Confidence distributions for treatment effects in clinical trials: Posteriors without priors.

An attractive feature of using a Bayesian analysis for a clinical trial is that knowledge and uncertainty about the treatment effect is summarized in a posterior probability distribution. Researchers often find probability statements about treatment effects highly intuitive and the fact that this is not accommodated in frequentist inference is a disadvantage. At the same time, the requirement to specify a prior distribution in order to obtain a posterior distribution is sometimes an artificial process that may introduce subjectivity or complexity into the analysis. This paper considers a compromise involving confidence distributions, which are probability distributions that summarize uncertainty about the treatment effect without the need for a prior distribution and in a way that is fully compatible with frequentist inference. The concept of a confidence distribution provides a posterior-like probability distribution that is distinct from, but exists in tandem with, the relative frequency interpretation of probability used in frequentist inference. Although they have been discussed for decades, confidence distributions are not well known among clinical trial statisticians and the goal of this paper is to discuss their use in analyzing treatment effects from randomized trials. As well as providing an introduction to confidence distributions, some illustrative examples relevant to clinical trials are presented, along with various case studies based on real clinical trials. It is recommended that trial statisticians consider presenting confidence distributions for treatment effects when reporting analyses of clinical trials.

Analysis of Nonconcurrent Controls in Adaptive Platform Trials: Separating Randomized and Nonrandomized Information.

Adaptive platform trials allow treatments to be added or dropped during the study, meaning that the control arm may be active for longer than the experimental arms. This leads to nonconcurrent controls, which provide nonrandomized information that may increase efficiency but may introduce bias from temporal confounding and other factors. Various methods have been proposed to control confounding from nonconcurrent controls, based on adjusting for time period. We demonstrate that time adjustment is insufficient to prevent bias in some circumstances where nonconcurrent controls are present in adaptive platform trials, and we propose a more general analytical framework that accounts for nonconcurrent controls in such circumstances. We begin by defining nonconcurrent controls using the concept of a concurrently randomized cohort, which is a subgroup of participants all subject to the same randomized design. We then use cohort adjustment rather than time adjustment. Due to flexibilities in platform trials, more than one randomized design may be in force at any time, meaning that cohort-adjusted and time-adjusted analyses may be quite different. Using simulation studies, we demonstrate that time-adjusted analyses may be biased while cohort-adjusted analyses remove this bias. We also demonstrate that the cohort-adjusted analysis may be interpreted as a synthesis of randomized and indirect comparisons analogous to mixed treatment comparisons in network meta-analysis. This allows the use of network meta-analysis methodology to separate the randomized and nonrandomized components and to assess their consistency. Whenever nonconcurrent controls are used in platform trials, the separate randomized and indirect contributions to the treatment effect should be presented.

Challenges of estimating treatment effects after a positive interim analysis.

BACKGROUND: This research investigates why a beneficial treatment effect reported at the first interim analysis (IA) may diminish at a subsequent analysis (SA). We examined three challenges in interpreting treatment effects from randomized clinical trials (RCTs) after the first positive IA: overestimation bias; non-proportional hazards; and heterogeneity in recruitment. We investigate how a penalized estimation method can address overestimation bias, and discuss additional factors to consider when interpreting positive IA results. METHODS: We identified oncology RCTs reporting positive results at the initial IA and a SA for event-free (EFS) and overall survival (OS). We modeled: (1) the hazard ratio at IA (HRIA) versus its timing as measured by the information fraction (IF; i.e., events at IA versus total events sought); and (2), the ratio of HRIA to HRSA (rHR) versus the IF. This was repeated for HRIA adjusted for overestimation bias. Examples of the other two challenges were sought. RESULTS: Amongst 71 RCTs, HRIA were positively associated with the IF (slope: EFS 0.83, 95 % CI 0.44-1.22; OS 0.25, 95 % CI 0.10-0.41). HRIA tended to exaggerate HRSA, and more so the lower the IF (slope rHR versus IF: EFS 0.10, 95 % CI - 0.22 to 0.42; OS 0.26, 95 % CI 0.07-0.46). Adjusted HRIA did not exaggerate HRSA (slope rHR versus IF: EFS - 0.14, 95 % CI - 0.67 to 0.39; OS 0.02, 95 % CI - 0.26 to 0.30). Examples of two other challenges are shown. CONCLUSION: Overestimation bias, non-proportional hazards, and heterogeneity in recruitment and other important treatments should be considered when communicating estimates of treatment effects from positive IAs.

A Randomized Trial of Nafamostat for Covid-19.

BACKGROUND: Nafamostat mesylate is a potent in vitro antiviral agent that inhibits the host transmembrane protease serine 2 enzyme used by severe acute respiratory syndrome coronavirus 2 for cell entry. METHODS: This open-label, pragmatic, randomized clinical trial in Australia, New Zealand, and Nepal included noncritically ill hospitalized patients with coronavirus disease 2019 (Covid-19). Participants were randomly assigned to usual care or usual care plus nafamostat. The primary end point was death (any cause) or receipt of new invasive or noninvasive ventilation or vasopressor support within 28 days after randomization. Analysis was with a Bayesian logistic model in which an adjusted odds ratio <1.0 indicates improved outcomes with nafamostat. Enrollment was closed due to falling numbers of eligible patients. RESULTS: We screened 647 patients in 21 hospitals (15 in Australia, 4 in New Zealand, and 2 in Nepal) and enrolled 160 participants from May 2021 to August 2022. In the intention-to-treat population, the primary end point occurred in 8 (11%) of 73 patients with usual care and 4 (5%) of 82 with nafamostat. The median adjusted odds ratio for the primary end point for nafamostat was 0.40 (95% credible interval, 0.12 to 1.34) with a posterior probability of effectiveness (adjusted odds ratio <1.0) of 93%. For usual care compared with nafamostat, hyperkalemia occurred in 1 (1%) of 67 and 7 (9%) of 78 participants, respectively, and clinically relevant bleeding occurred in 1 (1%) of 73 and 7 (8%) of 82 participants. CONCLUSIONS: Among hospitalized patients with Covid-19, there was a 93% posterior probability that nafamostat reduced the odds of death or organ support. Prespecified stopping criteria were not met, precluding definitive conclusions. Hyperkalemia and bleeding were more common with nafamostat. (Funded by ASCOT and others; ClinicalTrials.gov number, NCT04483960.)