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Rahul Bhattacharya - One of the best experts on this subject based on the ideXlab platform.
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A response Adaptive Design for ordinal categorical responses weighing the cumulative odds ratios
Biostatistics & Epidemiology, 2019Co-Authors: Atanu Biswas, Rahul Bhattacharya, Soumyadeep DasAbstract:ABSTRACTWeighing the cumulative odds ratios suitably, a two treatment response Adaptive Design for phase III clinical trial is proposed for ordinal categorical responses. Properties of the proposed...
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A response Adaptive Design for ordinal categorical responses.
Journal of biopharmaceutical statistics, 2018Co-Authors: Atanu Biswas, Rahul Bhattacharya, Soumyadeep DasAbstract:A two treatment response Adaptive Design is developed for phase III clinical trials with ordinal categorical treatment outcome using Goodman-Kruskal measure of association. Properties of the proposed Design are studied both empirically and theoretically and the acceptability is further illustrated using two real data-sets; one from a clinical trial with trauma patients and the other from a trial with patients having rheumatoid arthritis.
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A new response-Adaptive Design for continuous treatment responses for phase III clinical trials
Journal of Statistical Planning and Inference, 2011Co-Authors: Uttam Bandyopadhyay, Atanu Biswas, Rahul BhattacharyaAbstract:A new response-Adaptive Design, applicable for general class of continuous response distributions, is proposed. The allocation Design is studied both theoretically and numerically and compared with some existing procedures. The applicability of the proposed procedure is also illustrated using real life data sets.
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An optimal response-Adaptive Design with dual constraints
Statistics & Probability Letters, 2009Co-Authors: Atanu Biswas, Rahul BhattacharyaAbstract:The article deals with an optimal response-Adaptive Design for allocating patients among two competing treatments in a phase III clinical trial. An optimal response-Adaptive target is developed for a general class of response distributions subject to two clinically relevant constraints. Various theoretical and numerical properties of the proposed procedure are investigated and compared with some existing competitors. A data study is also included for the assessment of the proposed procedure in real situation.
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Kernel-Based Response-Adaptive Design for Continuous Responses
Communications in Statistics - Theory and Methods, 2009Co-Authors: Uttam Bandyopadhyay, Atanu Biswas, Rahul BhattacharyaAbstract:Response-Adaptive Designs are becoming popular in phase III clinical trials. We propose a response-Adaptive Design for a general class of continuous responses. The Design is based on two kernels and does not require the existence of any moment of the response variables, and thus applicable for any type of continuous responses, even if the distribution is non-standard or the existence of moments of the response distribution is not guaranteed. Some possible properties of the Design are discussed and are compared with some existing procedures. The methodology is illustrated by using some real life data.
Atanu Biswas - One of the best experts on this subject based on the ideXlab platform.
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A response Adaptive Design for ordinal categorical responses weighing the cumulative odds ratios
Biostatistics & Epidemiology, 2019Co-Authors: Atanu Biswas, Rahul Bhattacharya, Soumyadeep DasAbstract:ABSTRACTWeighing the cumulative odds ratios suitably, a two treatment response Adaptive Design for phase III clinical trial is proposed for ordinal categorical responses. Properties of the proposed...
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A response Adaptive Design for ordinal categorical responses.
Journal of biopharmaceutical statistics, 2018Co-Authors: Atanu Biswas, Rahul Bhattacharya, Soumyadeep DasAbstract:A two treatment response Adaptive Design is developed for phase III clinical trials with ordinal categorical treatment outcome using Goodman-Kruskal measure of association. Properties of the proposed Design are studied both empirically and theoretically and the acceptability is further illustrated using two real data-sets; one from a clinical trial with trauma patients and the other from a trial with patients having rheumatoid arthritis.
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A new response-Adaptive Design for continuous treatment responses for phase III clinical trials
Journal of Statistical Planning and Inference, 2011Co-Authors: Uttam Bandyopadhyay, Atanu Biswas, Rahul BhattacharyaAbstract:A new response-Adaptive Design, applicable for general class of continuous response distributions, is proposed. The allocation Design is studied both theoretically and numerically and compared with some existing procedures. The applicability of the proposed procedure is also illustrated using real life data sets.
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An optimal response-Adaptive Design with dual constraints
Statistics & Probability Letters, 2009Co-Authors: Atanu Biswas, Rahul BhattacharyaAbstract:The article deals with an optimal response-Adaptive Design for allocating patients among two competing treatments in a phase III clinical trial. An optimal response-Adaptive target is developed for a general class of response distributions subject to two clinically relevant constraints. Various theoretical and numerical properties of the proposed procedure are investigated and compared with some existing competitors. A data study is also included for the assessment of the proposed procedure in real situation.
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Kernel-Based Response-Adaptive Design for Continuous Responses
Communications in Statistics - Theory and Methods, 2009Co-Authors: Uttam Bandyopadhyay, Atanu Biswas, Rahul BhattacharyaAbstract:Response-Adaptive Designs are becoming popular in phase III clinical trials. We propose a response-Adaptive Design for a general class of continuous responses. The Design is based on two kernels and does not require the existence of any moment of the response variables, and thus applicable for any type of continuous responses, even if the distribution is non-standard or the existence of moments of the response distribution is not guaranteed. Some possible properties of the Design are discussed and are compared with some existing procedures. The methodology is illustrated by using some real life data.
Mark Chang - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Design methods in clinical trials - a review.
Orphanet journal of rare diseases, 2008Co-Authors: Shein-chung Chow, Mark ChangAbstract:In recent years, the use of Adaptive Design methods in clinical research and development based on accrued data has become very popular due to its flexibility and efficiency. Based on adaptations applied, Adaptive Designs can be classified into three categories: prospective, concurrent (ad hoc), and retrospective Adaptive Designs. An Adaptive Design allows modifications made to trial and/or statistical procedures of ongoing clinical trials. However, it is a concern that the actual patient population after the adaptations could deviate from the originally target patient population and consequently the overall type I error (to erroneously claim efficacy for an infective drug) rate may not be controlled. In addition, major adaptations of trial and/or statistical procedures of on-going trials may result in a totally different trial that is unable to address the scientific/medical questions the trial intends to answer. In this article, several commonly considered Adaptive Designs in clinical trials are reviewed. Impacts of ad hoc adaptations (protocol amendments), challenges in by Design (prospective) adaptations, and obstacles of retrospective adaptations are described. Strategies for the use of Adaptive Design in clinical development of rare diseases are discussed. Some examples concerning the development of Velcade intended for multiple myeloma and non-Hodgkin's lymphoma are given. Practical issues that are commonly encountered when implementing Adaptive Design methods in clinical trials are also discussed.
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Adaptive Design method based on sum of p-values.
Statistics in medicine, 2007Co-Authors: Mark ChangAbstract:Bauer and Kohne proposed an Adaptive Design using Fisher's combination of independent p-values based on subsamples from different stages (Biometrics 1994; 50(4):1029-1041). Their method provides great flexibility in the selection of statistical methods for hypothesis testing of subsamples. However, the choices for the stopping boundaries are not flexible enough to meet practical needs (Biometrics 2001; 57(3): 886-891). In this paper, an Adaptive Design method is proposed using linear combination of the independent p-values. The method provides great flexibility in the selection of stopping boundaries and no numerical integration is required for the two-stage Designs. The stopping boundaries and p-values can be calculated manually. The operating characteristics of the Adaptive Designs are studied using computer simulations with and without sample size adjustment. Examples are presented for superiority and non-inferiority trials with different endpoints (normal, binary, and survival) under different adaptations. The statistical efficiency of the proposed method is compared with other methods based on conditional power.
