Article

Subgroup Identification for Time-to-event Data Based on AFT Model

2019-12-17

Abstract excerpt

Considering the problem of identifying subgroup in a randomized clinical trial with respect to survival time, we present an analysis strategy to find subgroup of enhanced treatment effect. We fit univariate accelerated failure time (AFT) models with covariate-treatment interactions to identify predictive covariates. The false discovery rate is controlled by Benjamini-Hochberg procedure. Then a composite score conv...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 8bc1b754-98e2-570a-9b1b-b0048183e9ba
DOI: 10.21203/rs.2.18979/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Subgroup Identification for Time-to-event Data Based on AFT ModelDOI 10.21203/rs.2.18979/v1Read

Transfer learning for accelerated failure time model with microarray dataSemantic proximity Read

A Meta-Learner Framework to Estimate Individualized Treatment Effects for Survival OutcomesSemantic proximity Read

Variable selection in the accelerated failure time model via the bridge method.Semantic proximity Read

Summary measures in non-inferiority clinical trials with a time-to-event outcome: an empirical comparison of powerSemantic proximity Read

Do we need to adjust for interim analyses in a Bayesian adaptive trial design?Semantic proximity Read

Using Median Survival in Meta-analysis of Experimental Time-to-event DataSemantic proximity Read

When Does IPCW Help? Simulation and Real-World Evidence on Censoring Adjustment in Survival AnalysisSemantic proximity Read

Nonparametric estimation of the joint and conditional survival functions of the time to an event of interest and associated integrated covariate processesSemantic proximity Read

Non-Monotone Exponential Time (NEXT) Model for the Longitudinal Trend of a Continuous Outcome in Clinical TrialsSemantic proximity Read

Group sequential designs using early outcomes in the R package gsearly: implementation and application in randomized clinical trialsSemantic proximity Read

Accountable Survival Contrast-learning for Optimal Dynamic Treatment RegimeSemantic proximity Read

Restricted mean survival time approach versus time-varying coefficient Cox model for quantifying treatment effect when hazards are non-proportionalSemantic proximity Read