randCorrOR {BayesSenMC}

Model with nondifferential, randomly correlated misclassification

Description

Generate a stanfit object corresponding to a posterior distribution of corrected odds ratio given nondifferential misclassification that extends from the logit model but allows a random correlation between Sensitivity and Specificity.

Usage

randCorrOR(
  a,
  N1,
  c,
  N0,
  prior_list = NULL,
  m.lg.se = NULL,
  m.lg.sp = NULL,
  s.lg.se = NULL,
  s.lg.sp = NULL,
  lg.se = NULL,
  lg.sp = NULL,
  m.z = NULL,
  s.z = NULL,
  z = NULL,
  logitpi0_prior = c(0, 10),
  lor_prior = c(0, 2),
  chains = 2,
  traceplot = FALSE,
  inc_warmup = FALSE,
  window = NULL,
  refresh = 0,
  seed = 0,
  ...
)

Arguments

a

number of exposed subjects in the case group.

N1

number of total subjects in the case group.

c

number of exposed subjects in the control group.

N0

number of total subjects in the control group.

prior_list

list of priors. Can be replaced by the function call to paramEst, or a list of prior parameters (m.lg.se, s.lg.se, m.lg.sp, s.lg.sp, m.z, s.z). If prior_list is specified, the values for the corresponding function parameters will be disregarded.

m.lg.se

normal distribution of logit Se with (mean = m.lg.se, sd = s.lg.se). Do not have to specify this if prior_list is given - it will be disregarded.

m.lg.sp

conditional normal distribution of logit Sp given Se with (m.lg.sp, s.lg.sp). Do not have to specify this if prior_list is given - it will be disregarded.

s.lg.se

standard deviation of logit Se. Do not have to specify this if prior_list is given - it will be disregarded.

s.lg.sp

standard deviation of logit Sp. Do not have to specify this if prior_list is given - it will be disregarded.

lg.se

used as an initial value for logit Se. Default to m.lg.se. Do not have to specify this if prior_list is given - it will be disregarded. Default to m.lg.se

lg.sp

used as an initial value for logit Sp. Default to m.lg.sp. Do not have to specify this if prior_list is given - it will be disregarded. Default to m.lg.sp

m.z

normal distribution of Z with (mean = m.z, sd = s.z). Do not have to specify this if prior_list is given - it will be disregarded.

s.z

normal distribution of Z with (mean = m.z, sd = s.z). Do not have to specify this if prior_list is given - it will be disregarded.

z

used as an initial value of Fisher's Z transformed of rho, where correlation rho = (exp(2z)-1)/(1+exp(2z))). Do not have to specify this if prior_list is given - it will be disregarded. Default to m.z

logitpi0_prior

mean and sd of the prior normal distribution of logit(pi0). Default to c(0,10).

lor_prior

mean and sd of the prior normal distribution of corrected log odds ratio. Default to c(0,2).

chains

number of Markov Chains. Default to 2.

traceplot

Logical, defaulting to FALSE. If TRUE it will draw the traceplot corresponding to one or more Markov chains.

inc_warmup

Only evaluated when traceplot = TRUE. TRUE or FALSE, indicating whether or not to include the warmup sample in the traceplot; defaults to FALSE.

window

Only evaluated when traceplot = TRUE. A vector of length 2. Iterations between window[1] and window[2] will be shown in the plot. The default shows all iterations if inc_warmup is TRUE and all iterations from the sampling period only if inc_warmup is FALSE. If inc_warmup is FALSE the iterations specified in window do not include iterations from the warmup period. The default number of iterations is 2000 unless otherwise specified in the optional iter argument.

refresh

an integer value used to control how often the progress of sampling is reported. By default, the progress indicator is turned off, thus refresh <= 0. If on, refresh = max(iter/10, 1) is generally recommended.

seed

the seed for random number generation. Default to 0. See stan for more details.

...

optional parameters passed to stan.

Value

It returns a stanfit object of this model, which inherits stanfit class methods. See rstan for more details.

Examples

# Case-control study data of Bipolar Disorder with rheumatoid arthritis (Farhi et al. 2016)
# Data from \url{https://www.sciencedirect.com/science/article/pii/S0165032715303864#bib13}

mod <- nlmeNDiff(bd_meta, lower = 0) # see \code{nlmeNDiff()} for detailed example.
prior_list <- paramEst(mod)
randCorrOR(a = 66, N1 = 11782, c = 243, N0 = 57973, prior_list = prior_list,
chains = 3, iter = 10000)