1
|
Luh WM, Guo JH. Unequal allocation of sample/event sizes with considerations of sampling cost for testing equality, non-inferiority/superiority, and equivalence of two Poisson rates. Int J Biostat 2024; 20:143-156. [PMID: 36583245 DOI: 10.1515/ijb-2022-0039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 12/09/2022] [Indexed: 12/31/2022]
Abstract
For non-inferiority/superiority and equivalence tests of two Poisson rates, the determination of the required number of sample sizes has been studied but the studies for the number of events to be observed are very limited. To fill the gap, the present study first is aimed toward determining the number of events to be observed for testing non-inferiority/superiority and equivalence of two Poisson rates, respectively. Also, considering the cost for each event, the second purpose is to apply an exhaustive search to find the unequal but optimal allocation of events for each group such that the budget is minimal for a user-specified power level, or the statistical power is maximal for a user-specified budget. Four R Shiny apps were developed to obtain the number of events needed for each group. A simulation study showed the proposed approach to be valid in terms of Type I error and statistical power. A comparison of the proposed approach with extant methods from various disciplines was performed, and an illustrative example of comparing the adverse reactions to the COVID-19 vaccines was demonstrated. By applying the proposed approach, researchers also can estimate the most economical number of subjects or time intervals after determining the number of events.
Collapse
Affiliation(s)
- Wei-Ming Luh
- National Cheng Kung University, Tainan, 70101, Taiwan
| | | |
Collapse
|
2
|
Zhang W, Zhang Z, Liu A. Optimizing treatment allocation in randomized clinical trials by leveraging baseline covariates. Biometrics 2023; 79:2815-2829. [PMID: 37641532 PMCID: PMC10843680 DOI: 10.1111/biom.13914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 07/10/2023] [Indexed: 08/31/2023]
Abstract
We consider the problem of optimizing treatment allocation for statistical efficiency in randomized clinical trials. Optimal allocation has been studied previously for simple treatment effect estimators such as the sample mean difference, which are not fully efficient in the presence of baseline covariates. More efficient estimators can be obtained by incorporating covariate information, and modern machine learning methods make it increasingly feasible to approach full efficiency. Accordingly, we derive the optimal allocation ratio by maximizing the design efficiency of a randomized trial, assuming that an efficient estimator will be used for analysis. We then expand the scope of optimization by considering covariate-dependent randomization (CDR), which has some flavor of an observational study but provides the same level of scientific rigor as a standard randomized trial. We describe treatment effect estimators that are consistent, asymptotically normal, and (nearly) efficient under CDR, and derive the optimal propensity score by maximizing the design efficiency of a CDR trial (under the assumption that an efficient estimator will be used for analysis). Our optimality results translate into optimal designs that improve upon standard practice. Real-world examples and simulation results demonstrate that the proposed designs can produce substantial efficiency improvements in realistic settings.
Collapse
Affiliation(s)
- Wei Zhang
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Zhiwei Zhang
- Biostatistics Innovation Group, Gilead Sciences, Foster City, California, USA
| | - Aiyi Liu
- Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA
| |
Collapse
|
3
|
Zhou Y, Zhu Y, Wong WK. Statistical tests for homogeneity of variance for clinical trials and recommendations. Contemp Clin Trials Commun 2023; 33:101119. [PMID: 37143826 PMCID: PMC10151260 DOI: 10.1016/j.conctc.2023.101119] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 03/03/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023] Open
Abstract
In most clinical trials, the main interest is to test whether there are differences in the mean outcomes among the treatment groups. When the outcome is continuous, a common statistical test is a usual t-test for a two-group comparison. For more than 2 groups, an ANOVA setup is used and the test for equality for all groups is based on the F-distribution. A key assumption for these parametric tests is that data are normally, independently distributed and the response variances are equal. The robustness of these tests to the first two assumptions is quite well investigated, but the issues arising from heteroscedasticity are less studied. This paper reviews different methods for ascertaining homogeneity of variance across groups and investigates the consequences of heteroscedasticity on the tests. Simulations based on normal, heavy-tailed, and skewed normal data demonstrate that some of the less known methods, such as the Jackknife or Cochran's test, are quite effective in detecting differences in the variances.
