Canonical Causal Diagrams to Guide the Treatment of Missing Data in Epidemiologic Studies

Moreno‐Betancur, Margarita; Lee, Katherine J.; Leacy, Finbarr P.; White, Ian R.; Simpson, Julie A.

doi:10.1093/aje/kwy173

Cited by 48 publications

(82 citation statements)

References 34 publications

(46 reference statements)

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“…67 Some other extensions of DAGs include selection diagrams 68 and missingness graphs. 69 As addressed in this article, there are many pitfalls to avoid when using DAGs. Indeed, DAGs are not panacea, and there is no magic bullet for causal inference.…”

Section: Future Directionsmentioning

confidence: 99%

Causal Diagrams: Pitfalls and Tips

Suzuki

Shinozaki

Yamamoto

2020

Journal of Epidemiology

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Graphical models are useful tools in causal inference, and causal directed acyclic graphs (DAGs) are used extensively to determine the variables for which it is sufficient to control for confounding to estimate causal effects. We discuss the following ten pitfalls and tips that are easily overlooked when using DAGs: 1) Each node on DAGs corresponds to a random variable and not its realized values; 2) The presence or absence of arrows in DAGs corresponds to the presence or absence of individual causal effect in the population; 3) "Non-manipulable" variables and their arrows should be drawn with care; 4) It is preferable to draw DAGs for the total population, rather than for the exposed or unexposed groups; 5) DAGs are primarily useful to examine the presence of confounding in distribution in the notion of confounding in expectation; 6) Although DAGs provide qualitative differences of causal structures, they cannot describe details of how to adjust for confounding; 7) DAGs can be used to illustrate the consequences of matching and the appropriate handling of matched variables in cohort and case-control studies; 8) When explicitly accounting for temporal order in DAGs, it is necessary to use separate nodes for each timing; 9) In certain cases, DAGs with signed edges can be used in drawing conclusions about the direction of bias; and 10) DAGs can be (and should be) used to describe not only confounding bias but also other forms of bias. We also discuss recent developments of graphical models and their future directions.

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Section: Future Directionsmentioning

confidence: 99%

Causal Diagrams: Pitfalls and Tips

Suzuki

Shinozaki

Yamamoto

2020

Journal of Epidemiology

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“…Finally, our simulations considered only MCAR and MAR missingness mechanisms and the MI methods evaluated in our simulations are only guaranteed to produce unbiased estimates under MAR. In practice it is possible that the data are missing not at random (MNAR), although this does not preclude unbiased estimation from the approaches considered [63]. Future research should examine the performance of these methods under MNAR mechanisms.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of approaches for multiple imputation of three-level data

Wijesuriya

Moreno‐Betancur

Lee

2020

BMC Med Res Methodol

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Background: Three-level data arising from repeated measures on individuals who are clustered within larger units are common in health research studies. Missing data are prominent in such longitudinal studies and multiple imputation (MI) is a popular approach for handling missing data. Extensions of joint modelling and fully conditional specification MI approaches based on multilevel models have been developed for imputing three-level data. Alternatively, it is possible to extend single-and two-level MI methods to impute three-level data using dummy indicators and/or by analysing repeated measures in wide format. However, most implementations, evaluations and applications of these approaches focus on the context of incomplete two-level data. It is currently unclear which approach is preferable for imputing three-level data. Methods: In this study, we investigated the performance of various MI methods for imputing three-level incomplete data when the target analysis model is a three-level random effects model with a random intercept for each level. The MI methods were evaluated via simulations and illustrated using empirical data, based on a case study from the Childhood to Adolescence Transition Study, a longitudinal cohort collecting repeated measures on students who were clustered within schools. In our simulations we considered a number of different scenarios covering a range of different missing data mechanisms, missing data proportions and strengths of level-2 and level-3 intra-cluster correlations. Results: We found that all of the approaches considered produced valid inferences about both the regression coefficient corresponding to the exposure of interest and the variance components under the various scenarios within the simulation study. In the case study, all approaches led to similar results. Conclusion: Researchers may use extensions to the single-and two-level approaches, or the three-level approaches, to adequately handle incomplete three-level data. The two-level MI approaches with dummy indicator extension or the MI approaches based on three-level models will be required in certain circumstances such as when there are longitudinal data measured at irregular time intervals. However, the single-and two-level approaches with the DI extension should be used with caution as the DI approach has been shown to produce biased parameter estimates in certain scenarios.

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“…28 Even if the covariates are missing not at random (MNAR), it has been shown, theoretically and by simulation, that listwise deletion can give unbiased estimates for all or some of the parameters. 28,36–38 Alternatively, the next closest L or R with a non-missing covariate value could be selected, but this choice will increase uncertainty about the unobserved infection time 4 and lead to slightly larger standard errors (see Table S1). Again, the nature of the missing data will need to be carefully reviewed by the researcher.…”

Section: Discussionmentioning

confidence: 99%

Estimating trends in the incidence rate with interval censored data and time-dependent covariates

Vandormael

Tanser

Cuadros

et al. 2019

Stat Methods Med Res

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We propose a multiple imputation method for estimating the incidence rate with interval censored data and time-dependent (and/or time-independent) covariates. The method has two stages. First, we use a semi-parametric G-transformation model to estimate the cumulative baseline hazard function and the effects of the time-dependent (and/or time-independent covariates) on the interval censored infection times. Second, we derive the participant's unique cumulative distribution function and impute infection times conditional on the covariate values. To assess performance, we simulated infection times from a Cox proportional hazards model and induced interval censoring by varying the testing rate, e.g., participants test 100%, 75%, 50% of the time, etc. We then compared the incidence rate estimates from our G-imputation approach with single random-point and mid-point imputation. By comparison, our G-imputation approach gave more accurate incidence rate estimates and appropriate standard errors for models with time-independent covariates only, time-dependent covariates only, and a mixture of time-dependent and time-independent covariates across various testing rates. We demonstrate, for the first time, a multiple imputation approach for incidence rate estimation with interval censored data and time-dependent (and/or time-independent) covariates.

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Canonical Causal Diagrams to Guide the Treatment of Missing Data in Epidemiologic Studies

Cited by 48 publications

References 34 publications

Causal Diagrams: Pitfalls and Tips

Causal Diagrams: Pitfalls and Tips

Evaluation of approaches for multiple imputation of three-level data

Estimating trends in the incidence rate with interval censored data and time-dependent covariates

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