Managing Pregnancies in Clinical Trials: Regulatory Guidance and Best Practices

Clinical research historically has excluded pregnant women, creating major data gaps. Today, less than 1% of trial participants are pregnant, and most approved drugs lack pregnancy safety data. There are various reasons pregnant women are often excluded from clinical trials: 

• Scientific and medical concerns: potential fetal harm, unknown pharmacokinetics and dosing, Complex mother-fetus risks, and impact on maternal health.
• Religious or cultural considerations: Emphasis on fetal protection, Paternalistic IRB attitudes, Participation and social norms. 
• Liability and legal concerns: Fear of fetal-harm lawsuits, Insurance and compensation, Regulatory ambiguity, Cascading regulatory costs

Regulators such as US FDA now emphasize a balanced approach – gathering information on drug use in pregnancy by allowing inclusion when appropriate, while protecting fetal safetyf FDA guidance and recent legislation stress that trials should broadly reflect real-world populations, with any exclusions (like pregnancy) justified by safety and scientific rationale. In fact, pregnancy is explicitly identified as a historically underrepresented group in FDA diversity initiatives. This shift in thinking – codified by laws like FDARA and FDORA and by draft FDA guidances – is intended to improve evidence on treating pregnant patients.

Regulatory Context and Diversity Guidance

FDA regulations and guidances set the framework for handling pregnancy in trials. The FDA’s 21 CFR Part 50 Subpart B (pregnant women, fetuses, and neonates) and 21 CFR Part 56 (IRB requirements) require heightened protections for pregnant participants. For example, FDA recommends that if a study offers benefit solely to the fetus, both the pregnant woman and her partner must consent (with narrow exceptions). Crucially, FDA guidance mandates that “each individual providing consent is fully informed regarding the reasonably foreseeable impact of the research on the fetus or neonate”. In practice, this means consent forms and discussions must spell out known pregnancy risks (and uncertainties) based on available animal or prior data. IRBs reviewing these trials must include members experienced with maternal-fetal medicine and ensure extra safeguards (per 21 CFR 56.111(a)(2) and (b)).

At a higher level, FDA’s draft guidance on pregnant women (2018) explicitly endorses “judicious inclusion of pregnant women in clinical trials” to inform safe drug use in pregnancy. Similarly, the 2020 FDA guidance on broadening trial diversity urges sponsors to continuously “broaden eligibility criteria” as safety data accrues, narrowing unnecessary exclusions. These documents reinforce that excluding pregnant women should not be automatic: instead, inclusion should be considered whenever the potential benefits outweigh risks. In summary, the regulatory message is clear: plan trials for diversity (as now required by law with Diversity Action Plans) and, where scientifically justified, include or at least carefully manage pregnant participants.

Informed Consent and Pre-Screening Procedures

Before enrollment, trials must address pregnancy risk. Protocols almost universally exclude pregnant or breastfeeding women, and require women of childbearing potential (WOCBP) to use reliable contraception and have negative pregnancy tests at screeningonbiostatistics.blogspot.com. Per longstanding FDA advice, informed consent must cover pregnancy issues explicitly: if nonclinical reproduction studies are lacking, sponsors must “fully inform” women and advise on contraceptive measuresonbiostatistics.blogspot.com. In practice, consent forms and discussions should include pregnancy-specific language and summarize what is (and isn’t) known about fetal risk.

• Contraception and Testing: Investigators should ensure WOCBP agree to use effective birth control (including abstinence) and take a pregnancy test before each dosing period. FDA guidance has long recommended pregnancy tests at screening and counseling on contraceptiononbiostatistics.blogspot.com. Some trials repeat tests periodically to catch early pregnanciesonbiostatistics.blogspot.com.

• Risk Disclosure: Consent discussions must describe potential risks to a fetus. The FDA draft guidance stresses that subjects be informed about “reasonably foreseeable impact” of trial participation on the fetus or neonate. Even if evidence is limited, the consent should transparently explain any known animal or human data, and state unknowns. This empowers participants to make decisions with full awareness of pregnancy-related risks.

Managing Pregnancies Discovered During Trials

Despite precautions, some participants will become pregnant during a study. Best practices focus on timely detection, notification, and safety:

• Detection: Continue pregnancy testing throughout the study (e.g. at regular visits or follow-ups). One biostatistics review notes that trials “usually perform pregnancy tests periodically” so pregnancies can be caught earlyonbiostatistics.blogspot.com.

