predictODX {baclava} | R Documentation |
Using the posterior parameter distributions, calculates the infinite population estimates of the probability of overdiagnosis at each screening episode due to indolence and/or death by other causes.
predictODX(
object,
screening.schedule,
other.cause.rates,
groups.rateP = NULL,
screen.type = NULL,
burnin = 1000L,
verbose = TRUE
)
## S3 method for class 'baclava.ODX.pred'
plot(x, y, ...)
object |
An object of S3 class 'baclava'. The value object returned
by |
screening.schedule |
A numeric vector object. A vector of ages at which screenings occur. |
other.cause.rates |
A data.frame object. Must contain columns "Rate" and "Age". |
groups.rateP |
An integer scalar object. If model included groups with
different sojourn parameters, the group for which overdiagnosis is to
be estimated. Must be one of |
screen.type |
An integer scalar object. If model included screen-type,
specific sensitivity parameters, the screen-type for which
overdiagnosis is to be estimated. Must be one of |
burnin |
An integer object. Optional. The number of burn-in samples.
Used only for |
verbose |
A logical object. If TRUE, progress bars will be displayed. |
x |
A an object of S3 class 'baclava.PDX.pred' as returned by |
y |
Ignored. |
... |
Ignored. |
Provided birth cohort life table is an all cause tables obtained from the CDC Life Tables. Vital Statistics of the United States, 1974 Life Tables, Vol. II, Section 5. 1976. Estimated "other cause" mortality will thus be overestimated when using these tables. It is recommended that user provide data that has been corrected to exclude death due to the disease under analysis.
A list object. For each screen in screening.schedule
,
a matrix providing the mean total overdiagnosis and the mean overdiagnosis
due to indolent/progressive tumors, as well as their 95
Similarly, element overall
provides these estimates for the full
screening schedule.
plot(baclava.ODX.pred)
: Generate column plot of predicted overdiagnosis for each screen.
data(screen_data)
theta_0 <- list("rate_H" = 7e-4, "shape_H" = 2.0,
"rate_P" = 0.5 , "shape_P" = 1.0,
"beta" = 0.9, psi = 0.4)
prior <- list("rate_H" = 0.01, "shape_H" = 1,
"rate_P" = 0.01, "shape_P" = 1,
"a_psi" = 1/2 , "b_psi" = 1/2,
"a_beta" = 38.5, "b_beta" = 5.8)
# This is for illustration only -- the number of MCMC samples should be
# significantly larger and the epsilon values should be tuned.
example <- fit_baclava(data.assess = data.screen,
data.clinical = data.clinical,
t0 = 30.0,
theta_0 = theta_0,
prior = prior)
# if rates are not available, an all cause dataset is provided in the package
# NOTE: these predictions will be over-estimated
data(all_cause_rates)
all_cause_rates <- all_cause_rates[, c("Age", "both")]
colnames(all_cause_rates) <- c("Age", "Rate")
# using single screen for example speed
predicted_odx <- predictODX(object = example,
other.cause.rates = all_cause_rates,
screening.schedule = 40,
burnin = 10)
plot(predicted_odx)