bootstrap.incidence: Calculates incidence and bootstrap confidence intervals for...

In RDS: Respondent-Driven Sampling

Calculates incidence and bootstrap confidence intervals for immunoassay data collected with RDS

Description

Calculates incidence and bootstrap confidence intervals for immunoassay data collected with RDS

Usage

bootstrap.incidence(
  rds.data,
  recent.variable,
  hiv.variable,
  N = NULL,
  weight.type = c("Gile's SS", "RDS-I", "RDS-I (DS)", "RDS-II", "Arithmetic Mean", "HCG"),
  mean.duration = 200,
  frr = 0.01,
  post.infection.cutoff = 730,
  number.of.bootstrap.samples = 1000,
  se.mean.duration = 0,
  se.frr = 0,
  confidence.level = 0.95,
  verbose = TRUE,
  ...
)

Arguments

rds.data	an rds.data.frame
recent.variable	The name of the variable indicating recent infection
hiv.variable	The name of the variable indicating of hiv infection
N	Population size
weight.type	A string giving the type of estimator to use. The options are "Gile's SS", "RDS-I", "RDS-II", "RDS-I/DS", and "Arithemic Mean". It defaults to "Gile's SS".
mean.duration	Estimated mean duration of recent infection (MDRI) (days)
frr	Estimated false-recent rate (FRR)
post.infection.cutoff	Post-infection time cut-off T, separating "true-recent" from "false-recent" results (days)
number.of.bootstrap.samples	The number of bootstrap samples used to construct the interval.
se.mean.duration	The standard error of the mean.duration estimate
se.frr	The standard error of the false recency estimate
confidence.level	The level of confidence for the interval
verbose	verbosity control
...	additional arguments to compute.weights

Details

The recent.variable and hiv should be the names of logical variables. Otherwise they are converted to logical using as.numeric(x) > 0.5.

This function estimates incidence using RDS sampling wieghts. Confidence intervals are constucted using HCG bootstraps. See http://www.incidence-estimation.org/ for additional information on (non-RDS) incidence estimation.

Examples

data(faux)
faux$hiv <- faux$X == "blue"
faux$recent <- NA
faux$recent[faux$hiv] <- runif(sum(faux$hiv)) < .2
faux$recent[runif(nrow(faux)) > .5] <- NA
faux$hiv[is.na(faux$recent)][c(1,6,10,21)] <- NA
attr(faux,"time") <- "wave"
bootstrap.incidence(faux,"recent","hiv",weight.type="RDS-II", number.of.bootstrap.samples=100)

RDS documentation built on Aug. 20, 2023, 9:06 a.m.