Survival prediction models: an introduction to discrete-time modeling.
Background: Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods for building these models that have been designed...
| Published in: | BMC Medical Research Methodology Vol. 22; no. 1; pp. 1 - 19 |
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| Main Authors: | , , |
| Format: | research Journal Article |
| Published: |
BioMed Central
7/26/2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158381161&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158381161 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712288 1CI1 jtl: BMC Medical Research Methodology issn: 14712288 maglogo: N pubinfo: dt: 7/26/2022 vid: 22 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 158381161 158381161 NLM35883032 158381161 10.1186/s12874-022-01679-6 NLM35883032 158381161 ppf: 1 ppct: 18 formats: tig: atl: Survival prediction models: an introduction to discrete-time modeling. aug: au: Suresh, Krithika Severn, Cameron Ghosh, Debashis affil: Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, USA sug: subj: Algorithms Software Human Multivariate Analysis Cox Proportional Hazards Model Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales ab: Background: Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods for building these models that have been designed or modified to account for the censoring that occurs in time-to-event data. Discrete-time survival models, which have often been overlooked in the literature, provide an alternative approach for predictive modeling in the presence of censoring with limited loss in predictive accuracy. These models can take advantage of the range of nonparametric machine learning classification algorithms and their available software to predict survival outcomes.Methods: Discrete-time survival models are applied to a person-period data set to predict the hazard of experiencing the failure event in pre-specified time intervals. This framework allows for any binary classification method to be applied to predict these conditional survival probabilities. Using time-dependent performance metrics that account for censoring, we compare the predictions from parametric and machine learning classification approaches applied within the discrete time-to-event framework to those from continuous-time survival prediction models. We outline the process for training and validating discrete-time prediction models, and demonstrate its application using the open-source R statistical programming environment.Results: Using publicly available data sets, we show that some discrete-time prediction models achieve better prediction performance than the continuous-time Cox proportional hazards model. Random survival forests, a machine learning algorithm adapted to survival data, also had improved performance compared to the Cox model, but was sometimes outperformed by the discrete-time approaches. In comparing the binary classification methods in the discrete time-to-event framework, the relative performance of the different methods varied depending on the data set.Conclusions: We present a guide for developing survival prediction models using discrete-time methods and assessing their predictive performance with the aim of encouraging their use in medical research settings. These methods can be applied to data sets that have continuous time-to-event outcomes and multiple clinical predictors. They can also be extended to accommodate new binary classification algorithms as they become available. We provide R code for fitting discrete-time survival prediction models in a github repository. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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