Collapse
|
4
|
Mavrogonatou L, Sun Y, Robertson DS, Villar SS. A comparison of allocation strategies for optimising clinical trial designs under variance heterogeneity. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
|
5
|
Optimal allocations for two treatment comparisons within the proportional odds cumulative logits model. PLoS One 2021; 16:e0250119. [PMID: 33882086 PMCID: PMC8059828 DOI: 10.1371/journal.pone.0250119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 03/30/2021] [Indexed: 12/02/2022] Open
Abstract
This paper studies optimal treatment allocations for two treatment comparisons when the outcome is ordinal and analyzed by a proportional odds cumulative logits model. The variance of the treatment effect estimator is used as optimality criterion. The optimal design is sought so that this variance is minimal for a given total sample size or a given budget, meaning that the power for the test on treatment effect is maximal, or it is sought so that a required power level is achieved at a minimal total sample size or budget. Results are presented for three, five and seven ordered response categories, three treatment effect sizes and a skewed, bell-shaped or polarized distribution of the response probabilities. The optimal proportion subjects in the intervention condition decreases with the number of response categories and the costs for the intervention relative to those for the control. The relation between the optimal proportion and effect size depends on the distribution of the response probabilities. The widely used balanced design is not always the most efficient; its efficiency as compared to the optimal design decreases with increasing cost ratio. The optimal design is highly robust to misspecification of the response probabilities and treatment effect size. The optimal design methodology is illustrated using two pharmaceutical examples. A Shiny app is available to find the optimal treatment allocation, to evaluate the efficiency of the balanced design and to study the relation between budget or sample size and power.
Collapse
|
6
|
Frieri R, Zagoraiou M. Optimal and ethical designs for hypothesis testing in multi-arm exponential trials. Stat Med 2021; 40:2578-2603. [PMID: 33687086 DOI: 10.1002/sim.8919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 01/25/2021] [Accepted: 02/03/2021] [Indexed: 11/06/2022]
Abstract
Multi-arm clinical trials are complex experiments which involve several objectives. The demand for unequal allocations in a multi-treatment context is growing and adaptive designs are being increasingly used in several areas of medical research. For uncensored and censored exponential responses, we propose a constrained optimization approach in order to derive the design maximizing the power of the multivariate test of homogeneity, under a suitable ethical constraint. In the absence of censoring, we obtain a very simple closed-form solution that dominates the balanced design in terms of power and ethics. Our suggestion can also accommodate delayed responses and staggered entries, and can be implemented via response adaptive rules. While other targets proposed in the literature could present an unethical behavior, the suggested optimal allocation is frequently unbalanced by assigning more patients to the best treatment, both in the absence and presence of censoring. We evaluate the operating characteristics of our proposal theoretically and by simulations, also redesigning a real lung cancer trial, showing that the constrained optimal target guarantees very good performances in terms of ethical demands, power and estimation precision. Therefore, it is a valid and useful tool in designing clinical trials, especially oncological trials and clinical experiments for grave and novel infectious diseases, where the ethical concern is of primary importance.
Collapse
Affiliation(s)
- Rosamarie Frieri
- Department of Statistical Sciences, University of Bologna, Bologna, Italy
| | - Maroussa Zagoraiou
- Department of Statistical Sciences, University of Bologna, Bologna, Italy
| |
Collapse
|
7
|
Affiliation(s)
| | - A.S. Hedayat
- Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago, Chicago, IL
| | - Min Yang
- Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago, Chicago, IL
| | | |
Collapse
|
8
|
Chandereng T, Wei X, Chappell R. Imbalanced randomization in clinical trials. Stat Med 2020; 39:2185-2196. [PMID: 32246484 DOI: 10.1002/sim.8539] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 02/17/2020] [Accepted: 03/07/2020] [Indexed: 11/07/2022]
Abstract
Randomization is a common technique used in clinical trials to eliminate potential bias and confounders in a patient population. Equal allocation to treatment groups is the standard due to its optimal efficiency in many cases. However, in certain scenarios, unequal allocation can improve efficiency. In superiority trials with more than two groups, the optimal randomization is not always a balanced randomization. In noninferiority (NI) trials, additive margin with equal variance is the http://www.statlab.wisc.edu/shiny/SSNI/.