• Immediate Actions: If a pregnancy is identified, the standard practice is to stop the study drug to avoid further exposureonbiostatistics.blogspot.com. The investigator should promptly notify the sponsor and IRB, and arrange any needed medical follow-up. FDA guidance goes further: it recommends unblinding the subject’s treatment (drug vs placebo) so the woman and her physician can discuss risks based on actual exposure. The patient should then undergo a second informed-consent process that incorporates the new risk-benefit assessment. For example, if the drug may benefit the mother, continuation might be allowed if potential benefits outweigh fetal risks (with the mother’s informed agreement). Otherwise, she should be withdrawn from treatment.

• Data and Follow-Up: Regardless of continuation, sponsors must collect detailed follow-up data. FDA guidance explicitly states that “regardless of whether the woman continues in the trial, it is important to collect and report the pregnancy outcome”. In practice, this means recording gestational age at drug exposure, duration of exposure, and all outcomes (live birth, gestational age at delivery, congenital events, etc.). The pregnant participant should receive standard obstetrical care and fetal monitoring alongside the study’s safety assessments. For example, cord blood or neonatal samples might be collected for drug levels if relevant.

• Discontinuation and Missing Data: If the participant discontinues the study, subsequent efficacy visits are often ceased; statisticians typically handle missing data using predefined methods. Notably, many sponsors maintain a pregnancy registry or report form for study pregnanciesonbiostatistics.blogspot.com. These registers feed into post-market surveillance (and often meet regulatory reporting requirements). Any adverse fetal outcome (miscarriage, birth defect, etc.) must be reported as a serious adverse event per 21 CFR 312.32/64.

• Monitoring: Trials with pregnant subjects or exposures require specialized monitoring. For example, the protocol should specify involvement of obstetric/maternal-fetal experts on the safety monitoring team. Ongoing review of maternal and fetal safety signals is essential – a dedicated data monitoring committee may be warranted. In extreme cases (e.g. clear evidence of harm), the trial’s stopping criteria may trigger halting enrollment.

Reporting and IRB Notification

Pregnancy events are subject to regulatory and ethics oversight. Investigators should report any pregnancy to the IRB as soon as it is identified (often as an “unanticipated problem” in light of initial exclusion criteria). FDA guidance notes that IRBs reviewing such protocols must have the right expertise and must ensure “additional safeguards” are in place for pregnant subjects. For example, IRBs should verify that consent materials address pregnancy, that medical backup (e.g. obstetric care) is arranged, and that procedures (e.g. prohibition of termination inducements) are followed.

Sponsors, in turn, must follow safety-reporting rules. Under FDA IND regulations, any pregnancy with drug exposure is reported to FDA (particularly if it results in a serious fetal or neonatal outcome). The IRB and health authorities should be kept informed according to institutional policies. In practice, many trials use a structured form or registry entry to document trial pregnancies and outcomesonbiostatistics.blogspot.com. This ensures timely communication of safety information to all stakeholders.

Ethical Considerations of Inclusion vs. Exclusion

The exclusion of pregnant women raises ethical issues. Traditionally, fear of fetal harm led to a protective approach, but contemporary ethicists argue that systematic exclusion often causes more harm. As one commentary notes, refusing to study drugs in pregnancy “merely shifts risk to the clinical context” – doctors and patients then must decide on therapies with no evidence, which is “hardly an ethical approach”. High-profile cases (e.g. thalidomide) illustrate the dangers of not studying drugs in pregnancy. In fact, a commissioned analysis found no liability cases from including pregnant women in trials, whereas many lawsuits have arisen from unforeseen drug harms in pregnant patients after approval.

Conversely, including pregnant participants – with proper precautions – yields direct benefits. It allows rigorous collection of safety/efficacy data in a controlled setting, reducing uncertainty. Experts stress it is a humane and scientific responsibility to prioritize pregnant women’s inclusion when possible. Denying them evidence essentially “denies pregnant women—and their healthcare providers—the evidence necessary to make informed decisions”. Modern ethical guidance and FDA policy now urge balancing fetal protection with the pregnant woman’s health needs, rather than default exclusion.

Recommendations and Future Directions

Given this landscape, sponsors and investigators can follow several best practices:

• Education and Consent: Develop clear, patient-friendly consent materials that discuss pregnancy risks and emphasize the importance of contraception for WOCBP. Train staff to discuss these issues openly.

• Safety Monitoring: Include pregnancy in safety monitoring plans. Engage obstetric/maternal-fetal medicine consultants in trial planning and oversight.

• Trial Design: Where feasible, design trials to allow pregnant participants (or planned pregnancy cohorts) for conditions that affect women of childbearing age. FDA’s 2018 draft guidance and recent NIH recommendations encourage such trials for pregnancy-specific or relevant indications.

• Community Engagement: Build partnerships with obstetric care providers and clinics. Embedding trial activities in OB settings can greatly improve recruitment and trust among pregnant patients.

• Regulatory Planning: Anticipate the need for pregnancy considerations in regulatory submissions. Under the Pregnancy and Lactation Labeling Rule (PLLR), FDA expects clear labeling of pregnancy data (or lack thereof). Sponsors should be prepared to update labels as new pregnancy data emerge.

• Continued Advocacy: Engage with regulatory agencies. FDA’s task forces and guidance initiatives (e.g. on diversity action plans) reflect ongoing shifts. Industry input can help shape policies that balance scientific goals with patient safety.

In summary, managing trial pregnancies requires a structured approach: robust informed consent, vigilant monitoring, regulatory reporting, and ethical reflection. With these measures, sponsors can protect participants while generating the much-needed data on drug safety in pregnancy. As one expert put it, including pregnant women in research is no longer a question of if but when and how – given the broad benefits of more equitable, evidence-based care.

References: 

• FDA guidance (2018) "Pregnant Women: Scientific and Ethical Considerations for Inclusion in Clinical Trials"
• FDA guidance (2020) "Enhancing the Diversity of Clinical Trial Populations-Eligibility Criteria, Enrollment Practices, and Trial Designs". This guidance was subsequently removed, but the talking points are still there. 
• The FDA law blog (2025) "Clinical Research Representation: Pass it On"
• Onbiostatistics.blogspot.com (2017) "Exclusion of pregnant women from clinical trial and pregnancy test for women of childbearing potential (WOCBP)"
• Life Science Leader (2025) "Pregnant Women: An Underrepresented Group In Clinical Trials"
• Applied Clinical Trials (2025) "Pregnant Women: An Underrepresented Group in Clinical Trials"

Prognostic enrichment versus predictive enrichment

Prognostic enrichment and predictive enrichment are both strategies used in clinical trials to select patients for inclusion. Both strategies aim to improve trial efficiency but address different aspects of clinical trial design.

Prognostic enrichment
Selects patients who are more likely to experience a disease-related event or condition. This strategy can help reduce the sample size required for event-driven trials.

Predictive enrichment
Selects patients who are more likely to benefit from a treatment or intervention based on a physiological or biological mechanism.

From FDA's guidance "Enrichment Strategies for Clinical Trials to Support Determination of Effectiveness of Human Drugs and Biological Products":

FDA guidance includes some detail examples of using prognostic enrichment strategies or predictive enrichment strategies. 

The differences between prognostic enrichment and predictive enrichment can be summarized as following:

	Prognostic Enrichment	Predictive Enrichment
Definition	Selecting patients based on their likelihood of experiencing a specific outcome (e.g., disease progression or event) regardless of treatment.	Selecting patients based on their likelihood of responding to a specific treatment due to a biomarker or characteristic.
Goal	To increase the event rate or outcome frequency in the trial population, improving statistical power.	To identify patients who are more likely to benefit from the investigational treatment.
Focus	Focuses on the natural history of the disease or risk of an outcome.	Focuses on the interaction between the treatment and a specific patient characteristic (e.g., biomarker).
Patient Selection	Patients are selected based on prognostic factors (e.g., disease severity, biomarkers, or risk scores).	Patients are selected based on predictive factors (e.g., presence of a biomarker or genetic mutation).
Outcome	Increases the proportion of patients who experience the outcome of interest.	Increases the likelihood of observing a treatment effect in the selected population.
Example	Enrolling patients with advanced-stage cancer to ensure a higher rate of disease progression. Enrolling severe patients who may be more likely to develop clinical worsening events	Enrolling only patients with a specific genetic mutation (genetic biomarker) that is targeted by the therapy. Enrolling only patients in a specific etiology sub-group who are expected to be more responsive to the investigational treatment
Impact on Trial	Reduces sample size and trial duration by enriching for patients with a higher event rate.	Improves treatment effect size by focusing on patients who are more likely to respond.
Statistical Benefit	Increases statistical power by reducing variability in the control group.	Increases effect size by reducing heterogeneity in treatment response.
Risk	May exclude patients who could still benefit from the treatment.	May limit generalizability of trial results to a broader population.
Common Use Cases	Trials where the primary endpoint is time-to-event (e.g., survival, disease progression).	Trials where the treatment mechanism is targeted (e.g., precision medicine, biomarker-driven therapies).

Further Reading: 

• FDA's guidance for industry (2019) "Enrichment Strategies for Clinical Trials to Support Determination of Effectiveness of Human Drugs and Biological Products":
• Bob Temple (2013) Enrichment Strategies for Clinical Trials
• Stanski and Hong (2020) Prognostic and predictive enrichment in sepsis. Nature Reviews Nephrology
• Moscicki and Tandon (2017) Drug-Development Challenges for Small Biopharmaceutical Companies. NEJM

Comparing "In Vitro," "In Vivo," "Clinical Trial," and "In Silico": Understanding Research Approaches in Science

Scientific research relies on diverse methods to study complex biological systems, test hypotheses, and develop treatments. Four commonly used terms you might come across are "in vitro," "in vivo," "clinical trial," and "in silico." Each of these approaches plays a unique role in understanding how living systems function and how interventions—like new drugs or treatments—might affect them. Let’s break down these terms and see how they differ in purpose, application, and benefits.

