Estimating preferences for medical devices:does the number of profile in choice experiments matter?

Background: Most applications of choice-based conjoint analysis in health use choice tasks with two profiles, while marketing studies routinely use three or more. This study reports on a randomized trial comparing paired with triplet profile choice formats focused on hearing aids. Methods: Respondents with hearing loss were drawn from a nationally representative cohort, completed identical surveys, and were randomized to choice tasks with two or three profiles. The primary outcomes of differences in estimated preferences were explored using t-tests, likelihood ratio tests, and analyses of individual-level models estimated with ordinary least squares. Results: 500 respondents were recruited. 127 had no hearing loss, 28 had profound loss and 22 declined to participate and were not analyzed. Of the remaining 323 participants, 146 individuals were randomized to the pairs and 177 to triplets. Pairs and triplets produced identical rankings of attribute importance but homogeneity was rejected (P<0.0001). Pairs led to more variation, and were systematically biased toward the null because a third (32.2%) of respondents focused on only one attribute. This is in contrast to respondents in the triplet design who traded across all attributes. Discussion: The number of profiles in choice tasks affects the results of conjoint analysis studies. Here triplets are preferred to pairs as they avoid non-trading and allow for more accurate estimation of preferences models

Applying the AHP in Health Economic Evaluations of New Technology.

Much research in health care is devoted to health economical modelling. Even though the Analytic Hierarchy Process (AHP) is increasingly being applied in health care, its value to health economical modelling is still unrecognized. We explored the value of using AHP-derived results in a health economic model. We applied the AHP to provide input for a health economic evaluation of a new technology to diagnose breast cancer. No clinical data were available about the sensitivity and specificity of this technology. By means of the AHP, an expert panel estimated the sensitivity and specificity to be used in this model. Moreover, additional criteria including patient comfort and risks could be added to the health economic model. On the basis of the methodology suggested, the AHP proved to be feasible to support a comprehensive health economical evaluation of new technology, where clinical evidence is not yet available, or incomplet

Patient and Public Preferences for Treatment Attributes in Parkinson’s Disease

Background Patient and public preferences for therapeutic outcomes or medical technologies are often elicited, and discordance between the two is frequently reported. Objective Our main objective was to compare patient and public preferences for treatment attributes in Parkinson’s disease (PD). Methods A representative sample from Dutch PD patients and the general public were invited to complete a best–worst scaling case 2 experiment consisting of six health-related outcomes and one attribute describing the specific treatment (brain surgery, pump, oral medication). Data were analyzed using mixed logit models, and attribute impact was estimated and compared between populations (and population subgroups). Results Both the public (N = 276) and patient (N = 198) populations considered treatment modality the most important attribute, although patients assigned higher relative importance. Both groups assigned high disutility to pump infusion and brain surgery and preferred drug treatment. Most health outcomes were valued equally by patients and the public, with the exception of reducing dizziness (more important to the public) and improving slow movement (more important to patients). Discussion Although these data do not support definite conclusions on whether patients are less likely to undergo invasive treatments, the (predicted) choice probability of undergoing brain surgery or having pump infusion technology would be low based on the (un)desirability of the attribute levels. Patients with PD might have adapted to their condition and are not willing to undergo advanced treatments in order to receive health improvements. Both public and patient preferences entail information that is potentially relevant for decision makers, and patient preferences can inform decision makers about the likelihood of adaptation to a specific condition

Multidisciplinary Rehabilitation Treatment of Patients With Chronic Low Back Pain: A Prognostic Model for Its Outcome

Objectives: (1) To determine if treatment outcome in chronic low back pain can be predicted by a predefined multivariate prognostic model based on consistent predictors from the literature and (2) to explore the value of potentially prognostic factors further. Methods: Data were derived from a randomized controlled trial on the effect of a multidisciplinary rehabilitation program for chronic low back pain compared with usual care. The primary outcome measure was the Roland and Morris Disability Questionnaire and secondary outcomes were the Physical and Mental Component Summary Scales, derived from the Short Form Health Survey. Outcomes were expressed as the differences between baseline and follow-up (8 wk and 6 mo) values. A confirmatory and an exploratory model were defined. Baseline predictors included in the confirmatory model were pain intensity, work status, and Multidimensional Pain Inventory subgroup membership. The exploratory model included sick leave, compensation, depression, and fear-avoidance beliefs. Statistical analysis was performed using multiple linear regression analysis. Results: One hundred and sixty-three patients participated in the study. More pain was prognostic for more improvement in the rehabilitation group. No value was found for work status or the Multidimensional Pain Inventory subgroups. For the exploratory model, more depression and fear-avoidance beliefs predicted more improvement after rehabilitation. The explained variance ranged from 18.5% to 43.8% depending on the length of follow-up evaluation, the treatment group, and the outcome variable of interest. Discussion: The results of this study do not support the construction of a clinical prediction model. Future confirmative studies of homogeneous rehabilitation treatments and outcome measures are needed to shed more light on relevant prognostic factors

Multilevel Grouped Regression for Analyzing Self-reported Health in Relation to Environmental Factors: the Model and its Application

A method for modeling the relationship of polychotomous health ratings with predictors such as area characteristics, the distance to a source of environmental contamination, or exposure to environmental pollutants is presented. The model combines elements of grouped regression and multilevel analysis. The statistical model describes the entire response distribution as a function of the predictors so that any measure that summarizes this distribution can be calculated from the model. With the model, polychotomous health ratings can be used, and there is no need for a priori dichotomizing such variables which would lead to loss of information. It is described how, according to the model, various measures describing the response distribution are related to the exposure, and the confidence and tolerance intervals for these relationships are presented. Specific attention is given to the incorporation of random factors in the model. The application that here serves as an example, concerns annoyance from transportation noise. Exposure – response relationships obtained with the described method of modeling are presented for aircraft, road traffic, and railway noise