Simpson's Paradox - in integrated analyses or in sub-group analyses

 Simpson's paradox is a statistical paradox wherein the successes of groups seem reversed when the groups are combined. Simpson's paradox occurs when we combine data or when we perform the sub-group analyses.

According to Wikipedia, the Simpson's paradox is defined as the following:

Simpson's paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined. This result is often encountered in social-science and medical-science statistics, and is particularly problematic when frequency data are unduly given causal interpretations. The paradox can be resolved when confounding variables and causal relations are appropriately addressed in the statistical modeling. Simpson's paradox has been used to illustrate the kind of misleading results that the misuse of statistics can generate

It gave several examples of the Simpson's paradox including the kidney stone treatment example:

Kidney stone treatment

Another example comes from a real-life medical study comparing the success rates of two treatments for kidney stones. The table below shows the success rates (the term success rate here actually means the success proportion) and numbers of treatments for treatments involving both small and large kidney stones, where Treatment A includes open surgical procedures and Treatment B includes closed surgical procedures. The numbers in parentheses indicate the number of success cases over the total size of the group.

The paradoxical conclusion is that treatment A is more effective when used on small stones, and also when used on large stones, yet treatment B appears to be more effective when considering both sizes at the same time. In this example, the "lurking" variable (or confounding variable) causing the paradox is the size of the stones, which was not previously known to researchers to be important until its effects were included.

Here are some additional links to explain the Simpson's paradox:

• Bonovas & Piovani (2023) Simpson’s Paradox in Clinical Research: A Cautionary Tale
• 李永乐老师 (辛普森悖论)
• Stanford Encyclopedia of Philosophy (2021) Simpson’s Paradox
• Wagner (1982) Simpson's paradox in real life
• Fenton et al (2019) Simpson's paradox and the implication for medical trials
• Kievit et al (2013) Simpson's paradox in psychological science: a practical guide
• Shi and Chan (2020) Simpson's paradox in meta-analysis - choice of studies and summary statistics
• Simpson's Paradox (and How to Avoid Its Effects)

In clinical trials, Simpson's paradox can occur when the overall treatment effect observed in the entire study population is different from the treatment effect observed within subgroups of the population. This phenomenon can lead to misleading conclusions about the effectiveness of a treatment if not properly addressed.

For example, consider a clinical trial evaluating the effectiveness of two treatments (Drug A and Drug B) for a certain medical condition. The overall analysis of the trial data might suggest that Drug A is more effective than Drug B in improving patient outcomes. However, when the data is stratified by important demographic or clinical variables, such as age, gender, or disease severity, a different picture may emerge.

Let's say that within each age group, Drug B appears to be more effective than Drug A. However, the older age group constitutes a larger proportion of the study population, and within this group, patients tend to have poorer outcomes regardless of the treatment they receive. As a result, the overall analysis might erroneously indicate that Drug A is more effective, when in fact Drug B is more effective within each age group.

In this scenario, Simpson's paradox occurs because the distribution of confounding variables (in this case, age) differs between the treatment groups, leading to a reversal of the observed treatment effects when the data is aggregated. To avoid drawing misleading conclusions from clinical trial data affected by Simpson's paradox, it's crucial to conduct subgroup analyses and consider potential confounding variables that may influence treatment outcomes. Additionally, techniques such as propensity score matching or regression adjustment can help mitigate the impact of confounding variables and provide more accurate estimates of treatment effects.

Simpson's paradox can occur in pooling the data from multiple clinical trials, in meta analysis, in integrated summary of effectiveness (ISE), and in integrated summary of safety (ISS). 

FDA guidance for industry "Integrated Summary of Effectiveness" discussed 'pooled analyses of data from more than one study' and specifically mentioned  

Simpson's paradox can be a subject of the regulatory review comments when pooling the data or combining the data from multiple studies. 

Statistical review on BI's NDA of Empagliflozin for the treatment of type 2 diabetes:

FDA Briefing Document Cardiovascular and Renal Drugs Advisory Committee Meeting July 15, 2021 Roxadustat for the treatment of anemia due to chronic kidney disease (CKD)

FDA summary of review REPATHA for the treatment of Primary Hyperlipidemia and Mixed Dyslipidemia discussed the safety review:

... crude pooling of trials with varying allocation ratios can also lead to confounding by trial (i.e., Simpson’s paradox). 

In a DIA webinar presentation (2024) Integrated Safety Analyses in Drug Marketing Applications: Avoiding Common Mistakes, Mary Nilsson provided an example of Simpson's paradox when pooling the safety data from three randomized controlled clinical trials - Simpson's paradox can obscure the safety signals in the pooled analyses. She suggested that the stratification as an approach to address the Simpson's paradox issue. For the integrated safety analyses, the analytical methods should always be stratified by study - take differences within studies first, then takes the average of those differences.

This same issue was mentioned in a paper by SUH (2009) The use of atypical antipsychotics in dementia: rethinking Simpson’s paradox"

for safety analysis, pooling the data from multiple studies may obscure the finding of the safety signals. 

Science.org had an article in 2018 FDA's revolving door: Companies often hire agency staffers who managed their successful drug reviews. It described a case of pooled data analyses obscuring the signal of sudden cardiac death due to Simpson's paradox.  raise conflict of interest questions

In 2009, for example, an FDA panel weighed whether the agency should approve AstraZeneca's widely prescribed antipsychotic drug quetiapine (Seroquel) for a wider range of conditions. The panel heard from health policy expert Wayne Ray of Vanderbilt University in Nashville, who described his research linking the drug to sudden cardiac death when used with certain other medications. Ray recalls "an FDA staff member who gave a very negative presentation on our paper." And according to the meeting transcript, the agency's then-Director of Psychiatric Products Thomas Laughren, who was instrumental in shepherding Seroquel and similar drugs through the review process and personally signed their FDA approvals, also challenged Ray's results and defended AstraZeneca's clinical trial findings in the discussion that followed. The company's "analysis should have been able to pick up a difference in sudden cardiac death, and they didn't find any difference between drug and placebo," he said.

Ray told Laughren and the panel that AstraZeneca had pooled data from all its trials as though the data were one data set, causing a well-known statistical error called Simpson's paradox. To take the company's conclusion "as definitive" would be "very dangerous," Ray said, according to the transcript. Laughren responded by calling sudden death "a pretty definitive event."

Ultimately, the committee voted overwhelmingly to advise approval of the drug for new indications and made no recommendation on labeling it to warn about sudden cardiac death. Later evidence showed that the cardiac problems Ray described are real, and in 2011, FDA required adding a warning on Seroquel's label.

I consulted ChatGPT about strategies for addressing potential Simpson's paradox, and I wholeheartedly endorse the following responses.

To minimize the potential biases caused by Simpson's paradox, you can employ several strategies during data analysis:

Awareness: Be aware of the possibility of Simpson's paradox and actively look for it, especially when dealing with aggregated data or subgroup analyses.

Visualization: Visualize the data in various ways to explore relationships between variables. Use scatter plots, histograms, box plots, and other visualization techniques to identify patterns and potential confounding variables.

Stratification: Stratify the data by relevant variables to examine relationships within homogeneous subgroups. This allows you to identify any instances where the direction of association changes across subgroups.

Control Variables: Include potential confounding variables as control variables in your analysis. By controlling for these variables, you can assess the relationship between the variables of interest while holding other factors constant.

Causal Inference Techniques: Use causal inference techniques, such as causal mediation analysis or propensity score matching, to account for potential confounding variables and identify true causal relationships.

Sensitivity Analysis: Conduct sensitivity analyses to assess the robustness of your findings to different assumptions and model specifications. This helps you understand the potential impact of confounding variables on your results.

Expert Consultation: Consult with domain experts to ensure that you're considering all relevant variables and potential sources of bias in your analysis.

By incorporating these strategies into your data analysis, you can minimize the potential biases caused by Simpson's paradox and obtain more accurate and reliable results.

What's New in FDA's Draft Guidance "Use of Data Monitoring Committees in Clinical Trials"?

In early 2024, the FDA unveiled its latest drug guidance for the industry titled "Use of Data Monitoring Committees in Clinical Trials." This forthcoming guidance will supplant the existing DMC guidance, "Establishment and Operation of Clinical Trial Data Monitoring Committees," which dates back to March 2006. Notably, the title of the guidance has been modified.

What distinguishes this newly issued guidance? And why the necessity for a fresh approach to DMC guidance?

The subsequent articles endeavor to tackle these queries.

