bayesm (version 3.0-2)

detailing: Physician Detailing Data from Manchanda et al (2004)

Description

Monthly data on detailing (sales calls) on 1000 physicians. 23 mos of data for each physician. Includes physician covariates. Dependent variable (scripts) is the number of new prescriptions ordered by the physician for the drug detailed.

Usage

data(detailing)

Format

This R object is a list of two data frames, list(counts,demo).

List of 2:

$ counts:`data.frame': 23000 obs. of 4 variables: …$ id : int [1:23000] 1 1 1 1 1 1 1 1 1 1 …$ scripts : int [1:23000] 3 12 3 6 5 2 5 1 5 3 …$ detailing : int [1:23000] 1 1 1 2 1 0 2 2 1 1 …$ lagged_scripts: int [1:23000] 4 3 12 3 6 5 2 5 1 5

$ demo :`data.frame': 1000 obs. of 4 variables: …$ id : int [1:1000] 1 2 3 4 5 6 7 8 9 10 …$ generalphys : int [1:1000] 1 0 1 1 0 1 1 1 1 1 …$ specialist: int [1:1000] 0 1 0 0 1 0 0 0 0 0 …$ mean_samples: num [1:1000] 0.722 0.491 0.339 3.196 0.348

Details

generalphys is dummy for if doctor is a "general practitioner," specialist is dummy for if the physician is a specialist in the theraputic class for which the drug is intended, mean_samples is the mean number of free drug samples given the doctor over the sample.

Examples

data(detailing)
cat(" table of Counts Dep Var", fill=TRUE)
print(table(detailing$counts[,2]))
cat(" means of Demographic Variables",fill=TRUE)
mat=apply(as.matrix(detailing$demo[,2:4]),2,mean)
print(mat)

##
## example of processing for use with rhierNegbinRw
##
if(0)
{
data(detailing)
counts = detailing$counts
Z = detailing$demo

# Construct the Z matrix
Z[,1] = 1
Z[,2]=Z[,2]-mean(Z[,2])
Z[,3]=Z[,3]-mean(Z[,3])
Z[,4]=Z[,4]-mean(Z[,4])
Z=as.matrix(Z)
id=levels(factor(counts$id))
nreg=length(id)
nobs = nrow(counts$id)

regdata=NULL
for (i in 1:nreg) {
    X = counts[counts[,1] == id[i],c(3:4)]
    X = cbind(rep(1,nrow(X)),X)
    y = counts[counts[,1] == id[i],2]
    X = as.matrix(X)
    regdata[[i]]=list(X=X, y=y)
}
nvar=ncol(X)            # Number of X variables
nz=ncol(Z)              # Number of Z variables
rm(detailing,counts)              
cat("Finished Reading data",fill=TRUE)
fsh()

Data = list(regdata=regdata, Z=Z)
deltabar = matrix(rep(0,nvar*nz),nrow=nz)
Vdelta = 0.01 * diag(nz)
nu = nvar+3
V = 0.01*diag(nvar)
a = 0.5
b = 0.1
Prior = list(deltabar=deltabar, Vdelta=Vdelta, nu=nu, V=V, a=a, b=b)

R = 10000
keep =1
s_beta=2.93/sqrt(nvar)
s_alpha=2.93
c=2
Mcmc = list(R=R, keep = keep, s_beta=s_beta, s_alpha=s_alpha, c=c)
out = rhierNegbinRw(Data, Prior, Mcmc)

# Unit level mean beta parameters
Mbeta = matrix(rep(0,nreg*nvar),nrow=nreg)
ndraws = length(out$alphadraw)
for (i in 1:nreg) { Mbeta[i,] = rowSums(out$Betadraw[i, , ])/ndraws }

cat(" Deltadraws ",fill=TRUE)
summary(out$Deltadraw)
cat(" Vbetadraws ",fill=TRUE)
summary(out$Vbetadraw)
cat(" alphadraws ",fill=TRUE)
summary(out$alphadraw)

if(0){
## plotting examples
plot(out$betadraw)
plot(out$alphadraw)
plot(out$Deltadraw)
}
}