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The parametric g-formula for time-to-event data: intuition and a worked example.

ABSTRACT: The parametric g-formula can be used to estimate the effect of a policy, intervention, or treatment. Unlike standard regression approaches, the parametric g-formula can be used to adjust for time-varying confounders that are affected by prior exposures. To date, there are few published examples in which the method has been applied.We provide a simple introduction to the parametric g-formula and illustrate its application in an analysis of a small cohort study of bone marrow transplant patients in which the effect of treatment on mortality is subject to time-varying confounding.Standard regression adjustment yields a biased estimate of the effect of treatment on mortality relative to the estimate obtained by the g-formula.The g-formula allows estimation of a relevant parameter for public health officials: the change in the hazard of mortality under a hypothetical intervention, such as reduction of exposure to a harmful agent or introduction of a beneficial new treatment. We present a simple approach to implement the parametric g-formula that is sufficiently general to allow easy adaptation to many settings of public health relevance.

SUBMITTER: Keil AP

PROVIDER: S-EPMC4310506 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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The parametric g-formula for time-to-event data: intuition and a worked example.

Keil Alexander P AP Edwards Jessie K JK Richardson David B DB Naimi Ashley I AI Cole Stephen R SR

Epidemiology (Cambridge, Mass.) 20141101 6

<h4>Background</h4>The parametric g-formula can be used to estimate the effect of a policy, intervention, or treatment. Unlike standard regression approaches, the parametric g-formula can be used to adjust for time-varying confounders that are affected by prior exposures. To date, there are few published examples in which the method has been applied.<h4>Methods</h4>We provide a simple introduction to the parametric g-formula and illustrate its application in an analysis of a small cohort study o ...[more]

PMID: 25140837

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Project description:BackgroundParametric g-computation is an attractive analytic framework to study the health effects of air pollution. Yet, the ability to explore biologically relevant exposure windows within this framework is underdeveloped.ObjectivesWe outline a novel framework for how to incorporate complex lag-responses using distributed lag models (DLMs) into parametric g-computation analyses for survival data. We call this approach "g-survival-DLM" and illustrate its use examining the association between PM2.5 during pregnancy and the risk of preterm birth (PTB).MethodsWe applied the g-survival-DLM approach to estimate the hypothetical static intervention of reducing average PM2.5 in each gestational week by 20% on the risk of PTB among 9,403 deliveries from Beth Israel Deaconess Medical Center, Boston, Massachusetts, 2011-2016. Daily PM2.5 was taken from a 1-km grid model and assigned to address at birth. Models were adjusted for sociodemographics, time trends, nitrogen dioxide, and temperature. To facilitate implementation, we provide a detailed description of the procedure and accompanying R syntax.ResultsThere were 762 (8.1%) PTBs in this cohort. The gestational week-specific median PM2.5 concentration was relatively stable across pregnancy at ∼7μg/m3. We found that our hypothetical intervention strategy changed the cumulative risk of PTB at week 36 (i.e., the end of the preterm period) by -0.009 (95% confidence interval: -0.034, 0.007) in comparison with the scenario had we not intervened, which translates to about 86 fewer PTBs in this cohort. We also observed that the critical exposure window appeared to be weeks 5-20.DiscussionWe demonstrate that our g-survival-DLM approach produces easier-to-interpret, policy-relevant estimates (due to the g-computation); prevents immortal time bias (due to treating PTB as a time-to-event outcome); and allows for the exploration of critical exposure windows (due to the DLMs). In our illustrative example, we found that reducing fine particulate matter [particulate matter (PM) with aerodynamic diameter ≤2.5μm (PM2.5)] during gestational weeks 5-20 could potentially lower the risk of PTB. https://doi.org/10.1289/EHP13891.

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The parametric g-formula for time-to-event data: intuition and a worked example.

Publications

The parametric g-formula for time-to-event data: intuition and a worked example.

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