scholarly journals The use of propensity score methods with survival or time‐to‐event outcomes: reporting measures of effect similar to those used in randomized experiments

2013 ◽  
Vol 33 (7) ◽  
pp. 1242-1258 ◽  
Author(s):  
Peter C. Austin
Keyword(s):  
Propensity Score ◽  
Randomized Experiments ◽  
Time To Event ◽  
Propensity Score Methods

scholarly journals Propensity Score Methods for Analyzing Observational Data Like Randomized Experiments: Challenges and Solutions for Rare Outcomes and Exposures

2015 ◽  
Vol 181 (12) ◽  
pp. 989-995 ◽  
Author(s):  
M. E. Ross ◽  
A. R. Kreider ◽  
Y.-S. Huang ◽  
M. Matone ◽  
D. M. Rubin ◽  
...  
Keyword(s):  
Propensity Score ◽  
Observational Data ◽  
Randomized Experiments ◽  
Propensity Score Methods

scholarly journals Assessing the performance of the generalized propensity score for estimating the effect of quantitative or continuous exposures on survival or time-to-event outcomes

2018 ◽  
Vol 28 (8) ◽  
pp. 2348-2367
Author(s):  
Peter C Austin
Keyword(s):  
Propensity Score ◽  
Response Function ◽  
Dose Response ◽  
Covariate Adjustment ◽  
Time To Event ◽  
Probability Of Survival ◽  
Propensity Score Methods ◽  
Generalized Propensity Score ◽  
Dose Response Function ◽  
Do So

Propensity score methods are frequently used to estimate the effects of interventions using observational data. The propensity score was originally developed for use with binary exposures. The generalized propensity score (GPS) is an extension of the propensity score for use with quantitative or continuous exposures (e.g. pack-years of cigarettes smoked, dose of medication, or years of education). We describe how the GPS can be used to estimate the effect of continuous exposures on survival or time-to-event outcomes. To do so we modified the concept of the dose–response function for use with time-to-event outcomes. We used Monte Carlo simulations to examine the performance of different methods of using the GPS to estimate the effect of quantitative exposures on survival or time-to-event outcomes. We examined covariate adjustment using the GPS and weighting using weights based on the inverse of the GPS. The use of methods based on the GPS was compared with the use of conventional G-computation and weighted G-computation. Conventional G-computation resulted in estimates of the dose–response function that displayed the lowest bias and the lowest variability. Amongst the two GPS-based methods, covariate adjustment using the GPS tended to have the better performance. We illustrate the application of these methods by estimating the effect of average neighbourhood income on the probability of survival following hospitalization for an acute myocardial infarction.

scholarly journals Methodological comparison of marginal structural model, time-varying Cox regression, and propensity score methods: the example of antidepressant use and the risk of hip fracture

2016 ◽  
Vol 25 ◽  
pp. 114-121 ◽  
Author(s):  
M. Sanni Ali ◽  
Rolf H. H. Groenwold ◽  
Svetlana V. Belitser ◽  
Patrick C. Souverein ◽  
Elisa Martín ◽  
...  
Keyword(s):  
Hip Fracture ◽  
Propensity Score ◽  
Structural Model ◽  
Cox Regression ◽  
Time Varying ◽  
Marginal Structural Model ◽  
Propensity Score Methods ◽  
Antidepressant Use ◽  
Methodological Comparison

Using conventional and machine learning propensity score methods to examine the effectiveness of 12-step group involvement following inpatient addiction treatment

2021 ◽  
pp. 108943
Author(s):  
Mary Jean Costello ◽  
Yao Li ◽  
Yeying Zhu ◽  
Alyna Walji ◽  
Sarah Sousa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Propensity Score ◽  
Addiction Treatment ◽  
Propensity Score Methods ◽  
Group Involvement

Propensity Score Methods for Creating Covariate Balance in Observational Studies

2011 ◽  
Vol 64 (10) ◽  
pp. 897-903 ◽  
Author(s):  
Cassandra W. Pattanayak ◽  
Donald B. Rubin ◽  
Elizabeth R. Zell
Keyword(s):  
Propensity Score ◽  
Observational Studies ◽  
Propensity Score Methods ◽  
Covariate Balance

scholarly journals Plea for Standardized Reporting and Justification of Propensity Score Methods

2018 ◽  
Vol 68 (4) ◽  
pp. 710-711 ◽  
Author(s):  
Jan A Roth ◽  
Fabrice Juchler ◽  
Andreas F Widmer ◽  
Manuel Battegay
Keyword(s):  
Propensity Score ◽  
Propensity Score Methods ◽  
Standardized Reporting
Sign in / Sign up

Export Citation Format

Share Document