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Nunez DJ, Yao X, Lin J, Walker A, Zuo P, Webster L, Krug-Gourley S, Zamek-Gliszczynski MJ, Gillmor DS, Johnson SL. Glucose and lipid effects of the ileal apical sodium-dependent bile acid transporter inhibitor GSK2330672: double-blind randomized trials with type 2 diabetes subjects taking metformin. Diabetes Obes Metab 2016; 18:654-62. [PMID: 26939572 DOI: 10.1111/dom.12656] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2015] [Revised: 01/10/2016] [Accepted: 02/27/2016] [Indexed: 12/15/2022]
Abstract
AIMS To investigate the pharmacodynamics, pharmacokinetics and safety/tolerability of blocking reuptake of bile acids using the inhibitor GSK2330672 (GSK672) in patients with type 2 diabetes (T2D). METHODS Subjects with T2D taking metformin were enrolled in two studies in which they took metformin 850 mg twice daily for 2 weeks prior to and during the randomized treatment periods. In the first crossover study (n = 15), subjects received GSK672 45 mg, escalating to 90 mg, twice daily, or placebo for 7 days. The second parallel-group study (n = 75) investigated GSK672 10-90 mg twice daily, placebo or sitagliptin for 14 days. RESULTS In both studies, GSK672 reduced circulating bile acids and increased serum 7-α-hydroxy-4-cholesten-3-one (C4), an intermediate in the hepatic synthesis of bile acids. Compared with placebo, in the parallel-group study 90 mg GSK672 twice daily reduced fasting plasma glucose [FPG; -1.21 mmol/l; 95% confidence interval (CI) -2.14, -0.28] and weighted-mean glucose area under the curve (AUC)0-24 h (-1.33 mmol/l; 95% CI -2.30, -0.36), as well as fasting and weighted-mean insulin AUC0 -24 h . GSK672 also reduced cholesterol (LDL, non-HDL and total cholesterol) and apolipoprotein B concentrations; the maximum LDL cholesterol reduction was ∼40%. There was no change in HDL cholesterol but there was a trend towards increased fasting triglyceride levels in the GSK672 groups compared with placebo. In both studies, the most common adverse events associated with GSK672 were gastrointestinal, mostly diarrhoea (22-100%), which appeared to be independent of dose. CONCLUSIONS In subjects with T2D on metformin, GSK672 improved glucose and lipids, but there was a high incidence of gastrointestinal adverse events.
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Affiliation(s)
- D J Nunez
- GlaxoSmithKline plc, Research Triangle Park, NC and Collegeville, PA, USA
| | - X Yao
- Alexion Pharmaceuticals, Inc., Cambridge, MA, USA
| | - J Lin
- Grifols Therapeutics Inc., Research Triangle Park, NC, USA
| | - A Walker
- GlaxoSmithKline plc, Research Triangle Park, NC and Collegeville, PA, USA
| | - P Zuo
- Parexel International, Durham, NC, USA
| | | | - S Krug-Gourley
- GlaxoSmithKline plc, Research Triangle Park, NC and Collegeville, PA, USA
| | | | - D S Gillmor
- Pharmaceutical Product Development LLC, Morrisville, NC, USA
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Ma J, Chan W, Tilley BC. Continuous time Markov chain approaches for analyzing transtheoretical models of health behavioral change: A case study and comparison of model estimations. Stat Methods Med Res 2016; 27:593-607. [PMID: 27048681 DOI: 10.1177/0962280216639859] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Continuous time Markov chain models are frequently employed in medical research to study the disease progression but are rarely applied to the transtheoretical model, a psychosocial model widely used in the studies of health-related outcomes. The transtheoretical model often includes more than three states and conceptually allows for all possible instantaneous transitions (referred to as general continuous time Markov chain). This complicates the likelihood function because it involves calculating a matrix exponential that may not be simplified for general continuous time Markov chain models. We undertook a Bayesian approach wherein we numerically evaluated the likelihood using ordinary differential equation solvers available from the gnu scientific library. We compared our Bayesian approach with the maximum likelihood method implemented with the R package MSM. Our simulation study showed that the Bayesian approach provided more accurate point and interval estimates than the maximum likelihood method, especially in complex continuous time Markov chain models with five states. When applied to data from a four-state transtheoretical model collected from a nutrition intervention study in the next step trial, we observed results consistent with the results of the simulation study. Specifically, the two approaches provided comparable point estimates and standard errors for most parameters, but the maximum likelihood offered substantially smaller standard errors for some parameters. Comparable estimates of the standard errors are obtainable from package MSM, which works only when the model estimation algorithm converges.