• RAPS.org FDA issues new draft guidance on data monitoring committees
• FDA Law Blog: How to Run DMC? It’s Tricky – FDA’s New Draft Guidance Provides Updated Recommendations on How to Best Use Data Monitoring Committees in Clinical Trials

The new guidance actually mentioned the following "Significant changes in DMC structure and practice since the 2006 guidance was issued include: 

• The increased use of DMCs in trials (Califf et al. 2012) of modest size as reflected in the clinical trials data bank housed at ClinicalTrials.gov 
• A trend for DMC charters to become longer and more detailed
• An increased use of DMCs to implement certain adaptive clinical trial designs
• An increased use of some DMCs to oversee an entire clinical development program rather than a single clinical trial
• The potential for expansion of functions of a DMC; for example, for review of aggregate data for safety reporting for trials under an investigational new drug  application (IND) 
• An increased globalization of medical product development and use of multiregional trials with DMCs

In the early 2000s, the concept of Data Monitoring Committees (DMCs) began gaining traction, inspired by their use in NIH studies since the 1980s, albeit without formal FDA acknowledgment until later. These committees, born from the need for independent oversight, particularly in trials with serious outcomes or severely ill patients, initially operated with considerable flexibility, leading to varied implementations. Recognizing the evolving landscape, stakeholders engaged in discussions, culminating in the Clinical Trial Transformation Initiative's partnership with the FDA in 2015. Through a series of meetings, including one in Washington, recommendations were made to address the observed diversity in DMC practices. This dialogue ultimately contributed to the drafting of a new guidance document in 2024, aimed at refining DMC usage. The proposed revisions underscore not only the importance of study rigor and integrity but also prioritize participant safety, potentially influencing the early termination or modification of trials. Moreover, the guidance acknowledges the increasing prevalence of adaptive trial designs, suggesting a responsiveness to emerging trends. The new guidance suggested adaptation committee for clinical trials utilizing the adaptive design. The adaptive committee may or may not be the same as the DMC. Overall, the iterative process of refining DMC guidance reflects a collaborative effort to adapt to evolving clinical trial practices and prioritize patient well-being.

In recent years, there has been considerable discourse surrounding the creation of a distinct committee, often referred to as a safety assessment committee, tasked with the ongoing review of aggregate safety data. The requirement was spurred by the FDA guidance "Safety Assessment for Investigational New Drug Application Safety Reporting;" in 2015 that was subsequently withdrew. I have contended that the oversight of aggregate safety data can effectively be carried out by the independent Data Monitoring Committee (DMC), obviating the need for an additional safety assessment committee. The newly issued DMC guidance reflects this perspective, affirming that either the DMC or an independent safety team may undertake the review of aggregate safety data. The guidance specifies the following:

We are witnessing a growing prevalence of DMC utilization across various types of clinical trials, spanning from those focusing on rare diseases to trials characterized by high morbidity and mortality rates, as well as trials featuring innovative designs like adaptive designs. Furthermore, DMCs may find application in open-label studies, dose escalation studies, gene therapy trials, and beyond. Occasionally, sponsors may opt to incorporate DMCs as a means to enhance the study's credibility.

New champion for the most expensive drug in the US

It's widely acknowledged that medications for rare diseases and gene therapies come with a hefty price tag. In the United States, the FDA oversees drug approval, but there's no governmental oversight on pricing. After FDA approval, drug sponsors and manufacturers have the autonomy to propose and set the drug's price. Typically, pricing considerations revolve around factors like patient population, investment in drug development and approval, and anticipated financial gains, rather than the drug's effectiveness or its benefit-risk profile.

Before this month (March, 2024), the top 10 most expensive drugs in the US are:

Brand Name	Cost	Manufacturer	Indication	FDA approval date	Reason for high cost
Hemgenix	$3.5 million per one-time dose	CSL Behring	Hemophilia B	November 22, 2022	Gene therapy
Elevidys	$3.2 million per one-time dose	Sarepta Therapeutics	Duchenne Muscular Dystrophy (DMD)	June 22, 2023	Gene therapy
Skysona	$3 million per one-time dose	Bluebird Bio, Inc.	Cerebral Adrenoleukodystrophy (CALD)	September 16, 2022	Gene therapy
Zynteglo	$2.8 million per one-time dose	Bluebird Bio, Inc	Beta-thalassemia	September 16, 2022	Gene therapy
Zolgensma	$2.1 million per one-time dose	Novartis	Spinal Muscular Atrophy	May 24, 2019	Gene therapy
Myalept	$1.3 Million annually	Amryt Pharmaceuticals	Lipodystrophy/Leptin deficiency	February 24, 2014	Ultra rare disease
Danyelza	$1.2 million annually	Y-mAbs Therapeutics	Neuroblastoma	November 25, 2020	Ultra rare disease
Zokinvy	$1.2 million annually	Eiger Biopharmaceuticals	Progeria and Progeroid Laminopathies	November 20, 2020	Ultra rare disease
Kimmtrak	$1.1 million annually	Immunocore	Uveal Melanoma	January 25, 2022	Ultra rare disease
Luxturna	$850,000 per one-time dose	Spark Therapeutics	Biallelic RPE65-Mediated Inherited Retinal Disease	December 19, 2017	Gene therapy

In the past month, a new titleholder emerged for the most expensive drug in the US. Orchard Therapeutics, a subsidiary of Japan's Kyowa Kirin, now claims the top spot for producing the priciest medication.

