1
|
Gao P, Zhang W. A systematic approach to adaptive sequential design for clinical trials: using simulations to select a design with desired operating characteristics. J Biopharm Stat 2024:1-16. [PMID: 38812413 DOI: 10.1080/10543406.2024.2358796] [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: 12/11/2022] [Accepted: 05/12/2024] [Indexed: 05/31/2024]
Abstract
The failure rates of phase 3 trials are high. Incorrect sample size due to uncertainty of effect size could be a critical contributing factor. Adaptive sequential design (ASD), which may include one or more sample size re-estimations (SSR), has been a popular approach for dealing with such uncertainties. The operating characteristics (OCs) of ASD, including the unconditional power and mean sample size, can be substantially affected by many factors, including the planned sample size, the interim analysis schedule and choice of critical boundaries and rules for interim analysis. We propose a systematic, comprehensive strategy which uses iterative simulations to investigate the operating characteristics of adaptive designs and help achieve adequate unconditional power and cost-effective mean sample size if the effect size is in a pre-identified range.
Collapse
Affiliation(s)
- Ping Gao
- Innovatio Statistics, Inc, Bridgewater, NJ, USA
| | | |
Collapse
|
2
|
Cui L. Sample size adaptation designs and efficiency comparison with group sequential designs. Stat Med 2024; 43:2203-2215. [PMID: 38545849 DOI: 10.1002/sim.10066] [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: 07/27/2023] [Revised: 01/25/2024] [Accepted: 03/12/2024] [Indexed: 05/18/2024]
Abstract
This study is to give a systematic account of sample size adaptation designs (SSADs) and to provide direct proof of the efficiency advantage of general SSADs over group sequential designs (GSDs) from a different perspective. For this purpose, a class of sample size mapping functions to define SSADs is introduced. Under the two-stage adaptive clinical trial setting, theorems are developed to describe the properties of SSADs. Sufficient conditions are derived and used to prove analytically that SSADs based on the weighted combination test can be uniformly more efficient than GSDs in a range of likely values of the true treatment differenceδ $$ \delta $$ . As shown in various scenarios, given a GSD, a fully adaptive SSAD can be obtained that has sufficient statistical power similar to that of the GSD but has a smaller average sample size for allδ $$ \delta $$ in the range. The associated sample size savings can be substantial. A practical design example and suggestions on the steps to find efficient SSADs are also provided.
Collapse
Affiliation(s)
- Lu Cui
- Independent Researcher, Washington DC, USA
| |
Collapse
|
3
|
Bokelmann B, Rauch G, Meis J, Kieser M, Herrmann C. Extension of a conditional performance score for sample size recalculation rules to the setting of binary endpoints. BMC Med Res Methodol 2024; 24:15. [PMID: 38243169 PMCID: PMC10797857 DOI: 10.1186/s12874-024-02150-4] [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: 05/08/2023] [Accepted: 01/12/2024] [Indexed: 01/21/2024] Open
Abstract
BACKGROUND Sample size calculation is a central aspect in planning of clinical trials. The sample size is calculated based on parameter assumptions, like the treatment effect and the endpoint's variance. A fundamental problem of this approach is that the true distribution parameters are not known before the trial. Hence, sample size calculation always contains a certain degree of uncertainty, leading to the risk of underpowering or oversizing a trial. One way to cope with this uncertainty are adaptive designs. Adaptive designs allow to adjust the sample size during an interim analysis. There is a large number of such recalculation rules to choose from. To guide the choice of a suitable adaptive design with sample size recalculation, previous literature suggests a conditional performance score for studies with a normally distributed endpoint. However, binary endpoints are also frequently applied in clinical trials and the application of the conditional performance score to binary endpoints is not yet investigated. METHODS We extend the theory of the conditional performance score to binary endpoints by suggesting a related one-dimensional score parametrization. We moreover perform a simulation study to evaluate the operational characteristics and to illustrate application. RESULTS We find that the score definition can be extended without modification to the case of binary endpoints. We represent the score results by a single distribution parameter, and therefore derive a single effect measure, which contains the difference in proportions [Formula: see text] between the intervention and the control group, as well as the endpoint proportion [Formula: see text] in the control group. CONCLUSIONS This research extends the theory of the conditional performance score to binary endpoints and demonstrates its application in practice.
