1
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Boumendil L, Chevret S, Lévy V, Biard L. Two-stage randomized clinical trials with a right-censored endpoint: Comparison of frequentist and Bayesian adaptive designs. Stat Med 2024; 43:3364-3382. [PMID: 38844988 DOI: 10.1002/sim.10130] [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: 09/21/2022] [Revised: 04/17/2024] [Accepted: 05/20/2024] [Indexed: 07/17/2024]
Abstract
Adaptive randomized clinical trials are of major interest when dealing with a time-to-event outcome in a prolonged observation window. No consensus exists either to define stopping boundaries or to combinep $$ p $$ values or test statistics in the terminal analysis in the case of a frequentist design and sample size adaptation. In a one-sided setting, we compared three frequentist approaches using stopping boundaries relying onα $$ \alpha $$ -spending functions and a Bayesian monitoring setting with boundaries based on the posterior distribution of the log-hazard ratio. All designs comprised a single interim analysis with an efficacy stopping rule and the possibility of sample size adaptation at this interim step. Three frequentist approaches were defined based on the terminal analysis: combination of stagewise statistics (Wassmer) or ofp $$ p $$ values (Desseaux), or on patientwise splitting (Jörgens), and we compared the results with those of the Bayesian monitoring approach (Freedman). These different approaches were evaluated in a simulation study and then illustrated on a real dataset from a randomized clinical trial conducted in elderly patients with chronic lymphocytic leukemia. All approaches controlled for the type I error rate, except for the Bayesian monitoring approach, and yielded satisfactory power. It appears that the frequentist approaches are the best in underpowered trials. The power of all the approaches was affected by the violation of the proportional hazards (PH) assumption. For adaptive designs with a survival endpoint and a one-sided alternative hypothesis, the Wassmer and Jörgens approaches after sample size adaptation should be preferred, unless violation of PH is suspected.
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Affiliation(s)
- Luana Boumendil
- INSERM U1153, Team ECSTRRA, Hôpital Saint Louis, Paris, France
- Université Paris Cité, Paris, France
- AP-HP Hôpital Saint Louis, Service de Biostatistique et Information Médicale, Paris, France
| | - Sylvie Chevret
- INSERM U1153, Team ECSTRRA, Hôpital Saint Louis, Paris, France
- Université Paris Cité, Paris, France
- AP-HP Hôpital Saint Louis, Service de Biostatistique et Information Médicale, Paris, France
| | - Vincent Lévy
- INSERM U1153, Team ECSTRRA, Hôpital Saint Louis, Paris, France
- Université Paris 13, Villetaneuse, France
- AP-HP Hôpital Avicenne, Unité de Recherche Clinique Bobigny, Bobigny, France
| | - Lucie Biard
- INSERM U1153, Team ECSTRRA, Hôpital Saint Louis, Paris, France
- Université Paris Cité, Paris, France
- AP-HP Hôpital Saint Louis, Service de Biostatistique et Information Médicale, Paris, France
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2
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Sharma P, Phadnis MA. Stochastic curtailment tests for phase II trial with time-to-event outcome using the concept of relative time in the case of non-proportional hazards. J Biopharm Stat 2024; 34:596-611. [PMID: 37574976 DOI: 10.1080/10543406.2023.2244056] [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: 04/15/2022] [Accepted: 07/15/2023] [Indexed: 08/15/2023]
Abstract
As part of the drug development process, interim analysis is frequently used to design efficient phase II clinical trials. A stochastic curtailment framework is often deployed wherein a decision to continue or curtail the trial is taken at each interim look based on the likelihood of observing a positive or negative treatment effect if the trial were to continue to its anticipated end. Thus, curtailment can take place due to evidence of early efficacy or futility. Traditionally, in the case of time-to-event endpoints, interim monitoring is conducted in a two-arm clinical trial using the log-rank test, often with the assumption of proportional hazards. However, when this is violated, the log-rank test may not be appropriate, resulting in loss of power and subsequently inaccurate sample sizes. In this paper, we propose stochastic curtailment methods for two-arm phase II trial with the flexibility to allow non-proportional hazards. The proposed methods are built utilizing the concept of relative time assuming that the survival times in the two treatment arms follow two different Weibull distributions. Three methods - conditional power, predictive power and Bayesian predictive probability - are discussed along with corresponding sample size calculations. The monitoring strategy is discussed with a real-life example.
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Affiliation(s)
- Palash Sharma
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Milind A Phadnis
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
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3
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Ristl R, Götte H, Schüler A, Posch M, König F. Simultaneous inference procedures for the comparison of multiple characteristics of two survival functions. Stat Methods Med Res 2024; 33:589-610. [PMID: 38465602 PMCID: PMC11025310 DOI: 10.1177/09622802241231497] [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] [Indexed: 03/12/2024]
Abstract
Survival time is the primary endpoint of many randomized controlled trials, and a treatment effect is typically quantified by the hazard ratio under the assumption of proportional hazards. Awareness is increasing that in many settings this assumption is a priori violated, for example, due to delayed onset of drug effect. In these cases, interpretation of the hazard ratio estimate is ambiguous and statistical inference for alternative parameters to quantify a treatment effect is warranted. We consider differences or ratios of milestone survival probabilities or quantiles, differences in restricted mean survival times, and an average hazard ratio to be of interest. Typically, more than one such parameter needs to be reported to assess possible treatment benefits, and in confirmatory trials, the according inferential procedures need to be adjusted for multiplicity. A simple Bonferroni adjustment may be too conservative because the different parameters of interest typically show considerable correlation. Hence simultaneous inference procedures that take into account the correlation are warranted. By using the counting process representation of the mentioned parameters, we show that their estimates are asymptotically multivariate normal and we provide an estimate for their covariance matrix. We propose according to the parametric multiple testing procedures and simultaneous confidence intervals. Also, the logrank test may be included in the framework. Finite sample type I error rate and power are studied by simulation. The methods are illustrated with an example from oncology. A software implementation is provided in the R package nph.
