ArticleBiostatistics (Oxford, England)2015
Sensitivity analyses for parametric causal mediation effect estimation.
Article in Biostatistics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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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.
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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
21 citing papers in PubMed, 37 citations in OpenAlex.
- Do baby teeth really matter? Changing parental perception and increasing dental care utilization for young children.Contemporary clinical trials · 2017Trial
- Methods for the Analysis of Multiple Epigenomic Mediators in Environmental Epidemiology.Current environmental health reports · 2024Review
- The impact of omitting confounders in parallel process latent growth curve mediation models: Three sensitivity analysis approaches.Structural equation modeling : a multidisciplinary journal · 2024Article
- Causal mediation and sensitivity analysis for mixed-scale data.Statistical methods in medical research · 2023Article
- A Review of High-Dimensional Mediation Analyses in DNA Methylation Studies.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Acute exercise effects on inhibitory control and the pupillary response in young adults.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2021Article
- Relationships between enriching early life experiences and cognitive function later in life are mediated by educational attainment.Journal of cognitive enhancement : towards the integration of theory and practice · 2021Article
- Latent class mediator for multiple indicators of mediation.Statistics in medicine · 2021Article
- Sensitivity Analysis in Nonrandomized Longitudinal Mediation Analysis.Frontiers in psychology · 2021Article
- Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges.Computational and structural biotechnology journal · 2021Review
- Socioeconomic status and survival after stroke - using mediation and sensitivity analyses to assess the effect of stroke severity and unmeasured confounding.BMC public health · 2020Article
- Continuous-time causal mediation analysis.Statistics in medicine · 2019Article
- Maximum Likelihood Analysis of Linear Mediation Models with Treatment-Mediator Interaction.Psychometrika · 2019Article
- Generalized causal mediation and path analysis: Extensions and practical considerations.Statistical methods in medical research · 2019Article
- Sensitivity analysis for mistakenly adjusting for mediators in estimating total effect in observational studies.BMJ open · 2017Article
- Confounding in statistical mediation analysis: What it is and how to address it.Journal of counseling psychology · 2017Article
- Causal Mediation Analysis for the Cox Proportional Hazards Model with a Smooth Baseline Hazard Estimator.Journal of the Royal Statistical Society. Series C, Applied statistics · 2017Article
- Article
- Causal mediation analysis with a latent mediator.Biometrical journal. Biometrische Zeitschrift · 2016Article
- Assessing natural direct and indirect effects for a continuous exposure and a dichotomous outcome.Journal of statistical theory and practice · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Causal mediation analysis uses a potential outcomes framework to estimate the direct effect of an exposure on an outcome and its indirect effect through an intermediate variable (or mediator). Causal interpretations of these effects typically rely on sequential ignorability. Because this assumption is not empirically testable, it is important to conduct sensitivity analyses. Sensitivity analyses so far offered for this situation have either focused on the case where the outcome follows a linear model or involve nonparametric or semiparametric models. We propose alternative approaches that are suitable for responses following generalized linear models. The first approach uses a Gaussian copula model involving latent versions of the mediator and the final outcome. The second approach uses a so-called hybrid causal-observational model that extends the association model for the final outcome, providing a novel sensitivity parameter. These models, while still assuming a randomized exposure, allow for unobserved (as well as observed) mediator-outcome confounders that are not affected by exposure. The methods are applied to data from a study of the effect of mother education on dental caries in adolescence.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.