ipdma.model.deft.onestage {bipd}

Make a (deft-approach) one-stage individual patient data meta-analysis object containing data, priors, and a JAGS model code

Description

This function sets up data and JAGS code that is needed to run (deft-approach) one-stage IPD-MA models in JAGS.

Usage

ipdma.model.deft.onestage(
  y = NULL,
  study = NULL,
  treat = NULL,
  X = NULL,
  response = "normal",
  type = "random",
  mean.alpha = 0,
  prec.alpha = 0.001,
  mean.beta = 0,
  prec.beta = 0.001,
  mean.gamma.within = 0,
  prec.gamma.within = 0.001,
  mean.gamma.across = 0,
  prec.gamma.across = 0.001,
  mean.delta = 0,
  prec.delta = 0.001,
  hy.prior = list("dhnorm", 0, 1)
)

Arguments

y

outcome of the study. Can be continuous or binary.

study

vector indicating which study the patient belongs to. Please change the study names into numbers (i.e. 1, 2, 3, etc)

treat

vector indicating which treatment the patient was assigned to (i.e. 1 for treatment, 0 for placebo)

X

matrix of covariate values for each patient. Dimension would be number of patients x number of covariates.

response

specification of the outcome type. Must specify either "normal" or "binomial".

type

assumption on the treatment effect: either "random" for random effects model or "fixed" for fixed effects model. Default is "random".

mean.alpha

prior mean for the study intercept

prec.alpha

prior precision for the study intercept

mean.beta

prior mean for the regression coefficients of the main effects of the covariates; main effects are assumed to have common effect.

prec.beta

prior precision for the regression coefficients of the main effects of the covariates

mean.gamma.within

prior mean for effect modifiers of within study information.

prec.gamma.within

prior precision for the effect modifiers of within study information.

mean.gamma.across

prior mean for the effect modifiers of across study information; effect modification is assumed to have common effect.

prec.gamma.across

prior precision for the effect modifiers of across study information

mean.delta

prior mean for the average treatment effect

prec.delta

prior precision for the average treatment effect

hy.prior

prior for the heterogeneity parameter. Supports uniform, gamma, and half normal for normal and binomial response It should be a list of length 3, where first element should be the distribution (one of dunif, dgamma, dhnorm) and the next two are the parameters associated with the distribution. For example, list("dunif", 0, 5) gives uniform prior with lower bound 0 and upper bound 5 for the heterogeneity parameter.

Value

data.JAGS

data organized in a list so that it can be used when running code in JAGS

code

JAGS code that is used to run the model. Use cat(code) to see the code in a readable format

model.JAGS

JAGS code in a function. This is used when running model in parallel

Xbar

study specific averages of covariates

References

Fisher DJ, Carpenter JR, Morris TP, et al. Meta-analytical methods to identify who benefits most from treatments: daft, deluded, or deft approach?. BMJ. 2017;356:j573 doi: 10.1136/bmj.j573

Examples

ds <- generate_ipdma_example(type = "continuous")
ipd <- with(ds, ipdma.model.deft.onestage(y = y, study = studyid, treat = treat, X = cbind(z1, z2), 
response = "normal"))

samples <- ipd.run(ipd)
treatment.effect(ipd, samples, newpatient= c(1,0.5), reference = c(0, 0))