In vitro modulator responsiveness of 655 CFTR variants found in people with cystic fibrosis.

Background: In 2017, the US Food and Drug Administration initiated expansion of drug labels for the treatment of cystic fibrosis (CF) to include CF transmembrane conductance regulator (CFTR) gene variants based on in vitro functional studies. This study aims to identify CFTR variants that result in increased chloride (Cl) transport function by the CFTR protein after treatment with the CFTR modulator combination elexacaftor/tezacaftor/ivacaftor (ELX/TEZ/IVA). These data may benefit people with CF (pwCF) who are not currently eligible for modulator therapies.

A general framework for inference on algorithm-agnostic variable importance.

In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response - in other words, to gauge the variable importance of features. Most recent work on variable importance assessment has focused on describing the importance of features within the confines of a given prediction algorithm. However, such assessment does not necessarily characterize the prediction potential of features, and may provide a misleading reflection of the intrinsic value of these features.

Multigroup, Adaptively Randomized Trials Are Advantageous for Comparing Coronavirus Disease 2019 (COVID-19) Interventions.

In this commentary, the authors explain and call for a broader use of outcome-adaptive randomization when designing clinical trials to test multiple COVID-19 interventions. This design potentially reduces the number of deaths or other adverse outcomes incurred during a trial.

Using Randomization Tests to Preserve Type I Error With Response-Adaptive and Covariate-Adaptive Randomization.

We demonstrate that clinical trials using response adaptive randomized treatment assignment rules are subject to substantial bias if there are time trends in unknown prognostic factors and standard methods of analysis are used. We develop a general class of randomization tests based on generating the null distribution of a general test statistic by repeating the adaptive randomized treatment assignment rule holding fixed the sequence of outcome values and covariate vectors actually observed in the trial. We develop broad conditions on the adaptive randomization method and the stochastic mechanism by which outcomes and covariate vectors are sampled that ensure that the type I error is controlled at the level of the randomization test.