Causal Proportional Hazards Models and Time-constant Exposure in Randomized Clinical Trials |
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Authors: | Email author" target="_blank">T?LoeysEmail author E?Goetghebeur A?Vandebosch |
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Institution: | (1) Department of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281, Gent, S9, 9000, Belgium |
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Abstract: | The last decade saw enormous progress in the development of causal inference tools to account for noncompliance in randomized
clinical trials. With survival outcomes, structural accelerated failure time (SAFT) models enable causal estimation of effects
of observed treatments without making direct assumptions on the compliance selection mechanism. The traditional proportional
hazards model has however rarely been used for causal inference. The estimator proposed by Loeys and Goetghebeur (2003, Biometrics vol. 59 pp. 100–105) is limited to the setting of all or nothing exposure. In this paper, we propose an estimation procedure
for more general causal proportional hazards models linking the distribution of potential treatment-free survival times to
the distribution of observed survival times via observed (time-constant) exposures. Specifically, we first build models for
observed exposure-specific survival times. Next, using the proposed causal proportional hazards model, the exposure-specific
survival distributions are backtransformed to their treatment-free counterparts, to obtain – after proper mixing – the unconditional
treatment-free survival distribution. Estimation of the parameter(s) in the causal model is then based on minimizing a test
statistic for equality in backtransformed survival distributions between randomized arms. |
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Keywords: | causal inference clinical trials compliance proportional hazards structural modelling |
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