prev: Estimated overall prevalence from a sample selection model

In GJRM: Generalised Joint Regression Modelling

Estimated overall prevalence from a sample selection model

Description

prev can be used to calculate the overall estimated prevalence from a sample selection model with binay outcome, with corresponding interval obtained using posterior simulation.

Usage

prev(x, sw = NULL, joint = TRUE, n.sim = 100, prob.lev = 0.05)

Arguments

x	A fitted gjrm object.
sw	Survey weights.
joint	If FALSE then the prevalence is obtained from the univariate model which neglects the presence of unobserved confounders. When TRUE, the prevalence is obtained from the simultaneous model which accounts for observed and unobserved confounders.
n.sim	Number of simulated coefficient vectors from the posterior distribution of the estimated model parameters. It may be increased if more precision is required.
prob.lev	Overall probability of the left and right tails of the prevalence distribution used for interval calculations.

Details

prev estimates the overall prevalence of a disease (e.g., HIV) when there are missing values that are not at random. An interval for the estimated prevalence can be obtained using posterior simulation.

Value

res	It returns three values: lower confidence interval limit, estimated prevalence and upper confidence interval limit.
prob.lev	Probability level used.
sim.prev	Vector containing simulated values of the prevalence. This is used to calculate an interval.

Author(s)

Authors: Giampiero Marra, Rosalba Radice, Guy Harling, Mark E McGovern

Maintainer: Giampiero Marra [e-mail]

References

Marra G., Radice R., Barnighausen T., Wood S.N. and McGovern M.E. (2017), A Simultaneous Equation Approach to Estimating HIV Prevalence with Non-Ignorable Missing Responses. Journal of the American Statistical Association, 112(518), 484-496.

See Also

GJRM-package, gjrm

GJRM documentation built on Oct. 25, 2024, 5:07 p.m.