calculate_screen: Calculate screen incidence in the presence of screening.

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

Description

Expand input data frame to indicate year of diagnosis and number of cases detected by screening.

Usage

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

Arguments

dset	A data frame of disease incidence as produced by generate_absence after processing by calculate_clinical.
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. Create new variables to record year of each screening round (exclusive of stopping year). The number of false negatives are bounded (1) below by 0 and screen round - 1 and (2) above by number of tests - 1 and sojourn time - 1 Check that incidence of onset matches diagnoses. Reshape dataset to indicate number of screen detections in each screen year.

Value

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

See Also

calculate_clinical

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)
sset <- ddply(cset,
              .(sojourn),
              calculate_screen,
              sensitivity=0.5,
              attendance=0.8,
              screen.start.year=0,
              screen.stop.year=10)
print(head(sset))

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