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Affiliation(s)
- Junsheng Ma
- 1 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, USA.,2 Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, USA
| | - Wenyaw Chan
- 2 Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, USA
| | - Barbara C Tilley
- 2 Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, USA
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Uozumi R, Hamada C. Interim decision-making strategies in adaptive designs for population selection using time-to-event endpoints. J Biopharm Stat 2016; 27:84-100. [PMID: 26881477 DOI: 10.1080/10543406.2016.1148714] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Adaptive designs in oncology clinical trials with interim analyses for population selection could be used in the development of targeted therapies if a predefined biomarker hypothesis exists. In this article, we consider an interim analysis using overall survival (OS), progression-free survival (PFS), and both OS and PFS, to determine whether the whole population or only the biomarker-positive population should continue into the subsequent stage of the trial, whereas the final decision is made based on OS data only. In order to increase the probability of selecting the most appropriate population at the interim analysis, we propose an interim decision-making strategy in adaptive designs with correlated endpoints considering the post-progression survival (PPS) magnitudes. In our approach, the interim decision is made on the basis of predictive power by incorporating information on OS as well as PFS to supplement the incomplete OS data. Simulation studies assuming a targeted therapy demonstrated that our interim decision-making procedure performs well in terms of selecting the proper population, especially under a scenario in which PPS affects the correlation between OS and PFS.
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Affiliation(s)
- Ryuji Uozumi
- a Department of Biomedicai Statistics and Bioinformatics , Kyoto University Graduate School of Medicine , Kyoto , Japan
| | - Chikuma Hamada
- b Department of Management Science, Graduate School of Engineering , Tokyo University of Science , Tokyo , Japan
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Fisch R, Jones I, Jones J, Kerman J, Rosenkranz GK, Schmidli H. Bayesian Design of Proof-of-Concept Trials. Ther Innov Regul Sci 2015; 49:155-162. [DOI: 10.1177/2168479014533970] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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55
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Dmitrienko A, Paux G, Pulkstenis E, Zhang J. Tradeoff-based optimization criteria in clinical trials with multiple objectives and adaptive designs. J Biopharm Stat 2015; 26:120-40. [PMID: 26391238 DOI: 10.1080/10543406.2015.1092032] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
The article discusses clinical trial optimization problems in the context of mid- to late-stage drug development. Using the Clinical Scenario Evaluation approach, main objectives of clinical trial optimization are formulated, including selection of clinically relevant optimization criteria, identification of sets of optimal and nearly optimal values of the parameters of interest, and sensitivity assessments. The paper focuses on a class of optimization criteria arising in clinical trials with several competing goals, termed tradeoff-based optimization criteria, and discusses key considerations in constructing and applying tradeoff-based criteria. The clinical trial optimization framework considered in the paper is illustrated using two case studies based on a clinical trial with multiple objectives and a two-stage clinical trial which utilizes adaptive decision rules.
