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The Implications of Using Lagged and Baseline Exposure Terms in Longitudinal Causal and Regression Models.

ABSTRACT: There are now many published applications of causal (structural) models for estimating effects of time-varying exposures in the presence of confounding by earlier exposures and confounders affected by earlier exposures. Results from these models can be highly sensitive to inclusion of lagged and baseline exposure terms for different visits. This sensitivity is often overlooked in practice; moreover, results from these models are not directly comparable to results from conventional time-dependent regression models, because the latter do not estimate the same causal parameter even when no bias is present. We thus explore the implications of including lagged and baseline exposure terms in causal and regression models, using a public data set (Caerphilly Heart Disease Study in the United Kingdom, 1979-1998) relating smoking to cardiovascular outcomes.

SUBMITTER: Mansournia MA

PROVIDER: S-EPMC8045477 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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The Implications of Using Lagged and Baseline Exposure Terms in Longitudinal Causal and Regression Models.

Mansournia Mohammad Ali MA Naimi Ashley I AI Greenland Sander S

American journal of epidemiology 20190401 4

There are now many published applications of causal (structural) models for estimating effects of time-varying exposures in the presence of confounding by earlier exposures and confounders affected by earlier exposures. Results from these models can be highly sensitive to inclusion of lagged and baseline exposure terms for different visits. This sensitivity is often overlooked in practice; moreover, results from these models are not directly comparable to results from conventional time-dependent ...[more]

PMID: 30576419

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Project description:ImportanceUnderstanding the longitudinal, bidirectional associations between disturbed sleep and depression in childhood and adolescence is crucial for the development of prevention and intervention programs.ObjectiveTo test for bidirectional associations and cascade processes between disturbed sleep and depressive symptoms covering both childhood and adolescence and to test for the moderating processes of sex and pubertal status in adolescence.Design, setting, and participantsA prospective cohort study using the Québec Longitudinal Study of Child Development (QLSCD; 1997-ongoing). QLSCD's objective is to identify early childhood factors associated with long-term psychosocial and academic adjustment. Data were collected across 8 waves between ages 5 years (2003) and 17 years (2015). Associations were tested through cross-lagged models in childhood (5, 7, and 8 years), and in adolescence (10, 12, 13, 15, and 17 years). Data were analyzed from February to October 2021.Main outcomes and measuresPrimary outcomes were disturbed sleep and depressive symptoms. Disturbed sleep was parent-reported and included sleep duration, time awake in bed, daytime sleepiness, sleep talking, sleepwalking, night terrors, and nightmares. Depressive symptoms were parent-reported in childhood (Child Behavior Checklist and Revised Ontario Child Health Study Scales), and self-reported in adolescence (Mental Health and Social Inadaptation Assessment for Adolescents).ResultsData on 1689 children (852 female [50.4%]) and 1113 adolescents (595 female [53.5%]) were included in the analyses. In childhood, significant bidirectional associations between depressive symptoms and disturbed sleep at all time points were found, indicating cascade processes (range β = 0.07; 95% CI, 0.02-012 to β = 0.15; 95% CI, 0.10-0.19). In adolescence, significant bidirectional associations from depressive symptoms to disturbed sleep (β = 0.09; 95% CI, 0.04-0.14) and vice versa (β = 0.10; 95% CI, 0.04-0.16) between 10 and 12 years were found. Between 12 and 13 years, depressive symptoms were modestly associated with disturbed sleep (β = 0.05; 95% CI, 0.001-0.10) but the reverse association was not significant. Cross-lagged estimates were nonsignificant after 13 years. The associations did not vary as a function of either sex or puberty-by-sex.Conclusions and relevanceThese findings suggest that disturbed sleep is associated with the consolidation of depressive symptoms starting in childhood, which, in turn, is associated with ongoing sleep problems. It is possible that timely and appropriate interventions for incipient disturbed sleep and depression prevent spiraling effects on both domains.

Project description:BackgroundConfounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method.MethodsFour causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM) were compared. The "do-calculus" was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies.ResultsAdjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal strategy was to adjust for the parent nodes of outcome, which obtained the highest precision.ConclusionsAll adjustment strategies through logistic regression were biased for causal effect estimation, while IPW-based-MSM could always obtain unbiased estimation when the adjusted set satisfied G-admissibility. Thus, IPW-based-MSM was recommended to adjust for confounders set.

Dataset Information

The Implications of Using Lagged and Baseline Exposure Terms in Longitudinal Causal and Regression Models.

Publications

The Implications of Using Lagged and Baseline Exposure Terms in Longitudinal Causal and Regression Models.

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