mplus, SAS, SPSS, and Stata".