Saturday, June 14, 2025

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At a higher level, FDA’s draft guidance on pregnant women (2018) explicitly endorses “judicious inclusion of pregnant women in clinical trials” to inform safe drug use in pregnancy.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: 

at No comments:

Sunday, June 08, 2025

Composite Strategy for Intercurrent Event and the Use of Trimmed Means in Clinical Trial Data Analyses

When composite strategy is used to handle the intercurrent event (ICE), specially the terminal event such as death, the occurrence of the ICE is integrated into the endpoint definition, often by assigning a specific value to participants who experience the event.

In ICH E9-R1 "Addendum on Estimands and Sensitivity Analysis in Clinical Trials" training material, about the composite strategy to handle the intercurrent event, trimmed mean is mentioned to be an approach in handling the intercurrent event.


A trimmed mean may also be called truncated mean and is the arithmetic mean of data values after a certain number or proportion of the highest and/or lowest data values have been discarded. The data values to be discarded can be one-sided or two-sided. A trimmed mean can be defined as a robust average computed by discarding a specified fraction of the lowest and highest observations and averaging the remainder. In effect, it “trims” the tails of the data, reducing the influence of outliers. For example, a 50% trimmed mean discards the bottom 25% and top 25% of values, averaging the middle 50% In general, an alpha‑trimmed mean removes the lowest and highest alpha/2 fraction of data (where alpha is expressed as a percentage of the total).

After trimming, the mean of the remaining values is computed by the usual arithmetic formula. In practical use, common trims range from 5–20% per tail (e.g. alpha=10%-40% total) in robust estimation, though some clinical examples have trimmed up to 50%. By removing extreme observations, trimmed means downweight outliers and model the assumption that missing or dropout outcomes are worse than any observed values.

Advantages of Trimmed Means in Handling Outliers and Skewed Data

Trimmed means are recognized as robust estimators of central tendency, demonstrating less sensitivity to deviations from assumed models or distributions, such as the presence of outliers or non-normality, when compared to classical methods like the sample mean. This robustness translates into more stable and reliable results under challenging data conditions.

For asymmetric distributions, where variability is more pronounced on one side, trimmed means can provide a superior estimation of the location of the main body of observations. By removing extreme values, they offer a more robust estimate of the central value and are less influenced by skewed data distributions. Furthermore, the standard error of the trimmed mean is less susceptible to the effects of outliers and asymmetry than that of the traditional mean, which can lead to increased statistical power for tests employing trimmed means.

The advantages of trimmed means extend beyond mere statistical robustness; they enable a more clinically meaningful interpretation of treatment effects, particularly in heterogeneous patient populations or when extreme outcomes (e.g., severe adverse events, rapid disease progression) might otherwise obscure the true effect in the majority of patients. This aligns with the need for statistical methods that accurately reflect real-world clinical practice and patient experience. If extreme values arise from factors not directly related to the treatment's intended effect on the typical patient—such as rare severe adverse events or non-adherence driven by external circumstances—then their removal allows for a clearer assessment of the treatment's impact on the majority. Conversely, if extreme values are an inherent part of the treatment effect, such as severe lack of efficacy leading to patient dropout, the

trimmed mean can define an estimand for the subpopulation that did not experience these extreme negative outcomes. Both scenarios offer a more focused and potentially more interpretable clinical picture.

It is important to acknowledge the inherent trade-off between robustness and efficiency: more robust methods, including trimmed means, may sacrifice some efficiency (precision or variability) compared to optimal methods under ideal statistical assumptions. The selection of a method ultimately depends on the nature of the data and the specific goals of the analysis.

Comparison with Traditional Measures of Central Tendency (Mean, Median)

The choice among the mean, median, and trimmed mean is not merely a statistical decision but reflects a fundamental determination about the target estimand and the specific clinical question being addressed.

● Mean: The traditional arithmetic mean calculates the average of all values in a dataset. It is highly sensitive to extreme values, which can significantly distort the measure of central tendency and lead to a less representative average, especially in the presence of outliers or skewed distributions.

● Median: The median represents the middle value in an ordered dataset and is highly resistant to the influence of extreme values.3 Conceptually, the median can be viewed as an extreme form of a trimmed mean, where all but one or two central observations are effectively removed. While both the trimmed mean and the median reduce the impact of outliers, the median is generally considered more robust in certain contexts due to its reliance solely on rank order.

