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Yoon DY, Daniels MJ, Willcocks RJ, Triplett WT, Morales JF, Walter GA, Rooney WD, Vandenborne K, Kim S. Five multivariate Duchenne muscular dystrophy progression models bridging six-minute walk distance and MRI relaxometry of leg muscles. J Pharmacokinet Pharmacodyn 2024:10.1007/s10928-024-09910-1. [PMID: 38609673 DOI: 10.1007/s10928-024-09910-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 02/15/2024] [Indexed: 04/14/2024]
Abstract
The study aimed to provide quantitative information on the utilization of MRI transverse relaxation time constant (MRI-T2) of leg muscles in DMD clinical trials by developing multivariate disease progression models of Duchenne muscular dystrophy (DMD) using 6-min walk distance (6MWD) and MRI-T2. Clinical data were collected from the prospective and longitudinal ImagingNMD study. Disease progression models were developed by a nonlinear mixed-effect modeling approach. Univariate models of 6MWD and MRI-T2 of five muscles were developed separately. Age at assessment was the time metric. Multivariate models were developed by estimating the correlation of 6MWD and MRI-T2 model variables. Full model estimation approach for covariate analysis and five-fold cross validation were conducted. Simulations were performed to compare the models and predict the covariate effects on the trajectories of 6MWD and MRI-T2. Sigmoid Imax and Emax models best captured the profiles of 6MWD and MRI-T2 over age. Steroid use, baseline 6MWD, and baseline MRI-T2 were significant covariates. The median age at which 6MWD is half of its maximum decrease in the five models was similar, while the median age at which MRI-T2 is half of its maximum increase varied depending on the type of muscle. The models connecting 6MWD and MRI-T2 successfully quantified how individual characteristics alter disease trajectories. The models demonstrate a plausible correlation between 6MWD and MRI-T2, supporting the use of MRI-T2. The developed models will guide drug developers in using the MRI-T2 to most efficient use in DMD clinical trials.
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Affiliation(s)
- Deok Yong Yoon
- Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, FL, USA
| | - Michael J Daniels
- Department of Statistics, University of Florida, Gainesville, FL, USA
| | | | - William T Triplett
- Department of Physical Therapy, University of Florida, Gainesville, FL, USA
| | - Juan Francisco Morales
- Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, FL, USA
| | - Glenn A Walter
- Department of Physiology and Aging, University of Florida, Gainesville, FL, USA
| | - William D Rooney
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA
| | - Krista Vandenborne
- Department of Physical Therapy, University of Florida, Gainesville, FL, USA
| | - Sarah Kim
- Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, FL, USA.
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Liang Y, Yew PY, Loth M, Adam TJ, Wolfson J, Tonellato PJ, Chi CL. Personalized statin treatment plan using counterfactual approach with multi-objective optimization over benefits and risks. Inform Med Unlocked 2023; 42:101362. [PMID: 37986733 PMCID: PMC10659576 DOI: 10.1016/j.imu.2023.101362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2023] Open
Abstract
Background Statins are a class of drugs that lower cholesterol levels in the blood by inhibiting an enzyme called 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase. High cholesterol levels can lead to plaque buildup in the arteries, which can cause Atherosclerotic Cardiovascular Disease(ASCVD). Statins can reduce the risk of ASCVD events by about 25-35% but they might be associated with symptoms such as muscle pain, liver damage, or diabetes. As a result, this leads to a strong reason to discontinue statin therapy, which increases the risk of cardiovascular events and mortality and becomes a public-health problem.To solve this problem, in the previous work, we proposed a framework to produce a proactive strategy, called a personalized statin treatment plan (PSTP) to minimize the risks of statin-associated symptoms and therapy discontinuation when prescribing statin. In our previous PSTP framework, three limitations remain, and they can influence PSTP usability: (1) Not taking the counterfactual predictions and confounding bias into account. (2) The balance between multiple drug-prescribing objectives (especially trade-off objectives), such as tradeoff between benefits and risks. (3) Evaluating PSTP in retrospective data. Objectives This manuscript aimed to provide solutions for the three abovementioned problems to improve PSTP robustness to produce a proactive strategy for statin prescription that can maximize the benefits (low-density lipoprotein cholesterol (LDL-C) reduction) and minimize risks (statin-associated symptoms and therapy discontinuation) at the same time. Methods We applied overlapping weighting counterfactual survival risk prediction (CP), multiple objective optimization (MOO), and clinical trial simulation (CTS) which consists of Random Arms, Clinical Guideline arms, PSTP Arms, and Practical Arms to improve the PSTP framework and usability. Results In addition to highly balanced covariates, in the CTS, the revised PSTP showed improvements in lowering the SAS risks overall compared to other arms across all time points by at most 7.5% to at least 1.0% (Fig. 8(a)). It also has the better flexibility of identifying the optimal Statin across all time points within one year. Conclusion We demonstrated feasibility of robust and trustworthy counterfactual survival risk prediction model. In CTS, we also demonstrated the PSTP with Pareto optimization can personalize optimal balance between Statin benefits and risks.