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Adaptive Design Theory and Implementation using SAS and R
2007Co-Authors: Mark ChangAbstract:Adaptive Design theory and implementation using SAS and R , Adaptive Design theory and implementation using SAS and R , کتابخانه دیجیتال جندی شاپور اهواز
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Adaptive Design Methods in Clinical Trials
2006Co-Authors: Shein-chung Chow, Mark ChangAbstract:Introduction What Is Adaptive Design Regulatory Perspectives Target Patient Population Statistical Inference Practical Issues Aims and Scope of the Book Protocol Amendment Introduction Moving Target Patient Population Analysis with Covariate Adjustment Assessment of Sensitivity Index Sample Size Adjustment Concluding Remarks Adaptive Randomization Conventional Randomization Treatment-Adaptive Randomization Covariate-Adaptive Randomization Response-Adaptive Randomization Issues with Adaptive Randomization Summary Adaptive Hypotheses Modifications of Hypotheses Switch from Superiority to Noninferiority Concluding Remarks Adaptive Dose-Escalation Trials Introduction CRM in Phase I Oncology Study Hybrid Frequentist-Bayesian Adaptive Design Design Selection and Sample Size Concluding Remarks Adaptive Group Sequential Design Sequential Methods General Approach for Group Sequential Design Early Stopping Boundaries Alpha Spending Function Group Sequential Design Based on Independent P-Values Calculation of Stopping Boundaries Group Sequential Trial Monitoring Conditional Power Practical Issues Statistical Tests for Seamless Adaptive Designs Why a Seamless Design Is Efficient Step-Wise Test and Adaptive Procedures Contrast Test and Naive P-Value Comparisons of Seamless Design Drop-the-Loser Adaptive Design Summary Adaptive Sample Size Adjustment Sample Size Re-Estimation without Unblinding Data Cui-Hung-Wang's Method Proschan-Hunsberger's Method Muller-Schafer Method Bauer-Koehne Method Generalization of Independent P-Value Approaches Inverse-Normal Method Concluding Remarks Two-Stage Adaptive Design Introduction Practical Issues Types of Two-Stage Adaptive Designs Analysis for Seamless Design with Same Study Objectives/Endpoints Analysis for Seamless Design with Different Endpoints Analysis for Seamless Design with Different Objectives/Endpoints Concluding Remarks Adaptive Treatment Switching Latent Event Times Proportional Hazard Model with Latent Hazard Rate Mixed Exponential Model Concluding Remarks Bayesian Approach Basic Concepts of Bayesian Approach Multiple-Stage Design for Single-Arm Trial Bayesian Optimal Adaptive Designs Concluding Remarks Biomarker Adaptive Trials Introduction Types of Biomarkers and Validation Design with Classifier Biomarker Adaptive Design with Prognostic Biomarker Adaptive Design with Predictive Marker Concluding Remarks Appendix Target Clinical Trials Introduction Potential Impact and Significance Evaluation of Treatment Effect Other Study Designs and Models Concluding Remarks Sample Size and Power Estimation Framework and Model/Parameter Assumptions Method Based on the Sum of P-Values Method Based on Product of P-Values Method with Inverse-Normal P-Values Sample Size Re-Estimation Summary Clinical Trial Simulation Introduction Software Application of ExpDesign Studio Early Phases Development Late Phases Development Concluding Remarks Regulatory Perspectives - A Review of FDA Draft Guidance Introduction The FDA Draft Guidance Well-Understood Designs Less Well-Understood Designs Adaptive Design Implementation Concluding Remarks Case Studies Basic Considerations Adaptive Group Sequential Design Adaptive Dose-Escalation Design Two-Stage Phase II/III Adaptive Design Bibliography Subject Index
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Adaptive Design in Clinical Research: Issues, Opportunities, and Recommendations
Journal of biopharmaceutical statistics, 2006Co-Authors: Mark Chang, Shein-chung Chow, Annpey PongAbstract:The issues and opportunities of Adaptive Designs are discussed. Starting with the definitions of an Adaptive Design, its validity and integrity are discussed. The three key components of an Adaptive Design, i.e., Type I error control, p-value adjustment, and unbiased estimation and confidence interval are addressed. Various seamless Designs are investigated. Recommendations are made in the following aspects: study planning, trial monitoring, analysis and reporting, trial simulation, and regulatory perspectives.
Shein-chung Chow - One of the best experts on this subject based on the ideXlab platform.
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On the independence of data monitoring committee in Adaptive Design clinical trials.
Journal of biopharmaceutical statistics, 2012Co-Authors: Shein-chung Chow, Ralph Corey, Min LinAbstract:In clinical trials, an independent data monitoring committee (DMC) is often established to perform both ongoing safety data monitoring and interim efficacy analysis. These evaluations are performed in a blinded fashion in order to avoid possible operational biases that may be introduced to the trial after the review of the data. The DMCs for clinical trials using Adaptive Design methods are also positioned to implement the adaptation decision according to the prospective adaptation algorithm. While the DMC plays an important role in maintaining the validity and integrity of the intended clinical trial, Adaptive Design clinical trials trigger a greater role and increased responsibility for the DMC. To assist the sponsor in establishing a DMC, the U.S. Food and Drug Administration (FDA) published a draft guidance entitled Establishment and Operation of Clinical Trial Data Monitoring Committees in 2006. In this article, the composition, role/responsibility, and function/activity of a DMC are described. Conce...