Collapse
Affiliation(s)
- Thevaa Chandereng
- Department of Statistics, University of Wisconsin-Madison, Wisconsin, USA.,Department of Biostatistics & Medical Informatics, University of Wisconsin-Madison, Wisconsin, USA.,Morgridge Institute of Research, Wisconsin, USA
| | - Xiaodan Wei
- Biostatistics and Programming, Sanofi Bridgewater, New Jersey, USA
| | - Rick Chappell
- Department of Statistics, University of Wisconsin-Madison, Wisconsin, USA.,Department of Biostatistics & Medical Informatics, University of Wisconsin-Madison, Wisconsin, USA
| |
Collapse
|
9
|
Sverdlov O, Ryeznik Y, Wong WK. On Optimal Designs for Clinical Trials: An Updated Review. JOURNAL OF STATISTICAL THEORY AND PRACTICE 2019. [DOI: 10.1007/s42519-019-0073-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
10
|
Kim YH, Graham AD, Li W, Radke CJ, Lin MC. Human Lacrimal Production Rate and Wetted Length of Modified Schirmer's Tear Test Strips. Transl Vis Sci Technol 2019; 8:40. [PMID: 31211005 PMCID: PMC6561131 DOI: 10.1167/tvst.8.3.40] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Accepted: 04/29/2019] [Indexed: 11/30/2022] Open
Abstract
Purpose To assess and compare the wetting kinetics of sheathed and unsheathed Schirmer's tear test (STT) strips, and to determine the repeatability of 5-minute wetted length (WL) and basal tear production rate (BTPR). Methods Seventeen subjects underwent two sheathed and unsheathed STTs each for both eyes on four visits on separate days. After administration of topical anesthetic, WLs were measured every 30 seconds for 5 minutes, and BTPRs were calculated for sheathed strips. Limits of agreement (LoA), difference-versus-mean plots (DVM), and the coefficient of repeatability (CR) assessed WL and BTPR repeatabilities. Variance estimates were used to calculate sample sizes for future study. Results For the unsheathed STT, the mean (SD) difference in WLs between visits was 0.74 (5.05) mm, LoA were [−9.17, 10.64], and CR was 9.17 mm; for the sheathed STT, the mean (SD) intervisit difference was 0.16 (5.94) mm, LoA were [−11.49, 11.8], and CR was 10.53 mm. Eight of 48 sheathed STTs and 20 of 44 unsheathed STTs showed constant WL for the final 90 seconds of the test. The mean (SD) difference between repeated visits for BTPR was approximately 0.0 μL/min, LoA were [−1.82, 1.82], and CR was 1.91 μL/min. Conclusions Repeatability of sheathed and unsheathed 5-minute WL and BTPR is inadequate for measuring within-subject changes, but is sufficient for group studies with moderate sample sizes. Constant WL for the final 90 seconds with the eight sheathed STT measurements suggests varying BTPR, whereas constant WL with the unsheathed STT can be explained by balancing evaporation and BTPR. Translational Relevance Repeatability of the modified STT is evaluated clinically to establish quantitative BTPRs rather than inference from a strip WL.
