Package 'bipd'

bipd: A package for individual patient data meta-analysis using 'JAGS'

Description

A package for individual patient data meta-analysis using 'JAGS'

Details

We use a Bayesian approach to run individual patient data meta-analysis and network meta-analysis using 'JAGS'. The methods incorporate shrinkage methods and calculate patient-specific treatment effects as described in Seo et al. (2021) <DOI:10.1002/sim.8859>. This package also includes user-friendly functions that impute missing data in an individual patient data using mice-related packages.

References

Audigier V, White I, Jolani S, et al. Multiple Imputation for Multilevel Data with Continuous and Binary Variables. Statistical Science. 2018;33(2):160-183. doi:10.1214/18-STS646

Debray TPA, Moons KGM, Valkenhoef G, et al. Get real in individual participant data (IPD) meta-analysis: a review of the methodology. Res Synth Methods. 2015;6(4):293-309. doi:10.1002/jrsm.1160

Dias S, Sutton AJ, Ades AE, et al. A Generalized Linear Modeling Framework for Pairwise and Network Meta-analysis of Randomized Controlled Trials. Medical Decision Making. 2013;33(5):607-617. doi:10.1177/0272989X12458724

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

O'Hara RB, Sillanpaa MJ. A review of Bayesian variable selection methods: what, how and which. Bayesian Anal. 2009;4(1):85-117. doi:10.1214/09-BA403

Riley RD, Debray TP, Fisher D, et al. Individual participant data meta-analysis to examine interactions between treatment effect and participant-level covariates: Statistical recommendations for conduct and planning. Stat Med. 2020:39(15):2115-2137. doi:10.1002/sim.8516

Seo M, White IR, Furukawa TA, et al. Comparing methods for estimating patient-specific treatment effects in individual patient data meta-analysis. Stat Med. 2021;40(6):1553-1573. doi:10.1002/sim.8859

---

Convenient function to add results (i.e. combine mcmc.list)

Description

This is a convenient function to add results (i.e. combine mcmc.list). This can be useful when combining results obtained from multiple imputation

Usage

add.mcmc(x, y)

Arguments

x	first result in a format of mcmc.list
y	second result in a format of mcmc.list

Examples

ds <- generate_ipdma_example(type = "continuous")
ds2 <- generate_ipdma_example(type = "continuous")
ipd <- with(ds, ipdma.model.onestage(y = y, study = studyid, treat = treat, X = cbind(z1, z2), 
response = "normal", shrinkage = "none"))
ipd2 <- with(ds2, ipdma.model.onestage(y = y, study = studyid, treat = treat, X = cbind(z1, z2), 
response = "normal", shrinkage = "none"))

samples <- ipd.run(ipd, pars.save = c("beta", "gamma", "delta"), n.chains = 3, n.burnin = 500, 
n.iter = 5000)
samples2 <- ipd.run(ipd2, pars.save = c("beta", "gamma", "delta"), n.chains = 3, n.burnin = 500, 
n.iter = 5000)
combined <- add.mcmc(samples, samples2)

---

Find missing data pattern in a given data

Description

Find missing data pattern in a given data i.e. whether variables are systematically missing or sporadically missing. Also calculates missing count and percentage for exploratory purposes.

Usage

findMissingPattern(
  dataset = NULL,
  covariates = NULL,
  typeofvar = NULL,
  studyname = NULL,
  treatmentname = NULL,
  outcomename = NULL
)

Arguments

dataset	data which contains variables of interests
covariates	vector of variable names that the user is interested in finding a missing data pattern
typeofvar	type of covariate variables; should be a vector of these values: "continuous", "binary", or "count". Order should follow that of covariates parameter.
studyname	study name in the data specified
treatmentname	treatment name in the data specified
outcomename	outcome name in the data specified

Value

missingcount	missing number of patients for each study and covariate
missingpercent	missing percentage of patients for each study and covariate
sys_missing	a vector indicating whether each covariate is systematically missing
spor_missing	a vector indicating whether each covariate is sporadically missing
sys_covariates	a vector of systematically missing covariates
spor_covariates	a vector of sporadically missing covariates
without_sys_covariates	a vector of covariates that are not systematically missing
covariates	vector of variable names that the user is interested in finding a missing data pattern
studyname	study name in the data specified
treatmentname	treatment name in the data specified
outcomename	outcome name in the data specified

