scholarly journals Subgroup identification in dose-finding trials via model-based recursive partitioning

2018 ◽  
Vol 37 (10) ◽  
pp. 1608-1624 ◽  
Author(s):  
Marius Thomas ◽  
Björn Bornkamp ◽  
Heidi Seibold
Keyword(s):  
Recursive Partitioning ◽  
Dose Finding ◽  
Model Based ◽  
Subgroup Identification

scholarly journals Subgroup identification in individual participant data meta-analysis using model-based recursive partitioning

2021 ◽  
Author(s):  
Cynthia Huber ◽  
Norbert Benda ◽  
Tim Friede
Keyword(s):  
Fixed Effects ◽  
Meta Analysis ◽  
Recursive Partitioning ◽  
Individual Participant Data ◽  
Controlled Clinical Trials ◽  
Randomized Controlled Clinical Trials ◽  
Randomized Controlled ◽  
Model Based ◽  
Subgroup Identification ◽  
Individual Participant

AbstractModel-based recursive partitioning (MOB) can be used to identify subgroups with differing treatment effects. The detection rate of treatment-by-covariate interactions and the accuracy of identified subgroups using MOB depend strongly on the sample size. Using data from multiple randomized controlled clinical trials can overcome the problem of too small samples. However, naively pooling data from multiple trials may result in the identification of spurious subgroups as differences in study design, subject selection and other sources of between-trial heterogeneity are ignored. In order to account for between-trial heterogeneity in individual participant data (IPD) meta-analysis random-effect models are frequently used. Commonly, heterogeneity in the treatment effect is modelled using random effects whereas heterogeneity in the baseline risks is modelled by either fixed effects or random effects. In this article, we propose metaMOB, a procedure using the generalized mixed-effects model tree (GLMM tree) algorithm for subgroup identification in IPD meta-analysis. Although the application of metaMOB is potentially wider, e.g. randomized experiments with participants in social sciences or preclinical experiments in life sciences, we focus on randomized controlled clinical trials. In a simulation study, metaMOB outperformed GLMM trees assuming a random intercept only and model-based recursive partitioning (MOB), whose algorithm is the basis for GLMM trees, with respect to the false discovery rates, accuracy of identified subgroups and accuracy of estimated treatment effect. The most robust and therefore most promising method is metaMOB with fixed effects for modelling the between-trial heterogeneity in the baseline risks.

scholarly journals Subgroup identification for differential cardio-respiratory fitness effect on cardiovascular disease risk factors: A model-based recursive partitioning approach

2021 ◽  
Vol 54 (2) ◽  
pp. 147-165
Author(s):  
Md Yasin Ali Parh ◽  
Munni Begum ◽  
Matthew Harber ◽  
Bradley S. Fleenor ◽  
Mitchell Whaley ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Disease Risk ◽  
Recursive Partitioning ◽  
Cvd Risk Factors ◽  
Cvd Risk ◽  
Treatment Variable ◽  
Model Based ◽  
Subgroup Identification ◽  
Cardio Respiratory Fitness

The goal of this study is twofold: i) identification of features associated with three cardiovascular disease (CVD) risk factors, and (ii) identification of subgroups with differential treatment effects. Multivariate analysis is performed to identify the features associated with the CVD risk factors: hypertension, diabetes, and dyslipidemia. For subgroup identification, we applied model-based recursive partitioning approach. This method fits a local model in each subgroup of the population rather than fitting one global model for the whole population. The method starts with a model for the overall effect of treatment and checks whether this effect is equally applicable for all individuals under the study based on parameter instability of M fluctuation test over a set of partitioning variables. The procedure produces a segmented model with a differential effect of cardio-respiratory fitness (CRF) corresponding to each subgroup. The subgroups are linked to predictive factors learned by the recursive partitioning approach. This approach is applied to the data from the Ball State Adult Fitness Program Longitudinal Lifestyle Study (BALL ST), where we considered the level of CRF as a treatment variable. The overall results indicate that CRF is inversely associated with hypertension, diabetes and dyslipidemia. The partitioning factors that are selected are related to these risk factors. The subgroup-specific results indicate that for each subgroup, the chance of hypertension, diabetes and dyslipidemia increases with low CRF.

