A Note on Using Regression Models to Analyze Randomized Trials: Asymptotically Valid Hypothesis Tests Despite Incorrectly Specified Models
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Publication:4919604
DOI10.1111/j.1541-0420.2012.01798.xzbMath1270.62098OpenAlexW2334869311WikidataQ43920726 ScholiaQ43920726MaRDI QIDQ4919604
Publication date: 14 May 2013
Published in: Biometrics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/j.1541-0420.2012.01798.x
likelihoodhypothesis testingmodel misspecificationCox proportional hazards modelparametric proportional hazards model
Linear regression; mixed models (62J05) Parametric hypothesis testing (62F03) Generalized linear models (logistic models) (62J12)
Related Items (1)
Cites Work
- Asymptotic distribution theory for Cox-type regression models with general relative risk form
- Cox's regression model for counting processes: A large sample study
- Some new estimators for Cox regression
- Robust inference for univariate proportional hazards frailty regression models
- Effects of Model Misspecification on Tests of No Randomized Treatment Effect Arising From Cox’s Proportional Hazards Model
- Bias correction for score tests arising from misspecified proportional hazards regression models
- Using Regression Models to Analyze Randomized Trials: Asymptotically Valid Hypothesis Tests Despite Incorrectly Specified Models
- The Robust Inference for the Cox Proportional Hazards Model
- Misspecified proportional hazard models
- A Jackknife Estimator of Variance for Cox Regression for Correlated Survival Data
- Robust covariate-adjusted logrank tests
- Comparing nonnested Cox models
- Semiparametric analysis of transformation models with censored data
- Rank-based inference for the accelerated failure time model
- Maximum Likelihood Estimation of Misspecified Models
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