Examples

simulated_dataset <- generate_sysmiss_ipdma_example(Nstudies = 10, Ncov = 5, sys_missing_prob = 0.3, 
magnitude = 0.2, heterogeneity = 0.1)

missP <- findMissingPattern(simulated_dataset, covariates = c("x1", "x2", "x3", "x4", "x5"), 
typeofvar = c("continuous", "binary", "binary", "continuous", "continuous"), studyname = "study",  
treatmentname = "treat", outcomename = "y")
missP

---

Generate a simulated IPD-MA data for demonstration

Description

Generate a simulated IPD-MA data for demonstration

Usage

generate_ipdma_example(type = "continuous")

Arguments

type	"continuous" for continuous outcome and "binary" for binary outcome

Value

returns simulated IPD-MA data

Examples

ds <- generate_ipdma_example(type = "continuous")
head(ds)

---

Generate a simualted IPD-NMA data for demonstration

Description

Generate a simulated IPD-NMA data for demonstration

Usage

generate_ipdnma_example(type = "continuous")

Arguments

type	"continuous" for continuous outcome and "binary" for binary outcome

Value

return simulated IPD-NMA data ds <- generate_ipdnma_example(type = "continuous") head(ds)

---

Generate a simulated IPD-MA data with systematically missing covariates

Description

Generate a simulated IPD-MA data with systematically missing covariates

Usage

generate_sysmiss_ipdma_example(
  Nstudies = 10,
  Ncov = 5,
  sys_missing_prob = 0.1,
  magnitude = 0.3,
  heterogeneity = 0.1,
  interaction = TRUE
)

Arguments

Nstudies	number of studies. Default is 10.
Ncov	number of covariates in total. Options are 5 or 10 studies. Default is set to 5.
sys_missing_prob	probability of systematically missing studies for each covariates. Default is set to 0.3.
magnitude	magnitude of the regression estimates (the mean). Default is set to 0.2.
heterogeneity	heterogeneity of regression estimates across studies. Default is set to 0.1.
interaction	whether to include treatment indicator and treatment

Value

returns simulated IPD-MA data with systematically missing covariates

Examples

simulated_dataset <- generate_sysmiss_ipdma_example(Nstudies = 10, Ncov = 5, sys_missing_prob = 0.3, 
magnitude = 0.2, heterogeneity = 0.1)
head(simulated_dataset)

---

Run the model using the ipd object

Description

This is the core function that runs the model in our program. Before running this function, we need to specify data, prior, JAGS code, etc. using ipd.model type function.

Usage

ipd.run(
  ipd,
  pars.save = NULL,
  inits = NULL,
  n.chains = 3,
  n.adapt = 1000,
  n.burnin = 1000,
  n.iter = 10000
)

Arguments

ipd	ipd object created from ipd.model type function
pars.save	parameters to save. For instance, "beta" - coefficients for main effects; "gamma" - coefficients for effect modifiers; "delta" - average treatment effect
inits	initial values specified for the parameters to save
n.chains	number of MCMC chains to sample
n.adapt	number of iterations for adaptation (Note that the samples from adaptation phase is non-Markovian and do not constitute a Markov chain)
n.burnin	number of iterations for burn-in
n.iter	number of iterations to run after the adaptation

Value

MCMC samples stored using JAGS. The returned samples have the form of mcmc.list and coda functions can be directly applied.

Examples

ds <- generate_ipdma_example(type = "continuous")
ipd <- with(ds, ipdma.model.onestage(y = y, study = studyid, treat = treat, X = cbind(z1, z2), 
response = "normal", shrinkage = "none"))

samples <- ipd.run(ipd, n.chains = 3, n.burnin = 500, n.iter = 5000)

---

Run the model using the ipd object with parallel computation

Description

This function runs the model through parallel computation using dclone R package. Before running this function, we need to specify data, prior, JAGS code, etc. using ipd.model type function.

Usage

ipd.run.parallel(
  ipd,
  pars.save = NULL,
  inits = NULL,
  n.chains = 2,
  n.adapt = 1000,
  n.burnin = 1000,
  n.iter = 10000
)

Arguments

ipd	ipd object created from ipd.model type function
pars.save	parameters to save. For instance, "beta" - coefficients for main effects; "gamma" - coefficients for effect modifiers; "delta" - average treatment effect
inits	initial values specified for the parameters to save
n.chains	number of MCMC chains to sample
n.adapt	number of iterations for adaptation (Note that the samples from adaptation phase is non-Markovian and do not constitute a Markov chain)
n.burnin	number of iterations for burn-in
n.iter	number of iterations to run after the adaptation

Value

MCMC samples stored using JAGS. The returned samples have the form of mcmc.list and coda functions can be directly applied.

Examples

ds <- generate_ipdma_example(type = "continuous")
ipd <- with(ds, ipdma.model.onestage(y = y, study = studyid, treat = treat, X = cbind(z1, z2), 
response = "normal", shrinkage = "none"))

samples <- ipd.run.parallel(ipd, n.chains = 2, n.burnin = 500, n.iter = 5000)

---

Impute missing data in individual participant data with two treatments (i.e. placebo and a treatment).

Description

Impute missing data in individual participant data with two treatments. Data is clustered by different studies. In the presence of systematically missing variables, the function defaults to 2l.2stage.norm, 2l.2stage.bin, and 2l.2stage.pois methods in micemd package. If there are no systematically missing variables, the function defaults to use 2l.pmm in miceadds package which generalizes predictive mean matching using linear mixed model. If there is only one study available, the function defaults to use pmm in mice package.

Usage

ipdma.impute(
  dataset = NULL,
  covariates = NULL,
  typeofvar = NULL,
  sys_impute_method = "2l.2stage",
  interaction = NULL,
  meth = NULL,
  pred = NULL,
  studyname = NULL,
  treatmentname = NULL,
  outcomename = NULL,
  m = 5
)

Arguments

dataset	data which contains variables of interests
covariates	vector of variable names to find missing data pattern
typeofvar	type of covariate variables; should be a vector of these values: "continuous", "binary", or "count". Order should follow that of covariates parameter specified. Covariates that are specified "binary" are automatically factored.
sys_impute_method	method used for systematically missing studies. Options are "2l.glm", "2l.2stage", or "2l.jomo". Default is set to "2l.2stage". There is also an option to ignore all the clustering level and impute using predictive mean matching by setting this parameter to "pmm".
interaction	indicator denoting whether treatment-covariate interactions should be included. Default is set to true.
meth	imputation method to be used in the mice package. If left unspecified, function picks a reasonable one.
pred	correct prediction matrix to be used in the mice package. If left unspecified, function picks a reasonable one.
studyname	study name in the data specified.
treatmentname	treatment name in the data specified.
outcomename	outcome name in the data specified.
m	number of imputed datasets. Default is set to 5.

Value

missingPattern	missing pattern object returned by running findMissingPattern function
meth	imputation method used with the mice function
pred	prediction matrix used with the mice function
imp	imputed datasets that is returned from the mice function
imp.list	imputed datasets in a list format

Examples

simulated_dataset <- generate_sysmiss_ipdma_example(Nstudies = 10, Ncov = 5, sys_missing_prob = 0.3, 
magnitude = 0.2, heterogeneity = 0.1)

# load in mice packages
library(mice) #for datasets with only one study level
library(miceadds) #for multilevel datasets without systematically missing predictors
library(micemd) #for multilevel datasets with systematically missing predictors.
imputation <- ipdma.impute(simulated_dataset, covariates = c("x1", "x2", "x3", "x4", "x5"), 
typeofvar = c("continuous", "binary", "binary", "continuous", "continuous"), interaction = TRUE, 
studyname = "study", treatmentname = "treat", outcomename = "y", m = 5)

---

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))

---

Make an 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 one-stage IPD-MA models in JAGS.

