Prognostic score matching methods for estimating the average effect of a non-reversible binary time-dependent treatment on the survival function

In evaluating the benefit of a treatment on survival, it is often of interest to compare post-treatment survival with the survival function that would have been observed in the absence of treatment. In many practical settings, treatment is time-dependent in the sense that subjects typically begin fo...

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Bibliographic Details
Published in:Lifetime data analysis 2020-07, Vol.26 (3), p.451-470
Main Authors: He, Kevin, Li, Yun, Rao, Panduranga S., Sung, Randall S., Schaubel, Douglas E.
Format: Article
Language:English
Subjects:
Causality
Economics
Finance
Health Sciences
Insurance
Management
Matching
Mathematics and Statistics
Medical prognosis
Medicine
Operations Research/Decision Theory
Quality Control
Reliability
Safety and Risk
Statistical inference
Statistics
Statistics for Business
Statistics for Life Sciences
Survival
Time dependence
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Summary:In evaluating the benefit of a treatment on survival, it is often of interest to compare post-treatment survival with the survival function that would have been observed in the absence of treatment. In many practical settings, treatment is time-dependent in the sense that subjects typically begin follow-up untreated, with some going on to receive treatment at some later time point. In observational studies, treatment is not assigned at random and, therefore, may depend on various patient characteristics. We have developed semi-parametric matching methods to estimate the average treatment effect on the treated (ATT) with respect to survival probability and restricted mean survival time. Matching is based on a prognostic score which reflects each patient’s death hazard in the absence of treatment. Specifically, each treated patient is matched with multiple as-yet-untreated patients with similar prognostic scores. The matched sets do not need to be of equal size, since each matched control is weighted in order to preserve risk score balancing across treated and untreated groups. After matching, we estimate the ATT non-parametrically by contrasting pre- and post-treatment weighted Nelson–Aalen survival curves. A closed-form variance is proposed and shown to work well in simulation studies. The proposed methods are applied to national organ transplant registry data.
ISSN:1380-7870
1572-9249
DOI:10.1007/s10985-019-09485-x