Multiple imputation in Cox regression when there are time-varying effects of covariates

Quantile regression when the covariates are functions

This paper deals with a linear model of regression on quantiles when the explanatory variable takes values in some functional space and the response is scalar. We propose a spline estimator of the functional coefficient that minimizes a penalized L type criterion. Then, we study the asymptotic behavior of this estimator. The penalization is of primary importance to get existence and convergence.

Multiple Imputation for M-Regression With Censored Covariates

Time-dependent covariates in the Cox proportional-hazards regression model.

The Cox proportional-hazards regression model has achieved widespread use in the analysis of time-to-event data with censoring and covariates. The covariates may change their values over time. This article discusses the use of such time-dependent covariates, which offer additional opportunities but must be used with caution. The interrelationships between the outcome and variable over time can ...

Understanding the Cox Regression Models with Time-Change Covariates

The Cox regression model is a cornerstone of modern survival analysis and is widely used in many other fields as well. But the Cox models with time-change covariates are not easy to understand or visualize. We therefore offer a simple and easy-to-understand interpretation of the (arbitrary) baseline hazard and time-change covariate. This interpretation also provides a way to simulate variables ...

When Are Inferences from Multiple Imputation Valid?

Multiple imputation, as described by Rubin, has seen a wide variety of applications. Counterexamples, presented by Fay (1991), and new methods, such as those of J.N.K. Rao and J. Shao, that can asymptotically disagree with the multiple imputation approach, have raised questions about the validity of multiple imputation. This paper identifies critical restrictions on the practical application of...