| Title: | Patient-Reported Outcome Data Analysis with Stan |
| Version: | 1.9.1.0 |
| Description: | Algorithms and subroutines for patient-reported outcome data analysis. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.2 |
| Biarch: | true |
| Depends: | R (≥ 3.5.0) |
| Imports: | methods, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), rstantools (≥ 2.1.1) |
| LinkingTo: | BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.26.0), StanHeaders (≥ 2.26.0) |
| SystemRequirements: | GNU make |
| NeedsCompilation: | yes |
| Packaged: | 2023-09-12 15:17:41 UTC; alkb |
| Author: | Bin Wang  [aut,
cre] |
| Maintainer: | Bin Wang <bwang831@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2023-09-12 22:00:02 UTC |
The 'prome' package.
Description
Algorithms to implenment the Bayesian methods to denoise the measurement errors in patient-reported outcome data with repeated measures. Also, two algorithms are included to discount the subgroup means or proportions for clinical studies with multiple subgroups.
Bayesian Hierarchical Model for Information Borrowing for Means
Description
To compute the mean values of subgroups based on a Bayesian hierarchical model.
Usage
MeanHM(x,sigma)
Arguments
x |
Numeric vector of observations for the subgroups. |
sigma |
hyper-parameter. to be estimated or can be given. |
Value
'theta': population mean.
'sigma': population standard deviation.
Examples
x1 <- rnorm(100,2,1)
x2 <- rnorm(100,3,1.5)
x3 <- rnorm(100,4,1.9)
x <- cbind(x1,x2,x3)
MeanHM(x,sigma=0.5)
Bayesian Hierarchical Model for Information Borrowing for Proportions
Description
To compute the proportions of the subgroups assuming the subgroups
follow the same binomial distribution with parameter p.
The approach on partial pooling by Bob Carpenter has been used –
"Hierarchical Partial Pooling for Repeated Binary Trials"
https://mc-stan.org/users/documentation/case-studies/pool-binary-trials.html
Usage
PropHM(x, n, kappa)
Arguments
x |
Numeric vector of events. |
n |
Numberic vector of group sample sizes. |
kappa |
|
Value
'data': data with estimates.
'alpha': parameter of the beta distribution.
'beta': parameter of the beta distribution.
Examples
out <- PropHM(x=c(5,10,2),n=c(20,50,30))
Bayesian Hierarchical Model for RPO data with repeated measures
Description
A Bayesian hierachical model to denoise PRO data using repeated measures.
Usage
bate(x0,x1,group,z,x.range,...)
ResponderAnalysis(x,mcid,type="absolute",conf.level=0.95,show=TRUE)
Arguments
x0, x1 |
Numeric vector/matrix of observations at T0 (baseline) and T1 (end point) of a study. |
z |
covariates |
group |
group assignments. Current version support one or two groups only |
x.range |
range of data 'x0' and 'x1' |
x |
An R object generated by |
mcid |
A threshold to define 'responder' |
type |
The type of responder analysis: absolute or relative changes |
conf.level |
Confidence level of the credible interval |
show |
control whether results should be displayed |
... |
Parameters ("adapt_delta","stepsize","max_treedepth") to improve model fitting/convergence. |
Value
'xfit': fitted results using stan.
'mu.t0': baseline mean.
'sig.t0': baseline SD.
'sig.me': SD of measurement errors.
'mu.active': mean effect size of active treatment.
'sig.active': sd of effect size of active treatment.
'mu.sham': mean effect size of sham treatment.
'sig.sham': sd of effect size of sham treatment.
Examples
data(n100x3)
out1 <- bate(x0=ex100x3$w0,x1=ex100x3$w1,group=ex100x3$group)
out1
ResponderAnalysis(out1,mcid=1,type="abs")
out2 <- bate(x0=ex100x3$w0,x1=ex100x3$w1,group=ex100x3$group,
control = list(adapt_delta = 0.8,
stepsize = 5,
max_treedepth = 10)
)
out2
ResponderAnalysis(out2,mcid=1,type="abs")
out <- out2
ResponderAnalysis(out,mcid=0.5,type="abs")
ResponderAnalysis(out,mcid=1,type="abs")
ResponderAnalysis(out,mcid=1.5,type="abs")
ResponderAnalysis(out,mcid=0.3,type="relative")
ResponderAnalysis(out,mcid=0.2,type="relative")
ResponderAnalysis(out,mcid=0.1,type="relative")
Sample PRO Data With Repeated Measures
Description
A simulated data set of patient-reported outcomes with repeated measures.
Format
A data frame with observations at beaseline and at a follow-up time.
w0 | matrix | measures at baseline |
w1 | matrix | measures at follow-up time |
group | character | group assignment |
Bayesian analysis of 2x2 crossover trial data
Description
A Bayesian hierachical model to analysis data from 2x2 (AB/BA) crossover trials.
Usage
xover(group,y1,y2,y0,...)
Arguments
y0, y1, y2 |
vectors of data from baseline, period 1, and period 2, respectively. |
group |
group or treatment sequence. |
... |
other parameters, i.e. 'control' for model fitting. |
Value
'stat': summary statistics.
'best': estimates using Bayesian analysis.
Examples
xover(y0=rnorm(20,34,1.5),y1=rnorm(20,30,2),
y2=rnorm(20,25,1.5),group=round(runif(20)<0.5))