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Affiliation(s)
- Alex Dmitrienko
- a Center for Statistics in Drug Development, Quintiles , Overland Park , Kansas , USA
| | - Gautier Paux
- b Oncology Biostatistics, Institut de Recherches Internationales Servier (I.R.I.S.) , Suresnes , France
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Walley RJ, Smith CL, Gale JD, Woodward P. Advantages of a wholly Bayesian approach to assessing efficacy in early drug development: a case study. Pharm Stat 2015; 14:205-15. [PMID: 25865949 DOI: 10.1002/pst.1675] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2014] [Revised: 11/12/2014] [Accepted: 02/17/2015] [Indexed: 01/21/2023]
Abstract
This paper illustrates how the design and statistical analysis of the primary endpoint of a proof-of-concept study can be formulated within a Bayesian framework and is motivated by and illustrated with a Pfizer case study in chronic kidney disease. It is shown how decision criteria for success can be formulated, and how the study design can be assessed in relation to these, both using the traditional approach of probability of success conditional on the true treatment difference and also using Bayesian assurance and pre-posterior probabilities. The case study illustrates how an informative prior on placebo response can have a dramatic effect in reducing sample size, saving time and resource, and we argue that in some cases, it can be considered unethical not to include relevant literature data in this way.
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Affiliation(s)
| | - Claire L Smith
- Eli Lilly and Company Limited, Erl Wood Manor, Windlesham, GU20 6PH, UK
| | | | - Phil Woodward
- Pfizer, Neusentis, The Portway Building, Granta Park, Cambridge, CB21 6GS, UK
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57
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Chiang AY, Wang MD. Incorporating Biomarkers Into the Analysis of Preclinical Cardiovascular Safety Studies. Stat Biopharm Res 2015. [DOI: 10.1080/19466315.2015.1005757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Zhang Q, Toubouti Y, Carlin BP. Design and analysis of Bayesian adaptive crossover trials for evaluating contact lens safety and efficacy. Stat Methods Med Res 2015; 26:1216-1236. [PMID: 25698715 DOI: 10.1177/0962280215572272] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
A crossover study, also referred to as a crossover trial, is a form of longitudinal study. Subjects are randomly assigned to different arms of the study and receive different treatments sequentially. While there are many frequentist methods to analyze data from a crossover study, random effects models for longitudinal data are perhaps most naturally modeled within a Bayesian framework. In this article, we introduce a Bayesian adaptive approach to crossover studies for both efficacy and safety endpoints using Gibbs sampling. Using simulation, we find our approach can detect a true difference between two treatments with a specific false-positive rate that we can readily control via the standard equal-tail posterior credible interval. We then illustrate our Bayesian approaches using real data from Johnson & Johnson Vision Care, Inc. contact lens studies. We then design a variety of Bayesian adaptive predictive probability crossover studies for single and multiple continuous efficacy endpoints, indicate their extension to binary safety endpoints, and investigate their frequentist operating characteristics via simulation. The Bayesian adaptive approach emerges as a crossover trials tool that is useful yet surprisingly overlooked to date, particularly in contact lens development.
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Affiliation(s)
- Quan Zhang
- 1 Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Youssef Toubouti
- 2 Johnson & Johnson Vision Care, Inc., 7500 Centurion Parkway, Jacksonville, FL, USA
| | - Bradley P Carlin
- 1 Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
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Wang M, Liu GF, Schindler J. Evaluation of program success for programs with multiple trials in binary outcomes. Pharm Stat 2015; 14:172-9. [DOI: 10.1002/pst.1670] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Revised: 12/05/2014] [Accepted: 12/18/2014] [Indexed: 11/09/2022]
Affiliation(s)
- Meihua Wang
- Merck Research Laboratories; North Wales PA USA
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Broglio KR, Connor JT, Berry SM. Not too big, not too small: a goldilocks approach to sample size selection. J Biopharm Stat 2014; 24:685-705. [PMID: 24697532 DOI: 10.1080/10543406.2014.888569] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
We present a Bayesian adaptive design for a confirmatory trial to select a trial's sample size based on accumulating data. During accrual, frequent sample size selection analyses are made and predictive probabilities are used to determine whether the current sample size is sufficient or whether continuing accrual would be futile. The algorithm explicitly accounts for complete follow-up of all patients before the primary analysis is conducted. We refer to this as a Goldilocks trial design, as it is constantly asking the question, "Is the sample size too big, too small, or just right?" We describe the adaptive sample size algorithm, describe how the design parameters should be chosen, and show examples for dichotomous and time-to-event endpoints.
