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Du Y, Li J, Raha S, Qu Y. A unified Bayesian framework for bias adjustment in multiple comparisons from clinical trials. Stat Med 2024; 43:2928-2943. [PMID: 38742595 DOI: 10.1002/sim.10064] [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: 06/05/2023] [Revised: 01/30/2024] [Accepted: 03/06/2024] [Indexed: 05/16/2024]
Abstract
In clinical trials, multiple comparisons arising from various treatments/doses, subgroups, or endpoints are common. Typically, trial teams focus on the comparison showing the largest observed treatment effect, often involving a specific treatment pair and endpoint within a subgroup. These findings frequently lead to follow-up pivotal studies, many of which do not confirm the initial positive results. Selection bias occurs when the most promising treatment, subgroup, or endpoint is chosen for further development, potentially skewing subsequent investigations. Such bias can be defined as the deviation in the observed treatment effects from the underlying truth. In this article, we propose a general and unified Bayesian framework to address selection bias in clinical trials with multiple comparisons. Our approach does not require a priori specification of a parametric distribution for the prior, offering a more flexible and generalized solution. The proposed method facilitates a more accurate interpretation of clinical trial results by adjusting for such selection bias. Through simulation studies, we compared several methods and demonstrated their superior performance over the normal shrinkage estimator. We recommended the use of Bayesian Model Averaging estimator averaging over Gaussian Mixture Models as the prior distribution based on its performance and flexibility. We applied the method to a multicenter, randomized, double-blind, placebo-controlled study investigating the cardiovascular effects of dulaglutide.
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Affiliation(s)
- Yu Du
- Global Statistical Sciences, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana
| | - Jianghao Li
- Global Statistical Sciences, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana
| | - Sohini Raha
- Global Statistical Sciences, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana
| | - Yongming Qu
- Global Statistical Sciences, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana
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2
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Khan JN, Kimani PK, Glimm E, Stallard N. Adjusting for treatment selection in phase II/III clinical trials with time to event data. Stat Med 2023; 42:146-163. [PMID: 36419206 PMCID: PMC10098876 DOI: 10.1002/sim.9606] [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: 03/22/2022] [Revised: 09/25/2022] [Accepted: 11/01/2022] [Indexed: 11/26/2022]
Abstract
Phase II/III clinical trials are efficient two-stage designs that test multiple experimental treatments. In stage 1, patients are allocated to the control and all experimental treatments, with the data collected from them used to select experimental treatments to continue to stage 2. Patients recruited in stage 2 are allocated to the selected treatments and the control. Combined data of stage 1 and stage 2 are used for a confirmatory phase III analysis. Appropriate analysis needs to adjust for selection bias of the stage 1 data. Point estimators exist for normally distributed outcome data. Extending these estimators to time to event data is not straightforward because treatment selection is based on correlated treatment effects and stage 1 patients who do not get events in stage 1 are followed-up in stage 2. We have derived an approximately uniformly minimum variance conditional unbiased estimator (UMVCUE) and compared its biases and mean squared errors to existing bias adjusted estimators. In simulations, one existing bias adjusted estimator has similar properties as the practically unbiased UMVCUE while the others can have noticeable biases but they are less variable than the UMVCUE. For confirmatory phase II/III clinical trials where unbiased estimators are desired, we recommend the UMVCUE or the existing estimator with which it has similar properties.
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Affiliation(s)
| | - Peter K Kimani
- Warwick Medical School, University of Warwick, Coventry, UK
| | | | - Nigel Stallard
- Warwick Medical School, University of Warwick, Coventry, UK
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3
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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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4
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Marschner IC, Schou M, Martin AJ. Estimation of the treatment effect following a clinical trial that stopped early for benefit. Stat Methods Med Res 2022; 31:2456-2469. [PMID: 36065593 DOI: 10.1177/09622802221122445] [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] [Indexed: 12/15/2022]
Abstract
When a clinical trial stops early for benefit, the maximum likelihood estimate (MLE) of the treatment effect may be subject to overestimation bias. Several authors have proposed adjusting for this bias using the conditional MLE, which is obtained by conditioning on early stopping. However, this approach has a fundamental problem in that the adjusted estimate may not be in the direction of benefit, even though the study has stopped early due to benefit. In this paper, we address this problem by embedding both the MLE and the conditional MLE within a broader class of penalised likelihood estimates, and choosing a member of the class that is a favourable compromise between the two. This penalised MLE, and its associated confidence interval, always lie in the direction of benefit when the study stops early for benefit. We study its properties using both simulations and analyses of the ENZAMET trial in metastatic prostate cancer. Conditional on stopping early for benefit, the method is found to have good unbiasedness and coverage properties, along with very favourable efficiency at earlier interim analyses. We recommend the penalised MLE as a supplementary analysis to a conventional primary analysis when a clinical trial stops early for benefit.
