Ely Dahan’s research while affiliated with University of California, Los Angeles and other places

Background. Conjoint analysis is widely used in studies of consumer preference but has only recently been applied to measure patient utilities for health outcomes. We compared the reliability, feasibility, and internal and predictive validity of conjoint scaling methods against better established rating scale and time tradeoff methods for assessing prostate cancer utilities in men at risk for prostate cancer. Methods. In total, 194 men who were biopsy negative for prostate cancer were randomly assigned to complete 2 preference assessment modules, either conjoint analysis and a rating scale module or conjoint analysis and a time tradeoff module. Each participant’s most important attribute was identified and evaluated in relation to age group (age <65, age 65 and older), education (high school, some college, college graduate), race/ethnicity (white, black, Latino), and relationship status (in significant relationship v. not). The methods were also evaluated in terms of ease of use and satisfaction. Results. Rating scales were rated as easiest to use and respondents were more satisfied with rating scales and conjoint in comparison to time tradeoffs. Rating scales and conjoint measures demonstrated significantly higher internal validity compared to time tradeoff when evaluated through R ² of the fitted utility function. The 3 methods were similar in terms of predictive validity, but conjoint analysis outperformed the rating scale method when patients were presented with novel combinations of attribute levels (68% correct v. 43%, P = 0.003). Conclusions. Rating scales and conjoint analysis exercises offer greater ease of use and higher satisfaction when measuring patient preferences in men biopsied for prostate cancer in comparison to time tradeoff exercises. Conjoint analysis may be a more robust approach to preference measurement for men at risk for prostate cancer.

Background Shared decision making (SDM) has been advocated as an approach to medical decision making that can improve decisional quality. Decision aids are tools that facilitate SDM in the context of limited physician time; however, many decision aids do not incorporate preference measurement. Objectives We aim to understand whether adding preference measurement to a standard patient educational intervention improves decisional quality and is feasible in a busy clinical setting. Methods Men with incident localized prostate cancer (n = 122) were recruited from the Greater Los Angeles Veterans Affairs (VA) Medical Center urology clinic, Olive View UCLA Medical Center, and Harbor UCLA Medical Center from January 2011 to May 2015 and randomized to education with a brochure about prostate cancer treatment or software-based preference assessment in addition to the brochure. Men undergoing preference assessment received a report detailing the relative strength of their preferences for treatment outcomes used in review with their doctor. Participants completed instruments measuring decisional conflict, knowledge, SDM, and patient satisfaction with care before and/or after their cancer consultation. Results Baseline knowledge scores were low (mean 62%). The baseline mean total score on the Decisional Conflict Scale was 2.3 (±0.9), signifying moderate decisional conflict. Men undergoing preference assessment had a significantly larger decrease in decisional conflict total score (p = 0.023) and the Perceived Effective Decision Making subscale (p = 0.003) post consult compared with those receiving education only. Improvements in satisfaction with care, SDM, and knowledge were similar between groups. Conclusions Individual-level preference assessment is feasible in the clinic setting. Patients with prostate cancer who undergo preference assessment are more certain about their treatment decisions and report decreased levels of decisional conflict when making these decisions.

In discrete choice experiments, patients are presented with sets of health states described by various attributes and asked to make choices from among them. Discrete choice experiments allow health care researchers to study the preferences of individual patients by eliciting trade-offs between different aspects of health-related quality of life. However, many discrete choice experiments yield data with incomplete ranking information and sparsity due to the limited number of choice sets presented to each patient, making it challenging to estimate patient preferences. Moreover, methods to identify outliers in discrete choice data are lacking. We develop a Bayesian hierarchical random effects rank-ordered multinomial logit model for discrete choice data. Missing ranks are accounted for by marginalizing over all possible permutations of unranked alternatives to estimate individual patient preferences, which are modeled as a function of patient covariates. We provide a Bayesian version of relative attribute importance, and adapt the use of the conditional predictive ordinate to identify outlying choice sets and outlying individuals with unusual preferences compared to the population. The model is applied to data from a study using a discrete choice experiment to estimate individual patient preferences for health states related to prostate cancer treatment.

Background: Many guidelines advocate the use of shared decision making for men with newly diagnosed prostate cancer. Decision aids can facilitate the process of shared decision making. Implicit in this approach is the idea that physicians understand which elements of treatment matter to patients. Little formal work exists to guide physicians or developers of decision aids in identifying these attributes. We use a mixed-methods technique adapted from marketing science, the 'Voice of the Patient', to describe and identify treatment elements of value for men with localized prostate cancer. Methods: We conducted semi-structured interviews with 30 men treated for prostate cancer in the urology clinic of the West Los Angeles Veteran Affairs Medical Center. We used a qualitative analysis to generate themes in patient narratives, and a quantitative approach, agglomerative hierarchical clustering, to identify attributes of treatment that were most relevant to patients making decisions about prostate cancer. Results: We identified five 'traditional' prostate cancer treatment attributes: sexual dysfunction, bowel problems, urinary problems, lifespan, and others' opinions. We further identified two novel treatment attributes: a treatment's ability to validate a sense of proactivity and the need for an incision (separate from risks of surgery). Conclusions: Application of a successful marketing technique, the 'Voice of the Customer', in a clinical setting elicits non-obvious attributes that highlight unique patient decision-making concerns. Use of this method in the development of decision aids may result in more effective decision support.