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Affiliation(s)
- Yue Liang
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, 55455, USA
- Optum Labs Visiting Fellow, Eden Prairie, MN, 55344, USA
| | - Pui Ying Yew
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, 55455, USA
- Optum Labs Visiting Fellow, Eden Prairie, MN, 55344, USA
| | - Matt Loth
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, 55455, USA
- School of Nursing, University of Minnesota, Minneapolis, MN, 55455, USA
- Optum Labs Visiting Fellow, Eden Prairie, MN, 55344, USA
| | - Terrence J. Adam
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, 55455, USA
- Department of Pharmaceutical Care & Health Systems, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Julian Wolfson
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Peter J. Tonellato
- Department of Health Management and Informatics, University of Missouri School of Medicine, Missouri, 65212, USA
| | - Chin-Lin Chi
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, 55455, USA
- School of Nursing, University of Minnesota, Minneapolis, MN, 55455, USA
- Optum Labs Visiting Fellow, Eden Prairie, MN, 55344, USA
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Caputo A, Racine A, Paule I, Tariot PN, Langbaum JB, Coello N, Riviere ME, Ryan JM, Lopez CL, Graf A. Rationale for the selection of dual primary endpoints in prevention studies of cognitively unimpaired individuals at genetic risk for developing symptoms of Alzheimer's disease. Alzheimers Res Ther 2023; 15:45. [PMID: 36879340 PMCID: PMC9987044 DOI: 10.1186/s13195-023-01183-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 02/06/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND There is a critical need for novel primary endpoints designed to detect early and subtle changes in cognition in clinical trials targeting the asymptomatic (preclinical) phase of Alzheimer's disease (AD). The Alzheimer's Prevention Initiative (API) Generation Program, conducted in cognitively unimpaired individuals at risk of developing AD (e.g., enriched by the apolipoprotein E (APOE) genotype), used a novel dual primary endpoints approach, whereby demonstration of treatment effect in one of the two endpoints is sufficient for trial success. The two primary endpoints were (1) time to event (TTE)-with an event defined as a diagnosis of mild cognitive impairment (MCI) due to AD and/or dementia due to AD-and (2) change from baseline to month 60 in the API Preclinical Composite Cognitive (APCC) test score. METHODS Historical observational data from three sources were used to fit models to describe the TTE and the longitudinal APCC decline, both in people who do and do not progress to MCI or dementia due to AD. Clinical endpoints were simulated based on the TTE and APCC models to assess the performance of the dual endpoints versus each of the two single endpoints, with the selected treatment effect ranging from a hazard ratio (HR) of 0.60 (40% risk reduction) to 1 (no effect). RESULTS A Weibull model was selected for TTE, and power and linear models were selected to describe the APCC score for progressors and non-progressors, respectively. Derived effect sizes in terms of reduction of the APCC change from baseline to year 5 were low (0.186 for HR = 0.67). The power for the APCC alone was consistently lower compared to the power of TTE alone (58% [APCC] vs 84% [TTE] for HR = 0.67). Also, the overall power was higher for the 80%/20% distribution (82%) of the family-wise type 1 error rate (alpha) between TTE and APCC compared to 20%/80% (74%). CONCLUSIONS Dual endpoints including TTE and a measure of cognitive decline perform better than the cognitive decline measure as a single primary endpoint in a cognitively unimpaired population at risk of AD (based on the APOE genotype). Clinical trials in this population, however, need to be large, include older age, and have a long follow-up period of at least 5 years to be able to detect treatment effects.