Collapse
Affiliation(s)
- Young Hyun Kim
- Vision Science Group, University of California, Berkeley, CA, USA.,Clinical Research Center, School of Optometry, University of California, Berkeley, CA, USA.,Chemical and Biomolecular Engineering Department, University of California, Berkeley, CA, USA
| | - Andrew D Graham
- Clinical Research Center, School of Optometry, University of California, Berkeley, CA, USA
| | - Wing Li
- Clinical Research Center, School of Optometry, University of California, Berkeley, CA, USA
| | - Clayton J Radke
- Vision Science Group, University of California, Berkeley, CA, USA.,Chemical and Biomolecular Engineering Department, University of California, Berkeley, CA, USA
| | - Meng C Lin
- Vision Science Group, University of California, Berkeley, CA, USA.,Clinical Research Center, School of Optometry, University of California, Berkeley, CA, USA
| |
Collapse
|
11
|
Sverdlov O, Ryeznik Y. Implementing unequal randomization in clinical trials with heterogeneous treatment costs. Stat Med 2019; 38:2905-2927. [DOI: 10.1002/sim.8160] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 12/28/2018] [Accepted: 03/15/2019] [Indexed: 11/11/2022]
Affiliation(s)
- Oleksandr Sverdlov
- Early Development BiostatisticsNovartis Pharmaceuticals East Hanover New Jersey
| | - Yevgen Ryeznik
- Department of MathematicsUppsala University Uppsala Sweden
- Department of Pharmaceutical BiosciencesUppsala University Uppsala Sweden
| |
Collapse
|
12
|
Ryeznik Y, Sverdlov O, Hooker AC. Implementing Optimal Designs for Dose-Response Studies Through Adaptive Randomization for a Small Population Group. AAPS JOURNAL 2018; 20:85. [PMID: 30027336 DOI: 10.1208/s12248-018-0242-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 06/18/2018] [Indexed: 11/30/2022]
Abstract
In dose-response studies with censored time-to-event outcomes, D-optimal designs depend on the true model and the amount of censored data. In practice, such designs can be implemented adaptively, by performing dose assignments according to updated knowledge of the dose-response curve at interim analysis. It is also essential that treatment allocation involves randomization-to mitigate various experimental biases and enable valid statistical inference at the end of the trial. In this work, we perform a comparison of several adaptive randomization procedures that can be used for implementing D-optimal designs for dose-response studies with time-to-event outcomes with small to moderate sample sizes. We consider single-stage, two-stage, and multi-stage adaptive designs. We also explore robustness of the designs to experimental (chronological and selection) biases. Simulation studies provide evidence that both the choice of an allocation design and a randomization procedure to implement the target allocation impact the quality of dose-response estimation, especially for small samples. For best performance, a multi-stage adaptive design with small cohort sizes should be implemented using a randomization procedure that closely attains the targeted D-optimal design at each stage. The results of the current work should help clinical investigators select an appropriate randomization procedure for their dose-response study.
Collapse
Affiliation(s)
- Yevgen Ryeznik
- Department of Mathematics, Uppsala University, Room Å14133 Lägerhyddsvägen 1, Hus 1, 6 och 7, 751 06, Uppsala, Sweden. .,Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
| | - Oleksandr Sverdlov
- Early Development Biostatistics, Novartis Institutes for Biomedical Research, East Hannover, New Jersey, USA
| | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| |
Collapse
|
13
|
van Breukelen GJP, Candel MJJM. Efficient design of cluster randomized trials with treatment-dependent costs and treatment-dependent unknown variances. Stat Med 2018; 37:3027-3046. [PMID: 29888393 PMCID: PMC6120518 DOI: 10.1002/sim.7824] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 03/23/2018] [Accepted: 04/19/2018] [Indexed: 11/30/2022]
Abstract
Cluster randomized trials evaluate the effect of a treatment on persons nested within clusters, where treatment is randomly assigned to clusters. Current equations for the optimal sample size at the cluster and person level assume that the outcome variances and/or the study costs are known and homogeneous between treatment arms. This paper presents efficient yet robust designs for cluster randomized trials with treatment‐dependent costs and treatment‐dependent unknown variances, and compares these with 2 practical designs. First, the maximin design (MMD) is derived, which maximizes the minimum efficiency (minimizes the maximum sampling variance) of the treatment effect estimator over a range of treatment‐to‐control variance ratios. The MMD is then compared with the optimal design for homogeneous variances and costs (balanced design), and with that for homogeneous variances and treatment‐dependent costs (cost‐considered design). The results show that the balanced design is the MMD if the treatment‐to control cost ratio is the same at both design levels (cluster, person) and within the range for the treatment‐to‐control variance ratio. It still is highly efficient and better than the cost‐considered design if the cost ratio is within the range for the squared variance ratio. Outside that range, the cost‐considered design is better and highly efficient, but it is not the MMD. An example shows sample size calculation for the MMD, and the computer code (SPSS and R) is provided as supplementary material. The MMD is recommended for trial planning if the study costs are treatment‐dependent and homogeneity of variances cannot be assumed.