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61
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Ibrahim JG, Chen MH, Lakshminarayanan M, Liu GF, Heyse JF. Bayesian probability of success for clinical trials using historical data. Stat Med 2014; 34:249-64. [PMID: 25339499 DOI: 10.1002/sim.6339] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Revised: 09/23/2014] [Accepted: 10/03/2014] [Indexed: 11/07/2022]
Abstract
Developing sophisticated statistical methods for go/no-go decisions is crucial for clinical trials, as planning phase III or phase IV trials is costly and time consuming. In this paper, we develop a novel Bayesian methodology for determining the probability of success of a treatment regimen on the basis of the current data of a given trial. We introduce a new criterion for calculating the probability of success that allows for inclusion of covariates as well as allowing for historical data based on the treatment regimen, and patient characteristics. A new class of prior distributions and covariate distributions is developed to achieve this goal. The methodology is quite general and can be used with univariate or multivariate continuous or discrete data, and it generalizes Chuang-Stein's work. This methodology will be invaluable for informing the scientist on the likelihood of success of the compound, while including the information of covariates for patient characteristics in the trial population for planning future pre-market or post-market trials.
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Affiliation(s)
- Joseph G Ibrahim
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, U.S.A
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62
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Bhattacharjee A. Application of Bayesian Approach in Cancer Clinical Trial. World J Oncol 2014; 5:109-112. [PMID: 29147387 PMCID: PMC5649812 DOI: 10.14740/wjon842e] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/27/2014] [Indexed: 01/20/2023] Open
Abstract
The application of Bayesian approach in clinical trials becomes more useful over classical method. It is beneficial from design to analysis phase. The straight forward statement is possible to obtain through Bayesian about the drug treatment effect. Complex computational problems are simple to handle with Bayesian techniques. The technique is only feasible to performing presence of prior information of the data. The inference is possible to establish through posterior estimates. However, some limitations are present in this method. The objective of this work was to explore the several merits and demerits of Bayesian approach in cancer research. The review of the technique will be helpful for the clinical researcher involved in the oncology to explore the limitation and power of Bayesian techniques.
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Affiliation(s)
- Atanu Bhattacharjee
- Division of Clinical Research and Biostatistics, Malabar Cancer Center, Thalassery, Kerala 670103, India.
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63
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Saville BR, Connor JT, Ayers GD, Alvarez J. The utility of Bayesian predictive probabilities for interim monitoring of clinical trials. Clin Trials 2014; 11:485-493. [PMID: 24872363 DOI: 10.1177/1740774514531352] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Bayesian predictive probabilities can be used for interim monitoring of clinical trials to estimate the probability of observing a statistically significant treatment effect if the trial were to continue to its predefined maximum sample size. PURPOSE We explore settings in which Bayesian predictive probabilities are advantageous for interim monitoring compared to Bayesian posterior probabilities, p-values, conditional power, or group sequential methods. RESULTS For interim analyses that address prediction hypotheses, such as futility monitoring and efficacy monitoring with lagged outcomes, only predictive probabilities properly account for the amount of data remaining to be observed in a clinical trial and have the flexibility to incorporate additional information via auxiliary variables. LIMITATIONS Computational burdens limit the feasibility of predictive probabilities in many clinical trial settings. The specification of prior distributions brings additional challenges for regulatory approval. CONCLUSIONS The use of Bayesian predictive probabilities enables the choice of logical interim stopping rules that closely align with the clinical decision-making process.
