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13 pages, 694 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Multiple Imputation of Composite Covariates in Survival Studies

by Lily Clements, Alan C. Kimber and Stefanie Biedermann

Stats 2022, 5(2), 358-370; https://doi.org/10.3390/stats5020020 - 29 Mar 2022

Cited by 1 | Viewed by 2669

Abstract

Missing covariate values are a common problem in survival studies, and the method of choice when handling such incomplete data is often multiple imputation. However, it is not obvious how this can be used most effectively when an incomplete covariate is a function of other covariates. For example, body mass index (BMI) is the ratio of weight and height-squared. In this situation, the following question arises: Should a composite covariate such as BMI be imputed directly, or is it advantageous to impute its constituents, weight and height, first and to construct BMI afterwards? We address this question through a carefully designed simulation study that compares various approaches to multiple imputation of composite covariates in a survival context. We discuss advantages and limitations of these approaches for various types of missingness and imputation models. Our results are a first step towards providing much needed guidance to practitioners for analysing their incomplete survival data effectively. Full article

(This article belongs to the Special Issue Survival Analysis: Models and Applications)

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19 pages, 878 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Bayesian Approach for Imputation of Censored Survival Data

by Shirin Moghaddam, John Newell and John Hinde

Stats 2022, 5(1), 89-107; https://doi.org/10.3390/stats5010006 - 26 Jan 2022

Cited by 7 | Viewed by 4784

Abstract

A common feature of much survival data is censoring due to incompletely observed lifetimes. Survival analysis methods and models have been designed to take account of this and provide appropriate relevant summaries, such as the Kaplan–Meier plot and the commonly quoted median survival time of the group under consideration. However, a single summary is not really a relevant quantity for communication to an individual patient, as it conveys no notion of variability and uncertainty, and the Kaplan–Meier plot can be difficult for the patient to understand and also is often mis-interpreted, even by some physicians. This paper considers an alternative approach of treating the censored data as a form of missing, incomplete data and proposes an imputation scheme to construct a completed dataset. This allows the use of standard descriptive statistics and graphical displays to convey both typical outcomes and the associated variability. We propose a Bayesian approach to impute any censored observations, making use of other information in the dataset, and provide a completed dataset. This can then be used for standard displays, summaries, and even, in theory, analysis and model fitting. We particularly focus on the data visualisation advantages of the completed data, allowing displays such as density plots, boxplots, etc, to complement the usual Kaplan–Meier display of the original dataset. We study the performance of this approach through a simulation study and consider its application to two clinical examples. Full article

(This article belongs to the Special Issue Survival Analysis: Models and Applications)

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25 pages, 467 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Resampling Plans and the Estimation of Prediction Error

by Bradley Efron

Stats 2021, 4(4), 1091-1115; https://doi.org/10.3390/stats4040063 - 20 Dec 2021

Cited by 5 | Viewed by 4801

Abstract

This article was prepared for the Special Issue on Resampling methods for statistical inference of the 2020s. Modern algorithms such as random forests and deep learning are automatic machines for producing prediction rules from training data. Resampling plans have been the key technology for evaluating a rule’s prediction accuracy. After a careful description of the measurement of prediction error the article discusses the advantages and disadvantages of the principal methods: cross-validation, the nonparametric bootstrap, covariance penalties (Mallows’

C_{p}

and the Akaike Information Criterion), and conformal inference. The emphasis is on a broad overview of a large subject, featuring examples, simulations, and a minimum of technical detail. Full article

(This article belongs to the Special Issue Re-sampling Methods for Statistical Inference of the 2020s)

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17 pages, 429 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Survival Augmented Patient Preference Incorporated Reinforcement Learning to Evaluate Tailoring Variables for Personalized Healthcare

by Yingchao Zhong, Chang Wang and Lu Wang

Stats 2021, 4(4), 776-792; https://doi.org/10.3390/stats4040046 - 27 Sep 2021

Cited by 3 | Viewed by 3291

Abstract

In this paper, we consider personalized treatment decision strategies in the management of chronic diseases, such as chronic kidney disease, which typically consists of sequential and adaptive treatment decision making. We investigate a two-stage treatment setting with a survival outcome that could be right censored. This can be formulated through a dynamic treatment regime (DTR) framework, where the goal is to tailor treatment to each individual based on their own medical history in order to maximize a desirable health outcome. We develop a new method, Survival Augmented Patient Preference incorporated reinforcement Q-Learning (SAPP-Q-Learning) to decide between quality of life and survival restricted at maximal follow-up. Our method incorporates the latent patient preference into a weighted utility function that balances between quality of life and survival time, in a Q-learning model framework. We further propose a corresponding m-out-of-n Bootstrap procedure to accurately make statistical inferences and construct confidence intervals on the effects of tailoring variables, whose values can guide personalized treatment strategies. Full article

(This article belongs to the Special Issue Survival Analysis: Models and Applications)

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