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Stat Med. 2016 Mar 30;35(7):1103-16. doi: 10.1002/sim.6777. Epub 2015 Oct 29.

Comparisons of the performance of different statistical tests for time-to-event analysis with confounding factors: practical illustrations in kidney transplantation.

Author information

1
SPHERE (EA 4275): bioStatistics, Pharmacoepidemiology & Human sciEnces REsearch, University of Nantes, Nantes, France.
2
IDBC/A2com, Espace Antrium Parc de la Teillais, 35740 PACE, France.
3
Transplantation, Urology and Nephrology Institute (ITUN), Nantes Hospital and University, Nantes, INSERM U1064, France.
4
Centre de recherche Epidémiologie et Biostatistique, INSERM U1153, Paris, France.
5
Centre d'Investigation clinique INSERM, Tours, CIC 1415, France.
6
Université François Rabelais de Tours, PRES Centre-Val de Loire Université, Tours, France.
7
CHRU de Tours, Tours, France.
8
Médecine néphrologie - Hémodialyse, Centre Hospitalier Départemental Vendée Site de La Roche sur Yon, France.

Abstract

Confounding factors are commonly encountered in observational studies. Several confounder-adjusted tests to compare survival between differently exposed subjects were proposed. However, only few studies have compared their performances regarding type I error rates, and no study exists evaluating their type II error rates. In this paper, we performed a comparative simulation study based on two different applications in kidney transplantation research. Our results showed that the propensity score-based inverse probability weighting (IPW) log-rank test proposed by Xie and Liu (2005) can be recommended as a first descriptive approach as it provides adjusted survival curves and has acceptable type I and II error rates. Even better performance was observed for the Wald test of the parameter corresponding to the exposure variable in a multivariable-adjusted Cox model. This last result is of primary interest regarding the exponentially increasing use of propensity score-based methods in the literature.

KEYWORDS:

adjusted Kaplan-Meier estimator; adjusted log-rank test; inverse probability weighting; propensity score; simulation study; survival data

PMID:
26514380
DOI:
10.1002/sim.6777
[Indexed for MEDLINE]

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