Usage

ipdma.model.onestage(
  y = NULL,
  study = NULL,
  treat = NULL,
  X = NULL,
  response = "normal",
  type = "random",
  shrinkage = "none",
  scale = TRUE,
  mean.alpha = 0,
  prec.alpha = 0.001,
  mean.beta = 0,
  prec.beta = 0.001,
  mean.gamma = 0,
  prec.gamma = 0.001,
  mean.delta = 0,
  prec.delta = 0.001,
  hy.prior = list("dhnorm", 0, 1),
  lambda.prior = NULL,
  p.ind = NULL,
  g = NULL,
  hy.prior.eta = NULL
)

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".
shrinkage	shrinkage method applied to the effect modifiers. "none" correspond to no shrinkage. "laplace" corresponds to a adaptive shrinkage with a Laplacian prior (ie often known as Bayesian LASSO). "SSVS" corresponds to the Stochastic Search Variable Selection method. SSVS is not strictly a shrinkage method, but pulls the estimated coefficient toward zero through variable selection in each iteration of the MCMC. See O'hara et al (2009) for more details.
scale	indicator for scaling the covariates by the overall average; default is TRUE.
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	prior mean for the effect modifiers. This parameter is not used if penalization is placed on effect modifiers.
prec.gamma	prior precision for the effect modifiers. This parameter is not used if penalization is placed on effect modifiers.
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.
lambda.prior	(only for shrinkage = "laplace") two options for laplace shrinkage. We can put a gamma prior on the lambda (i.e. list("dgamma",2,0.1)) or put a uniform prior on the inverse of lambda (i.e. list("dunif",0,5))
p.ind	(only for shrinkage = "SSVS") prior probability of including each of the effect modifiers. Length should be same as the total length of the covariates.
g	(only for shrinkage = "SSVS") multiplier for the precision of spike. Default is g = 1000.
hy.prior.eta	(only for shrinkage = "SSVS") standard deviation of the slab prior. Currently only support uniform distribution. Default is list("dunif", 0, 5)

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
scale.mean	mean used in scaling covariates
scale.sd	standard deviation used in scaling covariates

References

O'Hara RB, Sillanpaa MJ. A review of Bayesian variable selection methods: what, how and which. Bayesian Anal. 2009;4(1):85-117. doi:10.1214/09-BA403

Seo M, White IR, Furukawa TA, et al. Comparing methods for estimating patient-specific treatment effects in individual patient data meta-analysis. Stat Med. 2021;40(6):1553-1573. doi:10.1002/sim.8859

Examples

ds <- generate_ipdma_example(type = "continuous")
ipd <- with(ds, ipdma.model.onestage(y = y, study = studyid, treat = treat, X = cbind(z1, z2), 
response = "normal", shrinkage = "none"))

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

---

Make an one-stage individual patient data network 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 one-stage IPD-NMA models in JAGS.

Usage

ipdnma.model.onestage(
  y = NULL,
  study = NULL,
  treat = NULL,
  X = NULL,
  response = "normal",
  type = "random",
  shrinkage = "none",
  scale = TRUE,
  mean.alpha = 0,
  prec.alpha = 0.001,
  mean.beta = 0,
  prec.beta = 0.001,
  mean.gamma = 0,
  prec.gamma = 0.001,
  mean.delta = 0,
  prec.delta = 0.001,
  hy.prior = list("dhnorm", 0, 1),
  lambda.prior = NULL,
  p.ind = NULL,
  g = NULL,
  hy.prior.eta = NULL
)