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Affiliation(s)
- Benjamin R Saville
- Department of Biostatistics, School of Medicine, Vanderbilt University, Nashville, TN, USA
| | - Jason T Connor
- Berry Consultants, Austin, TX, USA College of Medicine, University of Central Florida, Orlando, FL, USA
| | - Gregory D Ayers
- Department of Biostatistics, School of Medicine, Vanderbilt University, Nashville, TN, USA
| | - JoAnn Alvarez
- Department of Biostatistics, School of Medicine, Vanderbilt University, Nashville, TN, USA
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64
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Lawrence Gould A, Zhang XD. Bayesian adaptive determination of the sample size required to assure acceptably low adverse event risk. Stat Med 2014; 33:940-57. [DOI: 10.1002/sim.5993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2012] [Revised: 06/25/2013] [Accepted: 09/06/2013] [Indexed: 11/11/2022]
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65
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Zhang J, Zhang JJ. Joint probability of statistical success of multiple phase III trials. Pharm Stat 2013; 12:358-65. [DOI: 10.1002/pst.1597] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2012] [Revised: 08/13/2013] [Accepted: 08/13/2013] [Indexed: 11/11/2022]
Affiliation(s)
| | - Jenny J. Zhang
- Gilead Sciences; 333 Lakeside Drive, Foster City CA 94404 USA
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Gallicano GI. Modeling to optimize terminal stem cell differentiation. SCIENTIFICA 2013; 2013:574354. [PMID: 24278782 PMCID: PMC3820305 DOI: 10.1155/2013/574354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2012] [Accepted: 12/18/2012] [Indexed: 06/02/2023]
Abstract
Embryonic stem cell (ESC), iPCs, and adult stem cells (ASCs) all are among the most promising potential treatments for heart failure, spinal cord injury, neurodegenerative diseases, and diabetes. However, considerable uncertainty in the production of ESC-derived terminally differentiated cell types has limited the efficiency of their development. To address this uncertainty, we and other investigators have begun to employ a comprehensive statistical model of ESC differentiation for determining the role of intracellular pathways (e.g., STAT3) in ESC differentiation and determination of germ layer fate. The approach discussed here applies the Baysian statistical model to cell/developmental biology combining traditional flow cytometry methodology and specific morphological observations with advanced statistical and probabilistic modeling and experimental design. The final result of this study is a unique tool and model that enhances the understanding of how and when specific cell fates are determined during differentiation. This model provides a guideline for increasing the production efficiency of therapeutically viable ESCs/iPSCs/ASC derived neurons or any other cell type and will eventually lead to advances in stem cell therapy.
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Affiliation(s)
- G. Ian Gallicano
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC 20057, USA
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Abstract
Although the frequentist paradigm has been the predominant approach to clinical trial design since the 1940s, it has several notable limitations. Advancements in computational algorithms and computer hardware have greatly enhanced the alternative Bayesian paradigm. Compared with its frequentist counterpart, the Bayesian framework has several unique advantages, and its incorporation into clinical trial design is occurring more frequently. Using an extensive literature review to assess how Bayesian methods are used in clinical trials, we find them most commonly used for dose finding, efficacy monitoring, toxicity monitoring, diagnosis/decision making, and studying pharmacokinetics/pharmacodynamics. The additional infrastructure required for implementing Bayesian methods in clinical trials may include specialized software programs to run the study design, simulation and analysis, and web-based applications, all of which are particularly useful for timely data entry and analysis. Trial success requires not only the development of proper tools but also timely and accurate execution of data entry, quality control, adaptive randomization, and Bayesian computation. The relative merit of the Bayesian and frequentist approaches continues to be the subject of debate in statistics. However, more evidence can be found showing the convergence of the two camps, at least at the practical level. Ultimately, better clinical trial methods lead to more efficient designs, lower sample sizes, more accurate conclusions, and better outcomes for patients enrolled in the trials. Bayesian methods offer attractive alternatives for better trials. More Bayesian trials should be designed and conducted to refine the approach and demonstrate their real benefit in action.
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Affiliation(s)
- J Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, U.S.A.
