A SAS macro for a clustered logrank test

Computer Methods and Programs in Biomedicine

Volumn 104, Issue 2, 2011, Pages 266-270

  Stedman, Margaret R ^b,d   Gagnon, David R ^a,c   Lew, Robert A ^a,c   Jung, Sin Ho ^e   Losina, Elena ^a,b,d   Brookhart, M Alan ^f  

^a BOSTON UNIVERSITY SCHOOL OF PUBLIC HEALTH   (United States)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

^c Boston VA Healthcare System   (United States)

^d HARVARD MEDICAL SCHOOL   (United States)

^e DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^f University of North Carolina at Chapel Hill   (United States)

Author keywords

Cluster randomized trial; Clustered logrank test; Logrank test; SAS macro

Indexed keywords

CLUSTER RANDOMIZED TRIALS; CLUSTERED LOGRANK TEST; LOGRANK TEST; NONPARAMETRIC METHODS; SAS MACRO; SIGNIFICANCE TESTING; SURVIVAL DATA; TREATMENT GROUP;

MACROS; PATIENT TREATMENT;

TESTING;

ARTICLE; CLUSTER ANALYSIS; COMPUTER SIMULATION; DATA ANALYSIS SOFTWARE; FOLLOW UP; HAZARD RATIO; HUMAN; INFORMATION PROCESSING; LOG RANK TEST; OBSERVATIONAL METHOD; RANDOMIZED CONTROLLED TRIAL (TOPIC); SAS MACRO; SURVIVAL;

CLUSTER ANALYSIS; MODELS, THEORETICAL; RANDOMIZED CONTROLLED TRIALS AS TOPIC;

EID: 80054101125     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2011.02.001     Document Type: Article

References (8)

1. Jung S., Jeong J. Rank tests for clustered survival data. Lifetime Data Analysis 2003, 9:21-33.
2. Harrington D.P., Fleming T.R. A class of rank test procedures for censored survival data. Biometrika 1982, 69(3):553-566.
3. Flemming T.R., Harrington D.P. Counting Processes and Survival Analysis 1991, Wiley, New York. 1st ed.
4. Solomon D.H., Polinski J.M., Stedman M., Truppo C., Breiner L., Egan C., Jan S., Patel M., Weiss T.W., Chen Y., Brookhart M.A. Improving care of patients at-risk for osteoporosis: a randomized controlled trial. Journal of General Internal Medicine 2007, 22:362-367.
5. Kelly P.J. A review of software packages for analyzing correlated survival data. The American Statistician 2004, 58(4):337-342.
6. SAS Institute Inc. 2007. SAS OnlineDoc® 9.2. Cary, NC: SAS Institute Inc.
7. E.W. Lee, L.J. Wei, D.A. Amato, Cox-type regression analysis for large numbers of small groups of correlated failure time observations, in: J. Klein, P. Goel (Eds.), Survival Analysis: State of the Art, 1992, pp. 237-245.
8. Jeong J., Jung S. Rank tests for clustered survival data when dependent subunits are randomized. Statistics in Medicine 2006, 25:361-373.