Multiple comparisons for survival data with propensity score adjustment

Hong Zhu, Bo Lu

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This article considers the practical problem in clinical and observational studies where multiple treatment or prognostic groups are compared and the observed survival data are subject to right censoring. Two possible formulations of multiple comparisons are suggested. Multiple Comparisons with a Control (MCC) compare every other group to a control group with respect to survival outcomes, for determining which groups are associated with lower risk than the control. Multiple Comparisons with the Best (MCB) compare each group to the truly minimum risk group and identify the groups that are either with the minimum risk or the practically minimum risk. To make a causal statement, potential confounding effects need to be adjusted in the comparisons. Propensity score based adjustment is popular in causal inference and can effectively reduce the confounding bias. Based on a propensity-score-stratified Cox proportional hazards model, the approaches of MCC test and MCB simultaneous confidence intervals for general linear models with normal error outcome are extended to survival outcome. This paper specifies the assumptions for causal inference on survival outcomes within a potential outcome framework, develops testing procedures for multiple comparisons and provides simultaneous confidence intervals. The proposed methods are applied to two real data sets from cancer studies for illustration, and a simulation study is also presented.

Original languageEnglish (US)
Pages (from-to)42-51
Number of pages10
JournalComputational Statistics and Data Analysis
Volume86
DOIs
StatePublished - 2015

Fingerprint

Propensity Score
Multiple Comparisons
Survival Data
Adjustment
Simultaneous Confidence Intervals
Causal Inference
Confounding
Hazards
Potential Outcomes
Right Censoring
Cox Proportional Hazards Model
Observational Study
Testing
Linear Model
Cancer
Simulation Study
Formulation

Keywords

  • Causal inference
  • Multiple comparisons
  • Propensity score stratification
  • Simultaneous confidence intervals

ASJC Scopus subject areas

  • Computational Mathematics
  • Computational Theory and Mathematics
  • Statistics and Probability
  • Applied Mathematics

Cite this

Multiple comparisons for survival data with propensity score adjustment. / Zhu, Hong; Lu, Bo.

In: Computational Statistics and Data Analysis, Vol. 86, 2015, p. 42-51.

Research output: Contribution to journalArticle

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