scholarly journals Gformula: Estimating Causal Effects in the Presence of Time-Varying Confounding or Mediation using the G-Computation Formula

2011 ◽  
Vol 11 (4) ◽  
pp. 479-517 ◽  
Author(s):  
Rhian M. Daniel ◽  
Bianca L. De Stavola ◽  
Simon N. Cousens
Keyword(s):  
Causal Effects ◽  
Time Varying ◽  
Computation Formula

scholarly journals Gformula: Estimating Causal Effects in the Presence of Time-Varying Confounding or Mediation using the G-Computation Formula

2011 ◽  
Vol 11 (4) ◽  
pp. 479-517 ◽  
Author(s):  
Rhian M. Daniel ◽  
Bianca L. De Stavola ◽  
Simon N. Cousens
Keyword(s):  
Indirect Effects ◽  
Related Problem ◽  
Causal Effect ◽  
Causal Effects ◽  
Time Varying ◽  
Direct And Indirect Effects ◽  
Computation Procedure ◽  
Computation Formula ◽  
Intermediate Variables

This article describes a new command, gformula, that is an implementation of the g-computation procedure. It is used to estimate the causal effect of time-varying exposures on an outcome in the presence of time-varying confounders that are themselves also affected by the exposures. The procedure also addresses the related problem of estimating direct and indirect effects when the causal effect of the exposures on an outcome is mediated by intermediate variables, and in particular when confounders of the mediator–outcome relationships are themselves affected by the exposures. A brief overview of the theory and a description of the command and its options are given, and illustrations using two simulated examples are provided.

scholarly journals Posterior predictive treatment assignment methods for causal inference in the context of time-varying treatments

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Shirley X. Liao ◽  
Lucas Henneman ◽  
Cory Zigler
Keyword(s):  
American Statistical Association ◽  
Treatment Patterns ◽  
Average Treatment Effect ◽  
Causal Effects ◽  
Time Varying ◽  
Medicare Beneficiaries ◽  
Finite Sample ◽  
Treatment Assignment ◽  
Power Plant Emissions ◽  
Plant Emissions

AbstractMarginal structural models (MSM) with inverse probability weighting (IPW) are used to estimate causal effects of time-varying treatments, but can result in erratic finite-sample performance when there is low overlap in covariate distributions across different treatment patterns. Modifications to IPW which target the average treatment effect (ATE) estimand either introduce bias or rely on unverifiable parametric assumptions and extrapolation. This paper extends an alternate estimand, the ATE on the overlap population (ATO) which is estimated on a sub-population with a reasonable probability of receiving alternate treatment patterns in time-varying treatment settings. To estimate the ATO within an MSM framework, this paper extends a stochastic pruning method based on the posterior predictive treatment assignment (PPTA) (Zigler, C. M., and M. Cefalu. 2017. “Posterior Predictive Treatment Assignment for Estimating Causal Effects with Limited Overlap.” eprint arXiv:1710.08749.) as well as a weighting analog (Li, F., K. L. Morgan, and A. M. Zaslavsky. 2018. “Balancing Covariates via Propensity Score Weighting.” Journal of the American Statistical Association 113: 390–400, https://doi.org/10.1080/01621459.2016.1260466.) to the time-varying treatment setting. Simulations demonstrate the performance of these extensions compared against IPW and stabilized weighting with regard to bias, efficiency, and coverage. Finally, an analysis using these methods is performed on Medicare beneficiaries residing across 18,480 ZIP codes in the U.S. to evaluate the effect of coal-fired power plant emissions exposure on ischemic heart disease (IHD) hospitalization, accounting for seasonal patterns that lead to change in treatment over time.

