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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Leveraging external controls -- relevant individual patient data undercontrol from external trials or real-world data -- has the potential to reducethe cost of randomized controlled trials (RCTs) while increasing the proportionof trial patients given access to novel treatments. However, due to lack ofrandomization, RCT patients and external controls may differ with respect tocovariates that may or may not have been measured. Hence, after controlling formeasured covariates, for instance by matching, testing for treatment effectusing external controls may still be subject to unmeasured biases. In thispaper, we propose a sensitivity analysis approach to quantify the magnitude ofunmeasured bias that would be needed to alter the study conclusion thatpresumed no unmeasured biases are introduced by employing external controls.Whether leveraging external controls increases power or not depends on theinterplay between sample sizes and the magnitude of treatment effect andunmeasured biases, which may be difficult to anticipate. This motivates acombined testing procedure that performs two highly correlated analyses, onewith and one without external controls, with a small correction for multipletesting using the joint distribution of the two test statistics. The combinedtest provides a new method of sensitivity analysis designed for data fusionproblems, which anchors at the unbiased analysis based on RCT only and spends asmall proportion of the type I error to also test using the external controls.In this way, if leveraging external controls increases power, the power gaincompared to the analysis based on RCT only can be substantial; if not, thepower loss is small. The proposed method is evaluated in theory and powercalculations, and applied to a real trial.

Testing for Treatment Effect Twice Using Internal and External Controls in Clinical Trials