The performance of different propensity score methods for estimating absolute effects of treatments on survival outcomes: A simulation study
Observational studies are increasingly being used to estimate the effect of treatments, interventions and exposures on outcomes that can occur over time. Historically, the hazard ratio, which is a relative measure of effect, has been reported. However, medical decision making is best informed when b...
| Main Authors: | , |
|---|---|
| Format: | Online |
| Language: | English |
| Published: |
SAGE Publications
2014
|
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5051602/ |
| id |
pubmed-5051602 |
|---|---|
| recordtype |
oai_dc |
| spelling |
pubmed-50516022016-10-12 The performance of different propensity score methods for estimating absolute effects of treatments on survival outcomes: A simulation study Austin, Peter C Schuster, Tibor Articles Observational studies are increasingly being used to estimate the effect of treatments, interventions and exposures on outcomes that can occur over time. Historically, the hazard ratio, which is a relative measure of effect, has been reported. However, medical decision making is best informed when both relative and absolute measures of effect are reported. When outcomes are time-to-event in nature, the effect of treatment can also be quantified as the change in mean or median survival time due to treatment and the absolute reduction in the probability of the occurrence of an event within a specified duration of follow-up. We describe how three different propensity score methods, propensity score matching, stratification on the propensity score and inverse probability of treatment weighting using the propensity score, can be used to estimate absolute measures of treatment effect on survival outcomes. These methods are all based on estimating marginal survival functions under treatment and lack of treatment. We then conducted an extensive series of Monte Carlo simulations to compare the relative performance of these methods for estimating the absolute effects of treatment on survival outcomes. We found that stratification on the propensity score resulted in the greatest bias. Caliper matching on the propensity score and a method based on earlier work by Cole and Hernán tended to have the best performance for estimating absolute effects of treatment on survival outcomes. When the prevalence of treatment was less extreme, then inverse probability of treatment weighting-based methods tended to perform better than matching-based methods. SAGE Publications 2014-01-23 2016-10 /pmc/articles/PMC5051602/ /pubmed/24463885 http://dx.doi.org/10.1177/0962280213519716 Text en © The Author(s) 2014 http://creativecommons.org/licenses/by-nc/2.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Austin, Peter C Schuster, Tibor |
| spellingShingle |
Austin, Peter C Schuster, Tibor The performance of different propensity score methods for estimating absolute effects of treatments on survival outcomes: A simulation study |
| author_facet |
Austin, Peter C Schuster, Tibor |
| author_sort |
Austin, Peter C |
| title |
The performance of different propensity score methods for estimating absolute effects of treatments on survival outcomes: A simulation study |
| title_short |
The performance of different propensity score methods for estimating absolute effects of treatments on survival outcomes: A simulation study |
| title_full |
The performance of different propensity score methods for estimating absolute effects of treatments on survival outcomes: A simulation study |
| title_fullStr |
The performance of different propensity score methods for estimating absolute effects of treatments on survival outcomes: A simulation study |
| title_full_unstemmed |
The performance of different propensity score methods for estimating absolute effects of treatments on survival outcomes: A simulation study |
| title_sort |
performance of different propensity score methods for estimating absolute effects of treatments on survival outcomes: a simulation study |
| description |
Observational studies are increasingly being used to estimate the effect of treatments, interventions and exposures on outcomes that can occur over time. Historically, the hazard ratio, which is a relative measure of effect, has been reported. However, medical decision making is best informed when both relative and absolute measures of effect are reported. When outcomes are time-to-event in nature, the effect of treatment can also be quantified as the change in mean or median survival time due to treatment and the absolute reduction in the probability of the occurrence of an event within a specified duration of follow-up. We describe how three different propensity score methods, propensity score matching, stratification on the propensity score and inverse probability of treatment weighting using the propensity score, can be used to estimate absolute measures of treatment effect on survival outcomes. These methods are all based on estimating marginal survival functions under treatment and lack of treatment. We then conducted an extensive series of Monte Carlo simulations to compare the relative performance of these methods for estimating the absolute effects of treatment on survival outcomes. We found that stratification on the propensity score resulted in the greatest bias. Caliper matching on the propensity score and a method based on earlier work by Cole and Hernán tended to have the best performance for estimating absolute effects of treatment on survival outcomes. When the prevalence of treatment was less extreme, then inverse probability of treatment weighting-based methods tended to perform better than matching-based methods. |
| publisher |
SAGE Publications |
| publishDate |
2014 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5051602/ |
| _version_ |
1613673790505484288 |