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Affiliation(s)
| | - Amy Racine
- Novartis Pharma AG, PostfachCH-4002, Basel, Switzerland
| | - Ines Paule
- Novartis Pharma AG, PostfachCH-4002, Basel, Switzerland
| | | | | | - Neva Coello
- Novartis Pharma AG, PostfachCH-4002, Basel, Switzerland
| | | | | | | | - Ana Graf
- Novartis Pharma AG, PostfachCH-4002, Basel, Switzerland
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Weinstein SM, Coates LC, Helliwell PS, Ogdie A, Stephens-Shields AJ. Simulation-based design of pragmatic trials in psoriatic arthritis using propensity scores. Clin Trials 2021; 18:541-551. [PMID: 34431409 DOI: 10.1177/17407745211023840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND/AIMS Design of clinical trials requires careful decision-making across several dimensions, including endpoints, eligibility criteria, and subgroup enrichment. Clinical trial simulation can be an informative tool in trial design, providing empirical evidence by which to evaluate and compare the results of hypothetical trials with varying designs. We introduce a novel simulation-based approach using observational data to inform the design of a future pragmatic trial. METHODS We utilize propensity score-adjusted models to simulate hypothetical trials under alternative endpoints and enrollment criteria. We apply our approach to the design of pragmatic trials in psoriatic arthritis, using observational data embedded within the Tight Control of Inflammation in Early Psoriatic Arthritis study to simulate hypothetical open-label trials comparing treatment with tumor necrosis factor-α inhibitors to methotrexate. We first validate our simulations of a trial with traditional enrollment criteria and endpoints against a recently published trial. Next, we compare simulated treatment effects in patient populations defined by traditional and broadened enrollment criteria, where the latter is consistent with a future pragmatic trial. In each trial, we also consider five candidate primary endpoints. RESULTS Our results highlight how changes in the enrolled population and primary endpoints may qualitatively alter study findings and the ability to detect heterogeneous treatment effects between clinical subgroups. For treatments of interest in the study of psoriatic arthritis, broadened enrollment criteria led to diluted estimated treatment effects. Endpoints with greater responsiveness to treatment compared with a traditionally used endpoint were identified. These considerations, among others, are important for designing a future pragmatic trial aimed at having high external validity with relevance for real-world clinical practice. CONCLUSION Observational data may be leveraged to inform design decisions in pragmatic trials. Our approach may be generalized to the study of other conditions where existing trial data are limited or do not generalize well to real-world clinical practice, but where observational data are available.