Optimal probability weights for estimating causal effects of time‐varying treatments with marginal structural Cox models

2018 ◽  
Vol 38 (10) ◽  
pp. 1891-1902 ◽  
Author(s):  
Michele Santacatterina ◽  
Celia García‐Pareja ◽  
Rino Bellocco ◽  
Anders Sönnerborg ◽  
Anna Mia Ekström ◽  
...  
Keyword(s):  
Causal Effects ◽  
Time Varying ◽  
Cox Models ◽  
Optimal Probability

11. Identification and Estimation of Causal Effects with Time-Varying Treatments and Time-Varying Outcomes

2007 ◽  
Vol 37 (1) ◽  
pp. 393-434 ◽  
Author(s):  
Jennie E. Brand ◽  
Yu Xie
Keyword(s):  
Employment Status ◽  
Causal Effect ◽  
Causal Effects ◽  
Potential Outcomes ◽  
Time Varying ◽  
Potential Outcome ◽  
Units Of Analysis ◽  
Wisconsin Longitudinal Study ◽  
Time Periods ◽  
Causal Variable

We develop an approach to identifying and estimating causal effects in longitudinal settings with time-varying treatments and time-varying outcomes. The classic potential outcome approach to causal inference generally involves two time periods: units of analysis are exposed to one of two possible values of the causal variable, treatment or control, at a given point in time, and values for an outcome are assessed some time subsequent to exposure. In this paper, we develop a potential outcome approach for longitudinal situations in which both exposure to treatment and the effects of treatment are time-varying. In this longitudinal setting, the research interest centers not on only two potential outcomes, but on a whole matrix of potential outcomes, requiring a complicated conceptualization of many potential counterfactuals. Motivated by sociological applications, we develop a simplification scheme—a weighted composite causal effect that allows identification and estimation of effects with a number of possible solutions. Our approach is illustrated via an analysis of the effects of disability on subsequent employment status using panel data from the Wisconsin Longitudinal Study.

scholarly journals Causal inference under over-simplified longitudinal causal models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Lola Étiévant ◽  
Vivian Viallon
Keyword(s):  
Causal Inference ◽  
Causal Effect ◽  
Sufficient Conditions ◽  
Repeated Measurements ◽  
Causal Models ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Time Varying ◽  
Weighted Averages

Abstract Many causal models of interest in epidemiology involve longitudinal exposures, confounders and mediators. However, repeated measurements are not always available or used in practice, leading analysts to overlook the time-varying nature of exposures and work under over-simplified causal models. Our objective is to assess whether – and how – causal effects identified under such misspecified causal models relates to true causal effects of interest. We derive sufficient conditions ensuring that the quantities estimated in practice under over-simplified causal models can be expressed as weighted averages of longitudinal causal effects of interest. Unsurprisingly, these sufficient conditions are very restrictive, and our results state that the quantities estimated in practice should be interpreted with caution in general, as they usually do not relate to any longitudinal causal effect of interest. Our simulations further illustrate that the bias between the quantities estimated in practice and the weighted averages of longitudinal causal effects of interest can be substantial. Overall, our results confirm the need for repeated measurements to conduct proper analyses and/or the development of sensitivity analyses when they are not available.

G-estimation of structural nested mean models for competing risks data using pseudo-observations

Biostatistics ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 860-875 ◽  
Author(s):  
Shiro Tanaka ◽  
M Alan Brookhart ◽  
Jason P Fine
Keyword(s):  
Type 2 Diabetes ◽  
Competing Risks ◽  
Causal Effects ◽  
Time Varying ◽  
Finite Sample ◽  
Subdistribution Hazard ◽  
Hazard Ratios ◽  
Competing Risks Data ◽  
Risk Ratios

Summary This article provides methods of causal inference for competing risks data. The methods are formulated as structural nested mean models of causal effects directly related to the cumulative incidence function or subdistribution hazard, which reflect the survival experience of a subject in the presence of competing risks. The effect measures include causal risk differences, causal risk ratios, causal subdistribution hazard ratios, and causal effects of time-varying exposures. Inference is implemented by g-estimation using pseudo-observations, a technique to handle censoring. The finite-sample performance of the proposed estimators in simulated datasets and application to time-varying exposures in a cohort study of type 2 diabetes are also presented.

scholarly journals G-estimation of Causal Effects, Allowing for Time-varying Confounding

2002 ◽  
Vol 2 (2) ◽  
pp. 164-182 ◽  
Author(s):  
Jonathan A. C. Sterne ◽  
Kate Tilling
Keyword(s):  
Survival Time ◽  
Causal Effects ◽  
Time Varying

This article describes the stgest command, which implements G-estimation (as proposed by Robins) to estimate the effect of a time-varying exposure on survival time, allowing for time-varying confounders.

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