est.prev: Estimated overall prevalence from sample selection model
Description
est.prev can be used to calculate the overall estimated prevalence from a sample selection model, with corresponding confidence intervals
obtained using the delta method.
A fitted SemiParBIVProbit object as produced by SemiParBIVProbit().
sig.lev
Significance level.
sw
Survey weights.
naive
If FALSE then the prevalence is calculated using the (naive/classic imputation) probit model. This option has
been introduced to compare adjusted (for non-random sample selection) and unadjusted estimates.
Value
resIt returns three values: lower confidence interval limit, estimated prevalence and upper confidence interval limit.
sig.levSignificance level used.
Details
est.prev estimates the overall prevalence of a disease (e.g., HIV) when there are missing values that are not at random.
References
McGovern M.E., Barnighausen T., Marra G. and Radice R. (2015), On the Assumption of Joint Normality in Selection Models: A Copula Approach Applied to Estimating HIV Prevalence. Epidemiology, 26(2), 229-237.