An E-value analysis of potential unmeasured or residual confounding in systematic reviews of post-tuberculosis mortality, respiratory disease, and cardiovascular disease

Introduction Unmeasured confounding poses a serious threat to observational studies of post-TB health outcomes. E-values have been recently proposed as a method to assess the magnitude of unmeasured confounding necessary to nullify, or to render non-significant, relative effect estimates from observational studies. Methods We calculated E-values for both the risk ratio (RR) point estimates and their lower 95% confidence limits (LCL) from studies of post-TB mortality, respiratory disease, and cardiovascular disease (CVD) included in published systematic reviews within and across post-TB outcome domains. We also employed a meta-analytic E-value approach to estimate the proportion of unconfounded study RRs greater than 1.1 at different levels of unmeasured confounding. Results Across post-TB health outcome domains, we observed a median E-value of 5.59 (IQR = 3.19-7.35) for RRs, and 2.95 (IQR = 1.71-4.61) for LCLs. Post-TB mortality studies had higher median E-values (E-value RR = 6.90 and E-value LCL = 4.54) than studies of respiratory disease (E-value RR = 5.59, E-value LCL = 2.94) or CVD (E-value RR = 3.90, E-value LCL = 1.81). The E-value at which the estimated proportion of studies with unconfounded RRs greater than 1.1 would remain over 0.7 was 3.45 for post-TB mortality, 3.96 for post-TB respiratory disease, and 1.71 for post-TB CVD meta-analyses. Conclusions Unmeasured confounding with an association of 2.95 or greater with both the exposure (TB) and outcome, on the risk ratio scale, could render most post-TB health studies' findings statistically non-significant. Post-TB mortality and respiratory disease studies had higher E-values than TB-CVD studies, indicating that either (a) TB-CVD studies may be more susceptible to unmeasured confounding bias, or (b) the true effect of TB on CVD is lower.