Combining Individual-Level Discrete Choice Experiment Estimates and Costs to Inform Health Care Management Decisions about Customized Care: The Case of Follow-Up Strategies after Breast Cancer Treatment

AbstractObjectiveCustomized care can be beneficial for patients when preferences for health care programs are heterogeneous. Yet, there is little guidance on how individual-specific preferences and cost data can be combined to inform health care decisions about customized care. Therefore, we propose a discrete choice experiment–based approach that illustrates how to analyze the cost-effectiveness of customized (and noncustomized) care programs to provide information for hospital managers.MethodsWe exploit the fact that choice models make it possible to determine whether preference heterogeneity exists and to obtain individual-specific parameter estimates. We present an approach of how to combine these individual-specific parameter estimates from a random parameter model (mixed logit model) with cost data to analyze the cost-effectiveness of customized care and demonstrate our method in the case of follow-up after breast cancer treatment.ResultsWe found that there is significant preference heterogeneity for all except two attributes of breast cancer treatment follow-up and that the fully customized care program leads to higher utility and lower costs than the current standardized program. Compared with the single alternative program, the fully customized care program has increased benefits and higher costs. Thus, it is necessary for health care decision makers to judge whether the use of resources for customized care is cost-effective.ConclusionsDecision makers should consider using the results obtained from our methodological approach when they consider implementing customized health care programs, because it may help to find ways to save costs and increase patient satisfaction

A Students’ Preferences-Based Approach to Select Methods for Detecting and Handling Free-Riding

Free-riding is a serious challenge in group projects. While there are various methods to reduce free-riding, marketing educators still face a difficult task when selecting an appropriate method for their course. In this study, we propose a students’ preferences-based approach that supports marketing educators with the selection of methods to detect and handle free-riding. To measure these preferences, students completed an online survey based on a choice task about two methods to detect free-riding and a ranking task about four methods to handle free-riding (n = 254). Their answers were analyzed using chi-squared tests, Borda scores, and rank-ordered logit models. The results show that (a) neither Dutch nor international students have a clear preference for one of the two detection methods (the reporting system vs. the process evaluation system), (b) grade discussion (a possible reduction of the free-rider’s grade based on a conversation with the course coordinator about each student’s contribution) is the most preferred method to handle free-riding, and (c) international students have a stronger preference for stricter handling methods. Marketing educators can apply the proposed approach, or use our specific findings, for designing methods to reduce free-riding in their courses

Exploring Outcomes to Consider in Economic Evaluations of Health Promotion Programs: What Broader Non-Health Outcomes Matter Most?

Background Attention is increasing on the consideration of broader non-health outcomes in economic evaluations. It is unknown which non-health outcomes are valued as most relevant in the context of health promotion. The present study fills this gap by investigating the relative importance of non-health outcomes in a health promotion context. Method We investigated the relative importance of ten non-health outcomes of health promotion programs not commonly captured in QALYs. Preferences were elicited from a sample of the Dutch general public (N = 549) by means of a ranking task. These preferences were analyzed using Borda scores and rank-ordered logit models. Results The relative order of preference (from most to least important) was: self-confidence, insights into own (un)healthy behavior, perceived life control, knowledge about a certain health problem, social support, relaxation, better educational achievements, increased labor participation and work productivity, social participation, and a reduction in criminal behavior. The weight given to a particular non-health outcome was affected by the demographic variables age, gender, income, and education. Furthermore, in an open question, respondents mentioned a number of other relevant non-health outcomes, which we classified into outcomes relevant for the individual, the direct social environment, and for society as a whole. Conclusion The study provides valuable insights in the non-health outcomes that are considered as most important by the Dutch general population. Ideally, researchers should include the most important non-health outcomes in economic evaluations of health promotio

Consumers’ evaluation of allocation policies for scarce health care services: Vested interest activation trumps spatial and temporal distance

The allocation of scarce health care service resources often requires trade-offs between individual and collective outcomes (e.g., when some individuals benefit more strongly from a given policy th

Justify your alpha

Benjamin et al. proposed changing the conventional “statistical significance” threshold (i.e.,the alpha level) from p ≤ .05 to p ≤ .005 for all novel claims with relatively low prior odds. They provided two arguments for why lowering the significance threshold would “immediately improve the reproducibility of scientific research.” First, a p-value near .05provides weak evidence for the alternative hypothesis. Second, under certain assumptions, an alpha of .05 leads to high false positive report probabilities (FPRP2 ; the probability that a significant finding is a false positive

Justify your alpha

In response to recommendations to redefine statistical significance to p ≤ .005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level

Effectiveness of a national quality improvement programme to improve survival after emergency abdominal surgery (EPOCH): a stepped-wedge cluster-randomised trial

BACKGROUND: Emergency abdominal surgery is associated with poor patient outcomes. We studied the effectiveness of a national quality improvement (QI) programme to implement a care pathway to improve survival for these patients. METHODS: We did a stepped-wedge cluster-randomised trial of patients aged 40 years or older undergoing emergency open major abdominal surgery. Eligible UK National Health Service (NHS) hospitals (those that had an emergency general surgical service, a substantial volume of emergency abdominal surgery cases, and contributed data to the National Emergency Laparotomy Audit) were organised into 15 geographical clusters and commenced the QI programme in a random order, based on a computer-generated random sequence, over an 85-week period with one geographical cluster commencing the intervention every 5 weeks from the second to the 16th time period. Patients were masked to the study group, but it was not possible to mask hospital staff or investigators. The primary outcome measure was mortality within 90 days of surgery. Analyses were done on an intention-to-treat basis. This study is registered with the ISRCTN registry, number ISRCTN80682973. FINDINGS: Treatment took place between March 3, 2014, and Oct 19, 2015. 22 754 patients were assessed for elegibility. Of 15 873 eligible patients from 93 NHS hospitals, primary outcome data were analysed for 8482 patients in the usual care group and 7374 in the QI group. Eight patients in the usual care group and nine patients in the QI group were not included in the analysis because of missing primary outcome data. The primary outcome of 90-day mortality occurred in 1210 (16%) patients in the QI group compared with 1393 (16%) patients in the usual care group (HR 1·11, 0·96-1·28). INTERPRETATION: No survival benefit was observed from this QI programme to implement a care pathway for patients undergoing emergency abdominal surgery. Future QI programmes should ensure that teams have both the time and resources needed to improve patient care. FUNDING: National Institute for Health Research Health Services and Delivery Research Programme

Reducing free-riding in group projects in line with students’ preferences: Does it matter if there is more at stake?

Reducing free-riding is an important challenge for educators who use group projects. In this study, we measure students’ preferences for group project characteristics and investigate if characteristics that better help to reduce free-riding become more important for students when stakes increase. We used a discrete choice experiment based on 12 choice tasks in which students chose between two group projects that differed on five characteristics of which each level had its own effect on free-riding. A different group project grade weight was presented before each choice task to manipulate how much there was at stake for students in the group project. Data of 257 student respondents were used in the analysis. Based on random parameter logit model estimates we find that students prefer (in order of importance) assignment based on schedule availability and motivation or self-selection (instead of random assignment), the use of one or two peer process evaluations (instead of zero), a small team size of three or two students (instead of four), a common grade (instead of a divided grade), and a discussion with the course coordinator without a sanction as a method to handle free-riding (instead of member expulsion). Furthermore, we find that the characteristic team formation approach becomes even more important (especially self-selection) when student stakes increase. Educators can use our findings to design group projects that better help to reduce free-riding by (1) avoiding random assignment as team formation approach, (2) using (one or two) peer process evaluations, and (3) creating small(er) teams