Collapse
Affiliation(s)
- Björn Bokelmann
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Biometry and Clinical Epidemiology, Charitéplatz 1, Berlin, 10117, Germany.
| | - Geraldine Rauch
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Biometry and Clinical Epidemiology, Charitéplatz 1, Berlin, 10117, Germany
- Technische Universität Berlin, Straße des 17. Juni 135, 10623, Berlin, Germany
| | - Jan Meis
- Institute of Medical Biometry, University Medical Center Ruprechts-Karls University Heidelberg, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
| | - Meinhard Kieser
- Institute of Medical Biometry, University Medical Center Ruprechts-Karls University Heidelberg, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
| | - Carolin Herrmann
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Biometry and Clinical Epidemiology, Charitéplatz 1, Berlin, 10117, Germany
| |
Collapse
|
4
|
Herrmann C, Kieser M, Rauch G, Pilz M. Optimization of adaptive designs with respect to a performance score. Biom J 2022; 64:989-1006. [PMID: 35426460 DOI: 10.1002/bimj.202100166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 02/09/2022] [Accepted: 02/12/2022] [Indexed: 11/08/2022]
Abstract
Adaptive designs are an increasingly popular method for the adaptation of design aspects in clinical trials, such as the sample size. Scoring different adaptive designs helps to make an appropriate choice among the numerous existing adaptive design methods. Several scores have been proposed to evaluate adaptive designs. Moreover, it is possible to determine optimal two-stage adaptive designs with respect to a customized objective score by solving a constrained optimization problem. In this paper, we use the conditional performance score by Herrmann et al. (2020) as the optimization criterion to derive optimal adaptive two-stage designs. We investigate variations of the original performance score, for example, by assigning different weights to the score components and by incorporating prior assumptions on the effect size. We further investigate a setting where the optimization framework is extended by a global power constraint, and additional optimization of the critical value function next to the stage-two sample size is performed. Those evaluations with respect to the sample size curves and the resulting design's performance can contribute to facilitate the score's usage in practice.
Collapse
Affiliation(s)
- Carolin Herrmann
- Institute of Biometry and Clinical Epidemiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Meinhard Kieser
- Institute of Medical Biometry, University Hospital Heidelberg, Heidelberg, Germany
| | - Geraldine Rauch
- Institute of Biometry and Clinical Epidemiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Maximilian Pilz
- Institute of Medical Biometry, University Hospital Heidelberg, Heidelberg, Germany
| |
Collapse
|
5
|
Li X, Hu F. Sample size re-estimation for response-adaptive randomized clinical trials. Pharm Stat 2022; 21:1058-1073. [PMID: 35191605 DOI: 10.1002/pst.2199] [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: 01/13/2020] [Revised: 01/23/2022] [Accepted: 02/02/2022] [Indexed: 11/10/2022]
Abstract
Clinical trials usually take a period of time to recruit volunteers, and they become a steady accumulation of data. Traditionally, the sample size of a trial is determined in advance and data is collected before analysis proceeds. Over the past decades, many strategies have been proposed and rigorous theoretical groundings have been provided to conduct sample size re-estimation. However, the application of these methodologies has not been well extended to take care of trials with adaptive designs. Therefore, we aim to fill the gap by proposing a sample size re-estimation procedure on response-adaptive randomized trial. For ethical and economical concerns, we use multiple stopping criteria with the allowance of early termination. Statistical inference is studied for the hypothesis testing under doubly-adaptive biased coin design. We also prove that the test statistics for each stage are asymptotic independently normally distributed, though dependency exists between the two stages. We find that under our methods, compared to fixed sample size design and other commonly used randomization procedures: (1) power is increased for all scenarios with adjusted sample size; (2) sample size is reduced up to 40% when underestimating the treatment effect; (3) the duration of trials is shortened. These advantages are evidenced by numerical studies and real examples.
