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On hazard ratio estimators by proportional hazards models in matched-pair cohort studies

Overview of attention for article published in Emerging Themes in Epidemiology, June 2017
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Title
On hazard ratio estimators by proportional hazards models in matched-pair cohort studies
Published in
Emerging Themes in Epidemiology, June 2017
DOI 10.1186/s12982-017-0060-8
Pubmed ID
Authors

Tomohiro Shinozaki, Mohammad Ali Mansournia, Yutaka Matsuyama

Abstract

In matched-pair cohort studies with censored events, the hazard ratio (HR) may be of main interest. However, it is lesser known in epidemiologic literature that the partial maximum likelihood estimator of a common HR conditional on matched pairs is written in a simple form, namely, the ratio of the numbers of two pair-types. Moreover, because HR is a noncollapsible measure and its constancy across matched pairs is a restrictive assumption, marginal HR as "average" HR may be targeted more than conditional HR in analysis. Based on its simple expression, we provided an alternative interpretation of the common HR estimator as the odds of the matched-pair analog of C-statistic for censored time-to-event data. Through simulations assuming proportional hazards within matched pairs, the influence of various censoring patterns on the marginal and common HR estimators of unstratified and stratified proportional hazards models, respectively, was evaluated. The methods were applied to a real propensity-score matched dataset from the Rotterdam tumor bank of primary breast cancer. We showed that stratified models unbiasedly estimated a common HR under the proportional hazards within matched pairs. However, the marginal HR estimator with robust variance estimator lacks interpretation as an "average" marginal HR even if censoring is unconditionally independent to event, unless no censoring occurs or no exposure effect is present. Furthermore, the exposure-dependent censoring biased the marginal HR estimator away from both conditional HR and an "average" marginal HR irrespective of whether exposure effect is present. From the matched Rotterdam dataset, we estimated HR for relapse-free survival of absence versus presence of chemotherapy; estimates (95% confidence interval) were 1.47 (1.18-1.83) for common HR and 1.33 (1.13-1.57) for marginal HR. The simple expression of the common HR estimator would be a useful summary of exposure effect, which is less sensitive to censoring patterns than the marginal HR estimator. The common and the marginal HR estimators, both relying on distinct assumptions and interpretations, are complementary alternatives for each other.

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Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 29%
Other 2 14%
Student > Master 2 14%
Professor 1 7%
Researcher 1 7%
Other 1 7%
Unknown 3 21%
Readers by discipline Count As %
Medicine and Dentistry 7 50%
Computer Science 1 7%
Agricultural and Biological Sciences 1 7%
Mathematics 1 7%
Engineering 1 7%
Other 0 0%
Unknown 3 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 June 2017.
All research outputs
#9,078,577
of 11,340,339 outputs
Outputs from Emerging Themes in Epidemiology
#82
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#192,208
of 267,421 outputs
Outputs of similar age from Emerging Themes in Epidemiology
#1
of 1 outputs
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