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Evaluating a Method to Estimate Mediation Effects With Discrete-Time Survival Outcomes.

ABSTRACT: The utility of evaluating mediation effects spans across research domains. The model facilitates investigation of underlying mechanisms of event timing and, as such, has the potential to help strengthen etiological research and inform intervention work that incorporates the evaluation of mediating variables. In order for the analyses to be maximally useful however, it is critical to employ methodology appropriate for the data under investigation. The purpose of this paper is to evaluate a regression-based approach to estimating mediation effects with discrete-time survival outcomes. We empirically evaluate the performance of the discrete-time survival mediation model in a statistical simulation study, and demonstrate that results are functionally equivalent to estimates garnered from a potential-outcomes framework. Simulation results indicate that parameter estimates of mediation in the model were statistically accurate and precise across the range of examined conditions. Type 1 error rates were also tolerable in the conditions studied. Adequate power to detect effects in the model, with binary X and continuous M variables, required effect sizes of the mediation paths to be medium or large. Possible extensions of the model are also considered.

SUBMITTER: Fairchild AJ

PROVIDER: S-EPMC6460901 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Evaluating a Method to Estimate Mediation Effects With Discrete-Time Survival Outcomes.

Fairchild Amanda Jane AJ Cai Chao C McDaniel Heather H Shi Dexin D Gottschall Amanda A Masyn Katherine E KE

Frontiers in psychology 20190405

The utility of evaluating mediation effects spans across research domains. The model facilitates investigation of underlying mechanisms of event timing and, as such, has the potential to help strengthen etiological research and inform intervention work that incorporates the evaluation of mediating variables. In order for the analyses to be maximally useful however, it is critical to employ methodology appropriate for the data under investigation. The purpose of this paper is to evaluate a regres ...[more]

PMID: 31024391

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Project description:When constructing discrete (binned) distributions from samples of a data set, applications exist where it is desirable to assure that all bins of the sample distribution have nonzero probability. For example, if the sample distribution is part of a predictive model for which we require returning a response for the entire codomain, or if we use Kullback-Leibler divergence to measure the (dis-)agreement of the sample distribution and the original distribution of the variable, which, in the described case, is inconveniently infinite. Several sample-based distribution estimators exist which assure nonzero bin probability, such as adding one counter to each zero-probability bin of the sample histogram, adding a small probability to the sample pdf, smoothing methods such as Kernel-density smoothing, or Bayesian approaches based on the Dirichlet and Multinomial distribution. Here, we suggest and test an approach based on the Clopper-Pearson method, which makes use of the binominal distribution. Based on the sample distribution, confidence intervals for bin-occupation probability are calculated. The mean of each confidence interval is a strictly positive estimator of the true bin-occupation probability and is convergent with increasing sample size. For small samples, it converges towards a uniform distribution, i.e., the method effectively applies a maximum entropy approach. We apply this nonzero method and four alternative sample-based distribution estimators to a range of typical distributions (uniform, Dirac, normal, multimodal, and irregular) and measure the effect with Kullback-Leibler divergence. While the performance of each method strongly depends on the distribution type it is applied to, on average, and especially for small sample sizes, the nonzero, the simple "add one counter", and the Bayesian Dirichlet-multinomial model show very similar behavior and perform best. We conclude that, when estimating distributions without an a priori idea of their shape, applying one of these methods is favorable.

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.

Project description:Background: Annually, over 1 billion people sustain traumatic injuries, resulting in over 900,000 deaths in Africa and 6 million deaths globally. Timely response, intervention, and transportation in the prehospital setting reduce morbidity and mortality of trauma victims. Our objective was to describe the existing literature evaluating trauma morbidity and mortality outcomes as a function of prehospital care time to identify gaps in literature and inform future investigation.Main body: We performed a scoping review of published literature in MEDLINE. Results were limited to English language publications from 2009 to 2020. Included articles reported trauma outcomes and prehospital time. We excluded case reports, reviews, systematic reviews, meta-analyses, comments, editorials, letters, and conference proceedings. In total, 808 articles were identified for title and abstract review. Of those, 96 articles met all inclusion criteria and were fully reviewed. Higher quality studies used data derived from trauma registries. There was a paucity of literature from studies in low- and middle-income countries (LMIC), with only 3 (3%) of articles explicitly including African populations. Mortality was an outcome measure in 93% of articles, predominantly defined as "in-hospital mortality" as opposed to mortality within a specified time frame. Prehospital time was most commonly assessed as crude time from EMS dispatch to arrival at a tertiary trauma center. Few studies evaluated physiologic morbidity outcomes such as multi-organ failure.Conclusion: The existing literature disproportionately represents high-income settings and most commonly assessed in-hospital mortality as a function of crude prehospital time. Future studies should focus on how specific prehospital intervals impact morbidity outcomes (e.g., organ failure) and mortality at earlier time points (e.g., 3 or 7?days) to better reflect the effect of early prehospital resuscitation and transport. Trauma registries may be a tool to facilitate such research and may promote higher quality investigations in Africa and LMICs.

Dataset Information

Evaluating a Method to Estimate Mediation Effects With Discrete-Time Survival Outcomes.

Publications

Evaluating a Method to Estimate Mediation Effects With Discrete-Time Survival Outcomes.

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