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1. In Vitro: "In the Glass"

• Definition: In vitro research refers to experiments conducted outside a living organism using isolated cells, organs, or tissues, typically in a controlled lab environment. The term literally means "in the glass," as many early studies were done in glass dishes or test tubes.

• Examples: Cell culture studies, molecular biology experiments, and biochemical tests are common examples of in vitro research. For instance, researchers may expose human cancer cells in a petri dish to a potential new drug to observe its effect on cell survival.

• Applications: This approach allows scientists to isolate specific variables and study biological processes or drug effects in a highly controlled way. It’s useful for preliminary testing of how compounds interact with specific cell types, enzymes, or receptors.

• Advantages:

  • Allows precise control of the experimental environment
  • Reduces complexity by focusing on specific cells or molecules
  • Often faster and more cost-effective than in vivo or clinical trials

• Limitations:

  • Lacks the complexity of whole-organism interactions
  • Results may not fully translate to living organisms, limiting their predictive power for real-life scenarios

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2. In Vivo: "In the Living"

• Definition: In vivo studies are performed within a living organism. This can involve testing in animals (like mice or zebrafish) or humans under controlled research conditions. Theoretically, in vivo tests consist of both pre-clinical (animal) tests and clinical trials (in human). 

• Examples: Animal studies that assess drug absorption, metabolism, and toxicity are examples of in vivo research. Researchers might administer a potential new medication to lab mice to monitor its effects on health and behavior over time.

• Applications: In vivo research is critical for understanding how treatments work within the complexity of a whole organism. It provides insights into drug absorption, distribution, metabolism, and excretion (ADME), and can help identify possible side effects before testing in humans.

• Advantages:

  • Captures interactions within a whole, living system
  • Helps predict how a treatment might work in humans
  • Essential for assessing safety and efficacy before clinical trials

• Limitations:

  • Often more expensive and time-consuming than in vitro studies
  • Ethical considerations, especially in animal testing
  • Results may not fully translate to humans due to species differences

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3. Clinical Trials: Testing in Humans

• Definition: Clinical trials are research studies conducted in human volunteers to evaluate the safety and effectiveness of medical, surgical, or behavioral interventions. They are typically divided into phases (Phase I-IV) to assess safety, dosage, efficacy, and long-term effects.

• Examples: A Phase I trial might test a new drug’s safety in a small group of healthy volunteers, while a Phase III trial could assess its efficacy in a larger group of patients with the target disease.

• Applications: Clinical trials are the gold standard for determining if a treatment is safe and effective in humans. They provide the final step before a new drug, therapy, or medical device can gain regulatory approval and reach the public.

• Advantages:

  • Directly measures effectiveness and safety in humans
  • Provides data necessary for regulatory approval
  • Helps identify real-world effectiveness and adverse effects

• Limitations:

  • High cost and time commitment
  • Ethical considerations, including informed consent and participant safety
  • Risk of unforeseen adverse effects or low efficacy in broader patient populations

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4. In Silico: "In the Computer"

• Definition: In silico research refers to studies conducted via computer simulations or computational models. This approach has grown with advances in bioinformatics, machine learning, and artificial intelligence.

• Examples: Using software to model how a drug might interact with a target protein or predict side effects based on chemical structure is an in silico approach. It can also include simulations to predict disease progression or drug outcomes.

• Applications: In silico methods allow researchers to screen vast numbers of compounds, optimize drug design, and predict potential outcomes with minimal laboratory resources. It’s particularly valuable for preliminary drug discovery and disease modeling.

• Advantages:

  • Reduces the need for animal or human testing in early stages
  • Cost-effective and can analyze vast amounts of data quickly
  • Enables virtual experiments that may not be feasible in the lab

• Limitations:

  • Models rely on available data, which may not be complete or entirely accurate
  • Predictions may not always match real-world biological systems
  • Still requires validation in in vitro, in vivo, or clinical settings to confirm results

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Summary Table

Final Thoughts

Each of these research methods—in vitro, in vivo, clinical trials, and in silico—serves a distinct role in scientific research. They are complementary and often used together, with insights from each approach informing the others. For example, in silico models may predict which compounds are worth testing in vitro, which, in turn, helps decide which treatments should move to in vivo studies and eventually to clinical trials.

