diffOR: Model with differential misclassification
In BayesSenMC: Different Models of Posterior Distributions of Adjusted Odds Ratio

diffOR

R Documentation

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,
  ...
)

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 μ 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.

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}

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)

BayesSenMC documentation built on Aug. 6, 2022, 9:06 a.m.