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Affiliation(s)
- Sarah M Weinstein
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura C Coates
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Philip S Helliwell
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK
| | - Alexis Ogdie
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Division of Rheumatology, Center for Pharmacoepidemiology Research and Training, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alisa J Stephens-Shields
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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D'Agate S, Wilson T, Adalig B, Manyak M, Palacios-Moreno JM, Chavan C, Oelke M, Roehrborn C, Della Pasqua O. Impact of disease progression on individual IPSS trajectories and consequences of immediate versus delayed start of treatment in patients with moderate or severe LUTS associated with BPH. World J Urol 2019; 38:463-472. [PMID: 31079189 PMCID: PMC6994451 DOI: 10.1007/s00345-019-02783-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Accepted: 04/23/2019] [Indexed: 01/07/2023] Open
Abstract
Purpose Despite superiority of tamsulosin–dutasteride combination therapy versus monotherapy for lower urinary tract symptoms due to benign prostatic hyperplasia (LUTS/BPH), patients at risk of disease progression are often initiated on α-blockers. This study evaluated the impact of initiating tamsulosin monotherapy prior to switching to tamsulosin–dutasteride combination therapy versus immediate combination therapy using a longitudinal model describing International Prostate Symptom Score (IPSS) trajectories in moderate/severe LUTS/BPH patients at risk of disease progression. Methods Clinical trial simulations (CTS) were performed using data from 10,238 patients from Phase III/IV dutasteride trials. The effect of varying disease progression rates was explored by comparing profiles on- and off-treatment. CTS scenarios were investigated, including a reference (immediate combination therapy) and six alternative virtual treatment arms (delayed combination therapy of 1–24 months). Clinical response (≥ 25% IPSS reduction relative to baseline) was analysed using log-rank test. Differences in IPSS relative to baseline at various on-treatment time points were assessed by t tests. Results Delayed combination therapy initiation led to significant (p < 0.01) decreases in clinical response. At month 48, clinical response rate was 79.7% versus 74.1%, 70.3% and 71.0% and IPSS was 6.3 versus 7.6, 8.1 and 8.0 (switchers from tamsulosin monotherapy after 6, 12 and 24 months, respectively) with immediate combination therapy. More patients transitioned from severe/moderate to mild severity scores by month 48. Conclusions CTS allows systematic evaluation of immediate versus delayed combination therapy. Immediate response to α-blockers is not predictive of long-term symptom improvement. Observed IPSS differences between immediate and delayed combination therapy (6–24 months) are statistically significant. Electronic supplementary material The online version of this article (10.1007/s00345-019-02783-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Salvatore D'Agate
- Clinical Pharmacology and Therapeutics Group, University College London, London, UK
| | | | - Burkay Adalig
- Classic and Established Products, GSK, Istanbul, Turkey
| | - Michael Manyak
- Classic and Established Products, GSK, Washington, DC, USA
| | | | | | - Matthias Oelke
- Department of Urology, St. Antonius Hospital, Gronau, Germany
| | - Claus Roehrborn
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Oscar Della Pasqua
- Clinical Pharmacology and Therapeutics Group, University College London, London, UK.
- Clinical Pharmacology Modelling and Simulation, GSK, Stockley Park, 1-3 Ironbridge Road, Uxbridge, Middlesex, UB11 1BT, UK.
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Abstract
BACKGROUND When designing studies it is common to search the literature to investigate variability estimates to use in sample size calculations. Proprietary data of previously designed trials in a particular indication are also used to obtain estimates of variability. Estimates of treatment effects are typically obtained from randomised controlled clinical trials (RCTs). Based on the observed estimates of treatment effect, variability and the minimum clinical relevant difference to detect, the sample size for a subsequent trial is estimated. However, data from real world evidence (RWE) studies, such as observational studies and other interventional studies in patients in routine clinical practice, are not widely used in a systematic manner when designing studies. In this paper, we propose a framework for inclusion of RWE in planning of a clinical development programme. METHODS In our proposed approach, all evidence, from both RCTs and RWE (i.e. from studies in routine clinical practice), available at the time of designing of a new clinical trial is combined in a Bayesian network meta-analysis (NMA). The results can be used to inform the design of the next clinical trial in the programme. The NMA was performed at key milestones, such as at the end of the phase II trial and prior to the design of key phase III studies. To illustrate the methods, we designed an alternative clinical development programme in multiple sclerosis using RWE through clinical trial simulations. RESULTS Inclusion of RWE in the NMA and the resulting trial simulations demonstrated that 284 patients per arm were needed to achieve 90% power to detect effects of predetermined size in the TRANSFORMS study. For the FREEDOMS and FREEDOMS II clinical trials, 189 patients per arm were required. Overall there was a reduction in sample size of at least 40% across the three phase III studies, which translated to a time savings of at least 6 months for the undertaking of the fingolimod phase III programme. CONCLUSION The use of RWE resulted in a reduced sample size of the pivotal phase III studies, which led to substantial time savings compared to the approach of sample size calculations without RWE.
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Affiliation(s)
- Reynaldo Martina
- Department of Health Sciences, University of Leicester, University Road, Leicester, UK
- Department of Biostatistics, University of Liverpool, 1-5 Brownlow Street, Liverpool, UK
| | - David Jenkins
- Department of Health Sciences, University of Leicester, University Road, Leicester, UK
- School of Health Sciences, University of Manchester, Oxford Road, Manchester, UK
| | - Sylwia Bujkiewicz
- Department of Health Sciences, University of Leicester, University Road, Leicester, UK
| | - Pascale Dequen
- Department of Health Sciences, University of Leicester, University Road, Leicester, UK
- Evidence Synthesis/Health Economics, Visible Analytics Ltd., Union Way, Oxon, UK
| | - Keith Abrams
- Department of Health Sciences, University of Leicester, University Road, Leicester, UK
| | - on behalf of GetReal Workpackage 1
- Department of Health Sciences, University of Leicester, University Road, Leicester, UK
- Department of Biostatistics, University of Liverpool, 1-5 Brownlow Street, Liverpool, UK
- School of Health Sciences, University of Manchester, Oxford Road, Manchester, UK
- Evidence Synthesis/Health Economics, Visible Analytics Ltd., Union Way, Oxon, UK
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Dockendorf MF, Vargo RC, Gheyas F, Chain ASY, Chatterjee MS, Wenning LA. Leveraging model-informed approaches for drug discovery and development in the cardiovascular space. J Pharmacokinet Pharmacodyn 2018; 45:355-364. [PMID: 29353335 PMCID: PMC5953982 DOI: 10.1007/s10928-018-9571-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 01/10/2018] [Indexed: 02/08/2023]
Abstract
Cardiovascular disease remains a significant global health burden, and development of cardiovascular drugs in the current regulatory environment often demands large and expensive cardiovascular outcome trials. Thus, the use of quantitative pharmacometric approaches which can help enable early Go/No Go decision making, ensure appropriate dose selection, and increase the likelihood of successful clinical trials, have become increasingly important to help reduce the risk of failed cardiovascular outcomes studies. In addition, cardiovascular safety is an important consideration for many drug development programs, whether or not the drug is designed to treat cardiovascular disease; modeling and simulation approaches also have utility in assessing risk in this area. Herein, examples of modeling and simulation applied at various stages of drug development, spanning from the discovery stage through late-stage clinical development, for cardiovascular programs are presented. Examples of how modeling approaches have been utilized in early development programs across various therapeutic areas to help inform strategies to mitigate the risk of cardiovascular-related adverse events, such as QTc prolongation and changes in blood pressure, are also presented. These examples demonstrate how more informed drug development decisions can be enabled by modeling and simulation approaches in the cardiovascular area.
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Affiliation(s)
- Marissa F Dockendorf
- Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co., Inc., Kenilworth, NJ, USA.
| | - Ryan C Vargo
- Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co., Inc., Kenilworth, NJ, USA
| | - Ferdous Gheyas
- Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co., Inc., Kenilworth, NJ, USA
| | - Anne S Y Chain
- Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co., Inc., Kenilworth, NJ, USA
| | - Manash S Chatterjee
- Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co., Inc., Kenilworth, NJ, USA
| | - Larissa A Wenning
- Pharmacokinetics, Pharmacodynamics, and Drug Metabolism, Merck & Co., Inc., Kenilworth, NJ, USA
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Tharayil JJ, Chiang S, Moss R, Stern JM, Theodore WH, Goldenholz DM. A big data approach to the development of mixed-effects models for seizure count data. Epilepsia 2017; 58:835-844. [PMID: 28369781 DOI: 10.1111/epi.13727] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/13/2017] [Indexed: 12/23/2022]
Abstract
OBJECTIVE Our objective was to develop a generalized linear mixed model for predicting seizure count that is useful in the design and analysis of clinical trials. This model also may benefit the design and interpretation of seizure-recording paradigms. Most existing seizure count models do not include children, and there is currently no consensus regarding the most suitable model that can be applied to children and adults. Therefore, an additional objective was to develop a model that accounts for both adult and pediatric epilepsy. METHODS Using data from SeizureTracker.com, a patient-reported seizure diary tool with >1.2 million recorded seizures across 8 years, we evaluated the appropriateness of Poisson, negative binomial, zero-inflated negative binomial, and modified negative binomial models for seizure count data based on minimization of the Bayesian information criterion. Generalized linear mixed-effects models were used to account for demographic and etiologic covariates and for autocorrelation structure. Holdout cross-validation was used to evaluate predictive accuracy in simulating seizure frequencies. RESULTS For both adults and children, we found that a negative binomial model with autocorrelation over 1 day was optimal. Using holdout cross-validation, the proposed model was found to provide accurate simulation of seizure counts for patients with up to four seizures per day. SIGNIFICANCE The optimal model can be used to generate more realistic simulated patient data with very few input parameters. The availability of a parsimonious, realistic virtual patient model can be of great utility in simulations of phase II/III clinical trials, epilepsy monitoring units, outpatient biosensors, and mobile Health (mHealth) applications.
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Affiliation(s)
- Joseph J Tharayil
- Clinical Epilepsy Section, NINDS, NIH, Bethesda, Maryland, U.S.A.,Department of Biomedical Engineering, Duke University, Durham, North Carolina, U.S.A
| | - Sharon Chiang
- Department of Statistics, Rice University, Houston, Texas, U.S.A.,Baylor College of Medicine, Houston, Texas, U.S.A
| | | | - John M Stern
- University of California Los Angeles Medical Center, Los Angeles, California, U.S.A
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Satlin A, Wang J, Logovinsky V, Berry S, Swanson C, Dhadda S, Berry DA. Design of a Bayesian adaptive phase 2 proof-of-concept trial for BAN2401, a putative disease-modifying monoclonal antibody for the treatment of Alzheimer's disease. Alzheimers Dement (N Y) 2016; 2:1-12. [PMID: 29067290 PMCID: PMC5644271 DOI: 10.1016/j.trci.2016.01.001] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Introduction Recent failures in phase 3 clinical trials in Alzheimer's disease (AD) suggest that novel approaches to drug development are urgently needed. Phase 3 risk can be mitigated by ensuring that clinical efficacy is established before initiating confirmatory trials, but traditional phase 2 trials in AD can be lengthy and costly. Methods We designed a Bayesian adaptive phase 2, proof-of-concept trial with a clinical endpoint to evaluate BAN2401, a monoclonal antibody targeting amyloid protofibrils. The study design used dose response and longitudinal modeling. Simulations were used to refine study design features to achieve optimal operating characteristics. Results The study design includes five active treatment arms plus placebo, a clinical outcome, 12-month primary endpoint, and a maximum sample size of 800. The average overall probability of success is ≥80% when at least one dose shows a treatment effect that would be considered clinically meaningful. Using frequent interim analyses, the randomization ratios are adapted based on the clinical endpoint, and the trial can be stopped for success or futility before full enrollment. Discussion Bayesian statistics can enhance the efficiency of analyzing the study data. The adaptive randomization generates more data on doses that appear to be more efficacious, which can improve dose selection for phase 3. The interim analyses permit stopping as soon as a predefined signal is detected, which can accelerate decision making. Both features can reduce the size and duration of the trial. This study design can mitigate some of the risks associated with advancing to phase 3 in the absence of data demonstrating clinical efficacy. Limitations to the approach are discussed.
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Affiliation(s)
- Andrew Satlin
- Neuroscience & General Medicine, Eisai Inc., Woodcliff Lake, NJ, USA
| | - Jinping Wang
- Neuroscience & General Medicine, Eisai Inc., Woodcliff Lake, NJ, USA
| | | | | | - Chad Swanson
- Neuroscience & General Medicine, Eisai Inc., Woodcliff Lake, NJ, USA
| | - Shobha Dhadda
- Neuroscience & General Medicine, Eisai Inc., Woodcliff Lake, NJ, USA
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