Mediation analysis <i>via</i> potential outcomes models

Albert, Jeffrey M.

doi:10.1002/sim.3016

Cited by 122 publications

(116 citation statements)

References 46 publications

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“…Mediation analysis assesses an intermediate variable as a mediator in the pathway between a risk factor and an outcome to estimate the extent to which the effect of the risk factor occurs through the mediator. [17][18][19][20] This method has been used in various research fields including studies of effects of genetic variants. [21][22][23][24] A mediation analysis in a model with CAD as the dependent variable, non-O versus O type as the independent variable, and LDLc level as the mediator variable, with age, sex, and hospitals as covariates, indicated that there was a 4.9% difference in CAD susceptibility between non-O type and O type (P=4.0×10 −5 ) and that 10% of this difference was mediated by increased LDLc level (0.5% of the difference in CAD susceptibility was mediated by LDLc level; P=7.8×10 −4 for mediation effect; Table 5).…”

Section: Discussionmentioning

confidence: 99%

Analysis of Circulating Cholesterol Levels as a Mediator of an Association Between ABO Blood Group and Coronary Heart Disease

Chen

et al. 2014

Circ Cardiovasc Genet

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Section: Discussionmentioning

confidence: 99%

Analysis of Circulating Cholesterol Levels as a Mediator of an Association Between ABO Blood Group and Coronary Heart Disease

Chen

et al. 2014

Circ Cardiovasc Genet

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“…However, the conventional mediation analysis largely based on linear models may not fit many dental outcomes, which are frequently count or zero-inflated count data. This article illustrates a useful new extension of a method based on the potential outcome framework (Albert 2008;Imai et al 2010) for dental data (Cheng et al unpublished data, 2014) to further understand the mechanism of the CAMBRA intervention.…”

Section: Discussionmentioning

confidence: 99%

Understanding Treatment Effect Mechanisms of the CAMBRA Randomized Trial in Reducing Caries Increment

et al. 2014

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The Caries Management By Risk Assessment (CAMBRA) randomized controlled trial showed that an intervention featuring combined antibacterial and fluoride therapy significantly reduced bacterial load and suggested reduced caries increment in adults with 1 to 7 baseline cavitated teeth. While trial results speak to the overall effectiveness of an intervention, insight can be gained from understanding the mechanism by which an intervention acts on putative intermediate variables (mediators) to affect outcomes. This study conducted mediation analyses on 109 participants who completed the trial to understand whether the intervention reduced caries increment through its action on potential mediators (oral bacterial load, fluoride levels, and overall caries risk based on the composite of bacterial challenge and salivary fluoride) between the intervention and dental outcomes. The primary outcome was the increment from baseline in decayed, missing, and filled permanent surfaces (ΔDMFS) 24 mo after completing restorations for baseline cavitated lesions. Analyses adjusted for baseline overall risk, bacterial challenge, and fluoride values under a potential outcome framework using generalized linear models. Overall, the CAMBRA intervention was suggestive in reducing the 24-mo DMFS increment (reduction in ΔDMFS: -0.96; 95% confidence interval [CI]: -2.01 to 0.08; P = 0.07); the intervention significantly reduced the 12-mo overall risk (reduction in overall risk: -19%; 95% CI, -7 to -41%;], P = 0.005). Individual mediators, salivary log 10 mutans streptococci, log 10 lactobacilli, and fluoride level, did not represent statistically significant pathways alone through which the intervention effect was transmitted. However, 36% of the intervention effect on 24-mo DMFS increment was through a mediation effect on 12-mo overall risk (P = 0.03). These findings suggest a greater intervention effect carried through the combined action on multiple aspects of the caries process rather than through any single factor. In addition, a substantial portion of the total effect of the CAMBRA intervention may have operated through unanticipated or unmeasured pathways not included among the potential mediators studied.

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“…X1 and X2), then at an individual level we can fit an equivalent IV model through the use of 2SLS. 14,51 In Stata, for example, we could use the following ivregress command:…”

Section: Treatment Effect Mediationmentioning

confidence: 99%

Evaluation and validation of social and psychological markers in randomised trials of complex interventions in mental health: a methodological research programme

Dunn

Emsley

Liu

et al. 2015

Health Technol Assess

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BackgroundThe development of the capability and capacity to evaluate the outcomes of trials of complex interventions is a key priority of the National Institute for Health Research (NIHR) and the Medical Research Council (MRC). The evaluation of complex treatment programmes for mental illness (e.g. cognitive–behavioural therapy for depression or psychosis) not only is a vital component of this research in its own right but also provides a well-established model for the evaluation of complex interventions in other clinical areas. In the context of efficacy and mechanism evaluation (EME) there is a particular need for robust methods for making valid causal inference in explanatory analyses of the mechanisms of treatment-induced change in clinical outcomes in randomised clinical trials.ObjectivesThe key objective was to produce statistical methods to enable trial investigators to make valid causal inferences about the mechanisms of treatment-induced change in these clinical outcomes. The primary objective of this report is to disseminate this methodology, aiming specifically at trial practitioners.MethodsThe three components of the research were (1) the extension of instrumental variable (IV) methods to latent growth curve models and growth mixture models for repeated-measures data; (2) the development of designs and regression methods for parallel trials; and (3) the evaluation of the sensitivity/robustness of findings to the assumptions necessary for model identifiability. We illustrate our methods with applications from psychological and psychosocial intervention trials, keeping the technical details to a minimum, leaving the reporting of the more theoretical and mathematically demanding results for publication in appropriate specialist journals.ResultsWe show how to estimate treatment effects and introduce methods for EME. We explain the use of IV methods and principal stratification to evaluate the role of putative treatment effect mediators and therapeutic process measures. These results are extended to the analysis of longitudinal data structures. We consider the design of EME trials. We focus on designs to create convincing IVs, bearing in mind assumptions needed to attain model identifiability. A key area of application that has become apparent during this work is the potential role of treatment moderators (predictive markers) in the evaluation of treatment effect mechanisms for personalised therapies (stratified medicine). We consider the role of targeted therapies and multiarm trials and the use of parallel trials to help elucidate the evaluation of mediators working in parallel.ConclusionsIn order to demonstrate both efficacy and mechanism, it is necessary to (1) demonstrate a treatment effect on the primary (clinical) outcome, (2) demonstrate a treatment effect on the putative mediator (mechanism) and (3) demonstrate a causal effect from the mediator to the outcome. Appropriate regression models should be applied for (3) or alternative IV procedures, which account for unmeasured confounding, provided that a valid instrument can be identified. Stratified medicine may provide a setting where such instruments can be designed into the trial. This work could be extended by considering improved trial designs, sample size considerations and measurement properties.FundingThe project presents independent research funded under the MRC–NIHR Methodology Research Programme (grant reference G0900678).

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Mediation analysis via potential outcomes models

Cited by 122 publications

References 46 publications

Analysis of Circulating Cholesterol Levels as a Mediator of an Association Between ABO Blood Group and Coronary Heart Disease

Analysis of Circulating Cholesterol Levels as a Mediator of an Association Between ABO Blood Group and Coronary Heart Disease

Understanding Treatment Effect Mechanisms of the CAMBRA Randomized Trial in Reducing Caries Increment

Evaluation and validation of social and psychological markers in randomised trials of complex interventions in mental health: a methodological research programme

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