ArticleBone marrow transplantation2022
A SAS macro for estimating direct adjusted survival functions for time-to-event data with or without left truncation.
Article in Bone marrow transplantation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- C-reactive protein and cardiovascular risk among women with no standard modifiable risk factors: evaluating the 'SMuRF-less but inflamed'.European heart journal · 2026Article
- Haploidentical Versus Matched Unrelated Donor Transplants Using Post-Transplantation Cyclophosphamide for Lymphomas.Transplantation and cellular therapy · 2023Article
- Statistical challenges in haematopoietic cell transplantation.Bone marrow transplantation · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.