Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects

2002 ◽  
Vol 21 (15) ◽  
pp. 2175-2197 ◽  
Author(s):  
Patrick Royston ◽  
Mahesh K. B. Parmar
Keyword(s):  
Survival Data ◽  
Treatment Effects ◽  
Proportional Hazards ◽  
Proportional Odds ◽  
Censored Survival Data ◽  
Proportional Odds Models ◽  
Prognostic Modelling

Tests of Proportional Hazards and Proportional Odds Models for Grouped Survival Data

Biometrics ◽  
2000 ◽  
Vol 56 (4) ◽  
pp. 1233-1240 ◽  
Author(s):  
Enrico A. Colosimo ◽  
Liciana V. A. S. Chalita ◽  
Clarice G. B. Demétrio
Keyword(s):  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Odds ◽  
Proportional Odds Models

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 Correction to: Baseline-adjusted proportional odds models for the quantification of treatment effects in trials with ordinal sum score outcomes

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Muriel Buri ◽  
Armin Curt ◽  
John Steeves ◽  
Torsten Hothorn
Keyword(s):  
Treatment Effects ◽  
Proportional Odds ◽  
Ordinal Sum ◽  
Sum Score ◽  
Proportional Odds Models

An amendment to this paper has been published and can be accessed via the original article.

Survival analysis via cox proportional hazards additive models

2017 ◽  
Vol 01 (01) ◽  
pp. 1650003
Author(s):  
Lu Bai ◽  
Daniel Gillen
Keyword(s):  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Generalized Additive Models ◽  
Cox Proportional Hazards ◽  
Additive Models ◽  
Cox Proportional Hazards Model ◽  
Linear Predictor ◽  
Censored Survival Data

The Cox proportional hazards model is commonly used to examine the covariate-adjusted association between a predictor of interest and the risk of mortality for censored survival data. However, it assumes a parametric relationship between covariates and mortality risk though a linear predictor. Generalized additive models (GAMs) provide a flexible extension of the usual linear model and are capable of capturing nonlinear effects of predictors while retaining additivity between the predictor effects. In this paper, we provide a review of GAMs and incorporate bivariate additive modeling into the Cox model for censored survival data with applications to estimating geolocation effects on survival in spatial epidemiologic studies.

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