ArticleEpidemiology (Cambridge, Mass.)2016
Diagnostics for Confounding of Time-varying and Other Joint Exposures.
Article in Epidemiology (Cambridge, Mass.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
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- De-Mystifying the Clone-Censor-Weight Method for Causal Research Using Observational Data: A Primer for Cancer Researchers.Cancer medicine · 2024Article
- Evaluating Effects of Multilevel Interventions on Disparity in Health and Healthcare Decisions.Prevention science : the official journal of the Society for Prevention Research · 2024Article
- Causal inference using observational intensive care unit data: a scoping review and recommendations for future practice.NPJ digital medicine · 2023Article
- Causal feature selection using a knowledge graph combining structured knowledge from the biomedical literature and ontologies: A use case studying depression as a risk factor for Alzheimer's disease.Journal of biomedical informatics · 2023Article
- Data Challenges for Externally Controlled Trials: Viewpoint.Journal of medical Internet research · 2023Article
- Association of age with the timing of acute spine surgery-effects on neurological outcome after traumatic spinal cord injury.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Article
- Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance.Journal of biomedical informatics · 2021Article
- Methodological Challenges and Proposed Solutions for Evaluating Opioid Policy Effectiveness.Health services & outcomes research methodology · 2021Article
- DATA VISUALIZATION TOOLS FOR CONFOUNDING AND SELECTION BIAS IN LONGITUDINAL DATA: THE %LENGTHEN, %BALANCE, AND %MAKEPLOT (CONFOUNDR) MACROS AND R PACKAGE.American journal of epidemiology · 2020Article
- The state of the science in opioid policy research.Drug and alcohol dependence · 2020Review
- Diagnosing Covariate Balance Across Levels of Right-Censoring Before and After Application of Inverse-Probability-of-Censoring Weights.American journal of epidemiology · 2019Article
- Survival Bias in Mendelian Randomization Studies: A Threat to Causal Inference.Epidemiology (Cambridge, Mass.) · 2019Article
- Propensity Scores in Pharmacoepidemiology: Beyond the Horizon.Current epidemiology reports · 2017Article
- Causal inference and longitudinal data: a case study of religion and mental health.Social psychiatry and psychiatric epidemiology · 2016Review
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
The effects of joint exposures (or exposure regimes) include those of adhering to assigned treatment versus placebo in a randomized controlled trial, duration of exposure in a cohort study, interactions between exposures, and direct effects of exposure, among others. Unlike the setting of a single point exposure (e.g., propensity score matching), there are few tools to describe confounding for joint exposures or how well a method resolves it. Investigators need tools that describe confounding in ways that are conceptually grounded and intuitive for those who read, review, and use applied research to guide policy. We revisit the implications of exchangeability conditions that hold in sequentially randomized trials, and the bias structure that motivates the use of g-methods, such as marginal structural models. From these, we develop covariate balance diagnostics for joint exposures that can (1) describe time-varying confounding, (2) assess whether covariates are predicted by prior exposures given their past, the indication for g-methods, and (3) describe residual confounding after inverse probability weighting. For each diagnostic, we present time-specific metrics that encompass a wide class of joint exposures, including regimes of multivariate time-varying exposures in censored data, with multivariate point exposures as a special case. We outline how to estimate these directly or with regression and how to average them over person-time. Using a simulated example, we show how these metrics can be presented graphically. This conceptually grounded framework can potentially aid the transparent design, analysis, and reporting of studies that examine joint exposures. We provide easy-to-use tools to implement it.
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Registered trials
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