Bayesian Additive Regression Trees (BART) with covariate adjusted borrowing in subgroup analyses

Pan, Jane; Bunn, Veronica; Hupf, Bradley; Lin, Jianchang

doi:10.1080/10543406.2022.2089160

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…Other design methods incorporate information borrowing by pooling all tumor types if deemed homogeneous at the interim analysis ( 53 ) or by pooling within subgroups that are defined using a clustering algorithm ( 54 ), clustering of tumor types ( 55 – 58 ), modeling patient-level data with a tree ensemble method ( 59 ), and using Bayesian commensurate priors ( 60 ). Baumann et al.…”

Section: Resultsmentioning

confidence: 99%

Basket trials in oncology: a systematic review of practices and methods, comparative analysis of innovative methods, and an appraisal of a missed opportunity

Kasim,

Bean,

Hendriksen

et al. 2023

Front. Oncol.

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BackgroundBasket trials are increasingly used in oncology drug development for early signal detection, accelerated tumor-agnostic approvals, and prioritization of promising tumor types in selected patients with the same mutation or biomarker. Participants are grouped into so-called baskets according to tumor type, allowing investigators to identify tumors with promising responses to treatment for further study. However, it remains a question as to whether and how much the adoption of basket trial designs in oncology have translated into patient benefits, increased pace and scale of clinical development, and de-risking of downstream confirmatory trials.MethodsInnovation in basket trial design and analysis includes methods that borrow information across tumor types to increase the quality of statistical inference within each tumor type. We build on the existing systematic reviews of basket trials in oncology to discuss the current practices and landscape. We conceptually illustrate recent innovative methods for basket trials, with application to actual data from recently completed basket trials. We explore and discuss the extent to which innovative basket trials can be used to de-risk future trials through their ability to aid prioritization of promising tumor types for subsequent clinical development.ResultsWe found increasing adoption of basket trial design in oncology, but largely in the design of single-arm phase II trials with a very low adoption of innovative statistical methods. Furthermore, the current practice of basket trial design, which does not consider its impact on the clinical development plan, may lead to a missed opportunity in improving the probability of success of a future trial. Gating phase II with a phase Ib basket trial reduced the size of phase II trials, and losses in the probability of success as a result of not using innovative methods may not be recoverable by running a larger phase II trial.ConclusionInnovative basket trial methods can reduce the size of early phase clinical trials, with sustained improvement in the probability of success of the clinical development plan. We need to do more as a community to improve the adoption of these methods.

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Section: Resultsmentioning

confidence: 99%

Basket trials in oncology: a systematic review of practices and methods, comparative analysis of innovative methods, and an appraisal of a missed opportunity

Kasim,

Bean,

Hendriksen

et al. 2023

Front. Oncol.

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“…BART is an efficient ensemble method for machine learning [78]. Within the past decade its popularity has been increasing and it has been applied in a diverse range of applications, e.g., [79,80]. It functions based on several decision trees in which parameters of the model are regularized in advance [78].…”

Section: Bayesian Additive Regression Trees (Bart)mentioning

confidence: 99%

Modeling Organizational Performance with Machine Learning

Pap

Makó

Illessy³

et al. 2022

Journal of Open Innovation: Technology, Market, and Complexity

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Identifying the performance factors of organizations is of utmost importance for labor studies for both empirical and theoretical research. The present study investigates the essential intra- and extra-organizational factors in determining the performance of firms using the European Company Survey (ECS) 2019 framework. The evolutionary computation method of genetic algorithm and the machine learning method of Bayesian additive regression trees (BART), are used to model the importance of each of the intra- and extra-organizational factors in identifying the firms’ performance as well as employee well-being. The standard metrics are further used to evaluate the accuracy of the proposed method. The mean value of the evaluation metrics for the accuracy of the impact of intra- and extra-organizational factors on firm performance are MAE = 0.225, MSE = 0.065, RMSE = 0.2525, and R2 = 0.9125, and the value of these metrics for the accuracy of the impact of intra- and extra-organizational factors on employee well-being are MAE = 0.18, MSE = 0.0525, RMSE = 0.2275, and R2 = 0.88. The low values of MAE, MSE and RMSE, and the high value of R2, indicate the high level of accuracy of the proposed method. The results revealed that the two variables of work organization and innovation are essential in improving firm performance well-being, and that the variables of collaboration and outsourcing, as well as job complexity and autonomy, have the greatest role in improving firm performance.

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“…More recently, there have been papers on modeling analysis based on regression trees approach in many fields. Such as the piecewise symbolic regression tree by Zhang et al [17], the boosted regression tree (Knierim et al [18], Said et al [19], Han et al [20], and Alnahit et al [21]), the logistic regression trees by Loh [22], the bayesian additive regression tree (Pan et al [23], Clark et al [24], Um et al [25]) and so on.…”

Section: Introductionmentioning

confidence: 99%

Partial Least Squares Regression Trees for Multivariate Response Data With Multicollinear Predictors

Yu,

Lee,

Cho

2024

IEEE Access

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Some problems arise in analyzing massive complex data consisting of multivariate response variables and a large number of multicollinear predictor variables, especially when the sample sizes compared to the number of predictors are small. Rather than ordinary linear regression modeling approaches, latent variable regression modeling approaches such as partial least squares regression can be used to capture the relationship between the response and predictor variables for such cases. However, for complex nonlinear relationships between the predictor and the response variable, the performance of inference and prediction using regression modeling approaches can be deflated. Regression trees can capture such complex relationships. Thus, we develop a partial least squares tree modeling algorithm that detects complex relationships and makes precise predictions by integrating the merits of partial least squares and regression trees. It is shown that it has better predictive performance than other methods through simulation and it is demonstrated that it generates interpretable predictive models with real data of usedcar and orthognathic surgrey.

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Bayesian Additive Regression Trees (BART) with covariate adjusted borrowing in subgroup analyses

Cited by 6 publications

References 26 publications

Basket trials in oncology: a systematic review of practices and methods, comparative analysis of innovative methods, and an appraisal of a missed opportunity

Basket trials in oncology: a systematic review of practices and methods, comparative analysis of innovative methods, and an appraisal of a missed opportunity

Modeling Organizational Performance with Machine Learning

Partial Least Squares Regression Trees for Multivariate Response Data With Multicollinear Predictors

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