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Type:

Package

Title:

Bayesian Subgroup Analysis in Clinical Trials

Description:

Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials. For details of the methodology, please refer to D.O. Dixon and R. Simon (1991), Biometrics, 47: 871-881.

Version:

2023.1.0

Date:

2023-10-14

Ravi Varadhan

Author:

Ravi Varadhan[aut, cre], Wenliang Yao[aut]

Maintainer:

Ravi Varadhan <ravi.varadhan@jhu.edu>

Depends:

R (≥ 2.15.1)

Imports:

License:

GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

NeedsCompilation:

Repository:

CRAN

Packaged:

2023-10-14 13:35:27 UTC; raviv

Date/Publication:

2023-10-14 14:10:08 UTC

Bayesian subgroup analysis in clinical trials

Description

Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials.

Details

Package:	DSBayes
Version:	1.1
Date:	Dec 27, 2013
Depends:	R (>= 2.15.1)
Imports:	BB
License:	GPL Version 2

The main functions in this package are:


DSBayes:  A function to calculate the posterior mode and credible interval of the parameters in the Dixon-Simon model, as well as the MLE regression coefficients.

Author(s)

Ravi Varadhan <rvaradhan@jhmi.edu> and Wenliang Yao <yaow080@gmail.com>

References

Dixon D. and Simon R. (1991). Bayesian Subset Analysis. Biometrics, 47, 871-881

Bayesian subgroup analysis in clinical trials

Description

Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials.

Usage

     DSBayes(obj, thetahat, C, lvector, control=list(), ...)

Arguments

obj

The object from a regression model, for example, linear regression or Cox proportional-hazards regression. If obj is specified, then thetahat and C should be set to NULL.

thetahat

A vector of regression coefficients without the intercept. If thetahat is specified, then C should be provided as well, and obj should be set at NULL.

C

A variance covariance matrix of regression. If C is given, then thetahat should also be provided, and obj should be set at NULL.

lvector

A vector or a matrix that denotes linear combination of the parameters for which posterior estimates are desired. Note that, the order of the lvector should be as follows: the first parameter should always be the treatment indicator, then a set of binary covariates, and then the interactions between the treatment with covariates. See *Examples*.

control

A list of control parameters. See *Details*.

...

Additional arguments.

Details

The control argument is a list that can supply any of the following components:

tol: A relative accuracy for numerical quadrature. Default is tol = 1.e-03.
epsilon: A small positive quantity to ensure proper posterior resulting from Jeffreys' prior. Default is epsilon = 0.005.
ci: Level of the credible interval. Default is ci = 0.95.
k: A constant value to determine the interval width for searching the Bayesian credible interval, from lower to upper for a maximum of the density function. Default value for k is, k = qnorm((6+ci)/7) = 2.45.
transform: = NULL, then no transformation is performed. If transform = "logit", which is at default, then logit transformation is applied for posterior density function to find the credibile interval, logit(x) = log(x/(1-x)).
print: = TRUE or FALSE, indicating whether or not we want to print control parameters and progress. Default is FALSE.

Author(s)

Ravi Varadhan <rvaradhan@jhmi.edu> and Wenliang Yao (maintainer) <yaow080@gmail.com>

References

Dixon D. and Simon R. (1991). Bayesian Subset Analysis. Biometrics, 47, 871-881

Examples


# ex1 - use given thetahat and C matrix, and set "obj=NULL".
# an example from the clinical trial reported by Fisher(1988)

thetahat  	<- c(-1.57,-0.52,-0.39,.68, 1.09, 0.68, 0.91)
names(thetahat) <- c("trt","Sex","Age","Stage","trt*sex","trt*age","trt*stage")
p <- length(thetahat)

C <- matrix(NA, p, p)
C[upper.tri(C, diag=TRUE)] <- c( .1502, .0141, .0505, .0198, .0042, .0506,
 .0389, -.0038, .0041, .0538, -.0361, -.0505, -.0042, .0039, .1037, -.0445,
 -.0042, -.0507, -.0041, -.0046, .1066,-.1209, .0037, -.0041, -.0536, -.0025,
 .0120, .1474)
C[lower.tri(C)] <- t(C)[lower.tri(t(C))]

# define lvector
trt 	<- rep(1,8)
cov 	<- as.data.frame(matrix(rep(0,24), ncol=3))
lmatrix<-as.matrix(cbind(trt,cov,rep(1:0,each=4),rep(rep(0:1,each=2),2), rep(0:1,4)))
dimnames(lmatrix)[[2]]<-c("trt","Sex","Age","Stage","trt*sex","trt*age","trt*stage")

lvector <- lmatrix[2,]    # for 1 subset  
#> lvector
#      trt       Sex       Age     Stage   trt*sex   trt*age trt*stage 
#        1         0         0         0         1         0         1 
# treatment effect for the subset of Female under 65 at stage C.
# in this case the reference group is Male, under 65 years, at stage B.

#lvector <- lmatrix       # for all 8 subsets

result <- DSBayes(NULL, thetahat, C, lvector)


################################################################################
# ex2 - use "obj" option, and set "thetahat=NULL" and "C=NULL" 
# To run ex2, you need to remove hashmark(#).

#data(simsolvd)
#simsolvd$event <- 1-simsolvd$censor
#obj <- glm(event~trt*(age+beat+lvef+cardratio+sodium),
#                 family = "binomial", data = simsolvd)
#
#para    <- as.data.frame(matrix(rep(rep(0,5),5), ncol=5))
#lmatrix <- as.matrix(cbind(rep(1,5),para[1:5,],diag(1,5)))
#dimnames(lmatrix)[[2]] <- c("trt","age","beat","lvef","cardratio","sodium",
#"trt*age","trt*beat","trt*lvef","trt*cardratio","trt*sodium")
  
#lvector   <- lmatrix[2,] 	    # for 1 subset 
#out <- DSBayes(obj, NULL, NULL, lvector)

Simulated SOLVD-Trial data set

Description

A simulated clinical trial based on the design of the Studies of Left Ventricular Dysfunction Trial (SOLVD-T), a placebo-controlled trial of the angiotensin-converting-enzyme inhibitor enalapril for patients with congestive heart failure.

Usage

data(simsolvd)

Format

A data frame with 2569 observations on the following 12 variables.

trt: indicator for enalapril group
age: age at baseline (centered and scaled)
beat: pulse at baseline (centered and scaled)
lymphocyte: lymphocyte count at baseline (centered and scaled)
lvef: left ventricular ejection fraction at baseline (centered and scaled)
noise: simulated vector of random uniform variables
nyha: indicator whether New York Heart Association score greater than 2
cardratio: indicator whether cardiothoracic ratio is greater than 0.5
creatinine: creatinine at baseline (centered and scaled)
sodium: sodium at baseline (centered and scaled)
ttodthorchfhosp: time to death or hospitalization in days
censor: indicator whether censored (1) or an event (0)
current: indicator whether current smoker

Source

Simulated data set based on the clinical study reported by: Yusuf, S. et al. (1991). Effect of Enalapril on Survival in Patients with Reduced Left-Ventricular Ejection Fractions and Congestive-Heart-Failure. NEJM 325:293-302.