Brand Name	Cost	Manufacturer	Indication	FDA approval date	Reason for high cost
Lenmeldy	$4.25 million per one-time treatment	Orchard Therapeutics, a unit of Kyowa Kirin	Metachromatic Leukodystrophy (MLD)	March 18, 2024	Gene therapy

There's an organization known as the Institute for Clinical and Economic Review (ICER), tasked with suggesting drug prices based on health economic assessments or Health Technology Assessments (HTAs). However, manufacturers often diverge from ICER's recommendations, frequently setting prices significantly higher. Take, for instance, a recent case where Merck, the drug's manufacturer, proposed a price nearly ten times higher than ICER's recommendation.

These exorbitant prices must ultimately be endorsed by health insurance companies or the Centers for Medicare & Medicaid Services (CMS). Consequently, it's the insurance companies or the Medicare & Medicaid program that bear the burden of these steep costs, as patients are unable to afford medications with such astronomical price tags.

Advancing Psychedelic Clinical Study Design - Virtual Public Meeting Organized by Reagan-Udall Foundation

Over the past decade, psychedelic compounds like psilocybin and ecstasy have emerged as potentially life-changing treatments for mental illnesses, including major depressive disorder and posttraumatic stress disorder. These psychedelic products may be synthetic compounds or extracts from natural products (such as magic mushroom). They usually have hallucinations effects and belongs to the substance control products. 

Academics and pharmaceutical/biotech companies are now interested in developing the psychedelic products for therapeutic uses in treating diseases like major depression, PTSD, Pakington's disease,...

In order to obtain the regulatory approval, various phases of clinical trials need to be conducted. The clinical trial designs for psychedelic drugs are more complicated than typical drugs because of its known side effects and the nature of the substance-controlled products. 

Reagan-Udall Foundation recently organized a virtual public meeting to discuss "advancing psychedelic clinical study design". During this public meeting, attendees discussed the experience of scientists working with psychedelics in FDA-authorized clinical studies and drug development, considerations for psychedelics in clinical trial designs, and perspectives and current research in psychedelic clinical trials. FDA presenters provided an overview of its newly issued guidance "Psychedelic Drugs: Considerations for Clinical Investigations."

Video recording of this virtual public meeting is available on Reagan-Udall Foundation website: 

Advancing Psychedelic Clinical Study Design
Virtual Public Meeting

Terminal events as intercurrent events in clinical trials

ICH E9 "Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials" contained discussions about intercurrent events and strategies for handling intercurrent events. Intercurrent events were defined as: 

Events occurring after treatment initiation that affect either the interpretation or the existence of the measurements associated with the clinical question of interest. It is necessary to address intercurrent events when describing the clinical question of interest in order to precisely define the treatment effect that is to be estimated.

The terminal events are one kind of intercurrent event. ICH E9 Addendum did not provide the formal definition for 'terminal events', but gave examples of the terminal events: 

Examples of intercurrent events that would affect the existence of the measurements include terminal events such as death and leg amputation (when assessing symptoms of diabetic foot ulcers), when these events are not part of the variable itself.

In a paper by Siegel et al "The role of occlusion: potential extension of the ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis for Time-to-Event oncology studies", the terminal events were described as the following: 

The estimands guidance also introduces the concept of a terminal event. Terminal events prevent the possibility of subsequent measurement. "For terminal events such as death, the variable cannot be measured after the intercurrent event, but neither should these data generally be regarded as missing." There are two examples given in the guidance, death and leg amputation. These examples clarify that terminal events physically prevent subsequent measurement, for any estimand in any study. 

Terminality is an objective property of an event which renders further observation physically impossible. If an event is terminal, it is impossible to devise a study that can look beyond it. Indeed there is no meaningful clinical question regarding the treatment effect that manifests after a terminal event. 