scholarly journals Using an Interaction Parameter in Model-Based Phase I Trials for Combination Treatments? A Simulation Study

2021 ◽  
Vol 18 (1) ◽  
pp. 345
Author(s):  
Pavel Mozgunov ◽  
Rochelle Knight ◽  
Helen Barnett ◽  
Thomas Jaki
Keyword(s):  
Phase I ◽  
Interaction Parameter ◽  
Simulation Study ◽  
Small Sample Size ◽  
Small Sample ◽  
Dose Finding ◽  
Phase I Trials ◽  
Model Based ◽  
Combination Toxicity ◽  
Toxicity Relationship

There is growing interest in Phase I dose-finding studies studying several doses of more than one agent simultaneously. A number of combination dose-finding designs were recently proposed to guide escalation/de-escalation decisions during the trials. The majority of these proposals are model-based: a parametric combination-toxicity relationship is fitted as data accumulates. Various parameter shapes were considered but the unifying theme for many of these is that typically between 4 and 6 parameters are to be estimated. While more parameters allow for more flexible modelling of the combination-toxicity relationship, this is a challenging estimation problem given the typically small sample size in Phase I trials of between 20 and 60 patients. These concerns gave raise to an ongoing debate whether including more parameters into combination-toxicity model leads to more accurate combination selection. In this work, we extensively study two variants of a 4-parameter logistic model with reduced number of parameters to investigate the effect of modelling assumptions. A framework to calibrate the prior distributions for a given parametric model is proposed to allow for fair comparisons. Via a comprehensive simulation study, we have found that the inclusion of the interaction parameter between two compounds does not provide any benefit in terms of the accuracy of selection, on average, but is found to result in fewer patients allocated to the target combination during the trial.

scholarly journals Embracing model-based designs for dose-finding trials

2017 ◽  
Vol 117 (3) ◽  
pp. 332-339 ◽  
Author(s):  
Sharon B Love ◽  
Sarah Brown ◽  
Christopher J Weir ◽  
Chris Harbron ◽  
Christina Yap ◽  
...  
Keyword(s):  
Dose Finding ◽  
Model Based

scholarly journals Dose transition pathways: a design, analysis and operational tool for dose-finding trials using model-based designs

Trials ◽  
2015 ◽  
Vol 16 (S2) ◽  
Author(s):  
Christina Yap ◽  
Lucinda Billingham ◽  
Charles Craddock ◽  
John O'Quigley
Keyword(s):  
Design Analysis ◽  
Dose Finding ◽  
Model Based ◽  
Transition Pathways ◽  
Operational Tool

scholarly journals A Comparison between Cure Model and Recursive Partitioning: A Retrospective Cohort Study of Iranian Female with Breast Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Mozhgan Safe ◽  
Javad Faradmal ◽  
Hossein Mahjub
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Cancer Patients ◽  
Simulation Study ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Recursive Partitioning ◽  
Breast Cancer Patients ◽  
Cure Model ◽  
Model Based

Background. Breast cancer which is the most common cause of women cancer death has an increasing incidence and mortality rates in Iran. A proper modeling would correctly detect the factors’ effect on breast cancer, which may be the basis of health care planning. Therefore, this study aimed to practically develop two recently introduced statistical models in order to compare them as the survival prediction tools for breast cancer patients.Materials and Methods. For this retrospective cohort study, the 18-year follow-up information of 539 breast cancer patients was analyzed by “Parametric Mixture Cure Model” and “Model-Based Recursive Partitioning.” Furthermore, a simulation study was carried out to compare the performance of mentioned models for different situations.Results. “Model-Based Recursive Partitioning” was able to present a better description of dataset and provided a fine separation of individuals with different risk levels. Additionally the results of simulation study confirmed the superiority of this recursive partitioning for nonlinear model structures.Conclusion. “Model-Based Recursive Partitioning” seems to be a potential instrument for processing complex mixture cure models. Therefore, applying this model is recommended for long-term survival patients.

