Dataset Information

Investigating Progression in Substance Use Initiation Using a Discrete-Time Multiple Event Process Survival Mixture (MEPSUM) Approach.

ABSTRACT: The order and timing of substance initiation has significant implications for later problematic patterns of use. Despite the need to study initiation from a multivariate framework, survival analytic methods typically cannot accommodate more than two substances in one model. The Discrete-Time Multiple Event Process Survival Mixture (MEPSUM; Dean, Bauer, & Shanahan, 2014) model represents an advance by incorporating more than two outcomes and enabling establishment of latent classes within a multivariate hazard distribution. Employing a MEPSUM approach, we evaluated patterns of tobacco, alcohol, and cannabis initiation in the National Longitudinal Study of Adolescent to Adult Health (N=18,923). We found four classes that differed in their ages and ordering of peak initiation risk. Demographics, externalizing psychopathology, and personality significantly predicted class membership. Sex differences in the association between delinquency and initiation patterns also emerged. Findings support the utility of the MEPSUM approach in elucidating developmental pathways underlying clinically relevant phenomena.

SUBMITTER: Richmond-Rakerd LS

PROVIDER: S-EPMC4844227 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Investigating Progression in Substance Use Initiation Using a Discrete-Time Multiple Event Process Survival Mixture (MEPSUM) Approach.

Richmond-Rakerd Leah S LS Fleming Kimberly A KA Slutske Wendy S WS

Clinical psychological science : a journal of the Association for Psychological Science 20150724 2

The order and timing of substance initiation has significant implications for later problematic patterns of use. Despite the need to study initiation from a multivariate framework, survival analytic methods typically cannot accommodate more than two substances in one model. The Discrete-Time Multiple Event Process Survival Mixture (MEPSUM; Dean, Bauer, & Shanahan, 2014) model represents an advance by incorporating more than two outcomes and enabling establishment of latent classes within a multi ...[more]

PMID: 27127730

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Project description:IntroductionMany research studies have well investigated Alzheimer's disease (AD) detection and progression. However, the continuous-time survival prediction of AD is not yet fully explored to support medical practitioners with predictive analytics. In this study, we develop a survival analysis approach to examine interactions between patients' inherent temporal and medical patterns and predict the probability of the AD next stage progression during a time period. The likelihood of reaching the following AD stage is unique to a patient, helping the medical practitioner analyze the patient's condition and provide personalized treatment recommendations ahead of time.MethodologiesWe simulate the disease progression based on patient profiles using non-linear survival methods-non-linear Cox proportional hazard model (Cox-PH) and neural multi-task logistic regression (N-MTLR). In addition, we evaluate the concordance index (C-index) and Integrated Brier Score (IBS) to describe the evolution to the next stage of AD. For personalized forecasting of disease, we also developed deep neural network models using the dataset provided by the National Alzheimer's Coordinating Center with their multiple-visit details between 2005 and 2017.ResultsThe experiment results show that our N-MTLR based survival models outperform the CoxPH models, the best of which gives Concordance-Index of 0.79 and IBS of 0.09. We obtained 50 critical features out of 92 by applying recursive feature elimination and random forest techniques on the clinical data; the top ones include normal cognition and behavior, criteria for dementia, community affairs, etc. Our study demonstrates that selecting critical features can improve the effectiveness of probabilities at each time interval.ConclusionsThe proposed deep learning-based survival method and model can be used by medical practitioners to predict the patients' AD shift efficiently and recommend personalized treatment to mitigate or postpone the effects of AD. More generally, our proposed survival analysis approach for predicting disease stage shift can be used for other progressive diseases such as cancer, Huntington's disease, and scleroderma, just to mention a few, using the corresponding clinical data.

Project description:BackgroundPrediction 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.MethodsDiscrete-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.ResultsUsing 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.ConclusionsWe 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.

Dataset Information

Investigating Progression in Substance Use Initiation Using a Discrete-Time Multiple Event Process Survival Mixture (MEPSUM) Approach.

Publications

Investigating Progression in Substance Use Initiation Using a Discrete-Time Multiple Event Process Survival Mixture (MEPSUM) Approach.

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