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rsu.dxtest <- function(se, sp, covar.pos, covar.neg, interpretation = "series"){
# Objects se, sp and covar must be of length 2 or 3:
if(length(se) < 2 | length(se) > 3) stop('se must be a vector of length 2 or 3.')
if(length(sp) < 2 | length(sp) > 3) stop('sp must be a vector of length 2 or 3.')
if(length(se) == 2 & length(covar.pos) != 1)
stop('covar.pos must be a vector of length 1 for assessment of two diagnostic tests.')
if(length(se) == 3 & length(covar.pos) != 4)
stop('covar.pos must be a vector of length 4 for assessment of three diagnostic tests.')
# Two tests:
if(length(se) == 2 & length(sp == 2)){
# Values of se and sp must range between 0 and 1:
if(se[1] < 0 | se[1] > 1) stop('se must be a number between 0 and 1.')
if(sp[1] < 0 | sp[1] > 1) stop('sp must be a number between 0 and 1.')
if(se[2] < 0 | se[2] > 1) stop('se must be a number between 0 and 1.')
if(sp[2] < 0 | sp[2] > 1) stop('sp must be a number between 0 and 1.')
# First element of covar is covariance for D+ group, second element is covariance for D- group.
# See Dohoo, Martin and Stryhn (2009) page 111.
# Minimums and maximums for the conditional covariance for sensitivity.
# See page 111 Gardner et al. (2000):
min.covse <- max(-1 * (1 - se[1]) * (1 - se[2]), -se[1] * se[2])
max.covse <- min(se[1] * (1 - se[2]), se[2] * (1 - se[1]))
# Minimums and maximums for the conditional covariance for specificity.
min.covsp <- max(-1 * (1 - sp[1]) * (1 - sp[2]), -sp[1] * sp[2])
max.covsp <- min(sp[1] * (1 - sp[2]), sp[2] * (1 - sp[1]))
# Check the values of covar entered by the user and return error if outside range:
if(covar.pos[1] < min.covse | covar.pos[1] > max.covse)
stop('The covariance estimate for diagnostic test sensitivity is outside of the plausible range given the sensitivities of the two tests.')
if(covar.neg[1] < min.covsp | covar.neg[1] > max.covsp)
stop('The covariance estimate for diagnostic test specificity is outside of the plausible range given the specificities of the two tests.')
# Series interpretation:
if(interpretation == "series") {
# Sensitivity and specificity assuming tests are INDEPENDENT.
# Equations 5.18 and 5.19 Dohoo et al. (2009) page 111:
sei <- se[1] * se[2]
spi <- sp[1] + sp[2] - (sp[1] * sp[2])
# Name each of the covariances to make code easier to read:
c012.pos <- covar.pos[1]
c012.neg <- covar.neg[1]
# Sensitivity and specificity assuming tests are DEPENDENT.
# Equations 5.24 and 5.25 Dohoo et al. (2009) page 113:
sed <- se[1] * se[2] + c012.pos
sed <- ifelse(sed < 0, 0, sed)
sed <- ifelse(sed > 1, 1, sed)
spd <- 1 - (1 - sp[1]) * (1 - sp[2]) - c012.neg
spd <- ifelse(spd < 0, 0, spd)
spd <- ifelse(sed > 1, 1, spd)
}
# Parallel interpretation:
if(interpretation == "parallel") {
# Sensitivity and specificity assuming tests are INDEPENDENT.
# Equations 5.16 and 5.17 Dohoo et al. (2009) page 111:
sei <- se[1] + se[2] - (se[1] * se[2])
spi <- sp[1] * sp[2]
# Name each of the covariances to make code easier to read:
c012.pos <- covar.pos[1]
c012.neg <- covar.neg[1]
# Sensitivity and specificity assuming tests are DEPENDENT.
# Equations 5.22 and 5.23 Dohoo et al. (2009) page 113:
sed <- 1 - (1 - se[1]) * (1 - se[2]) - c012.pos
sed <- ifelse(sed < 0, 0, sed)
sed <- ifelse(sed > 1, 1, sed)
spd <- sp[1] * sp[2] + c012.neg
spd <- ifelse(spd < 0, 0, spd)
spd <- ifelse(sed > 1, 1, spd)
}
}
# Three tests.
if(length(se) == 3 & length(sp == 3)){
# Values of se and sp must range between 0 and 1:
if(se[1] < 0 | se[1] > 1) stop('se must be a number between 0 and 1.')
if(sp[1] < 0 | sp[1] > 1) stop('sp must be a number between 0 and 1.')
if(se[2] < 0 | se[2] > 1) stop('se must be a number between 0 and 1.')
if(sp[2] < 0 | sp[2] > 1) stop('sp must be a number between 0 and 1.')
if(se[3] < 0 | se[3] > 1) stop('se must be a number between 0 and 1.')
if(sp[3] < 0 | sp[3] > 1) stop('sp must be a number between 0 and 1.')
