calculate_clinical: Calculate clinical incidence in the presence of screening.

In roman-gulati/overdiag: A package to investigate empirical estimates of overdiagnosis

Description

Append to input data frame number of screen tests offered and number of interval cases not detected by screening.

Usage

calculate_clinical(dset, sensitivity, attendance, screen.start.year,
  screen.stop.year)

Arguments

dset	A data frame of disease incidence as produced by generate_absence.
sensitivity	Proportion of relevant disease detected by screening.
attendance	Proportion of individuals who attend screening tests.
screen.start.year	Year of follow-up at which screening starts.
screen.stop.year	Year of follow-up at which screening stops.

Details

This function expects that the input data frame dset contains a variable sojourn with a single unique value. The number of false negatives are bounded by (1) the later of onset and the start of screening and (2) the earlier of clinical presentation and the end of screening. Disease presents clinically when all screening tests are false negatives. Imperfect attendance is represented by removing the expected number of cases in individuals who attend a sensitive test scaled by the number of tests offered. In other words, false negative tests represent either not attending a test or attending a test but it does not detect latent disease.

Value

A data frame of simulated disease incidence organized by year of preclinical onset, sojourn time, and year of clinical diagnosis.

See Also

calculate_screen

Examples

library(plyr)
library(reshape)
dset <- generate_absence(1000, 0.001, 0, 6, 10)
cset <- ddply(dset,
              .(sojourn),
              calculate_clinical,
              sensitivity=0.5,
              attendance=0.8,
              screen.start.year=0,
              screen.stop.year=10)
print(head(cset))

roman-gulati/overdiag documentation built on May 27, 2019, 1:49 p.m.