The trimmed mean strikes a balance between these two traditional measures. It retains more information from the dataset than the median, which discards a significant portion of the data, while simultaneously offering greater robustness to outliers than the arithmetic mean.3 This allows for a nuanced definition of "average effect" that acknowledges the presence of extreme outcomes without being unduly influenced by them (like the raw mean) or implicitly discarding them entirely (like the median). This highlights the importance of defining the estimand before selecting the statistical method, a principle strongly emphasized by ICH E9(R1).

Regulatory Context and Trial Examples

Trimmed means have been discussed in statistical literature and regulatory settings as a way to handle dropout or intercurrent events. Permutt and Li (FDA biostatisticians) first proposed using trimmed means for “symptom trials” with dropout, treating each dropout as a complete (nonnumeric) observation ranked as the worst outcome. Under this approach, all subjects who discontinue before the endpoint are implicitly assigned the worst possible values and then an equal percentage are trimmed from each arm. In effect, trimming favors treatments with fewer dropouts, since “having more completers is a beneficial effect of the drug”.

Notably, trimmed means correspond to a composite estimand in the ICH E9(R1) framework: one can assign intercurrent events (e.g. dropouts) a worst-case value and then summarize the outcome distribution by a median or trimmed mean. For instance, the ICH E9 addendum training materials explicitly cite trimmed means (alongside medians) as summary measures under a composite strategy when dropouts are scored as extreme unfavourable outcomes. A recent FDA review of glaucoma drug Rocklatan (netarsudil/latanoprost) illustrates this: the statistical reviewer computed an “adaptive trimmed mean” IOP reduction by coding patients who withdrew for lack of efficacy or adverse events as worst outcomes. The reviewer noted this analysis aligns with the composite strategy in ICH E9(R1) and can reinforce the main intent-to-treat result. 

However, we found no FDA-approved trial in which a trimmed mean was the pre‑specified primary endpoint analysis. In all examples, trimmed means have been used as sensitivity or supportive analyses rather than the main test. For example, in a uterine fibroid drug application (NDA 214846), a trimmed‐mean analysis of menstrual blood loss was reported: the trimmed mean in each arm used the 50% best-performing patients, reflecting a 50% trim. Similarly, an ophthalmology (geographic atrophy) trial review (NDA 217171) applied trimmed‐mean + multiple imputation as a sensitivity: certain dropouts were “excluded (trimmed)” in the reviewer’s analysis. In each case, the trimmed mean analysis “assumes missing data as ‘bad outcomes’” (i.e. MNAR). These examples underscore that trimmed means appear in regulatory documents as robust or conservative analyses (often labeled “completer” or MNAR analyses) rather than as the primary efficacy metric.

Key examples:

  • Glaucoma (Rocklatan NDA 208259): Reviewer performed an adaptive trimmed mean IOP analysis, excluding patients who withdrew for lack of effect and assigning worst values (citing Permutt & Li as method).

  • Uterine fibroids (NDA 214846): Primary efficacy (menstrual blood loss) was also examined by trimmed means (50% trim) as sensitivity. The FDA report notes the trimmed‐mean is based on the “top 50% best performers in each arm”.

  • Geographic atrophy (NDA 217171): A trimmed-mean + MI analysis was done by the reviewer; dropouts due to adverse events or lack of efficacy were “excluded (trimmed)” from one scenario.

These applications align with recent methodological studies showing trimmed means estimate a unique estimand: essentially, the mean outcome in the subpopulation of patients who would have remained on treatment. This estimand privileges treatments that prevent dropout, but it relies on the strong assumption that every dropout truly has an “unfavourable” outcome. As Wang et al. note, “the trimmed mean estimates a unique estimand” and its validity “hinges on the reasonableness of its assumptions: dropout is an equally bad outcome in all patients”. Ocampo et al. similarly emphasize that trimmed means work well when discontinuation is strongly associated with poor outcome, but give biased estimates if data are actually missing at random.

Calculation and Assumptions

Formula: To calculate an alpha‑trimmed mean, one typically sorts the data and discards a proportion alpha/2 from each end. For example, the 50% trimmed mean drops the lowest 25% and highest 25% of values, averaging the middle 50%. In general, if alpha (0–1) is the total fraction trimmed, remove the lowest alpha/2 and highest alpha/2 observations, then compute the mean of the remaining values. (The interquartile mean is the special case alpha=0.5.) Typical choices of alpha are guided by robustness needs: small trims (e.g. alpha=0.1 for 5% each tail) mildly reduce outlier influence, while large trims (up to 0.5) exclude half the data.

Statistical assumptions: Trimmed‐mean analyses assume that any trimmed/missing values are in fact the worst outcomes. In the dropout context, this treats early withdrawal as if the patient’s true result were extremely poor. As Ocampo et al. explain, the trimmed mean approach “sets missing values as the worst observed outcome and then trims away a fraction of the distribution”. Under this MNAR assumption, the trimmed mean can provide an unbiased estimate of the treatment effect (on the subpopulation that remains). However, if outcomes are actually missing at random (MAR) without relation to extreme values, trimmed means will be biased. In simulations, trimmed means were found to fail under MAR (because the assumption of trimming “bad” outcomes is then invalid).

Interpretation: Because the trimmed mean ignores equal fractions from each arm, it essentially compares the upper quantiles of the outcome distribution. Clinically, it reflects the mean of the best-performing subset of patients. This is why Permutt and Li emphasize that their method makes no assumptions beyond randomization: it is a nonparametric “exact” test for efficacy that includes all randomized subjects (by ranking dropouts worst). Regulators caution that the trimmed-mean estimand differs from a standard ITT mean; it answers the question, “What is the mean outcome among patients who would have remained in the trial?”.

SAS Implementation Example

Trimmed means can be computed in SAS using procedures like PROC UNIVARIATE or PROC MEANS. For instance, PROC UNIVARIATE supports a trimmed= option. The snippet below computes a 10% trimmed mean (i.e. removes 10% from each tail) of a variable Y and captures the result via ODS:


/* Example: Compute a 20% trimmed mean (10% each tail) for outcome Y */
ods output TrimmedMeans=TrimmedStats;
proc univariate data=trial_data trimmed=0.10;
   var Y;
run;

proc print data=TrimmedStats noobs; 
   title "Trimmed Mean of Y";
run;

Alternatively, PROC MEANS (SAS 9.4+) also supports trimmed means. For example:

/* Using PROC MEANS with OUTPUT to get trimmed mean */
proc means data=trial_data noprint trimmed=0.10;
   var Y;
   output out=Stats (drop=_TYPE_ _FREQ_) trimmed=Y_trimmed;
run;

proc print data=Stats noobs; 
   title "Trimmed Mean of Y via PROC MEANS";
run;

These code examples illustrate that one can easily obtain trimmed‐mean estimates in SAS by specifying the trimmed percentage (e.g. 0.10 for 10%) and directing the procedure output to a dataset for further use. (In practice, one would adjust trimmed= according to the planned trim proportion.)

Using Google Gemini, a comprehensive report on using trimmed means in clinical trials was generated and can be accessed here. 

at No comments:

Thursday, June 05, 2025

Baby KJ's Story - a remarkable success of the customized CRISPR gene editing treatment

This morning, FDA conducted a roundtable discussion on cell and gene therapies. The roundtable invited 23 panel members from the academic and industry and was attended by FDA commissionner Dr Marty Makary and FDA CBER director Dr. Vinay Prasad. At the second half of the roundtable discussion, HHS secretary, RF Kennedy Jr, NIH director, Dr Jay Bhattacharya, and CMS Administrator, Dr Mehmet Oz all gave commentary speeches. The overall tone of the roundtable is very positive and the government agencies are very supportive to the cell and gene therapy (including xenotransplantation) development. The panel suggests various ways to support the cell & gene therapies. Renewing the pediatric priority review voucher program, Reduction of CMC requirements, continued support of flexible trial design and accelerated approval, and more reliance on post approval requirements and real-world evidence (RWE) were highlighted as key tools for the agency to reduce the number of programs caught in the

During the roundtable discussions, the baby KJ's case was mentioned multiple times. It is worth discussing the baby KJ's story.

KJ Muldoon is a 10-month-old infant who became the first person in the world to receive a personalized CRISPR-based gene-editing therapy, marking a significant milestone in the treatment of rare genetic diseases.

Diagnosis and Condition

Shortly after birth, KJ was diagnosed with severe carbamoyl phosphate synthetase 1 (CPS1) deficiency, a rare and life-threatening genetic disorder that impairs the body's ability to eliminate ammonia. This condition can lead to toxic ammonia buildup, causing severe neurological damage or death in infancy. 

Development of Personalized Therapy

With conventional treatments offering limited hope, a multidisciplinary team from the Children's Hospital of Philadelphia (CHOP) and Penn Medicine embarked on an unprecedented effort to develop a customized gene-editing therapy tailored specifically to KJ's unique genetic mutations. Within six months, researchers identified two truncating variants in the CPS1 gene and designed a bespoke CRISPR-based therapy using base editing technology. This approach allowed precise correction of the faulty DNA without cutting the genetic code. 

