Effect of Mexiletine on Muscle Stiffness in Patients With Nondystrophic Myotonia Evaluated Using Aggregated N-of-1 Trials.

Importance: In rare diseases it is difficult to achieve high-quality evidence of treatment efficacy because of small cohorts and clinical heterogeneity. With emerging treatments for rare diseases, innovative trial designs are needed. Objective: To investigate the effectiveness of mexiletine in nondystrophic myotonia using an aggregated N-of-1 trials design and compare results between this innovative design and a previously conducted RCT. Design, Setting, and Participants: A series of aggregated, double-blind, randomized, placebo-controlled N-of-1-trials, performed in a single academic referral center. Thirty Dutch adult patients with genetically confirmed nondystrophic myotonia (38 patients screened) were enrolled between February 2014 and June 2015. Follow-up was completed in September 2016. Interventions: Mexiletine (600 mg daily) vs placebo during multiple treatment periods of 4 weeks. Main Outcomes and Measures: Reduction in daily-reported muscle stiffness on a scale of 1 to 9, with higher scores indicating more impairment. A Bayesian hierarchical model aggregated individual N-of-1 trial data to determine the posterior probability of reaching a clinically meaningful effect of a greater than 0.75-point difference. Results: Among 30 enrolled patients (mean age, 43.4 [SD, 15.24] years; 22% men; 19 CLCN1 and 11 SCN4A genotype), 27 completed the study and 3 dropped out (1 because of a serious adverse event). In 24 of the 27 completers, a clinically meaningful treatment effect was found. In the Bayesian hierarchical model, mexiletine resulted in a 100% posterior probability of reaching a clinically meaningful reduction in self-reported muscle stiffness for the nondystrophic myotonia group overall and the CLCN1 genotype subgroup and 93% posterior probability for the SCN4A genotype subgroup. In the total nondystrophic myotonia group, the median muscle stiffness score was 6.08 (interquartile range, 4.71-6.80) at baseline and was 2.50 (95% credible interval [CrI], 1.77-3.24) during the mexiletine period and 5.56 (95% CrI, 4.73-6.39) during the placebo period; difference in symptom score reduction, 3.06 (95% CrI, 1.96-4.15; n = 27) favoring mexiletine. The most common adverse event was gastrointestinal discomfort (21 mexiletine [70%], 1 placebo [3%]). One serious adverse event occurred (1 mexiletine [3%]; allergic skin reaction). Using frequentist reanalysis, mexiletine compared with placebo resulted in a mean reduction in daily-reported muscle stiffness of 3.12 (95% CI, 2.46-3.78), consistent with the previous RCT treatment effect of 2.69 (95% CI, 2.12-3.26). Conclusions and Relevance: In a series of N-of-1 trials of mexiletine vs placebo in patients with nondystrophic myotonia, there was a reduction in mean daily-reported muscle stiffness that was consistent with the treatment effect in a previous randomized clinical trial. These findings support the efficacy of mexiletine for treatment of nondystrophic myotonia as well as the feasibility of N-of-1 trials for assessing interventions in some chronic rare diseases. Trial Registration: ClinicalTrials.gov Identifier: NCT02045667.

Combined N-of-1 trials to investigate mexiletine in non-dystrophic myotonia using a Bayesian approach; study rationale and protocol.

BACKGROUND: To obtain evidence for the clinical and cost-effectiveness of treatments for patients with rare diseases is a challenge. Non-dystrophic myotonia (NDM) is a group of inherited, rare muscle diseases characterized by muscle stiffness. The reimbursement of mexiletine, the expert opinion drug for NDM, has been discontinued in some countries due to a lack of independent randomized controlled trials (RCTs). It remains unclear however, which concessions can be accepted towards the level 1 evidence needed for coverage decisions, in rare diseases. Considering the large number of rare diseases with a lack of treatment evidence, more experience with innovative trial designs is needed. Both NDM and mexiletine are well suited for an N-of-1 trial design. A Bayesian approach allows for the combination of N-of-1 trials, which enables the assessment of outcomes on the patient and group level simultaneously. METHODS/DESIGN: We will combine 30 individual, double-blind, randomized, placebo-controlled N-of-1 trials of mexiletine (600 mg daily) vs. placebo in genetically confirmed NDM patients using hierarchical Bayesian modeling. Our results will be compared and combined with the main results of an international cross-over RCT (mexiletine vs. placebo in NDM) published in 2012 that will be used as an informative prior. Similar criteria of eligibility, treatment regimen, end-points and measurement instruments are employed as used in the international cross-over RCT. DISCUSSION: The treatment of patients with NDM with mexiletine offers a unique opportunity to compare outcomes and efficiency of novel N-of-1 trial-based designs and conventional approaches in producing evidence of clinical and cost-effectiveness of treatments for patients with rare diseases. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02045667.

Synthesis of evidence for reimbursement decisions: a Bayesian reanalysis.

OBJECTIVES: The aim of this study was to compare Bayesian methods with the standard methods that are used for evidence-based policy making. METHODS: We performed a Bayesian reanalysis of the data underlying a reimbursement advice by the Dutch National Health Insurance Board (CVZ) regarding the anti-diabetic drug exenatide (an alternative to insulin). We synthesized evidence from various sources that was available when the CVZ advice was drafted: expert opinion (as elicited from internists), experimental data (from direct comparison studies), and observational data. Subsequently, the original frequentist results and the results from the Bayesian reanalysis were compared in terms of outcomes and interpretations. These results were presented in a meeting with staff from CVZ, whose opinions about the usefulness of a Bayesian approach were assessed using a questionnaire. RESULTS: The Bayesian approach yields outcomes that summarize different pieces of evidence, which would have been difficult to obtain otherwise. Moreover, there are conceptual differences, and the Bayesian approach allows for determining probabilities of clinically relevant differences. The staff at CVZ were fairly positive with respect to the use of Bayesian methods, although practical barriers were also seen as important. CONCLUSIONS: The Bayesian outcomes are different and could be more suited to the informational needs of policy makers. The response from staff at CVZ provides some support for this statement, but more research at the interface of science and policy is needed.

[Bayesian statistics: what, how and why?]. / Bayesiaanse statistiek: wat, hoe en waarom?

Bayesian statistics is an alternative form of statistics that provides a way to systematically integrate new information with existing information. Bayesian methods are very suitable for evidence synthesis. Bayesian outcomes are easier to interpret than standard statistical outcomes. For instance, Bayesian methods allow for determining the probability that a difference in effect between two treatments will be clinically relevant. The use of Bayesian methods is becoming more prevalent.

Study protocol of a randomized controlled trial comparing Mindfulness-Based Stress Reduction with treatment as usual in reducing psychological distress in patients with lung cancer and their partners: the MILON study.

BACKGROUND: Lung cancer is the leading cause of cancer death worldwide and characterized by a poor prognosis. It has a major impact on the psychological wellbeing of patients and their partners. Recently, it has been shown that Mindfulness-Based Stress Reduction (MBSR) is effective in reducing anxiety and depressive symptoms in cancer patients. The generalization of these results is limited since most participants were female patients with breast cancer. Moreover, only one study examined the effectiveness of MBSR in partners of cancer patients. Therefore, in the present trial we study the effectiveness of MBSR versus treatment as usual (TAU) in patients with lung cancer and their partners. METHODS/DESIGN: A parallel group, randomized controlled trial is conducted to compare MBSR with TAU. Lung cancer patients who have received or are still under treatment, and their partners are recruited. Assessments will take place at baseline, post intervention and at three-month follow-up. The primary outcome is psychological distress (i.e. anxiety and depressive symptoms). Secondary outcomes are quality of life (only for patients), caregiver appraisal (only for partners), relationship quality and spirituality. In addition, cost-effectiveness ratio (only in patients) and several process variables are assessed. DISCUSSION: This trial will provide information about the clinical and cost-effectiveness of MBSR compared to TAU in patients with lung cancer and their partners.

Cost-effectiveness on a local level: whether and when to adopt a new technology.

Cost-effectiveness analysis has become a widely accepted tool for decision making in health care. The standard textbook cost-effectiveness analysis focuses on whether to make the switch from an old or common practice technology to an innovative technology, and in doing so, it takes a global perspective. In this article, we are interested in a local perspective, and we look at the questions of whether and when the switch from old to new should be made. A new approach to cost-effectiveness from a local (e.g., a hospital) perspective, by means of a mathematical model for cost-effectiveness that explicitly incorporates time, is proposed. A decision rule is derived for establishing whether a new technology should be adopted, as well as a general rule for establishing when it pays to postpone adoption by 1 more period, and a set of decision rules that can be used to determine the optimal timing of adoption. Finally, a simple example is presented to illustrate our model and how it leads to optimal decision making in a number of cases.

Evidence based policy decisions through a Bayesian approach: the case of a statin appraisal in The Netherlands.

It has often been suggested that Bayesian statistics is more congenial to the informational needs of policy makers than the standard frequentist methods. In order to illustrate this claim, we use both a Bayesian and a frequentist approach for revisiting a recommendation by the Dutch National Health Insurance Board that for all patients requiring lipid reduction, the cheapest alternative (Simvastatin) should be prescribed. We investigate whether Simvastatin and Atorvastatin, the most commonly used alternative, can be considered equivalent in terms of lipid control for patients with heterozygous familial hypercholesterolemia. Priors were elicited from GPs, cardiologists and internists. A systematic review for studies comparing Simvastatin and Atorvastatin was performed. The data from these studies were combined with the priors in a Bayesian meta-analysis. For comparability a frequentist meta-analysis was also performed. The two approaches lead to similar point estimates and 95% intervals. However, the Bayesian outcomes are easier to understand and interpret, and our Bayesian analysis leads to additional outcomes that would have more direct pertinence for policy makers, and which could help them to assess what the data have to say about the questions that are most relevant to the problems they face.

Estimating the effectiveness of HPV vaccination in the open population: a Bayesian approach.

