[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.

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.