Conjoint analysis is a stated-preference survey method that can be used to elicit responses that reveal preferences, priorities, and the relative importance of individual features associated with health care interventions or services. Conjoint analysis methods, particularly discrete choice experiments (DCEs), have been increasingly used to quantify preferences of patients, caregivers, physicians, and other stakeholders. Recent consensus-based guidance on good research practices, including two recent task force reports from the International Society for Pharmacoeconomics and Outcomes Research, has aided in improving the quality of conjoint analyses and DCEs in outcomes research. Nevertheless, uncertainty regarding good research practices for the statistical analysis of data from DCEs persists. There are multiple methods for analyzing DCE data. Understanding the characteristics and appropriate use of different analysis methods is critical to conducting a well-designed DCE study. This report will assist researchers in evaluating and selecting among alternative approaches to conducting statistical analysis of DCE data. We first present a simplistic DCE example and a simple method for using the resulting data. We then present a pedagogical example of a DCE and one of the most common approaches to analyzing data from such a question format-conditional logit. We then describe some common alternative methods for analyzing these data and the strengths and weaknesses of each alternative. We present the ESTIMATE checklist, which includes a list of questions to consider when justifying the choice of analysis method, describing the analysis, and interpreting the results.

The use of the Gibbs sampler with fully conditionally specified models, where the distribution of each variable given the other variables is the starting point, has become a popular method to create imputations in incomplete multivariate data. The theoretical weakness of this approach is that the specified conditional densities can be incompatible, and therefore the stationary distribution to which the Gibbs sampler attempts to converge may not exist. This study investigates practical consequences of this problem by means of simulation. Missing data are created under four different missing data mechanisms. Attention is given to the statistical behavior under compatible and incompatible models. The results indicate that multiple imputation produces essentially unbiased estimates with appropriate coverage in the simple cases investigated, even for the incompatible models. Of particular interest is that these results were produced using only five Gibbs iterations starting from a simple draw from observed marginal distributions. It thus appears that, despite the theoretical weaknesses, the actual performance of conditional model specification for multivariate imputation can be quite good, and therefore deserves further study.

Abstract-A limited number of clinical studies have examined the effect of poststroke rehabilitation with robotic devices on hemiparetic arm function. We systematically reviewed the literature to assess the effect of robot-aided therapy on stroke patients' upper-limb motor control and functional abilities. Eight clinical trials were identified and reviewed. For four of these studies, we also pooled short-term mean changes in FuglMeyer scores before and after robot-aided therapy. We found that robot-aided therapy of the proximal upper limb improves short-and long-term motor control of the paretic shoulder and elbow in subacute and chronic patients; however, we found no consistent influence on functional abilities. In addition, robotaided therapy appears to improve motor control more than conventional therapy.

Time-varying covariance occurs when a covariate changes over time during the follow-up period. Such variable can be analyzed with the Cox regression model to estimate its effect on survival time. For this it is essential to organize the data in a counting process style. In situations when the proportional hazards assumption of the Cox regression model does not hold, we say that the effect of the covariate is time-varying. The proportional hazards assumption can be tested by examining the residuals of the model. The rejection of the null hypothesis induces the use of time varying coefficient to describe the data. The time varying coefficient can be described with a step function or a parametric time function. This article aims to illustrate how to carry out statistical analyses in the presence of time-varying covariates or coefficients with R.

BackgroundPatient-centered design that addresses patients’ preferences and needs is considered an important aim for improving health care systems. At present, within the field of pain rehabilitation, patients’ preferences regarding telerehabilitation remain scarcely explored and little is known about the optimal combination between human and electronic contact from the patients’ perspective. In addition, limited evidence is available about the best way to explore patients’ preferences. Therefore, the assessment of patients’ preferences regarding telemedicine is an important step toward the design of effective patient-centered care.ObjectiveTo identify which telerehabilitation treatment options patients with chronic pain are most likely to accept as alternatives to conventional rehabilitation and assess which treatment attributes are most important to them.MethodsA discrete choice experiment with 15 choice tasks, combining 6 telerehabilitation treatment characteristics, was designed. Each choice task consisted of 2 hypothetical treatment scenarios and 1 opt-out scenario. Relative attribute importance was estimated using a bivariate probit regression analysis. One hundred and thirty surveys were received, of which 104 were usable questionnaires; thus, resulting in a total of 1547 observations.ResultsPhysician communication mode, the use of feedback and monitoring technology (FMT), and exercise location were key drivers of patients’ treatment preferences (P<.001). Patients were willing to accept less frequent physician consultation offered mainly through video communication, provided that they were offered FMT and some face-to-face consultation and could exercise outside their home environment at flexible exercise hours. Home-based telerehabilitation scenarios with minimal physician supervision were the least preferred. A reduction in health care premiums would make these telerehabilitation scenarios as attractive as conventional clinic-based rehabilitation.Conclusions“Intermediate” telerehabilitation treatments offering FMT, some face-to-face consulting, and a gym-based exercise location should be pursued as promising alternatives to conventional chronic pain rehabilitation. Further research is necessary to explore whether strategies other than health care premium reductions could also increase the value of home telerehabilitation treatment.