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Adaptive Design methods in clinical trials - a review.
Orphanet journal of rare diseases, 2008Co-Authors: Shein-chung Chow, Mark ChangAbstract:In recent years, the use of Adaptive Design methods in clinical research and development based on accrued data has become very popular due to its flexibility and efficiency. Based on adaptations applied, Adaptive Designs can be classified into three categories: prospective, concurrent (ad hoc), and retrospective Adaptive Designs. An Adaptive Design allows modifications made to trial and/or statistical procedures of ongoing clinical trials. However, it is a concern that the actual patient population after the adaptations could deviate from the originally target patient population and consequently the overall type I error (to erroneously claim efficacy for an infective drug) rate may not be controlled. In addition, major adaptations of trial and/or statistical procedures of on-going trials may result in a totally different trial that is unable to address the scientific/medical questions the trial intends to answer. In this article, several commonly considered Adaptive Designs in clinical trials are reviewed. Impacts of ad hoc adaptations (protocol amendments), challenges in by Design (prospective) adaptations, and obstacles of retrospective adaptations are described. Strategies for the use of Adaptive Design in clinical development of rare diseases are discussed. Some examples concerning the development of Velcade intended for multiple myeloma and non-Hodgkin's lymphoma are given. Practical issues that are commonly encountered when implementing Adaptive Design methods in clinical trials are also discussed.
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On Two-stage Seamless Adaptive Design in Clinical Trials
Journal of the Formosan Medical Association, 2008Co-Authors: Shein-chung ChowAbstract:In recent years, the use of Adaptive Design methods in clinical research and development based on accrued data has become very popular because of its efficiency and flexibility in modifying trial and/or statistical procedures of ongoing clinical trials. One of the most commonly considered Adaptive Designs is probably a two-stage seamless Adaptive trial Design that combines two separate studies into one single study. In many cases, study endpoints considered in a two-stage seamless Adaptive Design may be similar but different (e.g. a biomarker versus a regular clinical endpoint or the same study endpoint with different treatment durations). In this case, it is important to determine how the data collected from both stages should be combined for the final analysis. It is also of interest to know how the sample size calculation/allocation should be done for achieving the study objectives originally set for the two stages (separate studies). In this article, formulas for sample size calculation/allocation are derived for cases in which the study endpoints are continuous, discrete (e.g. binary responses), and contain time-to-event data assuming that there is a well-established relationship between the study endpoints at different stages, and that the study objectives at different stages are the same. In cases in which the study objectives at different stages are different (e.g. dose finding at the first stage and efficacy confirmation at the second stage) and when there is a shift in patient population caused by protocol amendments, the derived test statistics and formulas for sample size calculation and allocation are necessarily modified for controlling the overall type I error at the prespecified level
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Adaptive Design Methods in Clinical Trials
2006Co-Authors: Shein-chung Chow, Mark ChangAbstract:Introduction What Is Adaptive Design Regulatory Perspectives Target Patient Population Statistical Inference Practical Issues Aims and Scope of the Book Protocol Amendment Introduction Moving Target Patient Population Analysis with Covariate Adjustment Assessment of Sensitivity Index Sample Size Adjustment Concluding Remarks Adaptive Randomization Conventional Randomization Treatment-Adaptive Randomization Covariate-Adaptive Randomization Response-Adaptive Randomization Issues with Adaptive Randomization Summary Adaptive Hypotheses Modifications of Hypotheses Switch from Superiority to Noninferiority Concluding Remarks Adaptive Dose-Escalation Trials Introduction CRM in Phase I Oncology Study Hybrid Frequentist-Bayesian Adaptive Design Design Selection and Sample Size Concluding Remarks Adaptive Group Sequential Design Sequential Methods General Approach for Group Sequential Design Early Stopping Boundaries Alpha Spending Function Group Sequential Design Based on Independent P-Values Calculation of Stopping Boundaries