G‐estimation of structural nested restricted mean time lost models to estimate effects of time‐varying treatments on a failure time outcome

Yasuhiro Hagiwara, Tomohiro Shinozaki and Yutaka Matsuyama

Biometrics, 2020, vol. 76, issue 3, 799-810

Abstract: G‐estimation of structural nested models (SNMs) plays an important role in estimating the effects of time‐varying treatments with appropriate adjustment for time‐dependent confounding. As SNMs for a failure time outcome, structural nested accelerated failure time models (SNAFTMs) and structural nested cumulative failure time models have been developed. The latter models are included in the class of structural nested mean models (SNMMs) and are not involved in artificial censoring, which induces several difficulties in g‐estimation of SNAFTMs. Recently, restricted mean time lost (RMTL), which corresponds to the area under a distribution function up to a restriction time, is attracting attention in clinical trial communities as an appropriate summary measure of a failure time outcome. In this study, we propose another SNMM for a failure time outcome, which is called structural nested RMTL model (SNRMTLM) and describe randomized and observational g‐estimation procedures that use different assumptions for the treatment mechanism in a randomized trial setting. We also provide methods to estimate marginal RMTLs under static treatment regimes using estimated SNRMTLMs. A simulation study evaluates finite‐sample performances of the proposed methods compared with the conventional intention‐to‐treat and per‐protocol analyses. We illustrate the proposed methods using data from a randomized controlled trial for cardiovascular disease with treatment changes. G‐estimation of SNRMTLMs is a useful tool to estimate the effects of time‐varying treatments on a failure time outcome.

Date: 2020
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