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. Since this is a network meta-analysis and there would be more than 2 treatments, careful naming of treatment is needed. This vector needs to be a sequence from 1:NT where NT is the total number of treatments. Treatment that is assigned 1 would be the baseline treatment.
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".
shrinkage	shrinkage method applied to the effect modifiers. "none" correspond to no shrinkage. "laplace" corresponds to a adaptive shrinkage with a Laplacian prior (ie often known as Bayesian LASSO). "SSVS" corresponds to the Stochastic Search Variable Selection method. SSVS is not strictly a shrinkage method, but pulls the estimated coefficient toward zero through variable selection in each iteration of the MCMC. See O'hara et al (2009) for more details.
scale	indicator for scaling the covariates by the overall average; default is TRUE.
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	prior mean for the effect modifiers. This parameter is not used if penalization is placed on effect modifiers.
prec.gamma	prior precision for the effect modifiers. This parameter is not used if penalization is placed on effect modifiers.
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.
lambda.prior	(only for shrinkage = "laplace") two options for laplace shrinkage. We can put a gamma prior on the lambda (i.e. list("dgamma",2,0.1)) or put a uniform prior on the inverse of lambda (i.e. list("dunif",0,5))
p.ind	(only for shrinkage = "SSVS") prior probability of including each of the effect modifiers. Length should be same as the total length of the covariates.
g	(only for shrinkage = "SSVS") multiplier for the precision of spike. Default is g = 1000.
hy.prior.eta	(only for shrinkage = "SSVS") standard deviation of the slab prior. Currently only support uniform distribution. Default is list("dunif", 0, 5)

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
scale.mean	mean used in scaling covariates
scale.sd	standard deviation used in scaling covariates

References

Dias S, Sutton AJ, Ades AE, et al. A Generalized Linear Modeling Framework for Pairwise and Network Meta-analysis of Randomized Controlled Trials. Medical Decision Making. 2013;33(5):607-617. doi:10.1177/0272989X12458724

O'Hara RB, Sillanpaa MJ. A review of Bayesian variable selection methods: what, how and which. Bayesian Anal. 2009;4(1):85-117. doi:10.1214/09-BA403

Seo M, White IR, Furukawa TA, et al. Comparing methods for estimating patient-specific treatment effects in individual patient data meta-analysis. Stat Med. 2021;40(6):1553-1573. doi:10.1002/sim.8859

Examples

ds <- generate_ipdnma_example(type = "continuous")
ipd <- with(ds, ipdnma.model.onestage(y = y, study = studyid, treat = treat, X = cbind(z1, z2), 
response = "normal", shrinkage = "none"))

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

---

Calculate patient-specific treatment effect

Description

Function for calculating the patient-specific treatment effect. Patient-specific treatment effect includes the main effect of treatment and treatment-covariate interaction effect (i.e. effect modification). Reports odds ratio for the binary outcome.

Usage

treatment.effect(
  ipd = NULL,
  samples = NULL,
  newpatient = NULL,
  scale_mean = NULL,
  scale_sd = NULL,
  reference = NULL,
  quantiles = c(0.025, 0.5, 0.975)
)

Arguments

ipd	IPD object created from running ipdma.model type function
samples	MCMC samples found from running ipd.run function
newpatient	covariate values of patients that you want to predict treatment effect on. Must have length equal to total number of covariates.
scale_mean	option to specify different overall mean compared to what was calculated in IPD object. can be useful when using multiple imputation.
scale_sd	option to specify different overall standard deviation compared to what was calculated in IPD object.
reference	reference group used for finding patient-specific treatment effect. This is only used for deft approach
quantiles	quantiles for credible interval of the patient-specific treatment effect

Value

patient-specific treatment effect with credible interval at specified quantiles

References

Seo M, White IR, Furukawa TA, et al. Comparing methods for estimating patient-specific treatment effects in individual patient data meta-analysis. Stat Med. 2021;40(6):1553-1573. doi:10.1002/sim.8859

Riley RD, Debray TP, Fisher D, et al. Individual participant data meta-analysis to examine interactions between treatment effect and participant-level covariates: Statistical recommendations for conduct and planning. Stat Med. 2020:39(15):2115-2137. doi:10.1002/sim.8516

Examples

ds <- generate_ipdma_example(type = "continuous")
ipd <- with(ds, ipdma.model.onestage(y = y, study = studyid, treat = treat, X = cbind(z1, z2), 
response = "normal", shrinkage = "none"))

samples <- ipd.run(ipd, pars.save = c("beta", "gamma", "delta"), n.chains = 3, n.burnin = 500, 
n.iter = 5000)
treatment.effect(ipd, samples, newpatient = c(1,0.5))

---