English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Covariate-adjusted randomization (CAR) can reduce the risk of covariateimbalance and, when accounted for in analysis, increase the power of a trial.Despite CAR advances, stratified randomization remains the most common CARmethod. Matched Randomization (MR) randomizes treatment assignment withinoptimally identified matched pairs based on covariates and a distance matrix.When participants enroll sequentially, Sequentially Matched Randomization (SMR)randomizes within matches found "on-the-fly" to meet a pre-specified matchingthreshold. However, pre-specifying the ideal threshold can be challenging andSMR yields less-optimal matches than MR. We extend SMR to allow multipleparticipants to be randomized simultaneously, to use a dynamic threshold, andto allow matches to break and rematch if a better match later enrolls(Sequential Rematched Randomization; SRR). In simplified settings and areal-world application, we assess whether these extensions improve covariatebalance, estimator/study efficiency, and optimality of matches. We investigatewhether adjusting for more covariates can be detrimental upon covariate balanceand efficiency as is the case of traditional stratified randomization. Assecondary objectives, we use the case study to assess how SMR schemes compareside-by-side with common and related CAR schemes and whether adjusting forcovariates in the design can be as powerful as adjusting for covariates in aparametric model. We find each SMR extension, individually and collectively, toimprove covariate balance, estimator efficiency, study power, and quality ofmatches. We provide a case-study where CAR schemes with randomization-basedinference can be as and more powerful than Non-CAR schemes with parametricadjustment for covariates.

Rematching on-the-fly: sequential matched randomization and a case for covariate-adjusted randomization