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Articles 61 - 74 of 74
Full-Text Articles in Statistical Methodology
Inference On Survival Data With Covariate Measurement Error - An Imputation-Based Approach, Yi Li, Louise Ryan
Inference On Survival Data With Covariate Measurement Error - An Imputation-Based Approach, Yi Li, Louise Ryan
Harvard University Biostatistics Working Paper Series
We propose a new method for fitting proportional hazards models with error-prone covariates. Regression coefficients are estimated by solving an estimating equation that is the average of the partial likelihood scores based on imputed true covariates. For the purpose of imputation, a linear spline model is assumed on the baseline hazard. We discuss consistency and asymptotic normality of the resulting estimators, and propose a stochastic approximation scheme to obtain the estimates. The algorithm is easy to implement, and reduces to the ordinary Cox partial likelihood approach when the measurement error has a degenerative distribution. Simulations indicate high efficiency and robustness. …
Implementation Of Estimating-Function Based Inference Procedures With Mcmc Sampler, Lu Tian, Jun S. Liu, L. J. Wei
Implementation Of Estimating-Function Based Inference Procedures With Mcmc Sampler, Lu Tian, Jun S. Liu, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Robust Inferences For Covariate Effects On Survival Time With Censored Linear Regression Models, Larry Leon, Tianxi Cai, L. J. Wei
Robust Inferences For Covariate Effects On Survival Time With Censored Linear Regression Models, Larry Leon, Tianxi Cai, L. J. Wei
Harvard University Biostatistics Working Paper Series
Various inference procedures for linear regression models with censored failure times have been studied extensively. Recent developments on efficient algorithms to implement these procedures enhance the practical usage of such models in survival analysis. In this article, we present robust inferences for certain covariate effects on the failure time in the presence of "nuisance" confounders under a semiparametric, partial linear regression setting. Specifically, the estimation procedures for the regression coefficients of interest are derived from a working linear model and are valid even when the function of the confounders in the model is not correctly specified. The new proposals are …
Cholesky Residuals For Assessing Normal Errors In A Linear Model With Correlated Outcomes: Technical Report, E. Andres Houseman, Louise Ryan, Brent Coull
Cholesky Residuals For Assessing Normal Errors In A Linear Model With Correlated Outcomes: Technical Report, E. Andres Houseman, Louise Ryan, Brent Coull
Harvard University Biostatistics Working Paper Series
Despite the widespread popularity of linear models for correlated outcomes (e.g. linear mixed models and time series models), distribution diagnostic methodology remains relatively underdeveloped in this context. In this paper we present an easy-to-implement approach that lends itself to graphical displays of model fit. Our approach involves multiplying the estimated margional residual vector by the Cholesky decomposition of the inverse of the estimated margional variance matrix. The resulting "rotated" residuals are used to construct an empirical cumulative distribution function and pointwise standard errors. The theoretical framework, including conditions and asymptotic properties, involves technical details that are motivated by Lange and …
Semiparametric Methods For Semi-Competing Risks Problem With Censoring And Truncation, Hongyu Jiang, Jason Fine, Richard J. Chappell
Semiparametric Methods For Semi-Competing Risks Problem With Censoring And Truncation, Hongyu Jiang, Jason Fine, Richard J. Chappell
Harvard University Biostatistics Working Paper Series
Studies of chronic life-threatening diseases often involve both mortality and morbidity. In observational studies, the data may also be subject to administrative left truncation and right censoring. Since mortality and morbidity may be correlated and mortality may censor morbidity, the Lynden-Bell estimator for left truncated and right censored data may be biased for estimating the marginal survival function of the non-terminal event. We propose a semiparametric estimator for this survival function based on a joint model for the two time-to-event variables, which utilizes the gamma frailty specification in the region of the observable data. Firstly, we develop a novel estimator …
The Optimal Confidence Region For A Random Parameter, Hajime Uno, Lu Tian, L.J. Wei
The Optimal Confidence Region For A Random Parameter, Hajime Uno, Lu Tian, L.J. Wei
Harvard University Biostatistics Working Paper Series
Under a two-level hierarchical model, suppose that the distribution of the random parameter is known or can be estimated well. Data are generated via a fixed, but unobservable realization of this parameter. In this paper, we derive the smallest confidence region of the random parameter under a joint Bayesian/frequentist paradigm. On average this optimal region can be much smaller than the corresponding Bayesian highest posterior density region. The new estimation procedure is appealing when one deals with data generated under a highly parallel structure, for example, data from a trial with a large number of clinical centers involved or genome-wide …
One- And Two-Sample Nonparametric Inference Procedures In The Presence Of Dependent Censoring, Yuhyun Park, Lu Tian, L. J. Wei
One- And Two-Sample Nonparametric Inference Procedures In The Presence Of Dependent Censoring, Yuhyun Park, Lu Tian, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Estimating Predictors For Long- Or Short-Term Survivors, Lu Tian, Wei Wang, L. J. Wei
Estimating Predictors For Long- Or Short-Term Survivors, Lu Tian, Wei Wang, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Nonparametric Comparison Of Conditional Distributions With Nonnegligible Cure Fractions, Yi Li, Jin Feng
A Nonparametric Comparison Of Conditional Distributions With Nonnegligible Cure Fractions, Yi Li, Jin Feng
Harvard University Biostatistics Working Paper Series
No abstract provided.
Survival Analysis With Heterogeneous Covariate Measurement Error, Yi Li, Louise Ryan
Survival Analysis With Heterogeneous Covariate Measurement Error, Yi Li, Louise Ryan
Harvard University Biostatistics Working Paper Series
No abstract provided.
Statistical Inference For Infinite Dimensional Parameters Via Asymptotically Pivotal Estimating Functions, Meredith A. Goldwasser, Lu Tian, L. J. Wei
Statistical Inference For Infinite Dimensional Parameters Via Asymptotically Pivotal Estimating Functions, Meredith A. Goldwasser, Lu Tian, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Semi-Parametric Box-Cox Power Transformation Models For Censored Survival Observations, Tianxi Cai, Lu Tian, L. J. Wei
Semi-Parametric Box-Cox Power Transformation Models For Censored Survival Observations, Tianxi Cai, Lu Tian, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Statistical Inferences Based On Non-Smooth Estimating Functions, Lu Tian, Jun S. Liu, Mary Zhao, L. J. Wei
Statistical Inferences Based On Non-Smooth Estimating Functions, Lu Tian, Jun S. Liu, Mary Zhao, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
On The Cox Model With Time-Varying Regression Coefficients, Lu Tian, David Zucker, L. J. Wei
On The Cox Model With Time-Varying Regression Coefficients, Lu Tian, David Zucker, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.