Treatment and Outcome

KJ received multiple infusions of the experimental therapy, delivered directly to his liver using lipid nanoparticles carrying the gene-editing components. The treatment aimed to correct the genetic defect responsible for his condition. Following the therapy, KJ showed significant improvement, including better tolerance to dietary protein and reduced dependence on supportive medications. After spending over 300 days in the hospital, he was discharged and returned home with his family.

Significance and Future Implications

KJ's case represents a groundbreaking advancement in personalized medicine and gene-editing therapies. It demonstrates the potential of customized CRISPR treatments to address ultra-rare genetic disorders by rapidly developing individualized therapies. While long-term outcomes and scalability remain areas for further research, this success story offers hope for treating other rare diseases through similar personalized approaches. 

For more detailed information, you can watch the FDA roundtable discussion on cell and gene therapies where KJ's case was mentioned: FDA Roundtable Discussion.

Impact of Baby's KJ's Case on Clinical Trial Design of Gene Therapies

Baby KJ's story was extensively discussed in a follow-up podcast "FDA Direct Ep. 7: This Week at the FDA!" by FDA commissioner Dr Makary and CBER director Dr Prasad. Their discussion of Baby KJ's story extended to the clinical trial designs for gene therapy studies and ultra rare diseases. 

The FDA's approach to clinical trial designs for gene therapy studies emphasizes flexibility and a nuanced understanding of the specific condition and therapy. Here are the key points:

  • Flexible Trial Designs: The FDA acknowledges that a "one-size-fits-all" approach is not suitable. They adapt trial designs based on the specific condition, its frequency, severity, and the uniqueness of the therapy.
  • "N of 1" Trials for Rare Diseases: For extremely rare and dire conditions, a single-patient ("N of 1") trial can be sufficient for approval, especially when there is a plausible mechanism of action and clear biological markers demonstrating effectiveness. An example highlighted is the rapid approval of a custom-tailored gene editing treatment for Baby KJ's rare condition.
  • Plausible Mechanism Pathway: A strong, scientifically sound mechanism of action can support approval, even without extensive large-scale trials, if it suggests safety and effectiveness through extrapolated data.
  • Challenges with Slowly Deteriorating Conditions: For conditions with slow and variable deterioration, relying on anecdotal evidence from "N of 1" studies without clear biological correlates is more difficult, as it's harder to distinguish treatment effects from the natural disease progression.
  • Surrogate Endpoints: The FDA accepts the use of surrogate endpoints, such as tumor shrinkage in cancer, as indicators of a drug's activity.
  • Industry Desire for Predictability: The industry seeks greater predictability regarding FDA expectations for endpoints, control arms, and when randomization is necessary. Improved communication and transparency are suggested to address this.
  • Concerns with International Trial Data: There are concerns about relying solely on clinical trial data from certain countries, particularly if the majority of participants are from a single foreign country, for U.S. regulatory decisions.
  • Re-evaluation of Trial Requirements: The FDA is open to re-evaluating requirements, such as the need for two clinical trials versus one for new drug approvals, to potentially streamline the process.

The overarching theme from recent FDA discussions is a shift towards a more flexible, common-sense, and scientifically driven approach to gene therapy regulation, prioritizing patient needs and scientific plausibility, especially for rare and life-threatening conditions.

References:

at No comments:

Sunday, June 01, 2025

Forwarded: "Data Distortions: When Statistics Can Lead Us Astray in Drug Safety"

The DIA Global Forum's article, "Data Distortions: When Statistics Can Lead Us Astray in Drug Safety," brought attention to a common pitfall: the practice of performing individual hypothesis tests for each adverse event (AE), calculating p-values, and then concluding statistical significance based on an arbitrary threshold like . This mirrors a concern I raised earlier in my blog article, "Should hypothesis tests be performed and p-values be provided for safety variables in efficacy evaluation clinical trials?"

It's a widely held statistical consensus that conducting hypothesis tests for individual AEs is unsound, and the p-values derived from them are prone to misinterpretation. Despite arguments that such tests could be performed for "exploratory purposes" with a disclaimer, the inherent risk is that these p-values will inevitably be misused to draw misleading conclusions from the data. Unfortunately, we've seen journal articles, often at the insistence of editors, include p-values for individual AEs, perpetuating this problematic practice.

at No comments: