A test for the correct specification of marginal structural models

Sall, Alioune; Aubé, Karine; Trudel, Xavier; Brisson, Chantal; Talbot, Denis

doi:10.1002/sim.8132

Cited by 3 publications

(4 citation statements)

References 26 publications

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“…Our results provide empirical evidence supporting the importance of using MSMs when estimating the effect of cumulative exposure to PSW, since traditional adjustment approaches have been observed to have non-negligibly more bias than MSM approaches in our area-specific simulations. These results supplement the theoretical arguments that we had previously advanced in favor of using MSMs in this context [ 8 ]. However, we recognize that MSMs are more difficult to implement than conventional methods.…”

Section: Discussionsupporting

confidence: 89%

“…Such adjustment methods may be inappropriate if some covariates have a double role of confounders and mediators, since adjustment for time-varying confounders leads to an overadjustment bias, and not adjusting for these variables leads to residual confounding bias. It has been argued that this exposure-confounder feedback could be present in the PSW context [ 8 ]. Marginal structural models (MSMs) are well-known methods to estimate the effect of time-varying exposures while controlling for time-varying covariates [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Marginal structural models (MSMs) are well-known methods to estimate the effect of time-varying exposures while controlling for time-varying covariates [ 9 ]. However, these models have only scarcely been used in the context of PSW (for example [ 8 , 10 – 13 ]). Various estimators of the parameters of MSMs have been proposed.…”

Section: Introductionmentioning

confidence: 99%

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Longitudinal plasmode algorithms to evaluate statistical methods in realistic scenarios: an illustration applied to occupational epidemiology

Souli,

Trudel,

Diop

et al. 2023

BMC Med Res Methodol

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Introduction Plasmode simulations are a type of simulations that use real data to determine the synthetic data-generating equations. Such simulations thus allow evaluating statistical methods under realistic conditions. As far as we know, no plasmode algorithm has been proposed for simulating longitudinal data. In this paper, we propose a longitudinal plasmode framework to generate realistic data with both a time-varying exposure and time-varying covariates. This work was motivated by the objective of comparing different methods for estimating the causal effect of a cumulative exposure to psychosocial stressors at work over time. Methods We developed two longitudinal plasmode algorithms: a parametric and a nonparametric algorithms. Data from the PROspective Québec (PROQ) Study on Work and Health were used as an input to generate data with the proposed plasmode algorithms. We evaluated the performance of multiple estimators of the parameters of marginal structural models (MSMs): inverse probability of treatment weighting, g-computation and targeted maximum likelihood estimation. These estimators were also compared to standard regression approaches with either adjustment for baseline covariates only or with adjustment for both baseline and time-varying covariates. Results Standard regression methods were susceptible to yield biased estimates with confidence intervals having coverage probability lower than their nominal level. The bias was much lower and coverage of confidence intervals was much closer to the nominal level when considering MSMs. Among MSM estimators, g-computation overall produced the best results relative to bias, root mean squared error and coverage of confidence intervals. No method produced unbiased estimates with adequate coverage for all parameters in the more realistic nonparametric plasmode simulation. Conclusion The proposed longitudinal plasmode algorithms can be important methodological tools for evaluating and comparing analytical methods in realistic simulation scenarios. To facilitate the use of these algorithms, we provide R functions on GitHub. We also recommend using MSMs when estimating the effect of cumulative exposure to psychosocial stressors at work.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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Longitudinal plasmode algorithms to evaluate statistical methods in realistic scenarios: an illustration applied to occupational epidemiology

Souli,

Trudel,

Diop

et al. 2023

BMC Med Res Methodol

Self Cite

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“…50 • "Stabilization" of the weights is not always acceptable (eg, dynamic-regime marginal structural models [72][73][74] ). • It is always important to check the fits of exposure probability models (eg, checking calibration or modeldiagnostic measures 78 and weight distributions 50 ) and marginal structural models (eg, comparing the estimating equation-based quasi-likelihood information criterion with that for less restricted models 79 or testing equivalence between asymptotic values of parameter estimates obtained through different weighting options 80 ). There are other practical concerns in real data analysis.…”

Section: Future Directionsmentioning

confidence: 99%

Understanding Marginal Structural Models for Time-Varying Exposures: Pitfalls and Tips

Shinozaki

Suzuki

2020

Journal of Epidemiology

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Epidemiologists are increasingly encountering complex longitudinal data, in which exposures and their confounders vary during follow-up. When a prior exposure affects the confounders of the subsequent exposures, estimating the effects of the time-varying exposures requires special statistical techniques, possibly with structural (ie, counterfactual) models for targeted effects, even if all confounders are accurately measured. Among the methods used to estimate such effects, which can be cast as a marginal structural model in a straightforward way, one popular approach is inverse probability weighting. Despite the seemingly intuitive theory and easy-to-implement software, misunderstandings (or "pitfalls") remain. For example, one may mistakenly equate marginal structural models with inverse probability weighting, failing to distinguish a marginal structural model encoding the causal parameters of interest from a nuisance model for exposure probability, and thereby failing to separate the problems of variable selection and model specification for these distinct models. Assuming the causal parameters of interest are identified given the study design and measurements, we provide a step-by-step illustration of generalized computation of standardization (called the gformula) and inverse probability weighting, as well as the specification of marginal structural models, particularly for time-varying exposures. We use a novel hypothetical example, which allows us access to typically hidden potential outcomes. This illustration provides steppingstones (or "tips") to understand more concretely the estimation of the effects of complex time-varying exposures.

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Evaluation and comparison of covariate balance metrics in studies with time‐dependent confounding

Adenyo,

Guertin,

Candas

et al. 2024

Statistics in Medicine

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Marginal structural models have been increasingly used by analysts in recent years to account for confounding bias in studies with time‐varying treatments. The parameters of these models are often estimated using inverse probability of treatment weighting. To ensure that the estimated weights adequately control confounding, it is possible to check for residual imbalance between treatment groups in the weighted data. Several balance metrics have been developed and compared in the cross‐sectional case but have not yet been evaluated and compared in longitudinal studies with time‐varying treatment. We have first extended the definition of several balance metrics to the case of a time‐varying treatment, with or without censoring. We then compared the performance of these balance metrics in a simulation study by assessing the strength of the association between their estimated level of imbalance and bias. We found that the Mahalanobis balance performed best. Finally, the method was illustrated for estimating the cumulative effect of statins exposure over one year on the risk of cardiovascular disease or death in people aged 65 and over in population‐wide administrative data. This illustration confirms the feasibility of employing our proposed metrics in large databases with multiple time‐points.

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A test for the correct specification of marginal structural models

Cited by 3 publications

References 26 publications

Longitudinal plasmode algorithms to evaluate statistical methods in realistic scenarios: an illustration applied to occupational epidemiology

Longitudinal plasmode algorithms to evaluate statistical methods in realistic scenarios: an illustration applied to occupational epidemiology

Understanding Marginal Structural Models for Time-Varying Exposures: Pitfalls and Tips

Evaluation and comparison of covariate balance metrics in studies with time‐dependent confounding

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