Search

Competing risks data are commonly encountered in randomized clinical trials and observational studies. This paper considers the situation where the ending statuses of competing events have different clinical interpretations and/or are of simultaneous interest. In clinical trials, often more than one competing event has meaningful clinical interpretations even though the trial effects of different events could be different or even opposite to each other. In this paper, we develop estimation procedures and inferential properties for the joint use of multiple cumulative incidence functions (CIFs). Additionally, by incorporating longitudinal marker information, we develop estimation and inference procedures for weighted CIFs and related metrics. The proposed methods are applied to a COVID‐19 in‐patient treatment clinical trial, where the outcomes of COVID‐19 hospitalization are either death or discharge from the hospital, two competing events with completely different clinical implications. [ABSTRACT FROM AUTHOR]

Rejoinder: Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment, for binary, ordinal, and time-to-event outcomes An alternative estimator to consider for time-to-event outcomes is the augmented, inverse probability weighted estimator from Díaz I et al i . M. Zhang and B. Zhang describe a general approach for constructing covariate adjusted estimators that applies to many estimands. [Extracted from the article]

Benkeser et al. present a very informative paper evaluating the efficiency gains of covariate adjustment in settings with binary, ordinal, and time‐to‐event outcomes. The adjustment method focuses on estimating the marginal treatment effect averaged over the covariate distribution in both arms combined. The authors show that covariate adjustment can achieve power gains that could find answers more quickly. The suggested approach is an important weapon in the armamentarium against epidemics like COVID‐19. I recommend evaluating the procedure against more traditional approaches for conditional analyses (e.g., logistic regression) and against blinded methods of building prediction models followed by randomization‐based inference. [ABSTRACT FROM AUTHOR]

5 Saville, B. R., Koch, G. G. (2013) Estimating covariate-adjusted log hazard ratios in randomized clinical trials using Cox proportional hazards models and non-parametric randomization based analysis of covariance. Noteworthy is the fact that the FDA guidance remains silent on the use of covariate adjustments to correct for imbalances between treatment groups for purposes of producing unbiased estimates of the drug's effects. The advantages of covariate adjustment in the much simpler linear model context have been known for a while, and the method is popular with clinical trialists for its ability to improve the precision of treatment effect estimates with minimal assumptions. [Extracted from the article]

In many randomized clinical trials of therapeutics for COVID‐19, the primary outcome is an ordinal categorical variable, and interest focuses on the odds ratio (OR; active agent vs control) under the assumption of a proportional odds model. Although at the final analysis the outcome will be determined for all subjects, at an interim analysis, the status of some participants may not yet be determined, for example, because ascertainment of the outcome may not be possible until some prespecified follow‐up time. Accordingly, the outcome from these subjects can be viewed as censored. A valid interim analysis can be based on data only from those subjects with full follow‐up; however, this approach is inefficient, as it does not exploit additional information that may be available on those for whom the outcome is not yet available at the time of the interim analysis. Appealing to the theory of semiparametrics, we propose an estimator for the OR in a proportional odds model with censored, time‐lagged categorical outcome that incorporates additional baseline and time‐dependent covariate information and demonstrate that it can result in considerable gains in efficiency relative to simpler approaches. A byproduct of the approach is a covariate‐adjusted estimator for the OR based on the full data that would be available at a final analysis. [ABSTRACT FROM AUTHOR]

Often HT ht is linear in I Y i . Suppose one postulates a model, likely misspecified, for HT ht , where HT ht is a vector of known basis functions. It is also equivalent to fitting HT ht using OLS. When HT ht , strategy (iii) has a very slight advantage. [Extracted from the article]

Time is of the essence in evaluating potential drugs and biologics for the treatment and prevention of COVID‐19. There are currently 876 randomized clinical trials (phase 2 and 3) of treatments for COVID‐19 registered on clinicaltrials.gov. Covariate adjustment is a statistical analysis method with potential to improve precision and reduce the required sample size for a substantial number of these trials. Though covariate adjustment is recommended by the U.S. Food and Drug Administration and the European Medicines Agency, it is underutilized, especially for the types of outcomes (binary, ordinal, and time‐to‐event) that are common in COVID‐19 trials. To demonstrate the potential value added by covariate adjustment in this context, we simulated two‐arm, randomized trials comparing a hypothetical COVID‐19 treatment versus standard of care, where the primary outcome is binary, ordinal, or time‐to‐event. Our simulated distributions are derived from two sources: longitudinal data on over 500 patients hospitalized at Weill Cornell Medicine New York Presbyterian Hospital and a Centers for Disease Control and Prevention preliminary description of 2449 cases. In simulated trials with sample sizes ranging from 100 to 1000 participants, we found substantial precision gains from using covariate adjustment–equivalent to 4–18% reductions in the required sample size to achieve a desired power. This was the case for a variety of estimands (targets of inference). From these simulations, we conclude that covariate adjustment is a low‐risk, high‐reward approach to streamlining COVID‐19 treatment trials. We provide an R package and practical recommendations for implementation. [ABSTRACT FROM AUTHOR]