Collapse
Affiliation(s)
- Gerard J P van Breukelen
- Department of Methodology and Statistics, CAPHRI Care and Public Health Research Institute, Maastricht University, PO Box 616, 6200 MD, The Netherlands.,Department of Methodology and Statistics, Graduate School of Psychology and Neuroscience, Maastricht University, PO Box 616, 6200 MD, The Netherlands
| | - Math J J M Candel
- Department of Methodology and Statistics, CAPHRI Care and Public Health Research Institute, Maastricht University, PO Box 616, 6200 MD, The Netherlands
| |
Collapse
|
14
|
Optimal response and covariate-adaptive biased-coin designs for clinical trials with continuous multivariate or longitudinal responses. Comput Stat Data Anal 2017. [DOI: 10.1016/j.csda.2016.05.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
15
|
Schou IM, Marschner IC. Design of clinical trials involving multiple hypothesis tests with a common control. Biom J 2016; 59:636-657. [DOI: 10.1002/bimj.201500230] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Revised: 06/27/2016] [Accepted: 07/12/2016] [Indexed: 11/11/2022]
Affiliation(s)
- I. Manjula Schou
- Department of Statistics; Macquarie University; NSW 2109 Australia
- NHMRC Clinical Trials Centre; University of Sydney; NSW 2006 Australia
| | - Ian C. Marschner
- Department of Statistics; Macquarie University; NSW 2109 Australia
- NHMRC Clinical Trials Centre; University of Sydney; NSW 2006 Australia
| |
Collapse
|
16
|
Normark S. Minimax designs for 2kfactorial experiments for generalized linear models. COMMUN STAT-THEOR M 2016. [DOI: 10.1080/03610926.2014.927502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
17
|
|
18
|
Lemme F, van Breukelen GJP, Candel MJJM, Berger MPF. The effect of heterogeneous variance on efficiency and power of cluster randomized trials with a balanced 2 × 2 factorial design. Stat Methods Med Res 2015; 24:574-93. [PMID: 25911332 DOI: 10.1177/0962280215583683] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Sample size calculation for cluster randomized trials (CRTs) with a [Formula: see text] factorial design is complicated due to the combination of nesting (of individuals within clusters) with crossing (of two treatments). Typically, clusters and individuals are allocated across treatment conditions in a balanced fashion, which is optimal under homogeneity of variance. However, the variance is likely to be heterogeneous if there is a treatment effect. An unbalanced allocation is then more efficient, but impractical because the optimal allocation depends on the unknown variances. Focusing on CRTs with a [Formula: see text] design, this paper addresses two questions: How much efficiency is lost by having a balanced design when the outcome variance is heterogeneous? How large must the sample size be for a balanced allocation to have sufficient power under heterogeneity of variance? We consider different scenarios of heterogeneous variance. Within each scenario, we determine the relative efficiency of a balanced design, as a function of the level (cluster, individual, both) and amount of heterogeneity of the variance. We then provide a simple correction of the sample size for the loss of power due to heterogeneity of variance when a balanced allocation is used. The theory is illustrated with an example of a published 2 x2 CRT.
Collapse
Affiliation(s)
- Francesca Lemme
- Department of Methodology and Statistics, Maastricht University, The Netherlands
| | - Gerard J P van Breukelen
- Department of Methodology and Statistics, CAPHRI School for Public Health and Primary Care, Maastricht University, The Netherlands
| | - Math J J M Candel
- Department of Methodology and Statistics, CAPHRI School for Public Health and Primary Care, Maastricht University, The Netherlands
| | - Martijn P F Berger
- Department of Methodology and Statistics, Maastricht University, The Netherlands
| |
Collapse
|
19
|
Biswas A, Bhattacharya R, Park E. On a class of optimal covariate-adjusted response adaptive designs for survival outcomes. Stat Methods Med Res 2014; 25:2444-2456. [PMID: 24619109 DOI: 10.1177/0962280214524177] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A class of optimal covariate-adjusted response adaptive procedures is developed for phase III clinical trials when the treatment response is a survival time and there is random censoring. The basic aim is to develop an allocation design by combining the ethical aspects with statistical precision in a reasonable way under the presence of covariate information. Considering minimisation of total hazards as the ethical requirement, the proposed procedure is assessed in terms of the assignment to the better treatment and the efficiency (i.e. power) to detect a small departure in treatment effectiveness. The applicability of the proposed methodology is also illustrated using a real data set.
