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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Most analyses of randomised trials with incomplete outcomes make untestableassumptions and should therefore be subjected to sensitivity analyses. However,methods for sensitivity analyses are not widely used. We propose a mean scoreapproach for exploring global sensitivity to departures from missing at randomor other assumptions about incomplete outcome data in a randomised trial. Weassume a single outcome analysed under a generalised linear model. One or moresensitivity parameters, specified by the user, measure the degree of departurefrom missing at random in a pattern mixture model. Advantages of our method arethat its sensitivity parameters are relatively easy to interpret and so can beelicited from subject matter experts; it is fast and non-stochastic; and itspoint estimate, standard error and confidence interval agree perfectly withstandard methods when particular values of the sensitivity parameters makethose standard methods appropriate. We illustrate the method using data from amental health trial.

A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials