Special Issue "Survival Analysis: Models and Applications"
Special Issue Editor
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 papers will be 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 1200 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
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Author: Yingchao Zhong, Chang Wang, and Lu Wang
Abstract: In this paper, we consider personalized treatment decision strategies in the management of chronic diseases, such as end stage renal 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 given their 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 the personalized treatment strategies.
Title: A double robust estimator of the causal t-year risk difference for right censored competing risks data, with applications to nationwide registry data
Author: Paul Blanche, Anders Holt, Thomas Scheike
Abstract: Registry data are often used to compare several treatments routinely prescribed to similar patients. With these data, we often aim to estimate (causal) treatment effects via the t-year risk of an event (e.g. stroke). This leads to challenges due to 1) confounding, 2) lost-of-follow-up (censoring) and 3) competing risks (death event free). We argue that methods for causal inference and survival data can tackle these challenges efficiently, when appropriately used in combination. We present a method that i) facilitates thorough modeling choices based on a priori clinical knowledge ii) enjoys a double robustness property and iii) is easy to implement using standard software. Simulation results show good finite sample properties and an illustration to nationwide registry data from 2003-2018 is presented (n=30,177). We compared the 33-month risk of cardiovascular events among patients treated with or without beta blocker, among stable patients after myocardial infarction.
