Survival estimation and testing via multiple imputation

Survival estimation and testing via multiple imputation

Multiple imputation is a technique for handling data sets with missing values. The method 0lls in each missing value several times, creating many augmented data sets. Each augmented data set is analyzed separately and the results combined to give a 0nal result consisting of an estimate and a measure of uncertainty. In this paper we consider nonparametric multiple-imputation methods to handle mi...

Survival analysis using auxiliary variables via non-parametric multiple imputation.

We develop an approach, based on multiple imputation, that estimates the marginal survival distribution in survival analysis using auxiliary variables to recover information for censored observations. To conduct the imputation, we use two working survival models to define a nearest neighbour imputing risk set. One model is for the event times and the other for the censoring times. Based on the ...

CrowdMI: Multiple Imputation via Crowdsourcing

Missing data is universal and methods to deal with it far ranging from simply ignoring it to using complex modelling strategies such as multiple imputation and maximum likelihood estimation.Missing data has only been effectively imputed by machines via statistical/machine learning models. In this paper we set to answer an important question ”Can humans perform reasonably well to fill in missing...

multiple imputation in survival models: applied on breast cancer data

Flexible modeling of survival data with covariates subject to detection limits via multiple imputation

Models for survival data generally assume that covariates are fully observed. However, in medical studies it is not uncommon for biomarkers to be censored at known detection limits. A computationally-efficient multiple imputation procedure for modeling survival data with covariates subject to detection limits is proposed. This procedure is developed in the context of an accelerated failure time...