By understanding these approaches, we gain a clearer view of the journey from basic research to new treatments that reach the public, illustrating how complex and collaborative scientific advancement truly is.

Some References:

• Emili & Rizk (2024) in Silico Technologies: Leading the Future of Drug Development Breakthroughs
• Marshall et al (2024) In Silico Lung Deposition Profiles of Three Single-inhaler Triple Therapy Combinations Assessed With Functional Respiratory Imaging (FRI) at a Low Inspiratory Flow Rate
• Khani et al (2022) Human in silico trials for parametric computational fluid dynamics investigation of cerebrospinal fluid drug delivery: impact of injection location, injection protocol, and physiology
• Crate et al (2022) Lung deposition of inhaled once-daily longacting muscarinic antagonists via standard jet nebulizer or dry powder inhaler, measured using functional respiratory imaging, in patients with chronic obstructive pulmonary disease 

Clinical trial succussed in phase 2, but failed in phase 3

Clinical trials are the backbone of drug developments, acting as the gateway between laboratory research and practical, commercial, real-world treatments. These trials typically progress through several phases, with Phase 2 and Phase 3 being crucial stages in the journey of a new treatment or drug. However, it is not uncommon for a treatment to show promise in Phase 2, only to stumble and fail in Phase 3. Understanding why these failures occur is key to improving future trials and ultimately enhancing patient care.

Several years ago, FDA published a report called "22 CASE STUDIES WHERE PHASE 2 AND PHASE 3 TRIALS HAD DIVERGENT RESULTS ". Raps.org had an article to discuss this report "22 Case Studies Where Phase 2 and 3 Results Diverge: New FDA Report". There are a lot of examples that the early phase (phase 2) clinical trial was successful, but the late phase (confirmatory, phase 3) study failed. As a matter of fact, when the drug development program moved to the phase 3 study stage, there was usually successful results from the early phase clinical trials and there was an expectation that the phase 3 study would reproduce the success observed from the phase 2 studies. However, we often see that the promising results from phase 2 can not be reproduced in the large scale phase 3 studies. 

Fiercebiotech.com tracks these trial flops (clinical trials succussed in phase 2, but failed in phase 3). 

• 2020’s top 10 clinical trial flops
• 2021's top 10 clinical trial flops
• 2022's 10 top clinical trial flops
• 2023's top 10 clinical trial flops
• 3 big recent trial flops
• 10 clinical trials to watch in the second half of 2024

The Reality Check of Phase 3

Phase 3 trials are more extensive, involving several hundred to several thousand participants. These trials are designed to confirm the efficacy and safety of the treatment on a larger scale, comparing it directly to existing standard treatments or placebos. Phase 3 trials are pivotal because they provide the comprehensive data needed for regulatory approval- so called 'licensure trial'.

Despite the promise shown in Phase 2 study, many treatments fail in Phase 3 study. The reasons for these failures are multifaceted and can be broadly categorized into four main areas:

1. Differences in Population and Scale:

   • Population Diversity: Phase 2 trials often involve more homogeneous patient groups, while Phase 3 trials encompass a broader and more diverse population. This diversity can introduce variables that were not accounted for in the smaller, more controlled Phase 2 trials. For example, genetic differences, comorbidities, and concurrent medications can all influence treatment outcomes.
   • Sample Size: The larger sample size in Phase 3 trials can reveal less common side effects or variations in treatment efficacy that were not apparent in Phase 2. What appeared as a clear benefit in a smaller group may not hold up when tested on a larger scale. The large sample size requires more clinical trial sites for patient enrollment and the study needs to be designed as the multi-regional clinical trial.

2. Study Design and Execution:

   • Study Rigidity: Phase 3 trials often have more rigid protocols and endpoints compared to Phase 2. The stringent criteria and predefined outcomes might not fully capture the treatment’s potential benefits, leading to negative results. Phase 3 trials are usually more statistically rigor. More stringent statistical analysis approaches are used in the analyses of the phase 3 study data including controlling type-1 error, adjustment for multiplicity, missing data handling, estimands and strategies for handling the intercurrent events...
   • Execution Challenges: The complexity of conducting large-scale trials can introduce logistical issues, variations in study conduct across different sites, and difficulties in maintaining consistent treatment administration. Execution challenges may also include the difficulties in patient retention, treatment compliance, and maintaining the treatment blinding,...

3. Efficacy and Endpoint Discrepancies:

   • Efficacy Overestimation: Positive results in Phase 2 might be due to smaller sample sizes, shorter follow-up periods, or more lenient statistical thresholds. When scaled up, the actual efficacy might be less impressive.
   • Endpoints and Metrics: The primary and secondary endpoints in Phase 3 trials may differ from those in Phase 2. A treatment might show improvement in a specific metric in Phase 2 but fail to meet the broader, more comprehensive endpoints required in Phase 3. Phase 2 study may be based on surrogate endpoints and biomarkers while phase 3 study needs to use the endpoints that measure patients' feel, function, and survival to meet the regulatory requirements. Phase 2 study is usually shorter in duration while phase 3 study is usually longer.