Group Sequential Trial Monitoring Conditional Power Practical Issues Statistical Tests for Seamless Adaptive Designs Why a Seamless Design Is Efficient Step-Wise Test and Adaptive Procedures Contrast Test and Naive P-Value Comparisons of Seamless Design Drop-the-Loser Adaptive Design Summary Adaptive Sample Size Adjustment Sample Size Re-Estimation without Unblinding Data Cui-Hung-Wang's Method Proschan-Hunsberger's Method Muller-Schafer Method Bauer-Koehne Method Generalization of Independent P-Value Approaches Inverse-Normal Method Concluding Remarks Two-Stage Adaptive Design Introduction Practical Issues Types of Two-Stage Adaptive Designs Analysis for Seamless Design with Same Study Objectives/Endpoints Analysis for Seamless Design with Different Endpoints Analysis for Seamless Design with Different Objectives/Endpoints Concluding Remarks Adaptive Treatment Switching Latent Event Times Proportional Hazard Model with Latent Hazard Rate Mixed Exponential Model Concluding Remarks Bayesian Approach Basic Concepts of Bayesian Approach Multiple-Stage Design for Single-Arm Trial Bayesian Optimal Adaptive Designs Concluding Remarks Biomarker Adaptive Trials Introduction Types of Biomarkers and Validation Design with Classifier Biomarker Adaptive Design with Prognostic Biomarker Adaptive Design with Predictive Marker Concluding Remarks Appendix Target Clinical Trials Introduction Potential Impact and Significance Evaluation of Treatment Effect Other Study Designs and Models Concluding Remarks Sample Size and Power Estimation Framework and Model/Parameter Assumptions Method Based on the Sum of P-Values Method Based on Product of P-Values Method with Inverse-Normal P-Values Sample Size Re-Estimation Summary Clinical Trial Simulation Introduction Software Application of ExpDesign Studio Early Phases Development Late Phases Development Concluding Remarks Regulatory Perspectives - A Review of FDA Draft Guidance Introduction The FDA Draft Guidance Well-Understood Designs Less Well-Understood Designs Adaptive Design Implementation Concluding Remarks Case Studies Basic Considerations Adaptive Group Sequential Design Adaptive Dose-Escalation Design Two-Stage Phase II/III Adaptive Design Bibliography Subject Index
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Adaptive Design in Clinical Research: Issues, Opportunities, and Recommendations
Journal of biopharmaceutical statistics, 2006Co-Authors: Mark Chang, Shein-chung Chow, Annpey PongAbstract:The issues and opportunities of Adaptive Designs are discussed. Starting with the definitions of an Adaptive Design, its validity and integrity are discussed. The three key components of an Adaptive Design, i.e., Type I error control, p-value adjustment, and unbiased estimation and confidence interval are addressed. Various seamless Designs are investigated. Recommendations are made in the following aspects: study planning, trial monitoring, analysis and reporting, trial simulation, and regulatory perspectives.
Jose Antonio Moler - One of the best experts on this subject based on the ideXlab platform.
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asymptotic behavior of a randomization test for a response Adaptive Design
Sequential Analysis, 2015Co-Authors: Arkaitz Galbete, Jose Antonio MolerAbstract:Abstract Response-Adaptive Designs allow the incorporation of ethical goals in the performance of a clinical trial, and they have been thoroughly studied assuming that treatment responses follow a population model. However, in some clinical trials, population models are not appropriate and randomization tests appear as a plausible alternative to make inference. Randomization-based tests can be devised but the calculation of their exact p-values when a response-Adaptive Design is used to allocate patients is either time consuming or not feasible for moderate to large sample sizes and so asymptotic results become helpful. Nevertheless, these asymptotic results are not available for response-Adaptive Designs with good properties. The Klein allocation rule is a response-Adaptive Design, with good ethical and inferential properties, that generalizes the classical Ehrenfest urn Design by making the replacement policy dependent on the response of the last patient. The goal of this article is to study the asympto...
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An Adaptive Design for clinical trials with non-dichotomous response and prognostic factors
Statistics & Probability Letters, 2006Co-Authors: Jose Antonio Moler, Fernando Plo, Miguel San MiguelAbstract:We present an Adaptive Design for multi-arm clinical trials with bounded response and prognostic factors. The allocation is ruled by an urn model that fits a Robbins-Monro scheme. We obtain asymptotic properties for the performance and allocation of treatments.
