A SAS macro for estimating direct adjusted survival functions for time-to-event data with or without left truncation. Bone Marrow Transplant 2022 Jan;57(1):6-10
Date
08/21/2021Pubmed ID
34413470Pubmed Central ID
PMC9396933DOI
10.1038/s41409-021-01435-2Scopus ID
2-s2.0-85113194011 (requires institutional sign-in at Scopus site) 5 CitationsAbstract
There are several statistical programmes to compute direct adjusted survival estimates from results of the Cox proportional hazards model. However, when used to analyze observational databases with large sample sizes or highly stratified treatment groups such as in registry-related datasets, these programmes are inefficient or unable to generate confidence bands and simultaneous p values. Also, these programmes do not consider potential left-truncation in retrospectively collected data. To address these deficiencies we developed a new SAS macro %adjsurvlt() able to produce direct adjusted survival estimates based on a stratified Cox model. The macro has improved computational performance and is able to handle left-truncated and right-censored time-to-event data. Several mechanisms were implemented to improve computational efficiency including choosing matrix operations over do-loops and reducing dimensions of co-variate matrices. Compared to the latest SAS macro, %adjsurvlt() used < 0.1% computational time to process a dataset with 100 treatment cohorts and a sample size of 20,000 and showed similar computational efficiency when analyzing left-truncated and right-censored data. We illustrate use of %adjsurvlt() to compare retrospectively collected survival data of 2 transplant cohorts.
Author List
Hu ZH, Wang HL, Gale RP, Zhang MJAuthor
Mei-Jie Zhang PhD Professor in the Institute for Health and Equity department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
HumansProportional Hazards Models
Retrospective Studies
Sample Size
Survival Analysis