Collapse
Affiliation(s)
- Xin Li
- Department of Statistics, George Washington University, Washington, District of Columbia, USA
| | - Feifang Hu
- Department of Statistics, George Washington University, Washington, District of Columbia, USA
| |
Collapse
|
6
|
Sample Size Re-estimation with the Com-Nougue Method to Evaluate Treatment Effect. STATISTICS IN BIOSCIENCES 2021. [DOI: 10.1007/s12561-021-09316-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
7
|
Li X, Ma W, Hu F. Sample size re-estimation for covariate-adaptive randomized clinical trials. Stat Med 2021; 40:2839-2858. [PMID: 33733513 DOI: 10.1002/sim.8939] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 01/25/2021] [Accepted: 02/13/2021] [Indexed: 11/08/2022]
Abstract
Covariate-adaptive randomization (CAR) procedures have been developed in clinical trials to mitigate the imbalance of treatments among covariates. In recent years, an increasing number of trials have started to use CAR for the advantages in statistical efficiency and enhancing credibility. At the same time, sample size re-estimation (SSR) has become a common technique in industry to reduce time and cost while maintaining a good probability of success. Despite the widespread popularity of combining CAR designs with SSR, few researchers have investigated this combination theoretically. More importantly, the existing statistical inference must be adjusted to protect the desired type I error rate when a model that omits some covariates is used. In this article, we give a framework for the application of SSR in CAR trials and study the underlying theoretical properties. We give the adjusted test statistic and derive the sample size calculation formula under the CAR setting. We can tackle the difficulties caused by the adaptive features in CAR and prove the asymptotic independence between stages. Numerical studies are conducted under multiple parameter settings and scenarios that are commonly encountered in practice. The results show that all advantages of CAR and SSR can be preserved and further improved in terms of power and sample size.
Collapse
Affiliation(s)
- Xin Li
- Department of Statistics, George Washington University, Washington, DC, USA
| | - Wei Ma
- Institute of Statistics and Big Data, Renmin University of China, Beijing, China
| | - Feifang Hu
- Department of Statistics, George Washington University, Washington, DC, USA
| |
Collapse
|
8
|
Cui L, Zhan T, Zhang L, Geng Z, Gu Y, Chan IS. An automation-based adaptive seamless design for dose selection and confirmation with improved power and efficiency. Stat Methods Med Res 2021; 30:1013-1025. [PMID: 33459183 DOI: 10.1177/0962280220984822] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In a drug development program, the efficacy and safety of multiple doses can be evaluated in patients through a phase 2b dose ranging study. With a demonstrated dose response in the trial, promising doses are identified. Their effectiveness then is further investigated and confirmed in phase 3 studies. Although this two-step approach serves the purpose of the program, in general, it is inefficient because of its prolonged development duration and the exclusion of the phase 2b data in the final efficacy evaluation and confirmation which are only based on phase 3 data. To address the issue, we propose a new adaptive design, which seamlessly integrates the dose finding and confirmation steps under one pivotal study. Unlike existing adaptive seamless phase 2b/3 designs, the proposed design combines the response adaptive randomization, sample size modification, and multiple testing techniques to achieve better efficiency. The design can be easily implemented through an automated randomization process. At the end, a number of targeted doses are selected and their effectiveness is confirmed with guaranteed control of family-wise error rate.
