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Survival Analysis: Models and Applications
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Survival Analysis: Models and Applications

A special issue of Stats (ISSN 2571-905X).

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 21498

Special Issue Editor

Dr. Haiming Zhou

E-Mail Website
Guest Editor
Biostatistics & Data Management, Daiichi Sankyo, Inc., Basking Ridge, NJ 07920, USA
Interests: survival analysis; regression models; Bayesian survival models; spatial survival models; competing risks models; cure rate models; software development; applications.

Special Issue Information

Dear Colleagues,

I am pleased to announce a Special Issue on “Survival Analysis: Models and Applications”. Survival analysis has a broad range of applications in fields that deal with time-to-event data, such as public health, engineering, biomedical science, actuarial science, and environmental science. This Special Issue will present a collection of the latest developments in survival models and their applications to new subject-matter challenges. Suitable topics include, but are not limited to, flexible but interpretable regression models, Bayesian survival models, spatial survival models, competing risk models, cure rate models, discrete survival models, methods for analyzing data in non-standard settings, and software development. Manuscripts that apply state-of-the-art survival models to new and ongoing real-world problems (e.g., the COVID-19 epidemic) are especially welcome.

I look forward to receiving your submissions. 

Sincerely, 

Dr. Haiming Zhou
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Stats is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • survival analysis
  • Bayesian inference
  • spatial models
  • censoring
  • regression models
  • competing risks
  • cure fraction

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Published Papers (7 papers)

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Research

14 pages, 11029 KiB  
Article
Semiparametric Survival Analysis of 30-Day Hospital Readmissions with Bayesian Additive Regression Kernel Model
by Sounak Chakraborty, Peng Zhao, Yilun Huang and Tanujit Dey
Stats 2022, 5(3), 617-630; https://doi.org/10.3390/stats5030038 - 14 Jul 2022
Cited by 2 | Viewed by 2707
Abstract
In this paper, we introduce a kernel-based nonlinear Bayesian model for a right-censored survival outcome data set. Our kernel-based approach provides a flexible nonparametric modeling framework to explore nonlinear relationships between predictors with right-censored survival outcome data. Our proposed kernel-based model is shown [...] Read more.
In this paper, we introduce a kernel-based nonlinear Bayesian model for a right-censored survival outcome data set. Our kernel-based approach provides a flexible nonparametric modeling framework to explore nonlinear relationships between predictors with right-censored survival outcome data. Our proposed kernel-based model is shown to provide excellent predictive performance via several simulation studies and real-life examples. Unplanned hospital readmissions greatly impair patients’ quality of life and have imposed a significant economic burden on American society. In this paper, we focus our application on predicting 30-day readmissions of patients. Our survival Bayesian additive regression kernel model (survival BARK or sBARK) improves the timeliness of readmission preventive intervention through a data-driven approach. Full article
(This article belongs to the Special Issue Survival Analysis: Models and Applications)
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13 pages, 414 KiB  
Article
The Missing Indicator Approach for Accelerated Failure Time Model with Covariates Subject to Limits of Detection
by Norah Alyabs and Sy Han Chiou
Stats 2022, 5(2), 494-506; https://doi.org/10.3390/stats5020029 - 10 May 2022
Viewed by 2343
Abstract
The limit of detection (LOD) is commonly encountered in observational studies when one or more covariate values fall outside the measuring ranges. Although the complete-case (CC) approach is widely employed in the presence of missing values, it could result in biased estimations or [...] Read more.
The limit of detection (LOD) is commonly encountered in observational studies when one or more covariate values fall outside the measuring ranges. Although the complete-case (CC) approach is widely employed in the presence of missing values, it could result in biased estimations or even become inapplicable in small sample studies. On the other hand, approaches such as the missing indicator (MDI) approach are attractive alternatives as they preserve sample sizes. This paper compares the effectiveness of different alternatives to the CC approach under different LOD settings with a survival outcome. These alternatives include substitution methods, multiple imputation (MI) methods, MDI approaches, and MDI-embedded MI approaches. We found that the MDI approach outperformed its competitors regarding bias and mean squared error in small sample sizes through extensive simulation. Full article
(This article belongs to the Special Issue Survival Analysis: Models and Applications)
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13 pages, 694 KiB  
Article
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 2700
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 [...] Read more.
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  
Article
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 4863
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 [...] Read more.
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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17 pages, 429 KiB  
Article
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 3324
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 [...] Read more.
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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15 pages, 560 KiB  
Article
Fiducial Inference on the Right Censored Birnbaum–Saunders Data via Gibbs Sampler
by Kalanka P. Jayalath
Stats 2021, 4(2), 385-399; https://doi.org/10.3390/stats4020025 - 21 May 2021
Cited by 5 | Viewed by 2043
Abstract
In this article, we implement a flexible Gibbs sampler to make inferences for two-parameter Birnbaum–Saunders (BS) distribution in the presence of right-censored data. The Gibbs sampler is applied on the fiducial distributions of the BS parameters derived using the maximum likelihood, methods of [...] Read more.
In this article, we implement a flexible Gibbs sampler to make inferences for two-parameter Birnbaum–Saunders (BS) distribution in the presence of right-censored data. The Gibbs sampler is applied on the fiducial distributions of the BS parameters derived using the maximum likelihood, methods of moments, and their bias-reduced estimates. A Monte-Carlo study is conducted to make comparisons between these estimates for Type-II right censoring with various parameter settings, sample sizes, and censoring percentages. It is concluded that the bias-reduced estimates outperform the rest with increasing precision. Higher sample sizes improve the overall accuracy of all the estimates while the amount of censoring shows a negative effect. Further comparisons are made with existing methods using two real-world examples. Full article
(This article belongs to the Special Issue Survival Analysis: Models and Applications)
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11 pages, 456 KiB  
Article
Weighted Log-Rank Statistics for Accelerated Failure Time Model
by Seung-Hwan Lee
Stats 2021, 4(2), 348-358; https://doi.org/10.3390/stats4020023 - 3 May 2021
Cited by 3 | Viewed by 2380
Abstract
This paper improves the sensitivity of the Gρ family of weighted log-rank tests for the accelerated failure time model, accommodating realistic alternatives in survival analysis with censored data, such as heavy censoring and crossing hazards. The procedures are based on a weight [...] Read more.
This paper improves the sensitivity of the Gρ family of weighted log-rank tests for the accelerated failure time model, accommodating realistic alternatives in survival analysis with censored data, such as heavy censoring and crossing hazards. The procedures are based on a weight function with the censoring proportion incorporated as a component. Extensive simulations show that the weight function enhances the performance of the Gρ family, increasing its sensitivity and flexibility. The weight function method is illustrated with an example concerning vaginal cancer. Full article
(This article belongs to the Special Issue Survival Analysis: Models and Applications)
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