Please identify a quantitative research article evaluating mediation in your field and provide the citation.
Jackson JW (1,2), VanderWeele TJ(2,3), Blacker D(2,4), Schneeweiss S(1,2). Mediators of first- versus second-generation antipsychotic- related mortality in older adults. Epidemiology. 2015 September; 26(5): 700–709.
What is the primary discipline of the authors?
(1)Brigham and Women’s Hospital and Harvard Medical School, Division of Pharmacoepidemiology, Department of Medicine, Boston, Massachusetts, 02120
(2)Harvard School of Public Health, Department of Epidemiology, Boston, Massachusetts, 02115 (3)Harvard School of Public Health, Department of Biostatistics, Boston, Massachusetts, 02115
(4)Massachusetts General Hospital & Harvard Medical School, Gerontology Research Unit, Department of Psychiatry, Boston, MA 02114
What is the exposure of interest?
New user of first generation antipsychotic versus new user of second generation antipsychotic (binary). In other words, the authors defined exposure as a binary variable comparing first-generation to second-generation antipsychotic initiation (reference).
What is the outcome of interest?
Mortality with 180 days (binary)
What is the hypothesized mediator of interest and how is it measured?
Mediators selected based on previous literature. Authors compared the separate and combined contributions of medical events previously studied in the literature: stroke, ventricular arrhythmia, myocardial infarction, venous thromboembolism, pneumonia, bacterial infection (other than pneumonia), and hip fracture. These were defined as binary variables indicating their occurrence between the index prescription date (inclusive) and the end of follow up (180 days) or death, and were classified using diagnostic and procedure codes based on the International Classification of Diseases. Death during follow-up was defined as a binary variable.
Describe the modeling approach and briefly report the estimated total, direct, and indirect effects (if these are reported).
Using causal mediation analysis, the authors sought to decompose the total effect of antipsychotic- type (exposure) on mortality (outcome) into natural direct and indirect effects through various individual medical events (mediators) on the risk ratio scale, and the proportion of the total effect mediated by each medical event on the risk difference scale.
Using logistic regression, the authors estimated the crude risk at 180 days and the covariate-adjusted relative risk comparing first- and second- generation antipsychotic use. Within groups defined by antipsychotic type, the authors used g- computation (i.e. model-based standardization with 95% confidence intervals (CI) obtained. The authors then used a regression-based approach for causal mediation analysis involving a binary exposure, mediator, and outcome to estimate crude and adjusted direct and indirect effects of antipsychotic-type on 180-day mortality through each medical event on the risk-ratio scale. For each medical event, the authors estimated two models: (1) a logistic regression model for the mediator’s occurrence conditional on antipsychotic type and main effects for all baseline covariates, and (2) a Poisson regression model for mortality conditional on antipsychotic type, the mediator’s occurrence, a product term for their interaction, and the same baseline covariates. The parameter estimates from these models were combined to estimate the direct and indirect effect risk ratios using closed form estimators, which were then used to compute the proportion mediated on the risk difference scale.
In bias analyses the proportion mediated ranged from 6% to 16% for stroke, 3% to 9% for ventricular arrhythmia, 3% to 11% for myocardial infarction, 0% venous thromboembolism, 3% to 9% for pneumonia, 0% to 1% for other bacterial infection, and 1% to 3% for hip fracture.
The crude and covariate-adjusted analyses accounting for exposure-mediator interaction were similar to those ignoring such interaction. Crude indirect effects were close to the null and were lower after covariate adjustment. Although covariate adjustment attenuated the total effect from RR=1.23 (95%CI 1.16 to 1.32) to RR=1.14 (95%CI 1.06 to 1.22), the direct effects in crude and adjusted analyses were similar to the total effect in both cases (the proportion mediated was highest for stroke at 5%).
If the direct effect is reported, would you describe this as a natural direct effect, a controlled direct effect, or something else?
I would describe the direct effect reported as a natural direct effect.
Do you think there is potential measurement error in the mediator and how would that affect the results?
To avoid false positive medical events during follow-up, the authors used restrictive classification algorithms with high positive-predictive values. False negatives may occur more often among those who die, especially with events where pre-hospital mortality is common (e.g. ventricular arrhythmia) or do-not-resuscitate orders dictate whether aggressive treatment is pursued (e.g. pneumonia). To avoid misclassification, the authors performed a bias analysis to explore how results would change under various scenarios of non- differential and differential misclassification.
Do you think there are unmeasured confounders of the mediator-outcome association and how would that affect the results of the mediation analysis?
The authors acknowledged that the approach allows for possible exposure-mediator interactions, it also requires that all confounders of the exposure-outcome, exposure-mediator, and mediator-outcome relationships are measured and adjusted for, and prohibits the existence of any mediator- outcome confounder that is itself affected by exposure.
The authors stated how residual confounding may have influenced their results. Delirium is a strong predictor of mortality in older adults and when it is detected, it is frequently treated with haloperidol (a widely used first-generation antipsychotic) which could lead to exposure- mediator or exposure-outcome confounding. Delirium is also poorly captured in claims data, so residual confounding at baseline could bias the total and indirect effects upwards. Moreover, unmeasured behavioral risk factors (e.g. smoking, physical activity) could also bias the indirect effects for several mediators through mediator- outcome confounding. The authors also stated how their results may be subject to a subtle form of length bias, where associations between antipsychotic-type and medical events are under-estimated
Do you have any critiques of the paper?
This paper was relatively well written, and the authors clearly addressed any misclassification or confounding that may have been present during the analysis.