Bmc Med Res Methodol
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Bmc Med Res Methodol · Feb 2020
Framework for personalized prediction of treatment response in relapsing remitting multiple sclerosis.
Personalized healthcare promises to successfully advance the treatment of heterogeneous neurological disorders such as relapsing remitting multiple sclerosis by addressing the caveats of traditional healthcare. This study presents a framework for personalized prediction of treatment response based on real-world data from the NeuroTransData network. ⋯ Applying personalized predictive models in relapsing remitting multiple sclerosis patients is still new territory that is rapidly evolving and has many challenges. The proposed framework addresses the following challenges: robustness and accuracy of the predictions, generalizability to new patients and clinical sites and comparability of the predicted effectiveness of different therapies. The methodological and clinical soundness of the results builds the basis for a future support of patients and doctors when the current treatment is not generating the desired effect and they are considering a therapy switch. (A) The framework is developed using quality-proven real-world data of patients with relapsing remitting multiple sclerosis. Patients have heterogeneous individual characteristics and diverse disease profiles, indicated for example by variations in frequency of relapses and degree of disability. Longitudinal characteristics regarding disease history (e.g. number of previous relapses in the last 12 months) are extracted at the time of an intended therapy switch, i.e. at time point "Today" (left). All clinical parameters are captured in a standardized way (right). (B) The model predicts the course of the disease based on the observed data (panel A), and is able to account for the impact of various available therapies on chosen clinical endpoints. The resulting ranking of therapies has a dependency on patient characteristics, illustrated here by a different highest ranked therapy depending on the number of relapse in the previous 12 months. (C) The model is evaluated for various generalization properties. Compared to performance on the training set (gray) it is able to predict for new patients not part of the training set (red).Top: Prediction for new patients. Middle: Prediction for new clinical sites. Bottom: Prediction for different time windows. (D) In order to assess the clinical impact of the model, disease activity is compared between patients treated with the highest ranked therapy and those treated with any of the other therapies. Patients adhering to the highest ranked therapy are associated with a better disease outcome when compared to those who did not.
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Bmc Med Res Methodol · Jan 2020
Comparative StudyComparison of Bayesian and frequentist group-sequential clinical trial designs.
There is a growing interest in the use of Bayesian adaptive designs in late-phase clinical trials. This includes the use of stopping rules based on Bayesian analyses in which the frequentist type I error rate is controlled as in frequentist group-sequential designs. ⋯ Comparison of frequentist and Bayesian designs can encourage careful thought about design parameters and help to ensure appropriate design choices are made.
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Bmc Med Res Methodol · Dec 2019
Self-reported data in environmental health studies: mail vs. web-based surveys.
Internet has been broadly employed as a facilitator for epidemiological surveys, as a way to provide a more economical and practical alternative to traditional survey modes. A current trend in survey research is to combine Web-based surveys with other survey modes by offering the participant the possibility of choosing his/her preferred response method (i.e. mixed-mode approach). However, studies have also demonstrated that the use of different survey modes may produce different responses to the same questions, posing potential challenges on the use of mixed-mode approaches. ⋯ Our main findings suggest that the use of mail and Web surveys may produce different responses to the same questions posed to participants, but, at the same time, may reach different groups of respondents, given that the overall characteristics of both groups considerably differ. Therefore, the tradeoff between using mixed-mode survey as a way to increase response rate and obtaining undesirable measurement changes may be attentively considered in future survey studies.
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Bmc Med Res Methodol · Dec 2019
Randomized Controlled Trial Pragmatic Clinical TrialStrategies for recruitment in general practice settings: the iSOLVE fall prevention pragmatic cluster randomised controlled trial.
Falls are common among older people, and General Practitioners (GPs) could play an important role in implementing strategies to manage fall risk. Despite this, fall prevention is not a routine activity in general practice settings. The iSOLVE cluster randomised controlled trial aimed to evaluate implementation of a fall prevention decision tool in general practice. This paper sought to describe the strategies used and reflect on the enablers and barriers relevant to successful recruitment of general practices, GPs and their patients. ⋯ Recruitment in general practice settings can be successfully achieved through multiple recruitment strategies, effective communication and rapport building, ensuring research topic and design suit general practice needs, and using familiar communication strategies to engage patients.
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Bmc Med Res Methodol · Nov 2019
ReviewEuropean Medicines Agency Policy 0070: an exploratory review of data utility in clinical study reports for academic research.
Clinical study reports (CSRs) have been increasingly utilised within academic research in recent years. European Medicines Agency (EMA) Policy 0070 'Phase 1,' which came into effect in January 2015, requires the publication of regulatory documents such as CSRs from central applications in an anonymised format. EMA Policy 0070 requires sponsors to demonstrate careful consideration of data utility within anonymised CSRs published within the scope of the policy, yet the concept of data utility is not clearly defined in the associated anonymisation guidance. ⋯ This work provides an initial insight into the previous use of CSR data and current practices for including regulatory data in academic research. This work also provides early guidance to qualitatively assess and document data utility within anonymised CSRs published under EMA Policy 0070.