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An important issue for many economic experiments is how the experimenter can ensure sufficient power in order to reject one or more hypotheses. The paper illustrates how methods for testing multiple hypotheses simultaneously in adaptive, two-stage designs can be used to improve the power of economic experiments. We provide a concise overview of the relevant theory and illustrate the method in three different applications. These include a simulation study of a hypothetical experimental design, as well as illustrations using two data sets from previous experiments. The simulation results highlight the potential for sample size reductions, maintaining the power to reject at least one hypothesis while ensuring strong control of the overall Type I error probability.

Background In out-of-hours urgent care practices in Germany, physicians of different specialties care for a large number of patients, most of all unknown to them, resulting in a high workload and challenging diagnostic decision-making. As there is no common patient file, physicians have no information about patients’ previous conditions or received treatments. In this setting, a digital tool for medical history taking could improve the quality of medical care. This study aims to implement and evaluate a software application (app) that takes a structured symptom-oriented medical history from patients in urgent care settings. Methods We conduct a time-cluster-randomized trial in two out-of-hours urgent care practices in Germany for 12 consecutive months. Each week during the study defines a cluster. We will compare participants with (intervention group) and without app use (control group) prior to consultation and provision of the self-reported information for the physician. We expect the app to improve diagnostic accuracy (primary outcome), reduce physicians’ perceived diagnostic uncertainty, and increase patients’ satisfaction and the satisfaction with communication of both physician and patient (secondary outcomes). Discussion While similar tools have only been subject to small-scale pilot studies surveying feasibility and usability, the present study uses a rigorous study design to measure outcomes that are directly associated with the quality of delivered care. Trial registration The study was registered at the German Clinical Trials Register (No. DRKS00026659 registered Nov 03 2021. World Health Organization Trial Registration Data Set, https://trialsearch.who.int/Trial2.aspx? TrialID = DRKS00026659.

Model-based recursive partitioning (MOB) can be used to identify subgroups with differing treatment effects. The detection rate of treatment-by-covariate interactions and the accuracy of identified subgroups using MOB depend strongly on the sample size. Using data from multiple randomized controlled clinical trials can overcome the problem of too small samples. However, naively pooling data from multiple trials may result in the identification of spurious subgroups as differences in study design, subject selection and other sources of between-trial heterogeneity are ignored. In order to account for between-trial heterogeneity in individual participant data (IPD) meta-analysis random-effect models are frequently used. Commonly, heterogeneity in the treatment effect is modelled using random effects whereas heterogeneity in the baseline risks is modelled by either fixed effects or random effects. In this article, we propose metaMOB, a procedure using the generalized mixed-effects model tree (GLMM tree) algorithm for subgroup identification in IPD meta-analysis. Although the application of metaMOB is potentially wider, e.g. randomized experiments with participants in social sciences or preclinical experiments in life sciences, we focus on randomized controlled clinical trials. In a simulation study, metaMOB outperformed GLMM trees assuming a random intercept only and model-based recursive partitioning (MOB), whose algorithm is the basis for GLMM trees, with respect to the false discovery rates, accuracy of identified subgroups and accuracy of estimated treatment effect. The most robust and therefore most promising method is metaMOB with fixed effects for modelling the between-trial heterogeneity in the baseline risks.

Background To improve interprofessional collaboration between registered nurses (RNs) and general practitioners (GPs) for nursing home residents (NHRs), the interprof ACT intervention package was developed. This complex intervention includes six components (e.g., shared goal setting, standardized procedures for GPs’ nursing home visits) that can be locally adapted. The cluster-randomized interprof ACT trial evaluates the effects of this intervention on the cumulative incidence of hospital admissions (primary outcome) and secondary outcomes (e.g., length of hospital stays, utilization of emergency care services, and quality of life) within 12 months. It also includes a process evaluation which is subject of this protocol. The objectives of this evaluation are to assess the implementation of the interprof ACT intervention package and downstream effects on nurse–physician collaboration as well as preconditions and prospects for successive implementation into routine care. Methods This study uses a mixed methods triangulation design involving all 34 participating nursing homes (clusters). The quantitative part comprises paper-based surveys among RNs, GPs, NHRs, and nursing home directors at baseline and 12 months. In the intervention group (17 clusters), data on the implementation of preplanned implementation strategies (training and supervision of nominated IPAVs, interprofessional kick-off meetings) and local implementation activities will be recorded. Major outcome domains are the dose, reach and fidelity of the implementation of the intervention package, changes in interprofessional collaboration, and contextual factors. The qualitative part will be conducted in a subsample of 8 nursing homes (4 per study group) and includes repeated non-participating observations and semistructured interviews on the interaction between involved health professionals and their work processes. Quantitative and qualitative data will be descriptively analyzed and then triangulated by means of joint displays and mixed methods informed regression models. Discussion By integrating a variety of qualitative and quantitative data sources, this process evaluation will allow comprehensive assessment of the implementation of the interprof ACT intervention package, the changes induced in interprofessional collaboration, and the influence of contextual factors. These data will reveal expected and unexpected changes in the procedures of interprofessional care delivery and thus facilitate accurate conclusions for the further design of routine care services for NHRs. Trial registration ClinicalTrials.gov NCT03426475. Registered on 07/02/2018.

In 2001, the German Multiple Sclerosis Society, facing lack of data, founded the German MS Registry (GMSR) as a long-term data repository for MS healthcare research. By the establishment of a network of participating neurological centres of different healthcare sectors across Germany, GMSR provides observational real-world data on long-term disease progression, sociodemographic factors, treatment and the healthcare status of people with MS. This paper aims to illustrate the framework of the GMSR. Structure, design and data quality processes as well as collaborations of the GMSR are presented. The registry’s dataset, status and results are discussed. As of 08 January 2021, 187 centres from different healthcare sectors participate in the GMSR. Following its infrastructure and dataset specification upgrades in 2014, more than 196,000 visits have been recorded relating to more than 33,000 persons with MS (PwMS). The GMSR enables monitoring of PwMS in Germany, supports scientific research projects, and collaborates with national and international MS data repositories and initiatives. With its recent pharmacovigilance extension, it aligns with EMA recommendations and helps to ensure early detection of therapy-related safety signals.

BACKGROUND: An important goal of chronic obstructive pulmonary disease (COPD) treatment is to reduce the frequency of exacerbations. Some observations suggest a decline in exacerbation rates in clinical trials over time. A more systematic understanding would help to improve the design and interpretation of COPD trials. METHODS: We performed a systematic review and meta-regression of the placebo groups in published randomized controlled trials reporting exacerbations as an outcome. A Bayesian negative binomial model was developed to accommodate results that are reported in different formats; results are reported with credible intervals (CI) and posterior tail probabilities ($p_B$). RESULTS: Of 1114 studies identified by our search, 55 were ultimately included. Exacerbation rates decreased by 6.7% (95% CI (4.4, 9.0); $p_B$ < 0.001) per year, or 50% (95% CI (36, 61)) per decade. Adjusting for available study and baseline characteristics such as forced expiratory volume in 1 s (FEV1) did not alter the observed trend considerably. Two subsets of studies, one using a true placebo group and the other allowing inhaled corticosteroids in the "placebo" group, also yielded consistent results. CONCLUSIONS: In conclusion, this meta-regression indicates that the rate of COPD exacerbations decreased over the past two decades to a clinically relevant extent independent of important prognostic factors. This suggests that care is needed in the design of new trials or when comparing results from older trials with more recent ones. Also a considerable effect of adjunct therapy on COPD exacerbations can be assumed., 11 pages, 4 figures

Many studies have reported satisfactory clinical outcomes and low redislocation rates after reconstruction of the medial patellofemoral ligament (MPFL) for the treatment of lateral patellar instability. Despite uncorrected severe trochlear dysplasia (Dejour type B to D) being acknowledged as a major reason for less favourable clinical outcomes and a higher incidence of patellar redislocations after an isolated MPFL reconstruction, the evidence for a deepening trochleoplasty procedure remains scarce in the current literature. The hypothesis of this systematic review and meta-analysis was that a deepening trochleoplasty in combination with an a la carte extensor apparatus balancing procedure provides lower redislocation rates and superior clinical outcomes than isolated MPFL reconstruction in patients with lateral patellar instability caused by severe trochlear dysplasia. A systematic review of the literature was conducted using specific inclusion and exclusion criteria for clinical studies reporting index operations (trochleoplasty and MPFL reconstruction) for the treatment of patellar instability caused by severe trochlear dysplasia. The Kujala score was analysed as the primary clinical outcome parameter in a random effects meta-analysis. Ten uncontrolled studies with a total of 407 knees (374 patients) were included in this analysis. The MPFL group comprised 4 studies with a total of 221 knees (210 patients), and the trochleoplasty group comprised 6 studies with a total of 186 knees (164 patients). The mean preoperative Kujala score ranged between 50.4 and 70.5 in the MPFL group and between 44.8 and 75.1 in the trochleoplasty group. The pooled Kujala score increased significantly by 26.4 (95% CI 21.4, 31.3; P

When competing strategies for development programs, clinical trial designs, or data analysis methods exist, the alternatives need to be evaluated in a systematic way to facilitate informed decision making. Here we describe a refinement of the recently proposed clinical scenario evaluation framework for the assessment of competing strategies. The refinement is achieved by subdividing key elements previously proposed into new categories, distinguishing between quantities that can be estimated from preexisting data and those that cannot and between aspects under the control of the decision maker from those that are determined by external constraints. The refined framework is illustrated by an application to a design project for an adaptive seamless design for a clinical trial in progressive multiple sclerosis.

When designing a clinical trial, a number of design features including the sample size have to be decided upon. The sample size calculation usually requires some discussion of relevant effect sizes and information about nuisance parameters such as standard deviations or overall event rates, with nuisance parameters being estimated from previous studies. Using a novel endpoint or moving into a new indication, no or only very limited information might be available and the sample size calculation is therefore subject to considerable uncertainty. Internal pilot study designs that allow sample size reestimation midcourse of the ongoing study have been proposed to make trials more robust to misspecifications of nuisance parameters in the planning phase. In this article we present the design of a recently completed randomized active controlled trial in dermatology as a case study. Furthermore, we demonstrate how type I error rate control can be achieved when testing for noninferiority and explore operating characteristics such as power and sample size distributions through simulations motivated by the case study. Finally, relevant regulatory guidelines on sample size reestimation are referred to.

Modern drug development requires an efficient clinical development program to have a reasonable chance of successfully leading to the submission of the therapy, given that the therapy is effective, or to early stopping if this is not the case. Clinical trials and programs should be designed to effectively support this final goal. Currently, the statistical planning in drug development is based on parts of a clinical program in isolation, conditioned on one fixed setting, focusing on sample size calculation or simple design questions. There is, however, an increasing demand for a clinical program optimization and acceleration as well as an unconditional evaluation of relative program efficiency, robustness, and validity. The complexity of the development process, however, often does not allow for simple solutions, frequently requiring computer simulations to support these assessments. We propose a general framework for comparing competing options for clinical programs, trial designs, and analysis methods as a basis for decision making and to facilitate the internal and external dialogue with key stakeholders. The final decision making ultimately needs to factor in quantitative aspects as well as additional qualitative dimensions such as logistic feasibility, regulatory acceptance, and so on. A terminology is introduce that clearly describes the different aspects of such a framework, the range of underlying assumptions, the competing options, and the metrics that are used to assess and compare these options. Three specific case studies are presented that illustrate these concepts at three different levels: program planning, trial design, and analysis methods.

An economic analysis of telemedicine support to improve glycemic control in patients with type 2 diabetes mellitus, illustrating the use of an analytic framework that efficiently combines telemedicine program findings with published estimates of treatment cost effectiveness. The Pro-Active Call Centre Treatment Support (PACCTS) trial compared tailored, protocol-driven call-center support with usual care as methods to manage glycemic control in 591 patients with diabetes in Salford, UK. Economic analysis of the trial describes the cost of delivering telemedicine support and level of improved glycemic control achieved in patients. These findings are linked to current best evidence for the long-term cost effectiveness of treatment to help inform whether the provision of call-center support to improve glycemic control should become routine health policy. Under trial conditions, the cost effectiveness of the PACCTS intervention was estimated to be £43 400/ quality-adjusted life-year (QALY) [2003 costings]. Under routine-use call-center conditions (a full caseload of patients with moderate to poor glycemic control) cost effectiveness was estimated to be lower at £33 700/QALY. Set against a threshold of £30 000/QALY, Monte Carlo simulation suggests the probability of PACCTS being cost-effective in routine use is 29%. Despite being received well by patients and healthcare professionals alike, telemedicine support solely to achieve improved glycemic control in patients with type 2 diabetes was found to be borderline cost effective. Major uncertainties that could change this result include the underlying cost effectiveness of improved glycemic control, which is currently imprecisely known. Research is ongoing in patients with type 2 diabetes to extend call-center support to improve blood pressure and lipid management, where if similar improvements are obtained, the call center should prove highly cost effective. The novel analytic approach illustrated provides a clear framework for thinking about the design and analysis of behavioral change policies for healthcare.

To overcome the problem of uncertainty in the planning phase of clinical trials, study designs have been proposed that allow for mid-course reestimation of the sample size. Using a study in asthma patients, we illustrate how blind sample size adjustment can be performed in an equivalence trial. Thereby, we demonstrate how the type I error rate can be controlled. Further, we show that the well-known expectation-maximization algorithm-based procedure by Gould and Shih is inappropriate for the purpose of sample size reestimation.

For fixed sample size designs, there is a risk that expected trial outcomes may not obtain adequate power because there is usually some uncertainty about the variance of the outcome variable in the planning phase. This deficit can be remedied by reestimating the variance during the ongoing trial and modifying the initially planned sample size if necessary. From a regulatory view, any adjustment to sample size should occur without unblinding the treatment group membership of the data. Corresponding methods proposed until now are restricted to studies comparing two treatment groups. We present sample size recalculation procedures for multiarmed trials and compare their performance by Monte Carlo simulations.