A Comparison of Bayesian Multivariate Versus Univariate Normal Regression Models for Prediction

Li, Xun; Ghosh, Joyee; Villarini, Gabriele

doi:10.1080/00031305.2022.2087735

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…To address this issue, we capitalize on the wealth of correlated and clustered health outcomes collected in trials by utilizing multivariate models, which have demonstrated significant improvements in estimation and prediction accuracy compared to their univariate counterparts [13][14][15][16][17][18][19]. Although correlated and clustered observations are often modeled (in the frequentist paradigm) by a marginal model via generalized estimating equations or a generalized linear mixed model [20], Bayesian methods can handle highly complex hierarchical structures and efficiently estimate parameters via Markov Chain Monte Carlo sampling, making it an appealing and efficient strategy [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Improving Individualized Treatment Decisions: A Bayesian Multivariate Hierarchical Model for Developing a Treatment Benefit Index using Mixed Types of Outcomes

Wu,

Goldfeld,

Petkova

et al. 2023

Preprint

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BackgroundPrecision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs.MethodsTo address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the “borrowing of information” across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative treatment benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model.ResultsWe demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analysis demonstrates the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs.ConclusionThe study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment

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Section: Introductionmentioning

confidence: 99%

Improving Individualized Treatment Decisions: A Bayesian Multivariate Hierarchical Model for Developing a Treatment Benefit Index using Mixed Types of Outcomes

Wu,

Goldfeld,

Petkova

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes

Wu,

Goldfeld,

Petkova

et al. 2024

BMC Med Res Methodol

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Background Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs. Methods To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the “borrowing of information” across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model. Results We demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analyses demonstrate the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs. Conclusion The study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.

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Time Series Forecasting Techniques for Climate Trend Prediction

Zakari,

Lawal,

Kalinaki

et al. 2024

Advances in Environmental Engineering and Green Technologies

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Climate change is a pressing global issue that profoundly impacts ecosystems, economies, and societies. Accurate climate trend prediction is crucial for informed decision-making and mitigation strategies. This study focuses on time series forecasting techniques as vital tools in predicting climate trends. It explores the complexities of climate time series data and the challenges associated with the data. The study explores traditional methods like Autoregressive Integrated Moving Average (ARIMA), highlighting their applicability and limitations. It also showcases the power of machine learning and statistical techniques in addressing climate data intricacies through real-world examples. In the era of technology, deep learning (DL) approaches, including recurrent neural networks, Long short-term memory (LSTM), Gated Recurrent Unit (GRU), and transformer-based models, are emerging for climate change forecasting. The study looks ahead to ongoing research and trends in climate time series forecasting, outlining challenges and promising areas for exploration.

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A Comparison of Bayesian Multivariate Versus Univariate Normal Regression Models for Prediction

Cited by 3 publications

References 18 publications

Improving Individualized Treatment Decisions: A Bayesian Multivariate Hierarchical Model for Developing a Treatment Benefit Index using Mixed Types of Outcomes

Improving Individualized Treatment Decisions: A Bayesian Multivariate Hierarchical Model for Developing a Treatment Benefit Index using Mixed Types of Outcomes

A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes

Time Series Forecasting Techniques for Climate Trend Prediction

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