rsu.sssep.rb2st2rf {epiR}R Documentation

Sample size to achieve a desired surveillance system sensitivity assuming risk-based 2-stage sampling on two risk factors at either the cluster level, unit level, or both

Description

Calculates the sample size to achieve a desired surveillance system sensitivity assuming risk-based 2-stage sampling on two risk factors at either the cluster level, the unit level or both, imperfect test sensitivity and perfect test specificity.

Usage

rsu.sssep.rb2st2rf(rr.c, ppr.c, spr.c, pstar.c, se.c, 
   rr.u, ppr.u, spr.u, pstar.u, se.u, se.p)

Arguments

rr.c

vector, corresponding to the number of risk strata defining the relative risk values at the cluster level.

ppr.c

vector of length equal to that of rr.c defining the population proportions at the cluster level.

spr.c

vector of length equal to that of rr.c defining the planned surveillance proportions at the cluster level.

pstar.c

scalar (either a proportion or integer) defining the cluster level design prevalence.

se.c

scalar (proportion), the desired cluster level sensitivity.

rr.u

vector, corresponding to the number of risk strata defining the relative risk values at the surveillance unit level.

ppr.u

vector, of length equal to that of rr.u defining the population proportions at the surveillance unit level.

spr.u

vector of length equal to that of rr.u defining the planned surveillance proportions at the surveillance unit level.

pstar.u

scalar (either a proportion or integer) defining the surveillance unit level design prevalence.

se.u

scalar (0 to 1) representing the sensitivity of the diagnostic test at the surveillance unit level.

se.p

scalar (0 to 1) representing the desired surveillance system (population-level) sensitivity..

Value

A list comprised of two elements:

clusters

scalar, the total number of clusters to be sampled.

units

scalar, the total number of units to sample from each cluster.

Examples

## EXAMPLE 1:
## A cross-sectional study is to be carried out to confirm the absence of 
## disease using risk based sampling. Assume a design prevalence of 0.02 
## at the cluster (herd) level and a design prevalence of 0.10 at the 
## surveillance unit (individual) level. Clusters are categorised as 
## being either high, medium or low risk with the probability of disease for 
## clusters in the high and medium risk area 5 and 3 times the probability of 
## disease in the low risk area. The proportions of clusters in the high, 
## medium and low risk area are 0.10, 0.20 and 0.70, respectively. The 
## proportion of samples from the high, medium and low risk area will be 
## 0.40, 0.40 and 0.20, respectively. 

## Surveillance units (individuals) are categorised as being either high or 
## low risk with the probability of disease for units in the high risk group 
## 4 times the probability of disease in the low risk group. The proportions 
## of units in the high and low risk groups are 0.10 and 0.90, respectively. 
## All of your samples will be taken from units in the high risk group. 

## You intend to use a test with diagnostic sensitivity of 0.95 and you'd 
## like to take sufficient samples to be 95% certain that you've detected 
## disease at the population level, 95% certain that you've detected disease 
## at the cluster level and 95% at the surveillance unit level. How many 
## clusters and how many units need to be sampled to meet the requirements 
## of the study?

rsu.sssep.rb2st2rf(
   rr.c = c(5,3,1), ppr.c = c(0.1,0.2,0.7), spr.c = c(0.4,0.4,0.2),
   pstar.c = 0.02, se.c = 0.50, 
   rr.u = c(4,1), ppr.u = c(0.1, 0.9), spr.u = c(1,0),
   pstar.u = 0.10, se.u = 0.90,
   se.p = 0.95)

## A total of 82 clusters needs to be sampled: 33 from the high risk area, 
## 33 from the medium risk area and 16 from the low risk area. A total of 
## 9 units should be sampled from each cluster.

[Package epiR version 2.0.31 Index]