OBJECTIVES: Estimation of the effectiveness of human papillomavirus (HPV) vaccination in the open population on the basis of published data from various sources. METHODS: A Bayesian approach was used to reanalyze the data underlying a guidance by the Dutch National Health Insurance Board about the quadrivalent HPV vaccine Gardasil. Several studies document the vaccine's effectiveness in preventing cases in different subpopulations. None of these (sub)populations, however, is representative of the actual target population that the vaccination program will be applied to. We used a Bayesian approach for restructuring the data by means of reweighting the subpopulations by using HPV prevalence data, to estimate the effectiveness that can be expected in the actual target population. RESULTS: The original data show an effectiveness of 44% in the entire population and an effectiveness of 98% for women who were compliant and were HPV-free at the start of the study. In the study population, the HPV prevalence was below 4%. In the relevant target population, however, the actual prevalence could be very different. In fact, some publications find an HPV prevalence of around 10%. We used Bayesian techniques to estimate the effectiveness in the actual target population. We found a mean effectiveness of 25%, and the probability that the effectiveness in the target population exceeds 50% is virtually zero. The results are very sensitive to the HPV prevalence that is used. CONCLUSIONS: A supplementary analysis can put together the bits and pieces of information to arrive at more relevant answers. A Bayesian approach allows for integrating all the evidence into one model in a straightforward way and results in very intuitive probability statements.

Stepped wedge designs could reduce the required sample size in cluster randomized trials.

OBJECTIVE: The stepped wedge design is increasingly being used in cluster randomized trials (CRTs). However, there is not much information available about the design and analysis strategies for these kinds of trials. Approaches to sample size and power calculations have been provided, but a simple sample size formula is lacking. Therefore, our aim is to provide a sample size formula for cluster randomized stepped wedge designs. STUDY DESIGN AND SETTING: We derived a design effect (sample size correction factor) that can be used to estimate the required sample size for stepped wedge designs. Furthermore, we compared the required sample size for the stepped wedge design with a parallel group and analysis of covariance (ANCOVA) design. RESULTS: Our formula corrects for clustering as well as for the design. Apart from the cluster size and intracluster correlation, the design effect depends on choices of the number of steps, the number of baseline measurements, and the number of measurements between steps. The stepped wedge design requires a substantial smaller sample size than a parallel group and ANCOVA design. CONCLUSION: For CRTs, the stepped wedge design is far more efficient than the parallel group and ANCOVA design in terms of sample size.

Quantifying short run cost-effectiveness during a gradual implementation process.

This paper examines the short run inefficiencies that arise during gradual implementation of a new cost-effective technology in healthcare. These inefficiencies arise when health gains associated with the new technology cannot be obtained immediately because the new technology does not yet supply all patients, and when there is overcapacity for the old technology in the short run because the supply of care is divided among two mutually exclusive technologies. Such efficiency losses are not taken into account in standard textbook cost-effectiveness analysis in which a steady state is presented where costs and effects are assumed to be unchanging over time. A model is constructed to quantify such short run inefficiencies as well as to inform the decision maker about the optimal implementation pattern for the new technology. The model operates by integrating the incremental net benefit equations for both the period of co-existence of mutually exclusive technologies and the period after complete substitution of the old technology. It takes into account the rate of implementation of the new technology, depreciation of capital of the old technology as well as the demand curves for both technologies. The model is applied to the real world case of converting from screen film to digital mammography in the Netherlands.

Time to incorporate time in cost-effectiveness analysis.

Cost-effectiveness analysis as a means to evaluate medical innovations has become well accepted in the UK and several other Western countries. An important assumption underlying this method is that costs and effects are constant over time. In reality, however, and especially in the short run, variations in costs and effects are likely to occur. These variations can lead to considerable deviations from the outcome of a conventional economic evaluation, which in turn may lead to serious implementation problems at a local level. Taking time into account explicitly in economic evaluations in health care may enhance their utility for both societal and local decision making, and may ultimately smooth the adoption of new and basically cost-effective health care technologies.

A model to correct for short-run inefficiencies in economic evaluations in healthcare.

Important assumptions that underlie cost-effectiveness analysis (CEA) are that production technologies are convex and that production processes always perform at constant returns to scale. However, in the short run these assumptions are likely to be violated. Therefore, CEAs might overestimate cost-effectiveness in the short run. To come up with a more precise cost-effectiveness outcome, we present a model that is able to correct the long-run incremental net benefit (INB) for short-run inefficiencies. This provides decision makers with a more realistic view of the expected efficiency gains. This model starts by determining the initial efficiency losses inflicted by inflexible resources. Then the model is made dynamic in order to adjust the efficiency losses by allowing for refilling and writing off freed capacity. Finally, the model calculates the length of the short-run time frame in which such efficiency losses exist, and a correction term with which the usual long-run INB should be adjusted to account for short-run inefficiencies. The model is applied to two cases: dialysis and digitizing a radiography department. The dialysis case shows moderate short-run efficiency losses while in the radiography case short-run efficiency losses are sufficiently large to cause a switch in cost-effectiveness from favorable to inefficient in the short run.