Dataset Information

Including historical data in the analysis of clinical trials: Is it worth the effort?

ABSTRACT: Data of previous trials with a similar setting are often available in the analysis of clinical trials. Several Bayesian methods have been proposed for including historical data as prior information in the analysis of the current trial, such as the (modified) power prior, the (robust) meta-analytic-predictive prior, the commensurate prior and methods proposed by Pocock and Murray et al. We compared these methods and illustrated their use in a practical setting, including an assessment of the comparability of the current and the historical data. The motivating data set consists of randomised controlled trials for acute myeloid leukaemia. A simulation study was used to compare the methods in terms of bias, precision, power and type I error rate. Methods that estimate parameters for the between-trial heterogeneity generally offer the best trade-off of power, precision and type I error, with the meta-analytic-predictive prior being the most promising method. The results show that it can be feasible to include historical data in the analysis of clinical trials, if an appropriate method is used to estimate the heterogeneity between trials, and the historical data satisfy criteria for comparability.

SUBMITTER: van Rosmalen J

PROVIDER: S-EPMC6176344 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Including historical data in the analysis of clinical trials: Is it worth the effort?

van Rosmalen Joost J Dejardin David D van Norden Yvette Y Löwenberg Bob B Lesaffre Emmanuel E

Statistical methods in medical research 20170221 10

Data of previous trials with a similar setting are often available in the analysis of clinical trials. Several Bayesian methods have been proposed for including historical data as prior information in the analysis of the current trial, such as the (modified) power prior, the (robust) meta-analytic-predictive prior, the commensurate prior and methods proposed by Pocock and Murray et al. We compared these methods and illustrated their use in a practical setting, including an assessment of the comp ...[more]

PMID: 28322129

Similar Datasets

Project description:BackgroundClinical trials frequently spend considerable effort to collect data on patients who were assessed for eligibility but not enrolled. The Consolidated Standards of Reporting Trials (CONSORT) guidelines' recommended flow diagram for randomized clinical trials reinforces the belief that the collection of screening data is a necessary and worthwhile endeavor. The rationale for collecting screening data includes scientific, trial management, and ethno-socio-cultural reasons.PurposeWe posit that the cost of collecting screening data is not justified, in part due to inability to centrally monitor and verify the screening data in the same manner as other clinical trial data.MethodsTo illustrate the effort and site-to-site variability, we analyzed the screening data from a multicenter, randomized clinical trial of patients with transient ischemic attack or minor ischemic stroke (Platelet-Oriented Inhibition in New Transient Ischemic Attack and Minor Ischemic Stroke (POINT)).ResultsData were collected on over 27,000 patients screened across 172 enrolling sites, 95% of whom were not enrolled. Although the rate of return of screen failure logs was high overall (95%), there were a considerable number of logs that were returned with 'no data to report' (23%), often due to administrative reasons rather than no patients screened.ConclusionIn spite of attempts to standardize the collection of screening data, due to differences in site processes, multicenter clinical trials face challenges in collecting those data completely and uniformly. The efforts required to centrally collect high-quality data on an extensive number of screened patients may outweigh the scientific value of the data. Moreover, the lack of a standardized definition of 'screened' and the challenges of collecting meaningful characteristics for patients who have not signed consent limits the ability to compare across studies and to assess generalizability and selection bias as intended.

Project description:The majority of musculoskeletal modelling studies investigating healthy populations use generic models linearly scaled to roughly match an individual's anthropometry. Generic models disregard the considerable variation in musculoskeletal geometry and tissue properties between individuals. This study investigated the physiological implications of personalizing musculoskeletal model geometry (body segment mass, inertia, joint center, and maximum isometric muscle force). Nine healthy athletes performed ten repetitions of 15 meter sprints at 75-95% of their maximum sprinting speed and ten repetitions of unanticipated sidestep cut trials with a 4.5-5.5 m/s approach running speed. Structural magnetic resonance imaging was collected on the lower extremities, from which subject-specific musculoskeletal models were developed. A one-dimensional statistical parametric mapping paired t-test was used to compare generic and subject-specific musculoskeletal models for: lower-limb kinematics, kinetics, torque matching, as well as hamstrings, adductors, and quadriceps muscle activations and fiber dynamics. Percentage change of geometric parameters between generic and subject-specific models were determined. Compared to generic models, subject-specific models showed significantly lower ankle dorsi/plantar flexion angle during sprinting and several significantly different net joint moments during sprint and cut tasks. Additionally, subject-specific models demonstrated better torque matching, more physiologically plausible fiber lengths, higher fiber velocities, lower muscle forces, and lower simulated activations in a subset of investigated muscles and motor tasks. Furthermore, subject-specific models identified between-limb differences that were not identified with generic models. Use of subject-specific modeling, even in healthy populations, may result in more physiologically plausible muscle fiber mechanics. Implementing subject-specific models may be especially beneficial when investigating populations with substantial geometric between-limb differences, or unilateral musculoskeletal pathologies, as these are not captured by a generic model.

Dataset Information

Including historical data in the analysis of clinical trials: Is it worth the effort?

Publications

Including historical data in the analysis of clinical trials: Is it worth the effort?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets