UIS: UIS - The UMARU IMPACT Study - Data

In Raydonal/prLogistic: Estimation of Prevalence Ratios using Logistic Models

Description

Data were from randomized trials related to treatment for drug abuse obtained by the University of Massachusetts Aids Research Unit (UMARU) IMPACT Study (UIS). The main goal of the study was to compare treatment programs of different durations in the reduction of drug abuse and in the prevention of high-risk HIV behavior (Hosmer and Lemeshow, 2000). The variables on the dataset included here are age at enrollment, drug use history at admission, race, treatment group, treatment site, and patient's status at the end of the treatment program (remained drug free or otherwise). These data are copyrighted by John Wiley & Sons Inc. and are used here to illustrate the calculation of prevalence ratios and corresponding confidence intervals.

Usage

data(UIS)

Format

A data frame with 575 observations on the following 7 variables:

ID

Identification code.

Age

Age at enrollment (in years) recoded to 1= 32 years or younger, 0= more than 32 years-old.

DrugUse

Drug use history at admission (1=never, 2=previous, 3=recent). Recoded to 1= never and 0= previous or recent.

race

Subject's race (0=white, 1=other).

trt

Treatment group (0=short, 1=long).

site

Treatment site (0=A, 1=B).

drugFree

Returned to drug use prior to the scheduled end of the treatment program (1=remained drug free, 0=otherwise).

Source

The data come from
Hosmer and Lemeshow (2000) Applied Logistic Regression. 2nd Edition, New York: John-Wiley & Sons.
These data are copyrighted by John Wiley & Sons Inc.
We acknowledged it and use the data to illustrate the methodology included in this library.

References

Weisberg, S. (2005) Applied Linear Regression. Wiley-Interscience.

Examples

data("UIS", package = "prLogistic")
 attach(UIS) 
 tmp=table(drugFree,trt)   
 options(digits=2)             
 prop.table(tmp,2)             

Raydonal/prLogistic documentation built on May 28, 2019, 2:27 p.m.