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Cai G, Zhou T. A remark on ‘Bayesian predictive approach to interim monitoring in clinical trials’ by A. Dmitrienko and M-D. Wang. Stat Med 2012; 31:1774-6. [DOI: 10.1002/sim.4445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Gengqian Cai
- Quantitative Sciences; GlaxoSmithKline; 709 Swedeland Rd.; King of Prussia; PA; 19406; USA
| | - Tianhui Zhou
- BioTx Clinical Quantitative Sciences; Pfizer; Collegeville; PA; 19426; USA
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Invasive and Device Management of Refractory Angina. Coron Artery Dis 2012. [DOI: 10.1007/978-1-84628-712-1_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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Mukherjee SD, Goffin JR, Taylor V, Anderson KK, Pond GR. Early stopping rules in oncology: considerations for clinicians. Eur J Cancer 2011; 47:2381-6. [PMID: 21684153 DOI: 10.1016/j.ejca.2011.05.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2011] [Revised: 05/12/2011] [Accepted: 05/13/2011] [Indexed: 10/18/2022]
Abstract
The number of cancer-related clinical trials has been rapidly increasing over the past decade. Along with this increase, oncology studies stopped early for benefit or harm have also been more common. Clinicians treating cancer patients often are faced with the challenge of having to decide whether or not to incorporate information from these new studies into their daily clinical practice. This review article explains the role of the Data and Safety Monitoring Committee in stopping trials early; provides examples of oncology trials stopped early; and reviews some of the controversies and statistical concepts associated with early stopping rules. In addition, a simple and practical approach to interpreting the findings of trials that are stopped early is provided to assist clinicians in deciding how to incorporate information from these studies into their daily practice.
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Affiliation(s)
- Som D Mukherjee
- Department of Oncology, McMaster University, Juravinski Cancer Centre, Hamilton, Ontario, Canada.
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Sultan MB, Zhou D, Loftus J, Dombi T, Ice KS. A phase 2/3, multicenter, randomized, double-masked, 2-year trial of pegaptanib sodium for the treatment of diabetic macular edema. Ophthalmology 2011; 118:1107-18. [PMID: 21529957 DOI: 10.1016/j.ophtha.2011.02.045] [Citation(s) in RCA: 108] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2010] [Revised: 01/24/2011] [Accepted: 02/25/2011] [Indexed: 02/07/2023] Open
Abstract
PURPOSE To confirm the safety and compare the efficacy of intravitreal pegaptanib sodium 0.3 mg versus sham injections in subjects with diabetic macular edema (DME) involving the center of the macula associated with vision loss not due to ischemia. DESIGN Randomized (1:1), sham-controlled, multicenter, parallel-group trial. PARTICIPANTS Subjects with DME. INTERVENTION Subjects received pegaptanib 0.3 mg or sham injections every 6 weeks in year 1 (total = 9 injections) and could receive focal/grid photocoagulation beginning at week 18. During year 2, subjects received injections as often as every 6 weeks per prespecified criteria. MAIN OUTCOME MEASURES The primary efficacy endpoint was the proportion gaining ≥ 10 letters of visual acuity (VA) from baseline to year 1. Safety was monitored throughout. RESULTS In all, 260 (pegaptanib, n = 133; sham, n = 127) and 207 (pegaptanib, n = 107; sham, n = 100) subjects were included in years 1 and 2 intent-to-treat analyses, respectively. A total of 49 of the 133 (36.8%) subjects from the pegaptanib group and 25 of the 127 (19.7%) from the sham group experienced a VA improvement of ≥ 10 letters at week 54 compared with baseline (odds ratio [OR], 2.38; 95% confidence interval, 1.32-4.30; P = 0.0047). For pegaptanib-treated subjects, change in mean VA from baseline by visit was superior (P<0.05) to sham at weeks 6, 24, 30, 36, 42, 54, 78, 84, 90, 96, and 102. At week 102, pegaptanib-treated subjects gained, on average, 6.1 letters versus 1.3 letters for sham (P<0.01). Fewer pegaptanib- than sham-treated subjects received focal/grid laser treatment (week 54, 31/133 [23.3%] vs 53/127 [41.7%], respectively, P = 0.002; week 102, 27/107 [25.2%] vs 45/100 [45.0%], respectively, P = 0.003). The pegaptanib treatment group showed significantly better results on the National Eye Institute-Visual Functioning Questionnaire than sham for subscales important in this population. Pegaptanib was well tolerated; the frequencies of discontinuations, adverse events, treatment-related adverse events, and serious adverse events were comparable in the pegaptanib and sham groups. CONCLUSIONS Patients with DME derive clinical benefit from treatment with the selective vascular endothelial growth factor antagonist pegaptanib 0.3 mg. These findings indicate that intravitreal pegaptanib is effective in the treatment of DME and, taken together with prior study data, support a positive safety profile in this population. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references.