Collapse
Affiliation(s)
- Lu Cui
- Statistical Science and Innovation, UCB Biosciences, Raleigh, NC, USA
| | - Tianyu Zhan
- Data and Statistical Sciences, AbbVie Inc., North Chicago, IL, USA
| | - Lanju Zhang
- Data and Statistical Sciences, AbbVie Inc., North Chicago, IL, USA
| | - Ziqian Geng
- Data and Statistical Sciences, AbbVie Inc., North Chicago, IL, USA
| | - Yihua Gu
- Data and Statistical Sciences, AbbVie Inc., North Chicago, IL, USA
| | - Ivan Sf Chan
- Data and Statistical Sciences, AbbVie Inc., North Chicago, IL, USA
| |
Collapse
|
9
|
Herrmann C, Pilz M, Kieser M, Rauch G. A new conditional performance score for the evaluation of adaptive group sequential designs with sample size recalculation. Stat Med 2020; 39:2067-2100. [DOI: 10.1002/sim.8534] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 12/20/2019] [Accepted: 03/04/2020] [Indexed: 11/06/2022]
Affiliation(s)
- Carolin Herrmann
- Institute of Biometry and Clinical Epidemiology Charité ‐ Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt‐Universität zu Berlin, Berlin Institute of Health Berlin Germany
- Berlin Institute of Health (BIH) Berlin Germany
| | - Maximilian Pilz
- Institute of Medical Biometry and Informatics University Medical Center Ruprechts‐Karls University Heidelberg Heidelberg Germany
| | - Meinhard Kieser
- Institute of Medical Biometry and Informatics University Medical Center Ruprechts‐Karls University Heidelberg Heidelberg Germany
| | - Geraldine Rauch
- Institute of Biometry and Clinical Epidemiology Charité ‐ Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt‐Universität zu Berlin, Berlin Institute of Health Berlin Germany
- Berlin Institute of Health (BIH) Berlin Germany
| |
Collapse
|
10
|
Pilz M, Kunzmann K, Herrmann C, Rauch G, Kieser M. A variational approach to optimal two‐stage designs. Stat Med 2019; 38:4159-4171. [DOI: 10.1002/sim.8291] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 04/29/2019] [Accepted: 06/04/2019] [Indexed: 11/07/2022]
Affiliation(s)
- Maximilian Pilz
- Institute of Medical Biometry and InformaticsUniversity Medical Center Ruprecht‐Karls University Heidelberg Heidelberg Germany
| | - Kevin Kunzmann
- Institute of Medical Biometry and InformaticsUniversity Medical Center Ruprecht‐Karls University Heidelberg Heidelberg Germany
| | - Carolin Herrmann
- Institute of Biometry and Clinical Epidemiology Charité‐Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin, Humboldt‐Universität zu Berlin, and Berlin Institute of Health) Berlin Germany
- Berlin Institute of Health (BIH) Berlin Germany
| | - Geraldine Rauch
- Institute of Biometry and Clinical Epidemiology Charité‐Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin, Humboldt‐Universität zu Berlin, and Berlin Institute of Health) Berlin Germany
- Berlin Institute of Health (BIH) Berlin Germany
| | - Meinhard Kieser
- Institute of Medical Biometry and InformaticsUniversity Medical Center Ruprecht‐Karls University Heidelberg Heidelberg Germany
| |
Collapse
|
11
|
Cui L, Hung HJ, Wang SJ. Commentary on “Applying CHW method to 2-in-1 design: gain or lose”. J Biopharm Stat 2019; 29:722-727. [DOI: 10.1080/10543406.2019.1634088] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Lu Cui
- Data Statistical Science, AbbVie Inc., North Chicago, IL, USA
| | - H.M. James Hung
- Division of Biometrics I, OB/OTS/CDER, FDA, Silver Spring, MD, USA
| | - Sue Jane Wang
- Office of Biostatistics, OTS/CDER, FDA, Silver Spring, MD, USA
| |
Collapse
|
12
|
Cui L, Zhang L. On the efficiency of adaptive sample size design. Stat Med 2018; 38:933-944. [DOI: 10.1002/sim.8034] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 09/24/2018] [Accepted: 10/22/2018] [Indexed: 11/07/2022]
Affiliation(s)
- Lu Cui
- AbbVie Inc North Chicago Illinois
| | | |
Collapse
|
13
|
Cui L, Zhang L, Yang B. Optimal adaptive group sequential design with flexible timing of sample size determination. Contemp Clin Trials 2017; 63:8-12. [DOI: 10.1016/j.cct.2017.04.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 04/03/2017] [Accepted: 04/22/2017] [Indexed: 11/26/2022]
|
14
|
