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Point and interval estimation in two‐stage adaptive designs with time to event data and biomarker‐driven subpopulation selection
Author(s) -
Kimani Peter K.,
Todd Susan,
Renfro Lindsay A.,
Glimm Ekkehard,
Khan Josephine N.,
Kairalla John A.,
Stallard Nigel
Publication year - 2020
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8557
Subject(s) - estimator , event (particle physics) , categorical variable , population , statistics , interval (graph theory) , stage (stratigraphy) , hazard ratio , computer science , selection (genetic algorithm) , estimation , confidence interval , interval estimation , data mining , mathematics , medicine , artificial intelligence , biology , combinatorics , paleontology , physics , environmental health , management , quantum mechanics , economics
In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two‐stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous.