Propensity score applied to survival data analysis through proportional hazards models: a Monte Carlo study

2012 ◽  
Vol 11 (3) ◽  
pp. 222-229 ◽  
Author(s):  
Etienne Gayat ◽  
Matthieu Resche-Rigon ◽  
Jean-Yves Mary ◽  
Raphaël Porcher
Keyword(s):  
Monte Carlo ◽  
Data Analysis ◽  
Propensity Score ◽  
Survival Data ◽  
Proportional Hazards ◽  
Monte Carlo Study ◽  
Proportional Hazards Models

Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study

2007 ◽  
Vol 26 (4) ◽  
pp. 754-768 ◽  
Author(s):  
Peter C. Austin ◽  
Paul Grootendorst ◽  
Sharon-Lise T. Normand ◽  
Geoffrey M. Anderson
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Treatment Effect ◽  
Monte Carlo Study ◽  
Biased Estimation

Analysis of censored survival data using random regression models

2001 ◽  
Vol 72 (1) ◽  
pp. 1-10 ◽  
Author(s):  
R. F. Veerkamp ◽  
S. Brotherstone ◽  
B. Engel ◽  
T. H. E. Meuwissen
Keyword(s):  
Milk Yield ◽  
Survival Data ◽  
Variance Components ◽  
Regression Models ◽  
Proportional Hazards ◽  
Random Regression ◽  
Breeding Values ◽  
Censored Survival Data ◽  
Proportional Hazards Models ◽  
Randomly Censored Data

AbstractCensoring of records is a problem in the prediction of breeding values for longevity, because breeding values are required before actual lifespan is known. In this study we investigated the use of random regression models to analyse survival data, because this method combines some of the advantages of a multitrait approach and the more sophisticated proportional hazards models. A model was derived for the binary representation of survival data and links with proportional hazards models and generalized linear models are shown. Variance components and breeding values were predicted using a linear approximation, including time-dependent fixed effects and random regression coefficients. Production records in lactations 1 to 5 were available on 24741 cows in the UK, all having had the opportunity to survive five lactations. The random regression model contained a linear regression on milk yield within herd (no. = 1417) by lactation number (no. = 4), Holstein percentage and year-month of calving effect (no. = 72). The additive animal genetic effects were modelled using orthogonal polynomials of order 1 to 4 with random coefficients and the error terms were fitted for each lactation separately, either correlated or not. Variance components from the full (i.e. uncensored) data set, were used to predict breeding values for survival in each lactation from both uncensored and randomly censored data. In the uncensored data, estimates of heritabilities for culling probability in each lactation ranged from 0·02 to 0·04. Breeding values for lifespan (calculated from the survival breeding values) had a range of 2·4 to 3·6 lactations and a standard deviation of 0·25. Correlations between predicted breeding values for 129 bulls, each with more than 30 daughters, from the various data sets ranged from 0·81 to 0·99 and were insensitive to the model used. It is concluded that random regression analysis models used for test-day records analysis of milk yield, might also be of use in the analysis of censored survival data.

scholarly journals Optimal Caliper Width for Propensity Score Matching of Three Treatment Groups: A Monte Carlo Study

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e81045 ◽  
Author(s):  
Yongji Wang ◽  
Hongwei Cai ◽  
Chanjuan Li ◽  
Zhiwei Jiang ◽  
Ling Wang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Monte Carlo Study ◽  
Treatment Groups

Within-center matching performed better when using propensity score matching to analyze multicenter survival data: empirical and Monte Carlo studies

2013 ◽  
Vol 66 (9) ◽  
pp. 1029-1037 ◽  
Author(s):  
Etienne Gayat ◽  
Gabriel Thabut ◽  
Jason D. Christie ◽  
Alexandre Mebazaa ◽  
Jean-Yves Mary ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Survival Data ◽  
Monte Carlo Studies
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