著者

Etsuji Suzuki Tomohiro Shinozaki Eiji Yamamoto

出版者

Japan Epidemiological Association

雑誌

Journal of Epidemiology (ISSN:09175040)

巻号頁・発行日

pp.JE20190192, (Released:2020-02-01)

参考文献数

70

被引用文献数

9 37

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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.

著者

Tomohiro Shinozaki Etsuji Suzuki

出版者

Japan Epidemiological Association

雑誌

Journal of Epidemiology (ISSN:09175040)

巻号頁・発行日

vol.30, no.9, pp.377-389, 2020-09-05 (Released:2020-09-05)

参考文献数

84

被引用文献数

13 20

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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 g-formula) 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.

著者

Tomohiro Shinozaki Etsuji Suzuki

出版者

Japan Epidemiological Association

雑誌

Journal of Epidemiology (ISSN:09175040)

巻号頁・発行日

pp.JE20200226, (Released:2020-07-18)

参考文献数

84

被引用文献数

20

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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 (i.e., 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 g-formula) 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.

著者

Etsuji Suzuki Michio Yamamoto Eiji Yamamoto

出版者

Japan Epidemiological Association

雑誌

Journal of Epidemiology (ISSN:09175040)

巻号頁・発行日

pp.JE20210352, (Released:2022-01-22)

参考文献数

26

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Background: The counterfactual definition of confounding is often explained in the context of exchangeability between the exposed and unexposed groups. One recent approach is to examine whether the measures of association (e.g., associational risk difference) are exchangeable when exposure status is flipped in the population of interest. We discuss the meaning and utility of this approach, showing their relationships with the concept of confounding in the counterfactual framework.Methods: Three hypothetical cohort studies are used, in which the target population is the total population. After providing an overview of the notions of confounding in distribution and in measure, we discuss the approach from the perspective of exchangeability of measures of association (e.g., factual associational risk difference vs. counterfactual associational risk difference).Results: In general, if the measures of association are non-exchangeable when exposure status is flipped, confounding in distribution is always present, although confounding in measure may or may not be present. Even if the measures of association are exchangeable when exposure status is flipped, there could be confounding both in distribution and in measure. When we use risk difference or risk ratio as a measure of interest and the exposure prevalence in the population is 0.5, testing the exchangeability of measures of association is equivalent to testing the absence of confounding in the corresponding measures.Conclusions: The approach based on exchangeability of measures of association essentially does not provide a definition of confounding in the counterfactual framework. Subtly differing notions of confounding should be distinguished carefully.

著者

Etsuji Suzuki Michio Yamamoto Eiji Yamamoto

出版者

Japan Epidemiological Association

雑誌

Journal of Epidemiology (ISSN:09175040)

巻号頁・発行日

vol.33, no.8, pp.385-389, 2023-08-05 (Released:2023-08-05)

参考文献数

26

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Background: The counterfactual definition of confounding is often explained in the context of exchangeability between the exposed and unexposed groups. One recent approach is to examine whether the measures of association (eg, associational risk difference) are exchangeable when exposure status is flipped in the population of interest. We discuss the meaning and utility of this approach, showing their relationships with the concept of confounding in the counterfactual framework.Methods: Three hypothetical cohort studies are used, in which the target population is the total population. After providing an overview of the notions of confounding in distribution and in measure, we discuss the approach from the perspective of exchangeability of measures of association (eg, factual associational risk difference vs counterfactual associational risk difference).Results: In general, if the measures of association are non-exchangeable when exposure status is flipped, confounding in distribution is always present, although confounding in measure may or may not be present. Even if the measures of association are exchangeable when exposure status is flipped, there could be confounding both in distribution and in measure. When we use risk difference or risk ratio as a measure of interest and the exposure prevalence in the population is 0.5, testing the exchangeability of measures of association is equivalent to testing the absence of confounding in the corresponding measures.Conclusion: The approach based on exchangeability of measures of association essentially does not provide a definition of confounding in the counterfactual framework. Subtly differing notions of confounding should be distinguished carefully.

著者

Toshiharu Mitsuhashi Etsuji Suzuki Soshi Takao Hiroyuki Doi

出版者

Japan Society for Occupational Health

雑誌

Journal of Occupational Health (ISSN:13419145)

巻号頁・発行日

vol.54, no.1, pp.25-33, 2012 (Released:2012-03-05)

参考文献数

43

被引用文献数

13

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Objectives: There has been a growing concern that maternal employment could have adverse or beneficial effects on children’s health. Although recent studies demonstrated that maternal employment was associated with a higher risk of childhood overweight, the evidence remains sparse in Asian countries. We sought to examine the relationship between maternal working hours and early childhood overweight in a rural town in Okayama Prefecture. Methods: In February 2008, questionnaires were sent to parents of all preschool children aged ≥3 yr in the town to assess maternal working status (working hours and form of employment), children’s body mass index, and potential confounders. Childhood overweight was defined following the age and sex-specific criteria of the International Obesity Task Force. Odds ratios (ORs) and 95% confidence intervals (CIs) for childhood overweight were estimated in a logistic regression. We used generalized estimating equations with an exchangeable correlation matrix, considering the correlation between siblings. Results: We analyzed 364 preschool children. Adjusting for each child’s characteristics (age, sex), mother’s characteristics (age, obesity, educational attainment, smoking status, and social participation), and family’s characteristics (number of siblings), children whose mothers work Conclusion: Short maternal working hours are associated with a lower odds of early childhood overweight.