Please identify a quantitative research article evaluating mediation in your field and provide the citation.
Lu, Y., Hajifathalian, K., Rimm, E. B., Ezzati, M., & Danaei, G. (2015). Mediators of the effect of body mass index on coronary heart disease: decomposing direct and indirect effects. Epidemiology, 26(2), 153-162.
What is the primary discipline of the authors?
Lu, Y: doctoral student at Harvard School of Public Health. Research focuses on mediation analysis of effects of overweight/obesity on cardiovascular diseases.
Hajifathalian, K, MD: Gastroenterologist in Iran. Visiting Scientist at Harvard. His research is focused on the global distribution of CHD risks by country, evaluating the effect of antihypertensive treatment on blood pressure trend in U.S., advanced mediation analysis of effects of obesity on cardiovascular outcomes, and updating the global estimates of distribution of metabolic risk factors of cardiovascular disease.
Rimm, Eric.B., ScD: Professor at Harvard. Studies modifiable lifestyle choices (e.g. diet and physical activity) in relation to cardiovascular disease as well as the translation of these findings into public health interventions that are effective for schoolchildren, adults and the food insecure.
Ezzati, M: Professor at Imperial College London. Does population health and environmental health with focus on preventable risk factors.
Danaei, Goodarz: Professor at Harvard School of Public Health. Estimates the effect of risk factors and preventive interventions on non-communicable disease incidence and mortality at the population level.
Draw a DAG representing the implicit or explicit causal model explored in this paper (you do not need to post your DAG, but we will try to discuss in class).
From the article:
(See DAG in article)
What is the exposure of interest? Body Mass Index
What is the outcome of interest? Coronary Heart Disease
What is the hypothesized mediator of interest and how is it measured?
|
Mediator |
Measurement method |
|
Blood pressure (systolic) |
Investigators pooled data from 9 cardiovascular cohort studies. In most, systolic blood pressure was measured 2-3 times at the arm, after a standard resting period. Values were averaged into one estimate. |
Other mediators of interest included serum cholesterol, blood glucose, fibrinogen and inflammatory biomarkers. For simplicity, I focused only on blood pressure as the mediator of interest.
Describe the modeling approach and briefly report the estimated total, direct, and indirect effects (if these are reported).
Modeling approach: they used the 2-stage regression method proposed by VanderWeele to estimate direct and indirect effects (VanderWeele TJ, Epidemiology. 2011) which has a number of assumptions in order to provide valid estimates of direct and indirect effects. These include having a rare outcome, no unmeasured confounding, and no model misspecification. The first is a linear regression model for blood pressure conditional on BMI and known and measured confounder(s). The second is a Cox proportional hazards regression model of the risk of CHD on BMI, blood pressure, and BMI-blood pressure interaction, and confounders.
The natural direct effects are then estimated using the coefficients from the two regressions.
Formula for calculating % of excess relative risk mediated: (HRTE – HRNDE)/(HRTE - 1)
HRTE: total effect hazard ratio = HRTE = HRNDE x HRNIE
NDE = natural direct effect
NIE = natural indirect effect
TE = total effect
Estimated total: Those in overweight category had 1.22 times the rate (hazard) of CHD compared to normal weight (HR 1.22 [1.14-1.3]); and the obese (BMI > 30) had 1.42 times the rate (hazard) of CHD (HR 1.42 [1.25-1.6]) compared to normal weight.
Direct: HR 1.16 [1.09-1.24] among the overweight, and HR 1.28 [1.15-1.43] among the obese.
Indirect: Blood pressure was the primary mediator of the overweight-CHD association explaining 28% of the excess relative risk among overweight, and 37% for obese; indirect-effect HR of 1.06 [1.03-1.08] for overweight, and HR of 1.13 [1.07-1.19] for obese.
If the direct effect is reported, would you describe this as a natural direct effect, a controlled direct effect, or something else?
Natural Direct effect. They aimed to decompose the total effect into direct and indirect effects. This cannot be done with controlled direct effects.
Do you think there is potential measurement error in the mediator and how would that affect the results?
Blood pressure was measured as an average of multiple measurements at the baseline visit. However, measuring resting blood pressure at one visit is unlikely to provide all of the true variance for each participant. If this measurement error is independent and non-differential with respect to true values of other measurements, this is likely to bias their results towards the null.
The authors actually took this into consideration, and in their sensitivity analyses, they assessed the impact of measurement error by calibrating the regression coefficients, with the assumption that baseline blood pressure only explained 65% of true inter-individual variability. When they took into account this presumed measurement error, they found the percentage of excess relative risk increased (for all mediators considered) from 47% to 69% for overweight, and from 52% to 73% for the obese.
Do you think there are unmeasured confounders of the mediator-outcome association and how would that affect the results of the mediation analysis?
Yes. Unmeasured confounders are likely, including genetic traits. In addition, there is likely residual confounding from not measuring dietary intake or physical activity very well. It is hard to predict how the unmeasured confounders would affect the results; it could bias the results in either direction depending on the causal relationships.
Do you have any critiques of the paper?
One important critique is that exposure (BMI), mediators and confounders were all measured at baseline. Although it’s hard to imagine a scenario where we may observe reverse causation from blood pressure to BMI, it is possible that someone’s blood pressure at time t minus x may have affected BMI at baseline, as well as blood pressure at baseline. Temporality would need to be absolutely met in order to estimate a causal indirect and direct estimate of this relationship.
In addition, in a letter to the editor, Fritz et al. critiqued this paper. Specifically, they highlight that the effects of BMI on CHD decreases with age and recommended including this interaction effect in their analyses.