scholarly journals Identifying back pain subgroups: developing and applying approaches using individual patient data collected within clinical trials

2016 ◽  
Vol 4 (10) ◽  
pp. 1-278 ◽  
Author(s):  
Shilpa Patel ◽  
Siew Wan Hee ◽  
Dipesh Mistry ◽  
Jake Jordan ◽  
Sally Brown ◽  
...  
Keyword(s):  
Back Pain ◽  
Cost Effectiveness ◽  
Recursive Partitioning ◽  
Research Programme ◽  
Clinical Effectiveness ◽  
Individual Participant Data ◽  
Subgroup Identification ◽  
Individual Participant ◽  
Participant Characteristics ◽  
Treatment Moderators

BackgroundThere is good evidence that therapist-delivered interventions have modest beneficial effects for people with low back pain (LBP). Identification of subgroups of people with LBP who may benefit from these different treatment approaches is an important research priority.Aim and objectivesTo improve the clinical effectiveness and cost-effectiveness of LBP treatment by providing patients, their clinical advisors and health-service purchasers with better information about which participants are most likely to benefit from which treatment choices. Our objectives were to synthesise what is already known about the validity, reliability and predictive value of possible treatment moderators (patient factors that predict response to treatment) for therapist-delivered interventions; develop a repository of individual participant data from randomised controlled trials (RCTs) testing therapist-delivered interventions for LBP; determine which participant characteristics, if any, predict clinical response to different treatments for LBP; and determine which participant characteristics, if any, predict the most cost-effective treatments for LBP. Achieving these objectives required substantial methodological work, including the development and evaluation of some novel statistical approaches. This programme of work was not designed to analyse the main effect of interventions and no such interpretations should be made.MethodsFirst, we reviewed the literature on treatment moderators and subgroups. We initially invited investigators of trials of therapist-delivered interventions for LBP with > 179 participants to share their data with us; some further smaller trials that were offered to us were also included. Using these trials we developed a repository of individual participant data of therapist-delivered interventions for LBP. Using this data set we sought to identify which participant characteristics, if any, predict response to different treatments (moderators) for clinical effectiveness and cost-effectiveness outcomes. We undertook an analysis of covariance to identify potential moderators to apply in our main analyses. Subsequently, we developed and applied three methods of subgroup identification: recursive partitioning (interaction trees and subgroup identification based on a differential effect search); adaptive risk group refinement; and an individual participant data indirect network meta-analysis (NWMA) to identify subgroups defined by multiple parameters.ResultsWe included data from 19 RCTs with 9328 participants (mean age 49 years, 57% females). Our prespecified analyses using recursive partitioning and adaptive risk group refinement performed well and allowed us to identify some subgroups. The differences in the effect size in the different subgroups were typically small and unlikely to be clinically meaningful. Increasing baseline severity on the outcome of interest was the strongest driver of subgroup identification that we identified. Additionally, we explored the application of Bayesian indirect NWMA. This method produced varying probabilities that a particular treatment choice would be most likely to be effective for a specific patient profile.ConclusionsThese data lack clinical effectiveness or cost-effectiveness justification for the use of baseline characteristics in the development of subgroups for back pain. The methodological developments from this work have the potential to be applied in other clinical areas. The pooled repository database will serve as a valuable resource to the LBP research community.FundingThe National Institute for Health Research Programme Grants for Applied Research programme. This project benefited from facilities funded through Birmingham Science City Translational Medicine Clinical Research and Infrastructure Trials Platform, with support from Advantage West Midlands (AWM) and the Wolfson Foundation.

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