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Meta‐analysis of prognostic studies for a biomarker with a study‐specific cutoff value
Author(s) -
Sadashima Eiji,
Hattori Satoshi,
Takahashi Kunihiko
Publication year - 2016
Publication title -
research synthesis methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.376
H-Index - 35
eISSN - 1759-2887
pISSN - 1759-2879
DOI - 10.1002/jrsm.1201
Subject(s) - cutoff , value (mathematics) , biomarker , meta analysis , statistics , computer science , medicine , mathematics , biology , biochemistry , physics , quantum mechanics
In prognostic studies, a summary statistic such as a hazard ratio is often reported between low‐expression and high‐expression groups of a biomarker with a study‐specific cutoff value. Recently, several meta‐analyses of prognostic studies have been reported, but these studies simply combined hazard ratios provided by the individual studies, overlooking the fact that the cutoff values are study‐specific. We propose a method to summarize hazard ratios with study‐specific cutoff values by estimating the hazard ratio for a 1‐unit change of the biomarker in the underlying individual‐level model. To this end, we introduce a model for a relationship between a reported log‐hazard ratio for a 1‐unit expected difference in the mean biomarker value between the low‐expression and high‐expression groups, which approximates the individual‐level model, and propose to make an inference of the model by using the method for trend estimation based on grouped exposure data. Our combined estimator provides a valid interpretation if the biomarker distribution is correctly specified. We applied our proposed method to a dataset that examined the association between the biomarker Ki‐67 and disease‐free survival in breast cancer patients. We conducted simulation studies to examine the performance of our method. Copyright © 2016 John Wiley & Sons, Ltd.