Sample size or precision calculation

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Description

Sample size or precision calculation

Usage

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incprecision(I, RSE_I, PrevH, CR, MDRI, RSE_MDRI, FRR, RSE_FRR, BigT = 730,
  DE_H = 1, DE_R = 1, n = "out", step = 5)

Arguments

I

Expected Incidence.

RSE_I

Relative Standard Error of Incidence Estimate. If this is the desired output, set to "out".

PrevH

Prevalence of HIV.

CR

Coverage rate: probability (0-1) of being tested for recency when positive for HIV.

MDRI

mean duration of recent infection in days (vector/integer).

RSE_MDRI

Relative standard error of MDRI (vector/integer).

FRR

False recent rate (vector/integer).

RSE_FRR

Relative standard error of FRR (vector/integer).

BigT

post-infection time cut-off for true vs. false recency. Default is 730 days.

DE_H

Design effect of HIV prevalence test (vector/integer).

DE_R

Design effect of recency test (vector/integer).

n

Sample Size: Set to a hypothetical value if the desired output is RSE_I, othewise set to "out" to obtain required sample size.

step

number of steps between minimum I and maximum I in the calculation of a range of output.

Details

The package contains long form documentation in the form of vignettes that cover the use of the main fucntions. Use browseVignettes(package="inctools") to access them.

This function summarizes performance of a recent infection test into a standard error of the incidence estimate, given the estimated test properties and hypothetical survey context or the sample size necessary for a given level of precision.

Up to two arguments can be specified as ranges, with the input parameter 'step' specifying the number of increments between the endpoints of the two ranges supplied under the argument name. This yields output for each step. See the second and third example below for an illustration of this output.

Either the argument RSE_I or the argument n must be set to "out".

Value

Either sample size necessary for a given precision under a given set of testing characteristics and a hypothetical prevalence/incidence scenario, or precision under a particular sample size scenario, with a given hypothetical prevalence/incidence scenario.

Examples

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incprecision(I = 0.015, RSE_I = 0.25, PrevH = 0.2, CR = 1,
MDRI = 200, RSE_MDRI = 0.05, FRR = 0.01, RSE_FRR = 0.2,
BigT = 730, DE_H = 1.1, DE_R = 1, n = 'out')

incprecision(I = c(0.015,0.02), RSE_I = 0.25, PrevH = c(0.10,0.20),
CR = 1, MDRI = 200, RSE_MDRI = 0.05, FRR = 0.01, RSE_FRR = 0.2,
BigT = 700, DE_H = 1, DE_R = 1, n = 'out', step = 5)

incprecision(I = 0.017, RSE_I = 'out', PrevH = c(0.10,0.20),
CR = 1, MDRI = 211, RSE_MDRI = 0.05, FRR = 0.009, RSE_FRR = 0.2,
BigT = 720, n = 5000, step = 5)