# Minimums and maximums for the conditional covariance for sensitivity.
# See page 86 Toft et al. (2007):
min.covse <- max(-1 * (1 - se[2]) * (1 - se[3]), -se[2] * se[3])
max.covse <- min(se[2] * (1 - se[3]), se[2] * (1 - se[3]))
# Minimums and maximums for the conditional covariance for specificity.
min.covsp <- max(-1 * (1 - sp[2]) * (1 - sp[3]), -sp[2] * sp[3])
max.covsp <- min(sp[2] * (1 - sp[3]), sp[3] * (1 - sp[2]))
# Check the values of covar entered by the user and return error if outside range:
if(covar.pos[1] < min.covse | covar.pos[1] > max.covse)
stop('The covariance estimate for diagnostic test sensitivity is outside of the plausible range given the sensitivities of the three tests.')
if(covar.pos[2] < min.covse | covar.pos[2] > max.covse)
stop('The covariance estimate for diagnostic test sensitivity is outside of the plausible range given the sensitivities of the three tests.')
if(covar.pos[3] < min.covse | covar.pos[3] > max.covse)
stop('The covariance estimate for diagnostic test sensitivity is outside of the plausible range given the sensitivities of the three tests.')
if(covar.pos[4] < min.covse | covar.pos[4] > max.covse)
stop('The covariance estimate for diagnostic test specificity is outside of the plausible range given the specificities of the three tests.')
if(covar.neg[1] < min.covsp | covar.neg[1] > max.covsp)
stop('The covariance estimate for diagnostic test specificity is outside of the plausible range given the specificities of the three tests.')
if(covar.neg[2] < min.covsp | covar.neg[2] > max.covsp)
stop('The covariance estimate for diagnostic test specificity is outside of the plausible range given the specificities of the three tests.')
if(covar.neg[3] < min.covsp | covar.neg[3] > max.covsp)
stop('The covariance estimate for diagnostic test specificity is outside of the plausible range given the specificities of the three tests.')
if(covar.neg[4] < min.covsp | covar.neg[4] > max.covsp)
stop('The covariance estimate for diagnostic test specificity is outside of the plausible range given the specificities of the three tests.')
# Series interpretation:
if(interpretation == "series"){
# Sensitivity assuming tests are INDEPENDENT:
sei <- se[1] * se[2] * se[3]
# Specificity assuming tests are INDEPENDENT:
spi <- 1 - ((1 - sp[1]) * (1 - sp[2]) * (1 - sp[3]))
# Name each of the covariances to make code easier to read:
c012.pos <- covar.pos[1]
c013.pos <- covar.pos[2]
c023.pos <- covar.pos[3]
c123.pos <- covar.pos[4]
c012.neg <- covar.neg[1]
c013.neg <- covar.neg[2]
c023.neg <- covar.neg[3]
c123.neg <- covar.neg[4]
# Sensitivity assuming tests are DEPENDENT.
# Jones et al. (2009) Equation 7, page 857:
sed <- (se[1] * se[2] * se[3]) + (se[1] * c023.pos) + (se[2] * c013.pos) + (se[3] * c012.pos) - c123.pos
sed <- ifelse(sed < 0, 0, sed)
sed <- ifelse(sed > 1, 1, sed)
# Dohoo et al. (2009) Equation 5.24, page 113:
# sed <- se[1] * (se[2] * se[3] + c23.pos)
# Specificity assuming tests are DEPENDENT.
spd <- 1 - (((1 - sp[1]) * (1 - sp[2]) * (1 - sp[3])) + ((1 - sp[1]) * c023.neg) + ((1 - sp[2]) * c013.neg) + ((1 - sp[3]) * c013.neg)) + c123.neg
spd <- ifelse(spd < 0, 0, spd)
spd <- ifelse(spd > 1, 1, spd)
# Dohoo et al. (2009) Equation 5.24, page 113:
# spd <- 1 - (1 - sp[1]) * ((1 - sp[2]) * (1 - sp[3]) + c23.neg)
}
# Parallel interpretation:
if (interpretation == "parallel") {
# Sensitivity assuming tests are INDEPENDENT:
sei <- 1 - (1 - se[1]) * ((1 - se[2]) * (1 - se[3]))
# Specificity assuming tests are INDEPENDENT:
spi <- sp[1] * sp[2] * sp[3]
# Name each of the covariances to make code easier to read:
c012.pos <- covar.pos[1]
c013.pos <- covar.pos[2]
c023.pos <- covar.pos[3]
c123.pos <- covar.pos[4]
c012.neg <- covar.neg[1]
c013.neg <- covar.neg[2]
c023.neg <- covar.neg[3]
c123.neg <- covar.neg[4]
# Sensitivity assuming tests are DEPENDENT:
sed <- 1 - (((1 - se[1]) * (1 - se[2]) * (1 - se[3])) + ((1 - se[1]) * c023.pos) + ((1 - se[2]) * c013.pos) + ((1 - se[3]) * c013.pos)) + c123.pos
sed <- ifelse(sed < 0, 0, sed)
sed <- ifelse(sed > 1, 1, sed)
# Dohoo et al. (2009) Equation 5.22, page 113:
# sed <- 1 - (1 - se[1]) * ((1 - se[2]) * (1 - se[3]) + c023.pos)
# Specificity assuming tests are DEPENDENT:
spd <- (sp[1] * sp[2] * sp[3]) + (sp[1] * c023.neg) + (sp[2] * c013.neg) + (sp[3] * c012.neg) - c123.neg
spd <- ifelse(spd < 0, 0, spd)
spd <- ifelse(spd > 1, 1, spd)
# Dohoo et al. (2009) Equation 5.23, page 113:
# spd <- sp[1] * (sp[2] * sp[3] + c023.neg)
}
}
independent <- data.frame(se = sei, sp = spi)
dependent <- data.frame(se = sed, sp = spd)
rval <- list(independent = independent, dependent = dependent)
return(rval)
}
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