Collapse
Affiliation(s)
- Atanu Biswas
- Applied Statistics Unit, Indian Statistical Institute, India
| | | | - Eunsik Park
- Department of Statistics, Chonnam National University, Korea
| |
Collapse
|
20
|
Sverdlov O, Rosenberger WF. On Recent Advances in Optimal Allocation Designs in Clinical Trials. JOURNAL OF STATISTICAL THEORY AND PRACTICE 2013. [DOI: 10.1080/15598608.2013.783726] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
21
|
Guo JH, Luh WM. Efficient sample size allocation with cost constraints for heterogeneous-variance group comparison. J Appl Stat 2013. [DOI: 10.1080/02664763.2013.819417] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
22
|
Abstract
In February 2010, the U.S. Food and Drug Administration (FDA, 2010 ) drafted guidance that discusses the statistical, clinical, and regulatory aspects of various adaptive designs for clinical trials. An important class of adaptive designs is adaptive randomization, which is considered very briefly in subsection VI.B of the guidance. The objective of this paper is to review several important new classes of adaptive randomization procedures and convey information on the recent developments in the literature on this topic. Much of this literature has been focused on the development of methodology to address past criticisms and concerns that have hindered the broader use of adaptive randomization. We conclude that adaptive randomization is a very broad area of experimental design that has important application in modern clinical trials.
Collapse
|
23
|
Biswas A, Bhattacharya R. Response-adaptive designs for continuous treatment responses in phase III clinical trials: A review. Stat Methods Med Res 2012; 25:81-100. [DOI: 10.1177/0962280212441424] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A variety of response-adaptive randomization procedures have been proposed in literature assuming binary outcomes. However, the list is not so long for continuous outcomes though many real clinical trials deal with continuous treatment responses. In this paper, we attempt to explore the available procedures together with a comparison of their performances. Some real-life adaptive trial is also reviewed.
Collapse
Affiliation(s)
- Atanu Biswas
- Applied Statistics Unit, Indian Statistical
Institute, Kolkata, India
| | | |
Collapse
|
24
|
Sverdlov O, Tymofyeyev Y, Wong WK. Optimal response-adaptive randomized designs for multi-armed survival trials. Stat Med 2011; 30:2890-910. [DOI: 10.1002/sim.4331] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2010] [Accepted: 06/14/2011] [Indexed: 11/11/2022]
Affiliation(s)
- Oleksandr Sverdlov
- Bristol-Myers Squibb; Route 206 and Province Line Road Princeton NJ 08543 USA
| | - Yevgen Tymofyeyev
- Quantitative Decision Strategies Department; Janssen Research and Development; 1125 Trenton-Harbourton Road Titusville NJ 08560 USA
| | - Weng Kee Wong
- Department of Biostatistics; School of Public Health, UCLA; 10833 Le Conte Ave Los Angeles CA 90095-1772 USA
| |
Collapse
|
25
|
Stallard N, Posch M, Friede T, Koenig F, Brannath W. Optimal choice of the number of treatments to be included in a clinical trial. Stat Med 2009; 28:1321-38. [PMID: 19243083 DOI: 10.1002/sim.3551] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
It is common for a number of potentially effective treatments to be available for clinical evaluation. Limitations on resources mean that this inevitably leads to a decision as to how many, and which, treatments should be considered for inclusion in a clinical trial. This paper considers the problem of selection of possible treatments for inclusion in a phase III clinical trial. We assume that treatments will be compared using a standard frequentist hypothesis test, and propose a Bayesian decision-theoretic approach that leads to minimization of the total sample size of the trial subject to controlling the familywise type I error rate and the expected probability of rejecting at least one null hypothesis. The method is illustrated in the simplest situation, in which two experimental treatments could be included in the clinical trial, exploring the levels of evidence that are required to lead to an optimal trial that includes one or both of these treatments.
Collapse
Affiliation(s)
- Nigel Stallard
- Warwick Medical School, University of Warwick, Coventry CV4 7AL, U.K.
| | | | | | | | | |
Collapse
|