4. Unanticipated Safety Concerns:

   • Rare Adverse Events: Larger trials can uncover rare but serious adverse events that were not evident in the smaller Phase 2 trials. These safety concerns can overshadow the benefits observed, leading to a failed trial.
   • Long-Term Effects: Phase 3 trials typically have longer follow-up periods, which can reveal long-term side effects or diminishing efficacy over time.

In a paper by Fogel (2018) "Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review", the following reasons were given:

"There are many reasons that potentially efficacious drugs can still fail to demonstrate efficacy, including a flawed study design, an inappropriate statistical endpoint, or simply having an underpowered clinical trial (i.e., sample size too small to reject the null hypothesis), which may result from patient dropouts and insufficient enrollment."

Here are two new examples with promising phase 2 study results, but failed phase 3 study: 

The StarScape study is a Phase 3 trial designed to evaluate the efficacy and safety of Zinpentraxin Alfa in patients with Idiopathic Pulmonary Fibrosis (IPF). It was based on a Phase 2 trial that showed promising results over a 28-week period. However, the StarScape study failed significantly, as it did not meet the primary efficacy endpoint of change from baseline to week 52 in forced vital capacity (FVC), nor did it succeed in any of the secondary efficacy endpoints. A companion editorial suggested that the failure of the StarScape study might have been due to outliers in the FVC measurements of two patients in the placebo group, that resulted in false positive results in Phase 2 study. 

"...Prompted by these negative results, a post hoc reevaluation of the phase II trial revealed that the apparent benefit of zinpentraxin alfa was primarily driven by two outliers in the placebo group who had an FVC decrease of more than 2,000ml/yr."

The biotech company Amylyx conducted a Phase 2 trial, known as the CENTAUR trial, to evaluate the safety and efficacy of AMX0035 for the treatment of Amyotrophic Lateral Sclerosis (ALS). Despite the relatively small sample size, the CENTAUR study demonstrated statistically significant results in the primary efficacy endpoint, which was the ALS Functional Rating Scale-Revised (ALSFRS-R) slope change. As a result, Amylyx received regulatory approval from both Health Canada and the US FDA. However, as a condition of approval, the FDA required the completion of an ongoing Phase 3 study called the PHOENIX trial. When the results of the PHOENIX study were released, none of the primary, secondary, or subgroup analyses showed statistical significance, marking the study as a total failure.

The likely reason for the failure of the Phase 3 study is the difference in geographic regions and patient populations. The Phase 2 CENTAUR study was conducted entirely in the United States, with 25 sites across the country. In contrast, the Phase 3 PHOENIX study was a multinational trial conducted at 69 sites across 12 countries in the US and Europe. Unfortunately, due to the failure of the Phase 3 study, the already approved and marketed drug had to be withdrawn from the market.

Phased Clinical Trials - Phases 0, 1, 2, 3, 4

Clinical development programs for drugs and biological products include phased clinical trials ranging from Phase 0, 1, 2, 3, and 4.  Phase 0 study is not typically needed. Phases 1, 2, and 3 studies are typical pre-market clinical trials and Phase 4 studies are post-market clinical trials. There are a lot of articles and books discussing the clinical trial phases. I borrowed some illustrations/slides from FDA's 'Clinical Research Phase Studies" and other web resources:

Phase 0 Clinical Trials:

• Phase 0 trials, also known as exploratory or pre-phase I trials, involve a small number of participants (usually fewer than 15) and are conducted very early in the drug development process.
• The primary goal of Phase 0 trials is to gather preliminary data on how the drug behaves in the human body, including its pharmacokinetics (how the drug is absorbed, distributed, metabolized, and excreted).
• These trials may involve administering subtherapeutic doses of the drug to minimize risks to participants while still providing valuable insight

Phase 1 Clinical Trials:

• Phase 1 trials are the first stage of testing in humans and typically involve a small number of healthy volunteers (or sometimes patients with the target condition).
• The main objectives of Phase 1 trials are to evaluate the safety and tolerability of the drug, determine its pharmacokinetics and pharmacodynamics, and establish an initial dose range for further testing.
• These trials are designed to identify any potential adverse effects and to determine the most appropriate dosage for subsequent studies.
  