Terminal events can be defined as events that make the outcome measures impossible and the events are not part of the outcome such as death and ankle amputation in a trial assessing ankle function). Sometimes, the outcome measure after the terminal events may still be possible, but the measures after the terminal events are not meaningful. For example, in clinical trials of pulmonary diseases with spirometry measure as the primary outcome, lung transplantation will be a terminal event. After the lung transplantation, the spirometry measure can still be performed, but the spirometry measure is a reflection of the transplanted lungs, not the intended measure of the clinical trial endpoint. 

Terminal events should be separated as fatal (death, mortality) and non-fatal terminal events (may be called 'terminal events excluding mortality'). While they are all considered intercurrent events, the strategies for handling the fatal and non-fatal terminal events need to be different. 

Strategies for Handling the Fatal Terminal Events

Treatment policy strategy can not be used for handling fatal terminal events (death events). ICH E9 Addendum mentioned the following: 

In general, the treatment policy strategy cannot be implemented for intercurrent events that are terminal events, since values for the variable after the intercurrent event do not exist. For example, an estimand based on this strategy cannot be constructed with respect to a variable that cannot be measured due to death.

Composite strategies (or composite variable strategies) are particularly useful for handling fatal terminal events (deaths). The occurrence of the fatal terminal intercurrent event is informative about the effect of the treatment and so it is incorporated in the endpoint. In practice, the outcomes after the fatal terminal intercurrent event can not be observed, but need to be assumed to have the worst values. 

With the composite strategy, the terminal intercurrent events will be assigned a failed value. A failed value may be:

• Worse possible measure (for example, 0 for 6MWD and 0 for FEV1 or FVC measures)
• Worst observed value across all subjects at the endpoint visit
• Trimmed means (trimmed means and quantiles were mentioned in ICH E9 addendum training materials)
• The worst change (from baseline) of all subjects plus a random error. The error can be randomly drawn from a normal distribution with a mean of 0 and a variance equal to the residual variance estimated from the mixed model for all observed values of change from baseline

In FDA's guidance, "Amyotrophic Lateral Sclerosis: Developing Drugs for Treatment Guidance for Industry", deaths were integrated into the functional measure by the ALS Functional Rating Scale-Revised (ALSFRS-R). The guidance said:

Functional endpoints can be confounded by loss of data because of patient deaths. To address this, FDA recommends sponsors use an analysis method that combines survival and function into a single overall measure, such as the joint rank test.

In pivotal clinical trials in ALS, the joint rank test is almost the default method for analyzing the primary efficacy endpoint of the ALSFRS-R. The Joint Rank statistic ranks study participants in each treatment group, first by survival and then by ALSFRS-R score. The Joint Rank can increase power relative to analysis of either ALSFRS-R or survival analysis alone in some circumstances, for example when mortality rates are high.  

Joint Rank test was described and used in the NEJM paper by Miller et al "Trial of Antisense Oligonucleotide Tofersen for SOD1 ALS".

Strategies for Handling the Non-Fatal Terminal Events

It is acceptable to use hypothetical strategy to handle the non-fatal terminal intercurrent events. "Hypothetical strategies: A scenario is envisaged in which the intercurrent event would not occur: the value of the variable to reflect the clinical question of interest is the value which the variable would have taken in the hypothetical scenario defined." 

The value of the variable to reflect the clinical question of interest is the value which the variable would have taken in the hypothetical scenario defined. The value to be considered would have been the one collected if patients had not had the non-fatal terminal event. Outcomes after the non-fatal terminal events do not need to be measured. If the outcomes after the non-fatal terminal events are measured (for example, the spirometry measure after lung transplantation), the measures can be disregarded and not used in the analyses. The outcomes after the non-fatal terminal events cannot be observed, can be left as missing values, and usually need to be implicitly or explicitly predicted/imputed.

Post-Marketing Requirement (PMR) versus Post-Marketing Commitment (PMC)

Following the NDA or BLA approval, the sponsors may be required to conduct post-marketing studies. The phrase post marketing requirements and commitments refers to studies and clinical trials that sponsors conduct after approval to gather additional information about a product's safety, efficacy, or optimal use. Some of the studies and clinical trials may be required; others may be studies or clinical trials a sponsor has committed to conduct.

Post-approval studies can be classified by FDA as a postmarketing requirement (PMR) or a postmarketing commitment (PMC). 