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Affiliation(s)
- Robin Ristl
- Medical University of Vienna, Center for Medical Data Science, Institute of Medical Statistics, Austria
| | | | | | - Martin Posch
- Medical University of Vienna, Center for Medical Data Science, Institute of Medical Statistics, Austria
| | - Franz König
- Medical University of Vienna, Center for Medical Data Science, Institute of Medical Statistics, Austria
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4
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Zhang X, Jia H, Xing L, Chen C. Application of group sequential methods to the 2-in-1 design and its extensions for interim monitoring. Stat Biopharm Res 2023. [DOI: 10.1080/19466315.2023.2197402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2023]
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5
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Robertson DS, Choodari‐Oskooei B, Dimairo M, Flight L, Pallmann P, Jaki T. Point estimation for adaptive trial designs I: A methodological review. Stat Med 2023; 42:122-145. [PMID: 36451173 PMCID: PMC7613995 DOI: 10.1002/sim.9605] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 10/21/2022] [Accepted: 11/01/2022] [Indexed: 12/02/2022]
Abstract
Recent FDA guidance on adaptive clinical trial designs defines bias as "a systematic tendency for the estimate of treatment effect to deviate from its true value," and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias. The conventional end-of-trial point estimates of the treatment effects are prone to bias in many adaptive designs, because they do not take into account the potential and realized trial adaptations. While much of the methodological developments on adaptive designs have tended to focus on control of type I error rates and power considerations, in contrast the question of biased estimation has received relatively less attention. This article is the first in a two-part series that studies the issue of potential bias in point estimation for adaptive trials. Part I provides a comprehensive review of the methods to remove or reduce the potential bias in point estimation of treatment effects for adaptive designs, while part II illustrates how to implement these in practice and proposes a set of guidelines for trial statisticians. The methods reviewed in this article can be broadly classified into unbiased and bias-reduced estimation, and we also provide a classification of estimators by the type of adaptive design. We compare the proposed methods, highlight available software and code, and discuss potential methodological gaps in the literature.
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Affiliation(s)
| | | | - Munya Dimairo
- School of Health and Related Research (ScHARR)University of SheffieldSheffieldUK
| | - Laura Flight
- School of Health and Related Research (ScHARR)University of SheffieldSheffieldUK
| | | | - Thomas Jaki
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
- Faculty of Informatics and Data ScienceUniversity of RegensburgRegensburgGermany
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6
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A gated group sequential design for seamless Phase II/III trial with subpopulation selection. BMC Med Res Methodol 2023; 23:2. [PMID: 36597042 PMCID: PMC9809114 DOI: 10.1186/s12874-022-01825-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 12/19/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Due to the high cost and high failure rate of Phase III trials where a classical group sequential design (GSD) is usually used, seamless Phase II/III designs are more and more popular to improve trial efficiency. A potential attraction of Phase II/III design is to allow a randomized proof-of-concept stage prior to committing to the full cost of a Phase III trial. Population selection during the trial allows a trial to adapt and focus investment where it is most likely to provide patient benefit. Previous methods have been developed for this problem when there is a single primary endpoint and two possible populations. METHODS To find the population that potentially benefits with one or two primary endpoints (e.g., progression free survival (PFS), overall survival (OS)), we propose a gated group sequential design for a seamless Phase II/III trial design with adaptive population selection. RESULTS The investigated design controls the familywise error rate and allows multiple interim analyses to enable early stopping for efficacy or futility. Simulations and an illustrative example suggest that the proposed gated group sequential design has more power and requires less time and resources compared to the group sequential design and adaptive design. CONCLUSIONS Combining the group sequential design and adaptive design, the gated group sequential design has more power and higher efficiency while controlling for the familywise error rate. It has the potential to save drug development cost and more quickly fulfill unmet medical needs.
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7
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Posch M, Ristl R, König F. Testing and interpreting the ”right” hypothesis - comment on ”Non-proportional hazards — An evaluation of the MaxCombo Test in cancer clinical trials”. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2090431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Martin Posch
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna
| | - Robin Ristl
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna
| | - Franz König
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna
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8
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Beyersmann J, Friede T, Schmoor C. Design aspects of COVID-19 treatment trials: Improving probability and time of favorable events. Biom J 2022; 64:440-460. [PMID: 34677829 PMCID: PMC8653377 DOI: 10.1002/bimj.202000359] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 08/13/2021] [Accepted: 09/04/2021] [Indexed: 12/24/2022]
Abstract
As a reaction to the pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a multitude of clinical trials for the treatment of SARS-CoV-2 or the resulting corona disease 2019 (COVID-19) are globally at various stages from planning to completion. Although some attempts were made to standardize study designs, this was hindered by the ferocity of the pandemic and the need to set up clinical trials quickly. We take the view that a successful treatment of COVID-19 patients (i) increases the probability of a recovery or improvement within a certain time interval, say 28 days; (ii) aims to expedite favorable events within this time frame; and (iii) does not increase mortality over this time period. On this background, we discuss the choice of endpoint and its analysis. Furthermore, we consider consequences of this choice for other design aspects including sample size and power and provide some guidance on the application of adaptive designs in this particular context.
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Affiliation(s)
| | - Tim Friede
- Institut für Medizinische StatistikUniversitätsmedizin GöttingenGöttingenGermany
- Deutsches Zentrum für Herz‐Kreislaufforschung (DZHK)Standort GöttingenGöttingenGermany
| | - Claudia Schmoor
- Zentrum Klinische Studien, Universitätsklinikum Freiburg, Medizinische FakultätAlbert‐Ludwigs Universität FreiburgFreiburg im BreisgauGermany
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9
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Danzer MF, Terzer T, Berthold F, Faldum A, Schmidt R. Confirmatory adaptive group sequential designs for single-arm phase II studies with multiple time-to-event endpoints. Biom J 2022; 64:312-342. [PMID: 35152459 DOI: 10.1002/bimj.202000205] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 05/26/2021] [Accepted: 05/30/2021] [Indexed: 11/07/2022]
Abstract
Existing methods concerning the assessment of long-term survival outcomes in one-armed trials are commonly restricted to one primary endpoint. Corresponding adaptive designs suffer from limitations regarding the use of information from other endpoints in interim design changes. Here we provide adaptive group sequential one-sample tests for testing hypotheses on the multivariate survival distribution derived from multi-state models, while making provision for data-dependent design modifications based on all involved time-to-event endpoints. We explicitly elaborate application of the methodology to one-sample tests for the joint distribution of (i) progression-free survival (PFS) and overall survival (OS) in the context of an illness-death model, and (ii) time to toxicity and time to progression while accounting for death as a competing event. Large sample distributions are derived using a counting process approach. Small sample properties are studied by simulation. An already established multi-state model for non-small cell lung cancer is used to illustrate the adaptive procedure.
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Affiliation(s)
- Moritz Fabian Danzer
- Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany
| | - Tobias Terzer
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Frank Berthold
- Department of Pediatric Oncology and Hematology, Center for Integrated Oncology, University of Cologne, Cologne, Germany
| | - Andreas Faldum
- Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany
| | - Rene Schmidt
- Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany
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10
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Kyr M, Svobodnik A, Stepanova R, Hejnova R. N-of-1 Trials in Pediatric Oncology: From a Population-Based Approach to Personalized Medicine-A Review. Cancers (Basel) 2021; 13:5428. [PMID: 34771590 PMCID: PMC8582573 DOI: 10.3390/cancers13215428] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 10/11/2021] [Accepted: 10/27/2021] [Indexed: 12/02/2022] Open
Abstract
Pediatric oncology is a critical area where the more efficient development of new treatments is urgently needed. The speed of approval of new drugs is still limited by regulatory requirements and a lack of innovative designs appropriate for trials in children. Childhood cancers meet the criteria of rare diseases. Personalized medicine brings it even closer to the horizon of individual cases. Thus, not all the traditional research tools, such as large-scale RCTs, are always suitable or even applicable, mainly due to limited sample sizes. Small samples and traditional versus subject-specific evidence are both distinctive issues in personalized pediatric oncology. Modern analytical approaches and adaptations of the paradigms of evidence are warranted. We have reviewed innovative trial designs and analytical methods developed for small populations, together with individualized approaches, given their applicability to pediatric oncology. We discuss traditional population-based and individualized perspectives of inferences and evidence, and explain the possibilities of using various methods in pediatric personalized oncology. We find that specific derivatives of the original N-of-1 trial design adapted for pediatric personalized oncology may represent an optimal analytical tool for this area of medicine. We conclude that no particular N-of-1 strategy can provide a solution. Rather, a whole range of approaches is needed to satisfy the new inferential and analytical paradigms of modern medicine. We reveal a new view of cancer as continuum model and discuss the "evidence puzzle".
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Affiliation(s)
- Michal Kyr
- Department of Paediatric Oncology, University Hospital Brno and School of Medicine, Masaryk University, Cernopolni 9, 613 00 Brno, Czech Republic
- International Clinical Research Centre, St. Anne’s University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Adam Svobodnik
- Department of Pharmacology, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; (A.S.); (R.S.) (R.H.)
| | - Radka Stepanova
- Department of Pharmacology, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; (A.S.); (R.S.) (R.H.)
| | - Renata Hejnova
- Department of Pharmacology, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; (A.S.); (R.S.) (R.H.)
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11
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Feld J, Faldum A, Schmidt R. Adaptive group sequential survival comparisons based on log-rank and pointwise test statistics. Stat Methods Med Res 2021; 30:2562-2581. [PMID: 34641702 PMCID: PMC8649467 DOI: 10.1177/09622802211043262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Whereas the theory of confirmatory adaptive designs is well understood for uncensored data, implementation of adaptive designs in the context of survival trials remains challenging. Commonly used adaptive survival tests are based on the independent increments structure of the log-rank statistic. This implies some relevant limitations: On the one hand, essentially only the interim log-rank statistic may be used for design modifications (such as data-dependent sample size recalculation). Furthermore, the treatment arm allocation ratio in these classical methods is assumed to be constant throughout the trial period. Here, we propose an extension of the independent increments approach to adaptive survival tests that addresses some of these limitations. We present a confirmatory adaptive two-sample log-rank test that allows rejection regions and sample size recalculation rules to be based not only on the interim log-rank statistic, but also on point-wise survival rate estimates, simultaneously. In addition, the possibility is opened to adapt the treatment arm allocation ratio after each interim analysis in a data-dependent way. The ability to include point-wise survival rate estimators in the rejection region of a test for comparing survival curves might be attractive, e.g., for seamless phase II/III designs. Data-dependent adaptation of the allocation ratio could be helpful in multi-arm trials in order to successively steer recruitment into the study arms with the greatest chances of success. The methodology is motivated by the LOGGIC Europe Trial from pediatric oncology. Distributional properties are derived using martingale techniques in the large sample limit. Small sample properties are studied by simulation.
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Affiliation(s)
- Jannik Feld
- 352489Institute of Biostatistics and Clinical Research, 9185University of Münster, Muenster, Germany
| | - Andreas Faldum
- 352489Institute of Biostatistics and Clinical Research, 9185University of Münster, Muenster, Germany
| | - Rene Schmidt
- 352489Institute of Biostatistics and Clinical Research, 9185University of Münster, Muenster, Germany
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12
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Liu Y, Xu H. Sample size re-estimation for pivotal clinical trials. Contemp Clin Trials 2020; 102:106215. [PMID: 33217555 DOI: 10.1016/j.cct.2020.106215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 10/13/2020] [Accepted: 11/10/2020] [Indexed: 10/22/2022]
Abstract
It is well known that if the hypothesis test is left unchanged, the Type I error rate may be inflated for sample size re-estimation (SSR) designs. To address this issue, three main approaches have been proposed in the literature: combination test, conditional error and conventional test with sample size increase in the allowable region (AR) only. These three seemingly different approaches are in fact connected. For each combination test, there is a corresponding conditional error function and AR. Designing adaptation rules in this AR with conventional test guarantees the Type I error rate control but at the same time always leads to smaller power comparing to the corresponding combination test (or conditional error) approach. In cases where conventional test is still preferable, step-wise type adaptation rules that do not fully reside in the AR can be alternatively considered. We believe controversies in the statistical community on the efficiency comparisons between group sequential (GS) and SSR design stem partially from the misalignment of performance metrics and conditional versus unconditional evaluations. We advocate summary metrics, such as median, variance or tail probabilities of the sample size in addition to expectation and personalizing efficiency definition for each trial sponsor. Conditional metrics by favorable, promising and unfavorable zones of the interim results provide additional insights and should always be incorporated into the decision-making process.
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Affiliation(s)
- Yi Liu
- Nektar Therapeutics, San Francisco, CA 94107, USA.
| | - Heng Xu
- Nektar Therapeutics, San Francisco, CA 94107, USA
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13
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Jiménez JL. Quantifying treatment differences in confirmatory trials under non-proportional hazards. J Appl Stat 2020; 49:466-484. [PMID: 35707213 PMCID: PMC9196085 DOI: 10.1080/02664763.2020.1815673] [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: 09/20/2019] [Accepted: 08/19/2020] [Indexed: 10/23/2022]
Abstract
Proportional hazards are a common assumption when designing confirmatory clinical trials in oncology. With the emergence of immunotherapy and novel targeted therapies, departure from the proportional hazard assumption is not rare in nowadays clinical research. Under non-proportional hazards, the hazard ratio does not have a straightforward clinical interpretation, and the log-rank test is no longer the most powerful statistical test even though it is still valid. Nevertheless, the log-rank test and the hazard ratio are still the primary analysis tools, and traditional approaches such as sample size increase are still proposed to account for the impact of non-proportional hazards. The weighed log-rank test and the test based on the restricted mean survival time (RMST) are receiving a lot of attention as a potential alternative to the log-rank test. We conduct a simulation study comparing the performance and operating characteristics of the log-rank test, the weighted log-rank test and the test based on the RMST, including a treatment effect estimation, under different non-proportional hazards patterns. Results show that, under non-proportional hazards, the hazard ratio and weighted hazard ratio have no straightforward clinical interpretation whereas the RMST ratio can be interpreted regardless of the proportional hazards assumption. In terms of power, the RMST achieves a similar performance when compared to the log-rank test.
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14
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Nguyen Duc A, Heinzmann D, Berge C, Wolbers M. A pragmatic adaptive enrichment design for selecting the right target population for cancer immunotherapies. Pharm Stat 2020; 20:202-211. [PMID: 32869509 DOI: 10.1002/pst.2066] [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: 03/18/2020] [Revised: 06/09/2020] [Accepted: 08/11/2020] [Indexed: 02/02/2023]
Abstract
One of the challenges in the design of confirmatory trials is to deal with uncertainties regarding the optimal target population for a novel drug. Adaptive enrichment designs (AED) which allow for a data-driven selection of one or more prespecified biomarker subpopulations at an interim analysis have been proposed in this setting but practical case studies of AEDs are still relatively rare. We present the design of an AED with a binary endpoint in the highly dynamic setting of cancer immunotherapy. The trial was initiated as a conventional trial in early triple-negative breast cancer but amended to an AED based on emerging data external to the trial suggesting that PD-L1 status could be a predictive biomarker. Operating characteristics are discussed including the concept of a minimal detectable difference, that is, the smallest observed treatment effect that would lead to a statistically significant result in at least one of the target populations at the interim or the final analysis, respectively, in the setting of AED.
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Affiliation(s)
- Anh Nguyen Duc
- Biostatistics, Hoffmann-La Roche Pharma AG, Basel, Switzerland
| | | | - Claude Berge
- Biostatistics, Hoffmann-La Roche Pharma AG, Basel, Switzerland
| | - Marcel Wolbers
- Biostatistics, Hoffmann-La Roche Pharma AG, Basel, Switzerland
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15
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Extensions of the 2-in-1 adaptive design. Contemp Clin Trials 2020; 95:106053. [DOI: 10.1016/j.cct.2020.106053] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 03/30/2020] [Accepted: 05/31/2020] [Indexed: 11/20/2022]
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16
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Uozumi R, Hamada C. Utility-Based Interim Decision Rule Planning in Adaptive Population Selection Designs With Survival Endpoints. Stat Biopharm Res 2019. [DOI: 10.1080/19466315.2019.1689844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Ryuji Uozumi
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Chikuma Hamada
- Department of Information and Computer Technology, Tokyo University of Science, Tokyo, Japan
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17
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Bayar MA, Le Teuff G, Koenig F, Le Deley MC, Michiels S. Group sequential adaptive designs in series of time-to-event randomised trials in rare diseases: A simulation study. Stat Methods Med Res 2019; 29:1483-1498. [PMID: 31354106 DOI: 10.1177/0962280219862313] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In rare diseases, fully powered large trials may not be doable in a reasonable time frame even with international collaborations. In a previous work, we proposed an approach based on a series of smaller parallel group two-arm randomised controlled trials (RCT) performed over a long research horizon. Within the series of trials, the treatment selected after each trial becomes the control treatment of the next one. We concluded that running more trials with smaller sample sizes and relaxed α-levels leads in the long term and under reasonable assumptions to larger survival benefits with a moderate increase of risk as compared to traditional designs based on larger but fewer trials designed to meet stringent evidence criteria. We now extend this quantitative framework with more 'flexible' designs including interim analyses for futility and/or efficacy, and three-arm adaptive designs with treatment selection at interim. In the simulation study, we considered different disease severities, accrual rates, and hypotheses of how treatments improve over time. For each design, we estimated the long-term survival benefit as the relative difference in hazard rates between the end and the start of the research horizon, and the risk defined as the probability of selecting at the end of the research horizon a treatment inferior to the initial control. We assessed the impact of the α-level and the choice of the stopping rule on the operating characteristics. We also compared the performance of series based on two- vs. three-arm trials. We show that relaxing α-levels within the limit of 0.1 is associated with larger survival gains and moderate increase of risk which remains within acceptable ranges. Including an interim analysis with a futility rule is associated with an additional survival gain and a better risk control as compared to series with no interim analysis, when the α-level is below or equal to 0.1, whereas the benefit of including an interim analysis is rather small for higher α-levels. Including an interim analysis for efficacy yields almost no additional gain. Series based on three-arm trials are associated with a systematic improvement in terms of survival gain and risk control as compared to series of two-arm trials.
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Affiliation(s)
- Mohamed Amine Bayar
- Service de Biostatistique et d'Épidémiologie, Gustave Roussy, Villejuif, France.,CESP, Faculté de médecine - Université Paris-Sud, Faculté de médecine - INSERM, Université Paris-Saclay, Villejuif, France
| | - Gwénaël Le Teuff
- Service de Biostatistique et d'Épidémiologie, Gustave Roussy, Villejuif, France.,CESP, Faculté de médecine - Université Paris-Sud, Faculté de médecine - INSERM, Université Paris-Saclay, Villejuif, France
| | - Franz Koenig
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Marie-Cécile Le Deley
- Service de Biostatistique et d'Épidémiologie, Gustave Roussy, Villejuif, France.,CESP, Faculté de médecine - Université Paris-Sud, Faculté de médecine - INSERM, Université Paris-Saclay, Villejuif, France.,Unité de Méthodologie et Biostatistique, Centre Oscar Lambret, Lille, France
| | - Stefan Michiels
- Service de Biostatistique et d'Épidémiologie, Gustave Roussy, Villejuif, France.,CESP, Faculté de médecine - Université Paris-Sud, Faculté de médecine - INSERM, Université Paris-Saclay, Villejuif, France
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18
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Jörgens S, Wassmer G, König F, Posch M. Nested combination tests with a time-to-event endpoint using a short-term endpoint for design adaptations. Pharm Stat 2019; 18:329-350. [PMID: 30652401 DOI: 10.1002/pst.1926] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 10/16/2018] [Accepted: 12/14/2018] [Indexed: 12/11/2022]
Abstract
Adaptive trial methodology for multiarmed trials and enrichment designs has been extensively discussed in the past. A general principle to construct test procedures that control the family-wise Type I error rate in the strong sense is based on combination tests within a closed test. Using survival data, a problem arises when using information of patients for adaptive decision making, which are under risk at interim. With the currently available testing procedures, either no testing of hypotheses in interim analyses is possible or there are restrictions on the interim data that can be used in the adaptation decisions as, essentially, only the interim test statistics of the primary endpoint may be used. We propose a general adaptive testing procedure, covering multiarmed and enrichment designs, which does not have these restrictions. An important application are clinical trials, where short-term surrogate endpoints are used as basis for trial adaptations, and we illustrate how such trials can be designed. We propose statistical models to assess the impact of effect sizes, the correlation structure between the short-term and the primary endpoint, the sample size, the timing of interim analyses, and the selection rule on the operating characteristics.
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Affiliation(s)
- Silke Jörgens
- Innovation Center, ICON Clinical Research Inc, Cologne, Germany
| | - Gernot Wassmer
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Franz König
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Martin Posch
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
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19
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Jiménez JL, Stalbovskaya V, Jones B. Properties of the weighted log-rank test in the design of confirmatory studies with delayed effects. Pharm Stat 2018; 18:287-303. [PMID: 30592138 DOI: 10.1002/pst.1923] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 10/08/2018] [Accepted: 11/21/2018] [Indexed: 11/09/2022]
Abstract
Proportional hazards are a common assumption when designing confirmatory clinical trials in oncology. This assumption not only affects the analysis part but also the sample size calculation. The presence of delayed effects causes a change in the hazard ratio while the trial is ongoing since at the beginning we do not observe any difference between treatment arms, and after some unknown time point, the differences between treatment arms will start to appear. Hence, the proportional hazards assumption no longer holds, and both sample size calculation and analysis methods to be used should be reconsidered. The weighted log-rank test allows a weighting for early, middle, and late differences through the Fleming and Harrington class of weights and is proven to be more efficient when the proportional hazards assumption does not hold. The Fleming and Harrington class of weights, along with the estimated delay, can be incorporated into the sample size calculation in order to maintain the desired power once the treatment arm differences start to appear. In this article, we explore the impact of delayed effects in group sequential and adaptive group sequential designs and make an empirical evaluation in terms of power and type-I error rate of the of the weighted log-rank test in a simulated scenario with fixed values of the Fleming and Harrington class of weights. We also give some practical recommendations regarding which methodology should be used in the presence of delayed effects depending on certain characteristics of the trial.
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20
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Collignon O, Koenig F, Koch A, Hemmings RJ, Pétavy F, Saint-Raymond A, Papaluca-Amati M, Posch M. Adaptive designs in clinical trials: from scientific advice to marketing authorisation to the European Medicine Agency. Trials 2018; 19:642. [PMID: 30454061 PMCID: PMC6245528 DOI: 10.1186/s13063-018-3012-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 10/21/2018] [Indexed: 12/15/2022] Open
Abstract
Background In recent years, experience on the application of adaptive designs in confirmatory clinical trials has accumulated. Although planning such trials comes at the cost of additional operational complexity, adaptive designs offer the benefit of flexibility to update trial design and objectives as data accrue. In 2007, the European Medicines Agency (EMA) provided guidance on confirmatory clinical trials with adaptive (or flexible) designs. In order to better understand how adaptive trials are implemented in practice and how they may impact medicine approval within the EMA centralised procedure, we followed on 59 medicines for which an adaptive clinical trial had been submitted to the EMA Scientific Advice (SA) and analysed previously in a dedicated EMA survey of scientific advice letters. We scrutinized in particular the submission of the corresponding medicines for a marketing authorisation application (MAA). We also discuss the current regulatory perspective as regards the implementation of adaptive designs in confirmatory clinical trials. Methods Using the internal EMA MAA database, the AdisInsight database and related trial registries, we analysed how many of these 59 trials actually started, the completion status, results, the time to trial start, the adaptive elements finally implemented after SA, their possible influence on the success of the trial and corresponding product approval. Results Overall 31 trials out of 59 (53%) were retrieved. Thirty of them (97%) have been started and 23 (74%) concluded. Nine of these trials (39% out of 23) demonstrated a significant treatment effect on their primary endpoint and 4 (17% out of 23) supported a marketing authorisation (MA). An additional two trials were stopped using pre-defined criteria for futility, efficiently identifying trials on which further resources should not be spent. Median time to trial start after SA letter was given by EMA was 5 months. In the investigated trial registries, at least 18 trial (58% of 31 retrieved trials) designs were implemented with adaptive elements, which were predominantly dose selection, sample size reassessment (SSR) and stopping for futility (SFF). Among the 11 completed trials including adaptive elements, 6 demonstrated a significant treatment effect on their primary endpoint (55%). Conclusions Adaptive designs are now well established in the drug development landscape. If properly pre-planned, adaptations can play a key role in the success of some of these trials, for example to help successfully select the most promising dose regimens for phase II/III trials. Interim analyses can also enable stopping of trials for futility when they do not hold their promises. Type I error rate control, trial integrity and results consistency between the different stages of the analyses are fundamental aspects to be discussed thoroughly. Engaging early dialogue with regulators and implementing the scientific advice received is strongly recommended, since much experience in discussing adaptive designs and assessing their results has been accumulated.
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Affiliation(s)
- Olivier Collignon
- European Medicines Agency, 30 Churchill Place, London, E14 5EU, UK. .,Competence Center for Methodology and Statistics, Luxembourg Institute of Health, 1A-B, rue Thomas Edison, L-1445, Strassen, Luxembourg.
| | - Franz Koenig
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria
| | - Armin Koch
- Institut für Biometrie, Medizinische Hochschule Hannover, OE 8410, 30625, Hanover, Germany
| | - Robert James Hemmings
- Medicines and Healthcare Products Regulatory Agency, 151 Buckingham Palace Road, London, SW1W 9SZ, UK
| | - Frank Pétavy
- European Medicines Agency, 30 Churchill Place, London, E14 5EU, UK
| | | | | | - Martin Posch
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria
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21
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Mitroiu M, Rengerink KO, Pontes C, Sancho A, Vives R, Pesiou S, Fontanet JM, Torres F, Nikolakopoulos S, Pateras K, Rosenkranz G, Posch M, Urach S, Ristl R, Koch A, Loukia S, van der Lee JH, Roes KCB. Applicability and added value of novel methods to improve drug development in rare diseases. Orphanet J Rare Dis 2018; 13:200. [PMID: 30419965 PMCID: PMC6233569 DOI: 10.1186/s13023-018-0925-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 10/02/2018] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The ASTERIX project developed a number of novel methods suited to study small populations. The objective of this exercise was to evaluate the applicability and added value of novel methods to improve drug development in small populations, using real world drug development programmes as reported in European Public Assessment Reports. METHODS The applicability and added value of thirteen novel methods developed within ASTERIX were evaluated using data from 26 European Public Assessment Reports (EPARs) for orphan medicinal products, representative of rare medical conditions as predefined through six clusters. The novel methods included were 'innovative trial designs' (six methods), 'level of evidence' (one method), 'study endpoints and statistical analysis' (four methods), and 'meta-analysis' (two methods) and they were selected from the methods developed within ASTERIX based on their novelty; methods that discussed already available and applied strategies were not included for the purpose of this validation exercise. Pre-requisites for application in a study were systematized for each method, and for each main study in the selected EPARs it was assessed if all pre-requisites were met. This direct applicability using the actual study design was firstly assessed. Secondary, applicability and added value were explored allowing changes to study objectives and design, but without deviating from the context of the drug development plan. We evaluated whether differences in applicability and added value could be observed between the six predefined condition clusters. RESULTS AND DISCUSSION Direct applicability of novel methods appeared to be limited to specific selected cases. The applicability and added value of novel methods increased substantially when changes to the study setting within the context of drug development were allowed. In this setting, novel methods for extrapolation, sample size re-assessment, multi-armed trials, optimal sequential design for small sample sizes, Bayesian sample size re-estimation, dynamic borrowing through power priors and fall-back tests for co-primary endpoints showed most promise - applicable in more than 40% of evaluated EPARs in all clusters. Most of the novel methods were applicable to conditions in the cluster of chronic and progressive conditions, involving multiple systems/organs. Relatively fewer methods were applicable to acute conditions with single episodes. For the chronic clusters, Goal Attainment Scaling was found to be particularly applicable as opposed to other (non-chronic) clusters. CONCLUSION Novel methods as developed in ASTERIX can improve drug development programs. Achieving optimal added value of these novel methods often requires consideration of the entire drug development program, rather than reconsideration of methods for a specific trial. The novel methods tested were mostly applicable in chronic conditions, and acute conditions with recurrent episodes.
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Affiliation(s)
- Marian Mitroiu
- Clinical Trial Methodology, Julius Center for Health Sciences and Primary Care, Biostatistics and Research Support, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Katrien Oude Rengerink
- Clinical Trial Methodology, Julius Center for Health Sciences and Primary Care, Biostatistics and Research Support, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Caridad Pontes
- Departament de Farmacologia, de Terapèutica i de Toxicologia, Universitat Autònoma de Barcelona, Unitat Docent Parc Taulí, c/ Parc Taulí 1, 08208 Sabadell, Spain
- Unitat de Farmacologia Clínica, Hospital de Sabadell, Institut d’Investigació i Innovació Parc Taulí I3PT - Universitat Autònoma de Barcelona, c/ Parc Taulí 1, 08208 Sabadell, Spain
| | - Aranzazu Sancho
- Departament de Farmacologia, de Terapèutica i de Toxicologia, Universitat Autònoma de Barcelona, Unitat Docent Parc Taulí, c/ Parc Taulí 1, 08208 Sabadell, Spain
- Clinical Pharmacology Department, Research Institute Puerta de Hierro, C/Manuel de Falla, 1, 28222 Majadahonda, Madrid, Spain
| | - Roser Vives
- Departament de Farmacologia, de Terapèutica i de Toxicologia, Universitat Autònoma de Barcelona, Unitat Docent Parc Taulí, c/ Parc Taulí 1, 08208 Sabadell, Spain
- Unitat de Farmacologia Clínica, Hospital de Sabadell, Institut d’Investigació i Innovació Parc Taulí I3PT - Universitat Autònoma de Barcelona, c/ Parc Taulí 1, 08208 Sabadell, Spain
| | - Stella Pesiou
- Departament de Farmacologia, de Terapèutica i de Toxicologia, Universitat Autònoma de Barcelona, Unitat Docent Parc Taulí, c/ Parc Taulí 1, 08208 Sabadell, Spain
| | - Juan Manuel Fontanet
- Departament de Farmacologia, de Terapèutica i de Toxicologia, Universitat Autònoma de Barcelona, Hospital de Sant Pau, C/St Antoni Maria Claret 167, 08025 Barcelona, Spain
| | - Ferran Torres
- Biostatistics Unit, Faculty of Medicine, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
- Medical Statistics Core Facility, IDIBAPS - Hospital Clinic Barcelona, C/Mallorca 183, Floor -1, 08036 Barcelona, Spain
| | - Stavros Nikolakopoulos
- Clinical Trial Methodology, Julius Center for Health Sciences and Primary Care, Biostatistics and Research Support, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Konstantinos Pateras
- Clinical Trial Methodology, Julius Center for Health Sciences and Primary Care, Biostatistics and Research Support, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Gerd Rosenkranz
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Martin Posch
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Susanne Urach
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Robin Ristl
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Armin Koch
- Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany
| | - Spineli Loukia
- Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany
| | - Johanna H. van der Lee
- Paediatric Clinical Research Office, Woman-Child Center, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Kit C. B. Roes
- Clinical Trial Methodology, Julius Center for Health Sciences and Primary Care, Biostatistics and Research Support, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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22
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Brückner M, Burger HU, Brannath W. Nonparametric adaptive enrichment designs using categorical surrogate data. Stat Med 2018; 37:4507-4524. [PMID: 30191578 DOI: 10.1002/sim.7936] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Revised: 07/14/2018] [Accepted: 07/16/2018] [Indexed: 11/06/2022]
Abstract
Adaptive survival trials are particularly important for enrichment designs in oncology and other life-threatening diseases. Current statistical methodology for adaptive survival trials provide type I error rate control only under restrictions. For instance, if we use stage-wise P values based on increments of the log-rank test, then the information used for the interim decisions need to be restricted to the primary survival endpoint. However, it is often desirable to base interim decisions also on correlated short-term endpoints like tumor response. Alternative statistical approaches based on a patient-wise splitting of the data require unnatural restrictions on the follow-up times and do not permit to efficiently account for an early rejection of the primary null hypothesis. We therefore suggest new approaches that enable us to use discrete surrogate endpoints (like tumor response status) and also to incorporate interim rejection boundaries. The new approaches are based on weighted Kaplan-Meier estimates and thereby have additional advantages. They permit us to account for nonproportional hazards and are robust against informative censoring based on the surrogate endpoint. We will show that nonproportionality is an intrinsic and relevant issue in enrichment designs. Moreover, informative censoring based on the surrogate endpoint is likely because of withdrawals and treatment switches after insufficient treatment response. It is shown and illustrated how nonparametric tests based on weighted Kaplan-Meier estimates can be used in closed combination tests for adaptive enrichment designs, such that type I error rate control is achieved and justified asymptotically.
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Affiliation(s)
- Matthias Brückner
- Competence Center for Clinical Trials and Institute for Statistics, University of Bremen, Bremen, Germany
| | | | - Werner Brannath
- Competence Center for Clinical Trials and Institute for Statistics, University of Bremen, Bremen, Germany
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23
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Chiu YD, Koenig F, Posch M, Jaki T. Design and estimation in clinical trials with subpopulation selection. Stat Med 2018; 37:4335-4352. [PMID: 30088280 PMCID: PMC6282861 DOI: 10.1002/sim.7925] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 05/23/2018] [Accepted: 07/06/2018] [Indexed: 11/10/2022]
Abstract
Population heterogeneity is frequently observed among patients' treatment responses in clinical trials because of various factors such as clinical background, environmental, and genetic factors. Different subpopulations defined by those baseline factors can lead to differences in the benefit or safety profile of a therapeutic intervention. Ignoring heterogeneity between subpopulations can substantially impact on medical practice. One approach to address heterogeneity necessitates designs and analysis of clinical trials with subpopulation selection. Several types of designs have been proposed for different circumstances. In this work, we discuss a class of designs that allow selection of a predefined subgroup. Using the selection based on the maximum test statistics as the worst‐case scenario, we then investigate the precision and accuracy of the maximum likelihood estimator at the end of the study via simulations. We find that the required sample size is chiefly determined by the subgroup prevalence and show in simulations that the maximum likelihood estimator for these designs can be substantially biased.
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Affiliation(s)
- Yi-Da Chiu
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancashire, UK
| | - Franz Koenig
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Martin Posch
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Thomas Jaki
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancashire, UK
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24
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Affiliation(s)
- Xiaodong Luo
- Research and Development, Sanofi, Bridgewater, NJ
| | - Hui Quan
- Research and Development, Sanofi, Bridgewater, NJ
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25
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Sugitani T, Posch M, Bretz F, Koenig F. Flexible alpha allocation strategies for confirmatory adaptive enrichment clinical trials with a prespecified subgroup. Stat Med 2018; 37:3387-3402. [PMID: 29945304 DOI: 10.1002/sim.7851] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 03/08/2018] [Accepted: 05/25/2018] [Indexed: 02/05/2023]
Abstract
Adaptive enrichment designs have recently received considerable attention as they have the potential to make drug development process for personalized medicine more efficient. Several statistical approaches have been proposed so far in the literature and the operating characteristics of these approaches are extensively investigated using simulation studies. In this paper, we improve on existing adaptive enrichment designs by assigning unequal weights to the significance levels associated with the hypotheses of the overall population and a prespecified subgroup. More specifically, we focus on the standard combination test, a modified combination test, the marginal combination test, and the partial conditional error rate approach and explore the operating characteristics of these approaches by a simulation study. We show that these approaches can lead to power gains, compared to existing approaches, if the weights are chosen carefully.
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Affiliation(s)
- Toshifumi Sugitani
- Biostatistics Group, Astellas Pharma Inc, Tokyo, Japan.,Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Martin Posch
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Frank Bretz
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria.,Statistical Methodology, Novartis Pharma AG, Basel, Switzerland
| | - Franz Koenig
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
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26
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Hilgers RD, Bogdan M, Burman CF, Dette H, Karlsson M, König F, Male C, Mentré F, Molenberghs G, Senn S. Lessons learned from IDeAl - 33 recommendations from the IDeAl-net about design and analysis of small population clinical trials. Orphanet J Rare Dis 2018; 13:77. [PMID: 29751809 PMCID: PMC5948846 DOI: 10.1186/s13023-018-0820-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 05/01/2018] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND IDeAl (Integrated designs and analysis of small population clinical trials) is an EU funded project developing new statistical design and analysis methodologies for clinical trials in small population groups. Here we provide an overview of IDeAl findings and give recommendations to applied researchers. METHOD The description of the findings is broken down by the nine scientific IDeAl work packages and summarizes results from the project's more than 60 publications to date in peer reviewed journals. In addition, we applied text mining to evaluate the publications and the IDeAl work packages' output in relation to the design and analysis terms derived from in the IRDiRC task force report on small population clinical trials. RESULTS The results are summarized, describing the developments from an applied viewpoint. The main result presented here are 33 practical recommendations drawn from the work, giving researchers a comprehensive guidance to the improved methodology. In particular, the findings will help design and analyse efficient clinical trials in rare diseases with limited number of patients available. We developed a network representation relating the hot topics developed by the IRDiRC task force on small population clinical trials to IDeAl's work as well as relating important methodologies by IDeAl's definition necessary to consider in design and analysis of small-population clinical trials. These network representation establish a new perspective on design and analysis of small-population clinical trials. CONCLUSION IDeAl has provided a huge number of options to refine the statistical methodology for small-population clinical trials from various perspectives. A total of 33 recommendations developed and related to the work packages help the researcher to design small population clinical trial. The route to improvements is displayed in IDeAl-network representing important statistical methodological skills necessary to design and analysis of small-population clinical trials. The methods are ready for use.
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Affiliation(s)
- Ralf-Dieter Hilgers
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany.
| | - Malgorzata Bogdan
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Carl-Fredrik Burman
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Holger Dette
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Mats Karlsson
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Franz König
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Christoph Male
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - France Mentré
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Geert Molenberghs
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Stephen Senn
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
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27
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Freidlin B, Korn EL, Abrams JS. Bias, Operational Bias, and Generalizability in Phase II/III Trials. J Clin Oncol 2018; 36:1902-1904. [PMID: 29698104 DOI: 10.1200/jco.2017.77.0479] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Affiliation(s)
- Boris Freidlin
- Boris Freidlin, National Cancer Institute, Bethesda, MD; Edward L. Korn, National Cancer Institute, Bethesda, MD; and Jeffrey S. Abrams, National Cancer Institute, Bethesda, MD
| | - Edward L Korn
- Boris Freidlin, National Cancer Institute, Bethesda, MD; Edward L. Korn, National Cancer Institute, Bethesda, MD; and Jeffrey S. Abrams, National Cancer Institute, Bethesda, MD
| | - Jeffrey S Abrams
- Boris Freidlin, National Cancer Institute, Bethesda, MD; Edward L. Korn, National Cancer Institute, Bethesda, MD; and Jeffrey S. Abrams, National Cancer Institute, Bethesda, MD
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28
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Ondra T, Jobjörnsson S, Beckman RA, Burman CF, König F, Stallard N, Posch M. Optimized adaptive enrichment designs. Stat Methods Med Res 2017; 28:2096-2111. [PMID: 29254436 PMCID: PMC6613177 DOI: 10.1177/0962280217747312] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Based on a Bayesian decision theoretic approach, we optimize frequentist single-
and adaptive two-stage trial designs for the development of targeted therapies,
where in addition to an overall population, a pre-defined subgroup is
investigated. In such settings, the losses and gains of decisions can be
quantified by utility functions that account for the preferences of different
stakeholders. In particular, we optimize expected utilities from the
perspectives both of a commercial sponsor, maximizing the net present value, and
also of the society, maximizing cost-adjusted expected health benefits of a new
treatment for a specific population. We consider single-stage and adaptive
two-stage designs with partial enrichment, where the proportion of patients
recruited from the subgroup is a design parameter. For the adaptive designs, we
use a dynamic programming approach to derive optimal adaptation rules. The
proposed designs are compared to trials which are non-enriched (i.e. the
proportion of patients in the subgroup corresponds to the prevalence in the
underlying population). We show that partial enrichment designs can
substantially improve the expected utilities. Furthermore, adaptive partial
enrichment designs are more robust than single-stage designs and retain high
expected utilities even if the expected utilities are evaluated under a
different prior than the one used in the optimization. In addition, we find that
trials optimized for the sponsor utility function have smaller sample sizes
compared to trials optimized under the societal view and may include the overall
population (with patients from the complement of the subgroup) even if there is
substantial evidence that the therapy is only effective in the subgroup.
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Affiliation(s)
- Thomas Ondra
- 1 Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | | | - Robert A Beckman
- 3 Departments of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, USA
| | - Carl-Fredrik Burman
- 2 Department of Mathematics, Chalmers University, Gothenburg, Sweden.,4 Statistical Innovation, AstraZeneca R&D, Molndal, Sweden
| | - Franz König
- 1 Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Nigel Stallard
- 5 Warwick Medical School, The University of Warwick, Coventry, UK
| | - Martin Posch
- 1 Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
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Schmidt R, Faldum A, Kwiecien R. Adaptive designs for the one-sample log-rank test. Biometrics 2017; 74:529-537. [DOI: 10.1111/biom.12776] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 08/01/2017] [Accepted: 08/01/2017] [Indexed: 12/01/2022]
Affiliation(s)
- Rene Schmidt
- Institute of Biostatistics and Clinical Research; University of Muenster; 48149 Muenster Germany
| | - Andreas Faldum
- Institute of Biostatistics and Clinical Research; University of Muenster; 48149 Muenster Germany
| | - Robert Kwiecien
- Institute of Biostatistics and Clinical Research; University of Muenster; 48149 Muenster Germany
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Abstract
BACKGROUND Sample size adjustment designs, which allow increasing the study sample size based on interim analysis of outcome data from a randomized clinical trial, have been increasingly promoted in the biostatistical literature. Although it is recognized that group sequential designs can be at least as efficient as sample size adjustment designs, many authors argue that a key advantage of these designs is their flexibility; interim sample size adjustment decisions can incorporate information and business interests external to the trial. Recently, Chen et al. (Clinical Trials 2015) considered sample size adjustment applications in the time-to-event setting using a design (CDL) that limits adjustments to situations where the interim results are promising. The authors demonstrated that while CDL provides little gain in unconditional power (versus fixed-sample-size designs), there is a considerable increase in conditional power for trials in which the sample size is adjusted. METHODS In time-to-event settings, sample size adjustment allows an increase in the number of events required for the final analysis. This can be achieved by either (a) following the original study population until the additional events are observed thus focusing on the tail of the survival curves or (b) enrolling a potentially large number of additional patients thus focusing on the early differences in survival curves. We use the CDL approach to investigate performance of sample size adjustment designs in time-to-event trials. RESULTS Through simulations, we demonstrate that when the magnitude of the true treatment effect changes over time, interim information on the shape of the survival curves can be used to enrich the final analysis with events from the time period with the strongest treatment effect. In particular, interested parties have the ability to make the end-of-trial treatment effect larger (on average) based on decisions using interim outcome data. Furthermore, in "clinical null" cases where there is no benefit due to crossing survival curves, the sample size adjustment design is shown to increase the probability of recommending an ineffective therapy. CONCLUSION Access to interim information on the shape of the survival curves may jeopardize the perceived integrity of trials using sample size adjustment designs. Therefore, given the lack of efficiency advantage over group sequential designs, sample size adjustment designs in time-to-event settings remain unjustified.
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Affiliation(s)
- Boris Freidlin
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA
| | - Edward L Korn
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA
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