Phase 2 Clinical Trials:

• Phase 2 trials involve a larger group of patients (typically several hundred) who have the condition or disease that the drug is intended to treat.
• The primary objectives of Phase 2 trials are to further assess the safety and efficacy of the drug, explore different dosages and dosing regimens, and gather preliminary data on the drug's effectiveness in treating the target condition.
• These trials help to provide more information about the drug's potential benefits and risks and inform the design of larger, more definitive Phase 3 trials

Phase 3 Clinical Trials:

• Phase 3 trials are large-scale studies that involve hundreds to thousands of patients and are designed to provide definitive evidence of the drug's safety and efficacy.
• The main goals of Phase 3 trials are to confirm the effectiveness of the drug in treating the target condition, further evaluate its safety profile, and compare it to existing treatments or placebo.
• Phase 3 trials are crucial for obtaining regulatory approval from health authorities such as the FDA (Food and Drug Administration) in the United States or the EMA (European Medicines Agency) in Europe.

Phase 4 Clinical Trials:

• Phase 4 trials, also known as post-marketing surveillance trials or post-approval studies, are conducted after a drug has been approved for marketing and made available to the general population.
• These trials continue to monitor the drug's safety and effectiveness in real-world settings, identify any rare or long-term adverse effects, and gather additional information about its optimal use.
• Phase 4 trials play a critical role in ensuring the ongoing safety and efficacy of medications after they have been approved for widespread use.

Overall, phased clinical trials are an essential part of the drug development process, providing valuable data at each stage to inform decision-making and ultimately bring safe and effective treatments to patients.

Mediation Analysis vs. Landmark Analysis for Clinical Trial Data

Mediation analysis and Landmark analysis are two valuable statistical tools in different contexts, providing insights into different aspects of data analysis. Both methods can be utilized to investigate if a surrogate endpoint or short-term measure can predict the long-term clinical endpoint or to investigate if short-term measures can be mediators for the long-term clinical endpoint. Both mediation analysis and landmark analysis are useful tools for post-hoc, exploratory analyses, but not the primary analysis method for the primary efficacy endpoint. 

Mediation analysis is a statistical method used to explore and understand the mechanism or process through which an independent variable influences a dependent variable. It helps to determine whether the effect of an independent variable on a dependent variable is mediated (i.e., transmitted through) one or more intermediate variables, often referred to as mediators.

In mediation analysis, the main focus is on understanding the causal chain of relationships between variables. It involves three key components:

• Independent variable (X): The variable that is hypothesized to influence the dependent variable directly or indirectly through one or more mediators.
• Mediator (M): The variable(s) that mediates the relationship between the independent variable and the dependent variable. It explains how and why the independent variable affects the dependent variable.
• Dependent variable (Y): The variable that is influenced by the independent variable either directly or indirectly through the mediators.

The analysis aims to estimate the direct effect of the independent variable on the dependent variable and the indirect effect mediated through the mediator(s). Several statistical techniques can be used for mediation analysis, such as regression-based methods (e.g., ordinary least squares regression) or more advanced methods like structural equation modeling.

In our published paper "Contemporary risk scores predict clinicalworsening in pulmonary arterial hypertension - Ananalysis of FREEDOM-EV",  mediation analysis was used to test the hypothesis that improvements in risk score (a surrogate endpoint) contributed to reduced likelihood for clinical worsening (a long-term clinical endpoint). 

 

In a paper by Blette et al, "Is low-risk status a surrogate outcome in pulmonary arterialhypertension? An analysis of three randomised trials ", the mediation analysis was used to investigate surrogacy. The author stated:

"we performed a mediation analysis considering each candidate surrogate as an intermediate outcome. To avoid inconsistent mediation (ie, when direct and indirect effects cancel each other out, the direct effect is even larger than the total effect, or other situations that can result in a negative proportion mediated), we first empirically tested four criteria to justify the mediation analysis. Next, Cox proportional hazards models were fit for the clinical worsening and survival outcomes conditional on each candidate surrogate separately, as well as treatment, corresponding baseline risk score, and a fixed effect variable with three levels for trial membership (to allow for similarity within each trial). Results from these models were combined with parallel models that did not condition on the candidate surrogates, but otherwise conditioned on the same set of variables to perform the difference method for mediation, estimating the total effect and direct effects of treatment on clinical worsening and survival, as well as the indirect effects through each candidate surrogate. The proportion of the effect mediated through each surrogate risk score was estimated, along with 95% CIs via a bootstrap procedure."

In a previous post, the mediation analysis was discussed. "Mediation analysis and SAS CAUSALMED procedure"

Landmark analysis, also known as landmark survival analysis, is a statistical method commonly used in survival analysis to investigate the impact of time-dependent variables on the occurrence of an event of interest. It allows for the assessment of time-varying effects in longitudinal studies or clinical trials where the values of variables may change over time.

