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

Kevin He (), Yun Li (), Panduranga S. Rao (), Randall S. Sung () and Douglas E. Schaubel ()
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Kevin He: University of Michigan
Yun Li: University of Pennsylvania Perelman School of Medicine
Panduranga S. Rao: University of Michigan
Randall S. Sung: University of Michigan
Douglas E. Schaubel: University of Pennsylvania Perelman School of Medicine

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2020, vol. 26, issue 3, No 2, 470 pages

Abstract: Abstract 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.

Keywords: Landmark analysis; Causal inference; Matching; Prognostic score; Semiparametric method; Survival function; Treatment effect (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10985-019-09485-x

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