Schüler S, Kieser M, Rauch G. Choice of futility boundaries for group sequential designs with two endpoints. BMC Med Res Methodol 2017; 17:119. [PMID: 28789615 PMCID: PMC5549398 DOI: 10.1186/s12874-017-0387-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Accepted: 06/30/2017] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND In clinical trials, the opportunity for an early stop during an interim analysis (either for efficacy or for futility) may relevantly save time and financial resources. This is especially important, if the planning assumptions required for power calculation are based on a low level of evidence. For example, when including two primary endpoints in the confirmatory analysis, the power of the trial depends on the effects of both endpoints and on their correlation. Assessing the feasibility of such a trial is therefore difficult, as the number of parameter assumptions to be correctly specified is large. For this reason, so-called 'group sequential designs' are of particular importance in this setting. Whereas the choice of adequate boundaries to stop a trial early for efficacy has been broadly discussed in the literature, the choice of optimal futility boundaries has not been investigated so far, although this may have serious consequences with respect to performance characteristics. METHODS In this work, we propose a general method to construct 'optimal' futility boundaries according to predefined criteria. Further, we present three different group sequential designs for two endpoints applying these futility boundaries. Our methods are illustrated by a real clinical trial example and by Monte-Carlo simulations. RESULTS By construction, the provided method of choosing futility boundaries maximizes the probability to correctly stop in case of small or opposite effects while limiting the power loss and the probability of stopping the study 'wrongly'. Our results clearly demonstrate the benefit of using such 'optimal' futility boundaries, especially compared to futility boundaries commonly applied in practice. CONCLUSIONS As the properties of futility boundaries are often not considered in practice and unfavorably chosen futility boundaries may imply bad properties of the study design, we recommend assessing the performance of these boundaries according to the criteria proposed in here.
Collapse
Affiliation(s)
- Svenja Schüler
- Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, Heidelberg, 69120, Germany.
| | - Meinhard Kieser
- Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, Heidelberg, 69120, Germany
| | - Geraldine Rauch
- Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, Heidelberg, 69120, Germany
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| |
Collapse
|
15
|
Zhang L, Cui L, Yang B. Optimal flexible sample size design with robust power. Stat Med 2016; 35:3385-96. [DOI: 10.1002/sim.6931] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Revised: 02/15/2016] [Accepted: 02/16/2016] [Indexed: 11/07/2022]
Affiliation(s)
- Lanju Zhang
- Data and Statistical Sciences; AbbVie Inc; 1 North Waukegan Rd North Chicago IL 60064 U.S.A
| | - Lu Cui
- Data and Statistical Sciences; AbbVie Inc; 1 North Waukegan Rd North Chicago IL 60064 U.S.A
| | - Bo Yang
- Data and Statistical Sciences; AbbVie Inc; 1 North Waukegan Rd North Chicago IL 60064 U.S.A
- Biometrics; Vertex Pharmaceuticals; 50 Northern Avenue Boston MA 02210 U.S.A
| |
Collapse
|
16
|
Kieser M, Rauch G. Two-stage designs for cross-over bioequivalence trials. Stat Med 2015; 34:2403-16. [PMID: 25809815 DOI: 10.1002/sim.6487] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2014] [Revised: 02/24/2015] [Accepted: 03/02/2015] [Indexed: 11/08/2022]
Abstract
The topic of applying two-stage designs in the field of bioequivalence studies has recently gained attention in the literature and in regulatory guidelines. While there exists some methodological research on the application of group sequential designs in bioequivalence studies, implementation of adaptive approaches has focused up to now on superiority and non-inferiority trials. Especially, no comparison of the features and performance characteristics of these designs has been performed, and therefore, the question of which design to employ in this setting remains open. In this paper, we discuss and compare 'classical' group sequential designs and three types of adaptive designs that offer the option of mid-course sample size recalculation. A comprehensive simulation study demonstrates that group sequential designs can be identified, which show power characteristics that are similar to those of the adaptive designs but require a lower average sample size. The methods are illustrated with a real bioequivalence study example.
Collapse
Affiliation(s)
- Meinhard Kieser
- Institute of Medical Biometry and Informatics, University of Heidelberg, D-69120 Heidelberg, Germany
| | - Geraldine Rauch
- Institute of Medical Biometry and Informatics, University of Heidelberg, D-69120 Heidelberg, Germany
| |
Collapse
|
17
|
Kieser M, Englert S. Performance of adaptive designs for single-armed phase II oncology trials. J Biopharm Stat 2014; 25:602-15. [PMID: 24905363 DOI: 10.1080/10543406.2014.920863] [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: 10/25/2022]
Abstract
When planning a single-armed clinical trial with binary endpoint, the sample size is determined such that the desired power is achieved for a single value of the target rate. However, there is usually some uncertainty with respect to the true treatment effect. It is therefore more realistic to specify an interval for the possible true rate to accommodate this uncertainty. For this situation, we examine comprehensively the overall performance of various Phase II oncology designs and sample size recalculation strategies. The methods and results of our investigations can be used to identify the most appropriate approach for a specific clinical trial situation at hand. Application is illustrated with a clinical trial in rectal cancer.
Collapse
Affiliation(s)
- Meinhard Kieser
- a Institute of Medical Biometry and Informatics , University of Heidelberg , Heidelberg , Germany
| | | |
Collapse
|
18
|
|
19
|
Wu X, Cui L. Group sequential and discretized sample size re-estimation designs: a comparison of flexibility. Stat Med 2012; 31:2844-57. [DOI: 10.1002/sim.5395] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2010] [Accepted: 03/13/2012] [Indexed: 11/06/2022]
Affiliation(s)
- Xiaoru Wu
- Gilead Sciences Inc.; Foster City; CA; U.S.A
| | - Lu Cui
- Eisai Medical Research Inc.; New York; NY; U.S.A
| |
Collapse
|
20
|
Liu Q, Li G, Anderson KM, Lim P. On efficient two-stage adaptive designs for clinical trials with sample size adjustment. J Biopharm Stat 2012; 22:617-40. [PMID: 22651105 DOI: 10.1080/10543406.2012.678226] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Group sequential designs are rarely used for clinical trials with substantial over running due to fast enrollment or long duration of treatment and follow-up. Traditionally, such trials rely on fixed sample size designs. Recently, various two-stage adaptive designs have been introduced to allow sample size adjustment to increase statistical power or avoid unnecessarily large trials. However, these adaptive designs can be seriously inefficient. To address this infamous problem, we propose a likelihood-based two-stage adaptive design where sample size adjustment is derived from a pseudo group sequential design using cumulative conditional power. We show through numerical examples that this design cannot be improved by group sequential designs. In addition, the approach may uniformly improve any existing two-stage adaptive designs with sample size adjustment. For statistical inference, we provide methods for sequential p-values and confidence intervals, as well as median unbiased and minimum variance unbiased estimates. We show that the claim of inefficiency of adaptive designs by Tsiatis and Mehta ( 2003 ) is logically flawed, and thereby provide a strong defense of Cui et al. ( 1999 ).
Collapse
Affiliation(s)
- Qing Liu
- Janssen Research and Development, LLC, Raritan, NJ 08869, USA.
| | | | | | | |
Collapse
|
21
|
Guidance for Industry: Adaptive Design Clinical Trials for Drugs and Biologics [excerpts]. Biotechnol Law Rep 2010. [DOI: 10.1089/blr.2010.9977] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
|
22
|
Gao P, Ware JH, Mehta C. Sample Size Re-Estimation for Adaptive Sequential Design in Clinical Trials. J Biopharm Stat 2008; 18:1184-96. [DOI: 10.1080/10543400802369053] [Citation(s) in RCA: 66] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Ping Gao
- a Biostatistics , The Medicines Company , Parsippany, New Jersey, USA
| | - James H. Ware
- b Harvard School of Public Health , Boston, Massachusetts, USA
| | - Cyrus Mehta
- b Harvard School of Public Health , Boston, Massachusetts, USA
- c Cytel Corporation , Cambridge, Massachusetts, USA
| |
Collapse
|