A PMR is a study or clinical trial that an applicant (or sponsor) is required by statute or regulation to conduct postapproval. A PMC is a study or clinical trial that an applicant (or sponsor) agrees in writing to conduct postapproval, but that is not required by statute or regulation. PMRs and PMCs can be issued upon approval of a drug or postapproval, if warranted.

As a result, failure to conduct a PMR would be a violation of the Federal Food, Drug, and Cosmetic Act (FDCA) and/or implementing regulations, subject to enforcement action.  Potential enforcement actions can include an FDA Warning Letter, charges under section 505(o)(1) of the FDCA, misbranding charges under section 502(z), or civil monetary penalties.  In contrast, failure to conduct a PMC would not be a violation of the FDCA or regulations, and therefore not subject to enforcement action.

The table below compares the features of the PMR versus PMC:

Feature	Post-Marketing Requirements (PMR)	Post-Marketing Commitments (PMC)
Definition	Regulatory obligations imposed by authorities	Voluntary commitments made by the sponsor
Purpose	Gather additional data on safety, efficacy, etc.	Obtain more information post-approval
Enforcement	Mandatory; non-compliance may lead to penalties	Voluntary, but sponsors are expected to fulfill
Imposition	Imposed by health regulatory agencies	Made voluntarily by the sponsor during approval
Consequences of Non-compliance	Regulatory actions, fines, or product withdrawal	Regulatory actions; may impact marketing authorization
Flexibility	Typically less flexible; regulatory mandates	Voluntary, but commitment should be honored
Origin	External (regulatory agency)	Internal (sponsor during regulatory approval)
Examples	Post-approval safety studies, surveillance	Additional clinical trials, long-term safety studies

According to FDA's guidance "Guidance for Industry Postmarketing Studies and Clinical Trials — Implementation of Section 505(o)(3) of the Federal Food, Drug, and Cosmetic Act", PMR may be required in the following situations:

PMC may be required in the following situations: 

These PMCs were generally agreed upon by FDA and the applicant. Prior to the passage of FDAAA, FDA required postmarketing studies or clinical trials only in the situations described below:

• Subpart H and subpart E accelerated approvals for products approved under 505(b) of the Act or section 351 of the PHS Act, respectively, which require postmarketing studies to demonstrate clinical benefit (21 CFR 314.510 and 601.41, respectively);

• Deferred pediatric studies, where studies are required under section 505B of the Act (21 CFR 314.55(b) and 601.27(b)); 6 and

• Subpart I and subpart H Animal Efficacy Rule approvals, where studies to demonstrate safety and efficacy in humans are required at the time of use (21 CFR 314.610(b)(1) and 601.91(b)(1), respectively). 7

Is the confirmatory trial after the accelerated approval PMR or PMC?  

As a condition of accelerated approval, the applicant should conduct confirmatory trials to verify the clinical benefit of the drug or demonstrate an effect on irreversible morbidity or mortality. If these trials completed post-approval verify the clinical benefit of an indication granted accelerated approval, the indication is granted traditional approval.

Per regulatory guidance, the confirmatory trial after the accelerated approval can be PMC, not PMR. However, without the results from the confirmatory trial to verify the clinical benefit, the approval will remain as 'conditional' and benefits will remain on the surrogate endpoint or biomarkers. A traditional approval (or full approval) can be obtained after the confirmatory trial verifies the benefit of clinical benefit. A drug with full approval will have advantages in the marketing and reimbursement positions. 

Searching the FDA's Postmarket Requirements and Commitments Database, almost all confirmatory trials after accelerated approval are PMRs, not PMCs. 

Is Post-Approval Pregnancy Study PMR or PMC?

TheFDAlawblog.com had a blog article "Why are Post-Approval Pregnancy Studies Post-Marketing Requirements Rather Than Post-Marketing Commitments?". It said the following: 

"Notably, of the 99 postmarketing pregnancy studies in the 10-year period, all but one were PMRs. The only example of a pregnancy PMC is for Paxlovid, for treatment of COVID-19, which is a distinguishable example because the sponsor committed to this study while the drug was still under an Emergency Use Authorization (EUA), not an NDA."

In general, the post-approval pregnancy's studies are PMR, not PMC.

What are examples of the PMR versus PMC?

Large pharmaceutical companies posted their PMRs and PMCs only for the purpose of transparency. For example, here are the lists of PMRs and PMCs for Amgen and Janssen. These PMRs and PMCs provide great examples what kind of studies they are. 

• Amgen United States Clinical Postmarketing Commitments/Requirements
• Janssen Pharmaceutical CompaniesU.S. Post-Marketing Requirements and Commitments