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Poitevineau J, Lecoutre B. Implementing Bayesian predictive procedures: The K-prime and K-square distributions. Comput Stat Data Anal 2010. [DOI: 10.1016/j.csda.2008.11.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Bretz F, Koenig F, Brannath W, Glimm E, Posch M. Adaptive designs for confirmatory clinical trials. Stat Med 2009; 28:1181-217. [PMID: 19206095 DOI: 10.1002/sim.3538] [Citation(s) in RCA: 167] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Adaptive designs play an increasingly important role in clinical drug development. Such designs use accumulating data of an ongoing trial to decide how to modify design aspects without undermining the validity and integrity of the trial. Adaptive designs thus allow for a number of possible adaptations at midterm: Early stopping either for futility or success, sample size reassessment, change of population, etc. A particularly appealing application is the use of adaptive designs in combined phase II/III studies with treatment selection at interim. The expectation has arisen that carefully planned and conducted studies based on adaptive designs increase the efficiency of the drug development process by making better use of the observed data, thus leading to a higher information value per patient.In this paper we focus on adaptive designs for confirmatory clinical trials. We review the adaptive design methodology for a single null hypothesis and how to perform adaptive designs with multiple hypotheses using closed test procedures. We report the results of an extensive simulation study to evaluate the operational characteristics of the various methods. A case study and related numerical examples are used to illustrate the key results. In addition we provide a detailed discussion of current methods to calculate point estimates and confidence intervals for relevant parameters.
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Affiliation(s)
- Frank Bretz
- Novartis Pharma AG, Lichtstrasse 35, 4002 Basel, Switzerland.
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Predictive checking for Bayesian interim analyses in clinical trials. Contemp Clin Trials 2008; 29:740-50. [PMID: 18571477 DOI: 10.1016/j.cct.2008.05.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2008] [Revised: 05/12/2008] [Accepted: 05/13/2008] [Indexed: 11/23/2022]
Abstract
Bayesian methodologies have been used for interim analyses of clinical trial data. In Bayesian interim analyses, decisions regarding the continuation of a trial are guided by a Bayesian model or indices, e.g., the predictive probability derived from it that specifies the conditions under which the clinical trial results might be judged sufficiently convincing to allow early stopping. Thus, its appropriateness for making such decisions depends on whether the model or the indices are reliable. In this paper we describe the use of both prior- and posterior- predictive checking approaches as a diagnostic tool for assessing the reliability of the model or indices on which the decision making is based. The proposed approach is illustrated with three examples, one of which is a simulation.
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Zhang X, Cutter G. Bayesian interim analysis in clinical trials. Contemp Clin Trials 2008; 29:751-5. [PMID: 18589003 DOI: 10.1016/j.cct.2008.05.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2007] [Revised: 05/12/2008] [Accepted: 05/30/2008] [Indexed: 11/25/2022]
Abstract
We propose a Bayesian approach to monitor clinical trials with clustered binary outcomes using multivariate probit models. Our monitoring is based on the calculated probability of the reduced incidence rate using a new treatment compared with the standard treatment greater than a target improvement under different prior scenarios for the treatment effect. We develop a Bayesian sampling algorithm for posterior inference allowing missing values in the outcomes. We illustrate our method using a published early trail of inhaled nitric oxide therapy in premature infants.
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Affiliation(s)
- Xiao Zhang
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
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77
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Abstract
We review a Bayesian predictive approach for interim data monitoring and propose its application to interim sample size reestimation for clinical trials. Based on interim data, this approach predicts how the sample size of a clinical trial needs to be adjusted so as to claim a success at the conclusion of the trial with an expected probability. The method is compared with predictive power and conditional power approaches using clinical trial data. Advantages of this approach over the others are discussed.
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Affiliation(s)
- Ming-Dauh Wang
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA.
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78
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