Sensitivity analysis: Probabilistic bias analysis
Chronic hypertension is subject to misclassification in these data
files, with sensitivity of 44% (and agreement of 98%) in comparison to
data abstracted from hospital medical charts.(28) To address potential
exposure misclassification and unmeasured confounding biases, we
undertook a sensitivity analysis through a probabilistic bias
analysis.(37, 38) Exposure (chronic hypertension) misclassification was
assumed to be differential with respect to the outcome (perinatal
mortality). Based on a uniform distribution, we assumed the priors for
sensitivity for chronic hypertension to range between 0.30 and 0.95
among those with perinatal deaths and 0.20 to 0.90 among live births;
the priors for specificity for chronic hypertension was assumed to range
between 0.98 and 1.00 both for deaths and live births, respectively.
Corrections for unmeasured confounding bias was based on the following
assumptions: (i) we assumed that the prevalence of the unmeasured
confounder among those with and without chronic hypertension, under a
log-normal distribution, ranged between 5% and 15%, and 3% and 10%,
respectively; and (ii) the RR for the confounder-outcome association was
varied between 1.25 and 3.00. Under these assumptions, we drew the bias
parameters 100,000 times from the prior distributions to address
exposure misclassification and unmeasured confounding (computational
strategy are provided in the R package “episenr”.(39)) From these
analyses, we report the median bias-corrected RR (RRbc)
and 95% CIbc.
Log-linear regression models and the mediation analysis were fit in SAS
(version 9.4; SAS Institute, Cary, NC) using the GENMOD and the
CAUSALMED procedures, respectively. The probabilistic bias analysis was
implemented in R (R Foundation for Statistical Computing, Vienna,
Austria) using the episensr package.(39)