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

Thomas' partial likelihood estimator of regression parameters is widely usedin the analysis of nested case-control data with Cox's model. This paperproposes a new estimator of the regression parameters, which is consistent andasymptotically normal. Its asymptotic variance is smaller than that of Thomas'estimator away from the null. Unlike some other existing estimators, theproposed estimator does not rely on any more data than strictly necessary forThomas' estimator and is easily computable from a closed form estimatingequation with a unique solution. The variance estimation is obtained as minusthe inverse of the derivative of the estimating function and therefore theinference is easily available. A numerical example is provided in support ofthe theory.

Statistical estimation in the proportional hazards model with risk set sampling