A C++ program to calculate sample sizes for cost-effectiveness trials in a Bayesian framework

Analysis of methods commonly used in biomedicine for treatment versus control comparison of very small samples

BACKGROUND AND OBJECTIVE

A rough estimate indicated that use of samples of size not larger than ten is not uncommon in biomedical research and that many of such studies are limited to strong effects due to sample sizes smaller than six. For data collected from biomedical experiments it is also often unknown if mathematical requirements incorporated in the sample comparison methods are satisfied.

METHODS

Computer simulated experiments were used to examine performance of methods for qualitative sample comparison and its dependence on the effectiveness of exposure, effect intensity, distribution of studied parameter values in the population, and sample size. The Type I and Type II errors, their average, as well as the maximal errors were considered.

RESULTS

The sample size 9 and the t-test method with p = 5% ensured error smaller than 5% even for weak effects. For sample sizes 6-8 the same method enabled detection of weak effects with errors smaller than 20%. If the sample sizes were 3-5, weak effects could not be detected with an acceptable error; however, the smallest maximal error in the most general case that includes weak effects is granted by the standard error of the mean method. The increase of sample size from 5 to 9 led to seven times more accurate detection of weak effects. Strong effects were detected regardless of the sample size and method used.

CONCLUSIONS

The minimal recommended sample size for biomedical experiments is 9. Use of smaller sizes and the method of their comparison should be justified by the objective of the experiment.

Cost-Effectiveness and Cost-Utility Analysis of the Treatment of Emotional Disorders in Primary Care: PsicAP Clinical Trial. Description of the Sub-study Design

Introduction: In the primary care (PC) setting in Spain, the prevalence of emotional disorders (EDs) such as anxiety, depression and somatoform disorder is high. In PC patients, these disorders are not always managed in accordance with the recommendations provided by clinical practice guidelines, resulting in major direct and indirect economic costs and suboptimal treatment outcomes. The aim is to analyze and compare the cost-effectiveness and cost-utility of group-based psychological therapy versus treatment as usual (TAU).

Methods: Multicenter, randomized controlled trial involving 300 patients recruited from PC centers in Madrid, Spain, with symptoms or possible diagnosis of anxiety, mood (mild or moderate), or somatoform disorders. Patients will be randomized to one of two groups: an experimental group, which will receive group-based transdiagnostic cognitive-behavioral therapy (TD-CBT); and a control group, which will receive TAU (mainly pharmacological interventions) prescribed by their general practitioner (GP). Clinical assessment will be performed with the Patient Health Questionnaire (PHQ). Direct and indirect costs will be calculated and relevant socio-demographic variables will be registered. The Spanish version of the EuroQol 5D-5L will be administered. Patients will be assessed at baseline, immediately after treatment finalization, and at 6 and 12 months post-treatment.

Discussion: To our knowledge, this is the first study to compare TD-CBT to TAU in the PC setting in Spain. This is the first comparative economic evaluation of these two treatment approaches in PC. The strength of the study is that it is a multicenter, randomized, controlled trial of psychotherapy and TAU for EDs in PC.

Trial registration: Protocol code: ISCRCTN58437086; 20/05/2013.

                                       EUDRACT: 2013-001955-11.

                                       Protocol Version: 6, 11/01/2014.

Bayesian sample size determination for cost-effectiveness studies with censored data

Cost-effectiveness models are commonly utilized to determine the combined clinical and economic impact of one treatment compared to another. However, most methods for sample size determination of cost-effectiveness studies assume fully observed costs and effectiveness outcomes, which presents challenges for survival-based studies in which censoring exists. We propose a Bayesian method for the design and analysis of cost-effectiveness data in which costs and effectiveness may be censored, and the sample size is approximated for both power and assurance. We explore two parametric models and demonstrate the flexibility of the approach to accommodate a variety of modifications to study assumptions.

Methodological developments in randomized controlled trial-based economic evaluations

Economic evaluation is a key contributor to decision making in health care, and it is important that it is carried out as effectively and reliably as possible. Studies carried out alongside randomised controlled trials are required to contribute real-world evidence to the decision-making process. However, the requirement that resource use be measured as well as effectiveness data within a trial results in additional complexity for trialists, and there are a number of methodological areas in which improvement is needed. This article reviews the literature in methodological work carried out to inform economic evaluation studies conducted alongside randomised controlled trials. Recent advances in areas including overall trial design, measuring resource use, measuring outcomes and reporting economic evaluations are discussed.