Journal ArticleDOI
Incomplete covariates in the Cox model with applications to biological marker data
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In this paper, the authors proposed a set of estimating equations to estimate the parameters of Cox's proportional hazards model with non-ignorably missing covariate data, and they extended those results to nonignorant data using Monte Carlo EM algorithm.Abstract:
A common occurrence in clinical trials with a survival end point is missing covariate data. With ignorably missing covariate data, Lipsitz and Ibrahim proposed a set of estimating equations to estimate the parameters of Cox's proportional hazards model. They proposed to obtain parameter estimates via a Monte Carlo EM algorithm. We extend those results to non-ignorably missing covariate data. We present a clinical trials example with three partially observed laboratory markers which are used as covariates to predict survival.read more
Citations
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Missing-data methods for generalized linear models: A comparative review
TL;DR: This work examines data that are missing at random and nonignorable missing, and compares four common approaches for inference in generalized linear models with missing covariate data: maximum likelihood (ML), multiple imputation (MI), fully Bayesian (FB), and weighted estimating equations (WEEs).
Journal ArticleDOI
Non‐ignorable missing covariate data in survival analysis: a case‐study of an International Breast Cancer Study Group trial
TL;DR: A method for estimating the parameters in the Cox proportional hazards model when missing covariates may be non-ignorable and continuous or discrete and, through sensitivity analysis, helps investigators to understand the potential effect of missing data on study results.
Journal ArticleDOI
Survival Analysis With Heterogeneous Covariate Measurement Error
Yi Li,Louise Ryan +1 more
TL;DR: In this paper, a time-to-event analysis where the covariate of interest was measured at the wrong time is studied, and the authors show that the problem can be formulated as a special case of survival analysis with heterogeneous covariate measurement error and develop a general analytic framework.
Journal ArticleDOI
Prognostic factor analysis of health-related quality of life data in cancer: a statistical methodological evaluation.
TL;DR: The statistical research methods employed, key issues for HRQOL prognostic factor-analysis parameters and proposes recommendations for future outcome research are discussed.
Journal ArticleDOI
Maximum likelihood inference for the Cox regression model with applications to missing covariates
TL;DR: An in-depth theoretical investigation for existence of maximum likelihood estimates for the Cox model both in the full data setting as well as in the presence of missing covariate data is carried out.
References
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Book
Statistical Analysis with Missing Data
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
Book
Multiple imputation for nonresponse in surveys
TL;DR: In this article, a survey of drinking behavior among men of retirement age was conducted and the results showed that the majority of the participants reported that they did not receive any benefits from the Social Security Administration.
Journal ArticleDOI
Categorical Data Analysis
TL;DR: In this article, categorical data analysis was used for categorical classification of categorical categorical datasets.Categorical Data Analysis, categorical Data analysis, CDA, CPDA, CDSA
Regression models and life tables (with discussion
TL;DR: The drum mallets disclosed in this article are adjustable, by the percussion player, as to balance, overall weight, head characteristics and tone production of the mallet, whereby the adjustment can be readily obtained.
Journal ArticleDOI
Inference and missing data
TL;DR: In this article, it was shown that ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are missing at random and the observed data are observed at random, and then such inferences are generally conditional on the observed pattern of missing data.