In landmark analysis, the follow-up time is divided into predefined intervals or "landmarks," and the analysis is performed separately for each landmark. The key steps in landmark analysis are as follows:

• Define landmarks: Choose specific time points of interest during the follow-up period.
• Create landmark cohorts: At each landmark, divide the study population into subgroups based on the status of the time-dependent variable(s) of interest.
• Analyze survival outcomes: Estimate survival probabilities or hazard rates for each subgroup defined by the landmark cohorts.
• Compare survival outcomes: Compare the survival outcomes between the different subgroups to assess the impact of the time-dependent variable(s) on the event occurrence.

Landmark analysis allows researchers to capture time-varying effects and observe how the relationship between variables changes over the course of a study. It is particularly useful when analyzing data with time-dependent covariates, treatment interventions, or changes in exposure levels over time.

In a paper by McLaughlin et al "Pulmonary Arterial Hypertension-Related Morbidity Is Prognostic for Mortality", the landmark analysis was used to assess the impact of morbidity events on the risk of subsequent mortality.

In a paper by Eisenstein et al "Clopidogrel use and long-term clinical outcomes after drug-eluting stent implantation" , landmark analyses were performed to explore the association of extended clopidogrel use and long-term clinical outcomes of patients receiving drug eluting stents (DES) and bare-metal stents (BMS) for treatment of coronary artery disease.

The tutorial paper "Landmark Analysis at the 25-Year Landmark Point" by Dr Dafni is a good reference for Landmark analysis. 

Mediation analysis and Landmark analysis differ in their goals, focus, and types of data they analyze:

Goals: Mediation analysis aims to understand the mechanism or process through which an independent variable influences a dependent variable, exploring direct and indirect effects. Landmark analysis, on the other hand, focuses on assessing time-varying effects and understanding how the occurrence of an event is influenced by time-dependent variables.

Focus: Mediation analysis emphasizes identifying mediators that explain the relationship between an independent variable and a dependent variable. Landmark analysis focuses on investigating the impact of time-dependent variables on survival outcomes or event occurrences.

Data: Mediation analysis typically requires cross-sectional or longitudinal data with variables measured at different time points. It is applicable to both continuous and categorical variables. Landmark analysis is commonly used in survival analysis, analyzing time-to-event data, and requires longitudinal data with time-dependent variables.

Both mediation analysis and landmark analysis are valuable statistical tools in different contexts, providing insights into different aspects of data analysis. Here is a table to compare the mediation analysis and the landmark analysis generated by ChatGPT, but I added the last row for implementing the mediation and landmark analyses. 

Aspect	Mediation Analysis	Landmark Analysis
Statistical Method	Investigates relationships and effects between variables	Analyzes time-varying effects and event occurrences
Time Dependency	Considers temporal aspect of data	Accounts for time-varying effects and changing values over time
Causal Inference	Aims to understand causal chain of relationships	Examines impact of time-dependent variables on event outcomes
Focus	Identifying mediators that explain relationships	Assessing time-dependent variables and event occurrences
Variables	Independent, mediator, and dependent variables	Time-dependent variables influencing event occurrences
Data Type	Cross-sectional or longitudinal with measured variables	Longitudinal data with time-dependent variables
Analytical Steps	Estimating direct and indirect effects using regression	Dividing follow-up into landmarks, comparing survival outcomes
Research Questions	Mechanisms and processes of variable influence	Impact of time-dependent variables on events or survival
Implementation	In SAS, Procedure CASUALMED allows you to estimate direct and indirect effects using different mediation models. It supports various regression-based mediation approaches, including Sobel, bootstrapping, and Bayesian estimation. In R, to conduct mediation analysis, the most commonly used package is "mediation." This package provides a comprehensive set of functions to estimate direct and indirect effects in mediation models. It supports various mediation methods, including the causal steps approach, bootstrapping, and structural equation modeling (SEM).	In SAS, Procedures LIFETEST, PHREG, and LIFEREG can all be used. You can divide the follow-up time into landmark intervals and analyze survival outcomes for different subgroups defined by the landmarks In R, you can utilize the "survival" package, which is widely used for survival analysis. The "survival" package provides functions to handle time-to-event data, perform survival analysis, and estimate survival probabilities. You can divide the follow-up time into landmarks and analyze survival outcomes for different landmark cohorts.

Personally, I prefer the mediation analysis to the landmark analysis. In the landmark analysis, the subjects who did not reach the landmark timepoint were excluded from the analysis and the analyses are performed on a subset of the overall population - sort of principle stratum. The endpoint measure at the landmark timepoint and whether or not the subjects reach the landmark timepoint itself is meaningful. Excluding it from the analysis is against the intention-to-treatment principle and may cause biases.