Re: Predicted lean body mass, fat mass, and all cause and cause specific mortality in men: prospective US cohort study
I calculated 459 confidence intervals (CI), 3 exposures, 5 primary outcomes and at least 8 covariates in study by Lee and coworkers (1). The authors used 0.05 threshold for statistical significance which is counterintuitive. Interestingly, a highest number of reported associations in a single observational study was 264 based on a survey published in 2004 (2).
Furthermore, Lee and coworkers described possibility of confounding and instantly explained it away in the Discussion: "...However, the homogeneity of the study population and comprehensive data on the risk factors minimized potential confounding..." (1). A recent survey of observational studies showed that only few (2/120) studies warranted cautious interpretation due to confounding (3).
E-value has been proposed for sensitivity analyses due to unmeasured confounding in observational studies (4,5). The definition of E-value is: "The E-value is the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and outcome, conditional on the measured covariates, to explain away a treatment–outcome association." (4).
For example, Lee et al reported hazard ratio of 1.35 (95% CI 1.26 to 1.46) for all-cause mortality when subjects with highest fifth of predicted fat mass were compared to subjects with lowest fifth (1). E-values based on the aforementioned hazard ratio and lower limit of CI are 1.76 and 1.63, respectively, if one ignores other (potential) biases and multiplicity of analyses.
1. Lee DH, Keum N, Hu FB, et al. Predicted lean body mass, fat mass, and all cause and cause specific mortality in men: prospective US cohort study. BMJ 2018;362:k2575.
2. Pocock SJ, Collier TJ, Dandreo KJ, et al. Issues in the reporting of epidemiological studies: a survey of recent practice. BMJ 2004;329:883.
3. Hemkens LG, Ewald H, Naudet F, et al. Interpretation of epidemiologic studies very often lacked adequate consideration of confounding. J Clin Epidemiol 2018;93:94-102.
4. VanderWeele TJ, Ding P. Sensitivity Analysis in Observational Research: Introducing the E-Value. Ann Intern Med 2017;167:268-274.
5. Mathur MB, Ding P, Riddell CA, VanderWeele TJ. Website and R Package for Computing E-Values. Epidemiology 2018 Jun 14. doi: 10.1097/EDE.0000000000000864.
Competing interests: I like statistics.