BayesSenMC (version 0.1.5)

diffOR: Model with differential misclassification

Description

Generate a stanfit object corresponding to a posterior distribution of corrected odds ratio given a four-variate differential misclassification.

Usage

diffOR(
  a,
  N1,
  c,
  N0,
  mu,
  s.lg.se0,
  s.lg.se1,
  s.lg.sp0,
  s.lg.sp1,
  corr.sesp0,
  corr.sesp1,
  corr.group = 0,
  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,
  ...
)

Value

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

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.

mu

vector of length 4; multivariate normal distribution of \(z \sim (mu, varz)\), where each \(\mu\) corresponds to the logit mean of \(Se_0\), \(Se_1\), \(Sp_0\) and \(Sp_1\) (0 for controls, 1 for cases group).

s.lg.se0

standard deviation of logit Se in the control group.

s.lg.se1

standard deviation of logit Se in the case group.

s.lg.sp0

standard deviation of logit Sp in the control group.

s.lg.sp1

standard deviation of logit Sp in the case group.

corr.sesp0

correlation between Se_0 and Sp_0.

corr.sesp1

correlation between Se_1 and Sp_1.

corr.group

correlation between Se_0 and Se_1, Sp_0 and Sp_1. Default to 0.

z

vector of length 4; used as an initial value for \(z \sim (mu, varz)\). Default to mu.

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.

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}

diffOR(a = 66, N1 = 11782, c = 243, N0 = 57973, mu = c(1.069, 1.069, 1.126, 1.126),
  s.lg.se0 = 0.712, s.lg.se1 = 0.712, s.lg.sp0 = 0.893, s.lg.sp1 = 0.893, corr.sesp0 = -0.377,
  corr.sesp1 = -0.377, corr.group = 0, chains = 3, iter = 10000)