Strength of Statistical Evidence for the Efficacy of Cancer Drugs: A Bayesian Re-Analysis of Randomized Trials Supporting FDA Approval.

OBJECTIVE: To quantify the strength of statistical evidence of randomised controlled trials (RCTs) for novel cancer drugs approved by the Food and Drug Administration (FDA) in the last two decades. STUDY DESIGN AND SETTING: We used data on overall survival (OS), progression-free survival (PFS), and tumour response (TR) for novel cancer drugs approved for the first time by the FDA between January 2000 and December 2020. We assessed strength of statistical evidence by calculating Bayes Factors (BFs) for all available endpoints, and we pooled evidence using Bayesian fixed-effect meta-analysis for indications approved based on two RCTs. Strength of statistical evidence was compared between endpoints, approval pathways, lines of treatment, and types of cancer. RESULTS: We analysed the available data from 82 RCTs corresponding to 68 indications supported by a single RCT and seven indications supported by two RCTs. Median strength of statistical evidence was ambiguous for OS (BF = 1.9; IQR 0.5-14.5), and strong for PFS (BF = 24,767.8; IQR 109.0-7.3*106) and TR (BF = 113.9; IQR 3.0-547,100). Overall, 44 indications (58.7%) were approved without clear statistical evidence for OS improvements and seven indications (9.3%) were approved without statistical evidence for improvements on any endpoint. Strength of statistical evidence was lower for accelerated approval compared to non-accelerated approval across all three endpoints. No meaningful differences were observed for line of treatment and cancer type. LIMITATIONS: This analysis is limited to statistical evidence. We did not consider non-statistical factors (e.g., risk of bias, quality of the evidence). CONCLUSION: BFs offer novel insights into the strength of statistical evidence underlying cancer drug approvals. Most novel cancer drugs lack strong statistical evidence that they improve OS, and a few lack statistical evidence for efficacy altogether. These cases require a transparent and clear explanation. When evidence is ambiguous, additional post-marketing trials could reduce uncertainty.

baymedr: an R package and web application for the calculation of Bayes factors for superiority, equivalence, and non-inferiority designs.

BACKGROUND: Clinical trials often seek to determine the superiority, equivalence, or non-inferiority of an experimental condition (e.g., a new drug) compared to a control condition (e.g., a placebo or an already existing drug). The use of frequentist statistical methods to analyze data for these types of designs is ubiquitous even though they have several limitations. Bayesian inference remedies many of these shortcomings and allows for intuitive interpretations, but are currently difficult to implement for the applied researcher. RESULTS: We outline the frequentist conceptualization of superiority, equivalence, and non-inferiority designs and discuss its disadvantages. Subsequently, we explain how Bayes factors can be used to compare the relative plausibility of competing hypotheses. We present baymedr, an R package and web application, that provides user-friendly tools for the computation of Bayes factors for superiority, equivalence, and non-inferiority designs. Instructions on how to use baymedr are provided and an example illustrates how existing results can be reanalyzed with baymedr. CONCLUSIONS: Our baymedr R package and web application enable researchers to conduct Bayesian superiority, equivalence, and non-inferiority tests. baymedr is characterized by a user-friendly implementation, making it convenient for researchers who are not statistical experts. Using baymedr, it is possible to calculate Bayes factors based on raw data and summary statistics.

A de novo transcription-dependent TAD boundary underpins critical multiway interactions during antibody class switch recombination.

Interactions between transcription and cohesin-mediated loop extrusion can influence 3D chromatin architecture. However, their relevance in biology is unclear. Here, we report a direct role for such interactions in the mechanism of antibody class switch recombination (CSR) at the murine immunoglobulin heavy chain locus (Igh). Using Tri-C to measure higher-order multiway interactions on single alleles, we find that the juxtaposition (synapsis) of transcriptionally active donor and acceptor Igh switch (S) sequences, an essential step in CSR, occurs via the interaction of loop extrusion complexes with a de novo topologically associating domain (TAD) boundary formed via transcriptional activity across S regions. Surprisingly, synapsis occurs predominantly in proximity to the 3' CTCF-binding element (3'CBE) rather than the Igh super-enhancer, suggesting a two-step mechanism whereby transcription of S regions is not topologically coupled to synapsis, as has been previously proposed. Altogether, these insights advance our understanding of how 3D chromatin architecture regulates CSR.

A review of applications of the Bayes factor in psychological research.

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The article is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Decisions about equivalence: A comparison of TOST, HDI-ROPE, and the Bayes factor.

Some important research questions require the ability to find evidence for two conditions being practically equivalent. This is impossible to accomplish within the traditional frequentist null hypothesis significance testing framework; hence, other methodologies must be utilized. We explain and illustrate three approaches for finding evidence for equivalence: The frequentist two one-sided tests procedure, the Bayesian highest density interval region of practical equivalence procedure, and the Bayes factor interval null procedure. We compare the classification performances of these three approaches for various plausible scenarios. The results indicate that the Bayes factor interval null approach compares favorably to the other two approaches in terms of statistical power. Critically, compared with the Bayes factor interval null procedure, the two one-sided tests and the highest density interval region of practical equivalence procedures have limited discrimination capabilities when the sample size is relatively small: Specifically, in order to be practically useful, these two methods generally require over 250 cases within each condition when rather large equivalence margins of approximately .2 or .3 are used; for smaller equivalence margins even more cases are required. Because of these results, we recommend that researchers rely more on the Bayes factor interval null approach for quantifying evidence for equivalence, especially for studies that are constrained on sample size. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Rethinking remdesivir for COVID-19: A Bayesian reanalysis of trial findings.

BACKGROUND: Following testing in clinical trials, the use of remdesivir for treatment of COVID-19 has been authorized for use in parts of the world, including the USA and Europe. Early authorizations were largely based on results from two clinical trials. A third study published by Wang et al. was underpowered and deemed inconclusive. Although regulators have shown an interest in interpreting the Wang et al. study, under a frequentist framework it is difficult to determine if the non-significant finding was caused by a lack of power or by the absence of an effect. Bayesian hypothesis testing does allow for quantification of evidence in favor of the absence of an effect. FINDINGS: Results of our Bayesian reanalysis of the three trials show ambiguous evidence for the primary outcome of clinical improvement and moderate evidence against the secondary outcome of decreased mortality rate. Additional analyses of three studies published after initial marketing approval support these findings. CONCLUSIONS: We recommend that regulatory bodies take all available evidence into account for endorsement decisions. A Bayesian approach can be beneficial, in particular in case of statistically non-significant results. This is especially pressing when limited clinical efficacy data is available.