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Affiliation(s)
- Ian C Marschner
- 110588NHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia
| | - Manjula Schou
- 110588NHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia
| | - Andrew J Martin
- 110588NHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia
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5
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Lee KM, Robertson DS, Jaki T, Emsley R. The benefits of covariate adjustment for adaptive multi-arm designs. Stat Methods Med Res 2022; 31:2104-2121. [PMID: 35876412 PMCID: PMC7613816 DOI: 10.1177/09622802221114544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Covariate adjustment via a regression approach is known to increase the precision
of statistical inference when fixed trial designs are employed in randomized
controlled studies. When an adaptive multi-arm design is employed with the
ability to select treatments, it is unclear how covariate adjustment affects
various aspects of the study. Consider the design framework that relies on
pre-specified treatment selection rule(s) and a combination test approach for
hypothesis testing. It is our primary goal to evaluate the impact of covariate
adjustment on adaptive multi-arm designs with treatment selection. Our secondary
goal is to show how the Uniformly Minimum Variance Conditionally Unbiased
Estimator can be extended to account for covariate adjustment analytically. We
find that adjustment with different sets of covariates can lead to different
treatment selection outcomes and hence probabilities of rejecting hypotheses.
Nevertheless, we do not see any negative impact on the control of the familywise
error rate when covariates are included in the analysis model. When adjusting
for covariates that are moderately or highly correlated with the outcome, we see
various benefits to the analysis of the design. Conversely, there is negligible
impact when including covariates that are uncorrelated with the outcome.
Overall, pre-specification of covariate adjustment is recommended for the
analysis of adaptive multi-arm design with treatment selection. Having the
statistical analysis plan in place prior to the interim and final analyses is
crucial, especially when a non-collapsible measure of treatment effect is
considered in the trial.
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Affiliation(s)
- Kim May Lee
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Thomas Jaki
- 47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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6
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Kimani PK, Todd S, Renfro LA, Glimm E, Khan JN, Kairalla JA, Stallard N. Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection. Stat Med 2020; 39:2568-2586. [PMID: 32363603 PMCID: PMC7785132 DOI: 10.1002/sim.8557] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 03/31/2020] [Accepted: 04/06/2020] [Indexed: 02/02/2023]
Abstract
In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two-stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous.
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Affiliation(s)
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, Reading, UK
| | - Lindsay A. Renfro
- Division of Biostatistics, University of Southern California, Los Angeles, CA
| | | | | | - John A. Kairalla
- Department of Biostatistics, University of Florida, Gainesville, Florida
| | - Nigel Stallard
- Warwick Medical School, University of Warwick, Coventry, UK
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7
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Stallard N, Hampson L, Benda N, Brannath W, Burnett T, Friede T, Kimani PK, Koenig F, Krisam J, Mozgunov P, Posch M, Wason J, Wassmer G, Whitehead J, Williamson SF, Zohar S, Jaki T. Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19. Stat Biopharm Res 2020; 12:483-497. [PMID: 34191981 PMCID: PMC8011600 DOI: 10.1080/19466315.2020.1790415] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/23/2020] [Accepted: 06/24/2020] [Indexed: 02/06/2023]
Abstract
The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this article, we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs.
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Affiliation(s)
- Nigel Stallard
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Lisa Hampson
- Advanced Methodology and Data Science, Novartis Pharma AG, Basel, Switzerland
| | - Norbert Benda
- The Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany
| | - Werner Brannath
- Institute for Statistics, University of Bremen, Bremen, Germany
| | - Thomas Burnett
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Peter K. Kimani
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Franz Koenig
- Section for Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria
| | - Johannes Krisam
- Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany
| | - Pavel Mozgunov
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Martin Posch
- Section for Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria
| | - James Wason
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | | | - John Whitehead
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - S. Faye Williamson
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Sarah Zohar
- INSERM, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, France
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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Ghosh P, Liu L, Mehta C. Adaptive multiarm multistage clinical trials. Stat Med 2020; 39:1084-1102. [PMID: 32048313 PMCID: PMC7065228 DOI: 10.1002/sim.8464] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 11/04/2019] [Accepted: 12/12/2019] [Indexed: 11/07/2022]
Abstract
Two methods for designing adaptive multiarm multistage (MAMS) clinical trials, originating from conceptually different group sequential frameworks are presented, and their operating characteristics are compared. In both methods pairwise comparisons are made, stage-by-stage, between each treatment arm and a common control arm with the goal of identifying active treatments and dropping inactive ones. At any stage one may alter the future course of the trial through adaptive changes to the prespecified decision rules for treatment selection and sample size reestimation, and notwithstanding such changes, both methods guarantee strong control of the family-wise error rate. The stage-wise MAMS approach was historically the first to be developed and remains the standard method for designing inferentially seamless phase 2-3 clinical trials. In this approach, at each stage, the data from each treatment comparison are summarized by a single multiplicity adjusted P-value. These stage-wise P-values are combined by a prespecified combination function and the resultant test statistic is monitored with respect to the classical two-arm group sequential efficacy boundaries. The cumulative MAMS approach is a more recent development in which a separate test statistic is constructed for each treatment comparison from the cumulative data at each stage. These statistics are then monitored with respect to multiplicity adjusted group sequential efficacy boundaries. We compared the powers of the two methods for designs with two and three active treatment arms, under commonly utilized decision rules for treatment selection, sample size reestimation and early stopping. In our investigations, which were carried out over a reasonably exhaustive exploration of the parameter space, the cumulative MAMS designs were more powerful than the stage-wise MAMS designs, except for the homogeneous case of equal treatment effects, where a small power advantage was discernable for the stage-wise MAMS designs.
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Affiliation(s)
| | | | - Cyrus Mehta
- Cytel Inc, Cambridge, Massachusetts.,Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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9
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Friede T, Stallard N, Parsons N. Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods, simulation model and their implementation in R. Biom J 2020; 62:1264-1283. [PMID: 32118317 PMCID: PMC8614126 DOI: 10.1002/bimj.201900020] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Revised: 01/10/2020] [Accepted: 01/12/2020] [Indexed: 11/12/2022]
Abstract
Adaptive seamless designs combine confirmatory testing, a domain of phase III trials, with features such as treatment or subgroup selection, typically associated with phase II trials. They promise to increase the efficiency of development programmes of new drugs, for example, in terms of sample size and/or development time. It is well acknowledged that adaptive designs are more involved from a logistical perspective and require more upfront planning, often in the form of extensive simulation studies, than conventional approaches. Here, we present a framework for adaptive treatment and subgroup selection using the same notation, which links the somewhat disparate literature on treatment selection on one side and on subgroup selection on the other. Furthermore, we introduce a flexible and efficient simulation model that serves both designs. As primary endpoints often take a long time to observe, interim analyses are frequently informed by early outcomes. Therefore, all methods presented accommodate interim analyses informed by either the primary outcome or an early outcome. The R package asd, previously developed to simulate designs with treatment selection, was extended to include subgroup selection (so‐called adaptive enrichment designs). Here, we describe the functionality of the R package asd and use it to present some worked‐up examples motivated by clinical trials in chronic obstructive pulmonary disease and oncology. The examples both illustrate various features of the R package and provide insights into the operating characteristics of adaptive seamless studies.
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Affiliation(s)
- Tim Friede
- Department of Medical StatisticsUniversity Medical Center GöttingenGöttingen Germany
| | - Nigel Stallard
- Division of Health SciencesWarwick Medical SchoolUniversity of WarwickCoventry UK
| | - Nicholas Parsons
- Division of Health SciencesWarwick Medical SchoolUniversity of WarwickCoventry UK
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10
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Barker KL, Newman M, Stallard N, Leal J, Lowe CM, Javaid MK, Noufaily A, Hughes T, Smith D, Gandhi V, Cooper C, Lamb SE. Physiotherapy rehabilitation for osteoporotic vertebral fracture-a randomised controlled trial and economic evaluation (PROVE trial). Osteoporos Int 2020; 31:277-289. [PMID: 31720722 DOI: 10.1007/s00198-019-05133-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 08/14/2019] [Indexed: 10/25/2022]
Abstract
UNLABELLED The trial compared three physiotherapy approaches: manual or exercise therapy compared with a single session of physiotherapy education (SSPT) for people with osteoporotic vertebral fracture(s). At 1 year, there were no statistically significant differences between the groups meaning there is inadequate evidence to support manual or exercise therapy. INTRODUCTION To evaluate the clinical and cost-effectiveness of different physiotherapy approaches for people with osteoporotic vertebral fracture(s) (OVF). METHODS >Prospective, multicentre, adaptive, three-arm randomised controlled trial. Six hundred fifteen adults with back pain, osteoporosis, and at least 1 OVF participated. INTERVENTIONS 7 individual physiotherapy sessions over 12 weeks focused on either manual therapy or home exercise compared with a single session of physiotherapy education (SSPT). The co-primary outcomes were quality of life and back muscle endurance measured by the QUALEFFO-41 and timed loaded standing (TLS) test at 12 months. RESULTS At 12 months, there were no statistically significant differences between groups. Mean QUALEFFO-41: - 1.3 (exercise), - 0.15 (manual), and - 1.2 (SSPT), a mean difference of - 0.2 (95% CI, - 3.2 to 1.6) for exercise and 1.3 (95% CI, - 1.8 to 2.9) for manual therapy. Mean TLS: 9.8 s (exercise), 13.6 s (manual), and 4.2 s (SSPT), a mean increase of 5.8 s (95% CI, - 4.8 to 20.5) for exercise and 9.7 s (95% CI, 0.1 to 24.9) for manual therapy. Exercise provided more quality-adjusted life years than SSPT but was more expensive. At 4 months, significant changes above SSPT occurred in endurance and balance in manual therapy, and in endurance for those ≤ 70 years, in balance, mobility, and walking in exercise. CONCLUSIONS Adherence was problematic. Benefits at 4 months did not persist and at 12 months, we found no significant differences between treatments. There is inadequate evidence a short physiotherapy intervention of either manual therapy or home exercise provides long-term benefits, but arguably short-term benefits are valuable. TRIAL REGISTRATION ISRCTN 49117867.
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Affiliation(s)
- K L Barker
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Botnar Research Centre, University of Oxford, Oxford, OX3 7LD, UK.
- Physiotherapy Research Unit, Physiotherapy Department, Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Windmill Road, Oxford, OX3 7LD, UK.
| | - M Newman
- Physiotherapy Research Unit, Physiotherapy Department, Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Windmill Road, Oxford, OX3 7LD, UK
| | - N Stallard
- Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - J Leal
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - C M Lowe
- Physiotherapy Research Unit, Physiotherapy Department, Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Windmill Road, Oxford, OX3 7LD, UK
| | - M K Javaid
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Botnar Research Centre, University of Oxford, Oxford, OX3 7LD, UK
| | - A Noufaily
- Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - T Hughes
- Physiotherapy Research Unit, Physiotherapy Department, Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Windmill Road, Oxford, OX3 7LD, UK
| | - D Smith
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Botnar Research Centre, University of Oxford, Oxford, OX3 7LD, UK
| | - V Gandhi
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Botnar Research Centre, University of Oxford, Oxford, OX3 7LD, UK
| | - C Cooper
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Botnar Research Centre, University of Oxford, Oxford, OX3 7LD, UK
| | - S E Lamb
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), Botnar Research Centre, University of Oxford, Oxford, OX3 7LD, UK
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Barker KL, Newman M, Stallard N, Leal J, Minns Lowe C, Javaid MK, Noufaily A, Adhikari A, Hughes T, Smith DJ, Gandhi V, Cooper C, Lamb SE. Exercise or manual physiotherapy compared with a single session of physiotherapy for osteoporotic vertebral fracture: three-arm PROVE RCT. Health Technol Assess 2019; 23:1-318. [PMID: 31456562 DOI: 10.3310/hta23440] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND A total of 25,000 people in the UK have osteoporotic vertebral fracture (OVF). Evidence suggests that physiotherapy may have an important treatment role. OBJECTIVE The objective was to investigate the clinical effectiveness and cost-effectiveness of two different physiotherapy programmes for people with OVF compared with a single physiotherapy session. DESIGN This was a prospective, adaptive, multicentre, assessor-blinded randomised controlled trial (RCT) with nested qualitative and health economic studies. SETTING This trial was based in 21 NHS physiotherapy departments. PARTICIPANTS The participants were people with symptomatic OVF. INTERVENTIONS Seven sessions of either manual outpatient physiotherapy or exercise outpatient physiotherapy compared with the best practice of a 1-hour single session of physiotherapy (SSPT). MAIN OUTCOME MEASURES Outcomes were measured at 4 and 12 months. The primary outcomes were quality of life and muscle endurance, which were measured by the disease-specific QUALEFFO-41 (Quality of Life Questionnaire of the European Foundation for Osteoporosis - 41 items) and timed loaded standing (TLS) test, respectively. Secondary outcomes were (1) thoracic kyphosis angle, (2) balance, evaluated via the functional reach test (FRT), and (3) physical function, assessed via the Short Physical Performance Battery (SPPB), 6-minute walk test (6MWT), Physical Activity Scale for the Elderly, a health resource use and falls diary, and the EuroQol-5 Dimensions, five-level version. RESULTS A total of 615 participants were enrolled, with 216, 203 and 196 randomised by a computer-generated program to exercise therapy, manual therapy and a SSPT, respectively. Baseline data were available for 613 participants, 531 (86.6%) of whom were women; the mean age of these participants was 72.14 years (standard deviation 9.09 years). Primary outcome data were obtained for 69% of participants (429/615) at 12 months: 175 in the exercise therapy arm, 181 in the manual therapy arm and 173 in the SSPT arm. Interim analysis met the criteria for all arms to remain in the study. For the primary outcomes at 12 months, there were no significant benefits over SSPT of exercise [QUALEFFO-41, difference -0.23 points, 95% confidence interval (CI) -3.20 to 1.59 points; p = 1.000; and TLS test, difference 5.77 seconds, 95% CI -4.85 to 20.46 seconds; p = 0.437] or of manual therapy (QUALEFFO-41, difference 1.35 points, 95% CI -1.76 to 2.93 points; p = 0.744; TLS test, difference 9.69 seconds (95% CI 0.09 to 24.86 seconds; p = 0.335). At 4 months, there were significant gains for both manual therapy and exercise therapy over SSPT in the TLS test in participants aged < 70 years. Exercise therapy was superior to a SSPT at 4 months in the SPPB, FRT and 6MWT and manual therapy was superior to a SSPT at 4 months in the TLS test and FRT. Neither manual therapy nor exercise therapy was cost-effective relative to a SSPT using the threshold of £20,000 per quality-adjusted life-year. There were no treatment-related serious adverse events. CONCLUSIONS This is the largest RCT to date assessing physiotherapy in participants with OVFs. At 1 year, neither treatment intervention conferred more benefit than a single 1-hour physiotherapy advice session. The focus of future work should be on the intensity and duration of interventions to determine if changes to these would demonstrate more sustained effects. TRIAL REGISTRATION Current Controlled Trials ISRCTN49117867. FUNDING This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 23, No. 44. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- Karen L Barker
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.,Physiotherapy Research Unit, Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Meredith Newman
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.,Physiotherapy Research Unit, Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Nigel Stallard
- Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Jose Leal
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Catherine Minns Lowe
- Physiotherapy Research Unit, Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Muhammad K Javaid
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Angela Noufaily
- Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Anish Adhikari
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Tamsin Hughes
- Physiotherapy Research Unit, Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - David J Smith
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Varsha Gandhi
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Cyrus Cooper
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Sarah E Lamb
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
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Lee KM, Wason J, Stallard N. To add or not to add a new treatment arm to a multiarm study: A decision-theoretic framework. Stat Med 2019; 38:3305-3321. [PMID: 31115078 PMCID: PMC6619445 DOI: 10.1002/sim.8194] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 03/05/2019] [Accepted: 04/17/2019] [Indexed: 12/16/2022]
Abstract
Multiarm clinical trials, which compare several experimental treatments against control, are frequently recommended due to their efficiency gain. In practise, all potential treatments may not be ready to be tested in a phase II/III trial at the same time. It has become appealing to allow new treatment arms to be added into on‐going clinical trials using a “platform” trial approach. To the best of our knowledge, many aspects of when to add arms to an existing trial have not been explored in the literature. Most works on adding arm(s) assume that a new arm is opened whenever a new treatment becomes available. This strategy may prolong the overall duration of a study or cause reduction in marginal power for each hypothesis if the adaptation is not well accommodated. Within a two‐stage trial setting, we propose a decision‐theoretic framework to investigate when to add or not to add a new treatment arm based on the observed stage one treatment responses. To account for different prospect of multiarm studies, we define utility in two different ways; one for a trial that aims to maximise the number of rejected hypotheses; the other for a trial that would declare a success when at least one hypothesis is rejected from the study. Our framework shows that it is not always optimal to add a new treatment arm to an existing trial. We illustrate a case study by considering a completed trial on knee osteoarthritis.
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Affiliation(s)
- Kim May Lee
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - James Wason
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.,Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK
| | - Nigel Stallard
- WMS - Statistics and Epidemiology, University of Warwick, Coventry, UK
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Glimm E. Adjusting for selection bias in assessing treatment effect estimates from multiple subgroups. Biom J 2018; 61:216-229. [PMID: 30474240 DOI: 10.1002/bimj.201800097] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 08/21/2018] [Accepted: 10/11/2018] [Indexed: 11/06/2022]
Abstract
This paper discusses a number of methods for adjusting treatment effect estimates in clinical trials where differential effects in several subpopulations are suspected. In such situations, the estimates from the most extreme subpopulation are often overinterpreted. The paper focusses on the construction of simultaneous confidence intervals intended to provide a more realistic assessment regarding the uncertainty around these extreme results. The methods from simultaneous inference are compared with shrinkage estimates arising from Bayesian hierarchical models by discussing salient features of both approaches in a typical application.
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Affiliation(s)
- Ekkehard Glimm
- Novartis Pharma AG, Novartis Campus, Basel, Switzerland.,Otto-von-Guericke University, Institute of Biometry and Medical Informatics, Magdeburg, Germany
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14
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Kimani PK, Todd S, Renfro LA, Stallard N. Point estimation following two-stage adaptive threshold enrichment clinical trials. Stat Med 2018; 37:3179-3196. [PMID: 29855066 PMCID: PMC6175016 DOI: 10.1002/sim.7831] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 03/16/2018] [Accepted: 04/30/2018] [Indexed: 11/11/2022]
Abstract
Recently, several study designs incorporating treatment effect assessment in biomarker-based subpopulations have been proposed. Most statistical methodologies for such designs focus on the control of type I error rate and power. In this paper, we have developed point estimators for clinical trials that use the two-stage adaptive enrichment threshold design. The design consists of two stages, where in stage 1, patients are recruited in the full population. Stage 1 outcome data are then used to perform interim analysis to decide whether the trial continues to stage 2 with the full population or a subpopulation. The subpopulation is defined based on one of the candidate threshold values of a numerical predictive biomarker. To estimate treatment effect in the selected subpopulation, we have derived unbiased estimators, shrinkage estimators, and estimators that estimate bias and subtract it from the naive estimate. We have recommended one of the unbiased estimators. However, since none of the estimators dominated in all simulation scenarios based on both bias and mean squared error, an alternative strategy would be to use a hybrid estimator where the estimator used depends on the subpopulation selected. This would require a simulation study of plausible scenarios before the trial.
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Affiliation(s)
- Peter K. Kimani
- Warwick Medical SchoolUniversity of WarwickCoventry CV4 7ALUK
| | - Susan Todd
- Department of Mathematics and StatisticsUniversity of ReadingReading RG6 6AXUK
| | - Lindsay A. Renfro
- Division of Biomedical Statistics and InformaticsMayo ClinicRochesterMN 55905USA
| | - Nigel Stallard
- Warwick Medical SchoolUniversity of WarwickCoventry CV4 7ALUK
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