A project funded by the Institute for the Study of Business Markets to develop an understanding of the current state of business-to-business marketing and a research agenda for the field identified a lack of understanding of how the marketing function can or should best contribute to firms’ innovation efforts as the top priority. A workshop of senior academics and research-oriented practitioners explored this topic further, identifying four specific themes: (1) improving customer needs understanding and customer involvement in developing new products, (2) innovating beyond the lab, (3) disseminating and implementing research findings in firms, and (4) marketing’s overall role in innovation. This article defines these themes, sketches the current status of knowledge about each theme, frames practitioners’ issues with them, and proposes research agendas for each theme to move the field forward. The goal is to encourage rigorously executed academic research that can also help firms innovate more successfully.

Purpose: We apply a newly developed preference measurement method, Adaptive Best-worst Conjoint (ABC), to improve questioning efficiency for patient treatment preferences. With this approach, utility functions with 7-10 parameters are estimated instantaneously at the individual level with as few as 12-15 tasks completed in 10 minutes. Method: Conjoint analysis respondents choose the best and worst of four treatment alternatives (attribute bundles). The method adaptively presents the next four treatment alternatives, and after 12-15 tasks a utility function is estimated and an individualized report is printed. This report summarizes the patient's treatment preferences and priorities, and serves to enhance the doctor-patient discussion of possible treatments. Adaptive Best-worst Conjoint with four options-at-a-time identifies five of the six possible paired comparisons (Best > option B, Best > option C, Best > Worst, option B > Worst, option C > Worst; only B is not compared to C). So ABC is 66% more efficient than traditional choice-based conjoint even without adaptive questioning. This inherent efficiency advantage of best-worst questioning is further enhanced through adaptive questioning based on transitivity of preference. That is, we assume that if full-profile A is preferred to full-profile B, and if B > E, then A is also > E, even though we never directly compared A to E. Such transitivity may resolve even more paired comparisons than direct questioning. For example, with 16-full-profiles, there are 16 x 15 / 2 = 120 possible paired comparisons, over 50% of which are resolved through transitivity. Results: The presentation highlights three key results: •Internal consistency: The estimated utility functions explain 88%, on average, of the variance in treatment scores. •Estimation and prediction: a comparison of linear regression, LINMAP, and Hierarchical Bayes shows that the abc method has high predictive accuracy (e.g., 68% first choice-out-of-four hit rate for holdout questions). Also, 80%-90% of paired comparisons in holdouts are consistent with the estimated utility function. •Respondent and physician reactionsto this system have been favorable as compared with a control group. Conclusion: Adaptive Best-worst Conjoint analysis compares favorably with Ratings Scale and Time Tradeoff as a way of measuring patient preferences. This presentation will lay out the Excel-based method as a direct takeaway from the SMDM conference.

Purpose: Shared decision making can help men with localized prostate cancer make better informed decisions. A key component of shared decision-making is identification of patient preferences for outcomes of care. However, existing methods of patient preference assessment suffer from significant challenges to validity and feasibility. Conjoint analysis, a method of preference assessment taken from Marketing Science, has shown superior validity compared with existing methods in early testing. We report pilot patient satisfaction and decision quality data from a trial of conjoint analysis-based preference assessment in men with newly diagnosed prostate cancer Method: We developed a conjoint analysis application which allowed real time, individual level conjoint analysis. 30 men with incident localized prostate cancer were recruited in the West LA Veterans Affairs Urology Clinic and randomized to education with a brochure about prostate cancer treatment or preference assessment using conjoint analysis in addition to the brochure. Men underwent the intervention in the clinic prior to their cancer counseling session. Men undergoing values clarification received a report detailing the strength of their preferences for treatment attributes (such as sexual dysfunction) intended for review with their doctor. After the cancer consultation, men were surveyed with instruments measuring elements of decision quality and patient satisfaction with care. Result: Pilot data indicate a trend towards improved patient satisfaction in men who had preference assessment with conjoint analysis vs. those who did not. Mean scores on two patient satisfaction items differed significantly between cohorts, “Overall satisfaction with care” (1.3 vs 2.0, p<0.04) and “Thoroughness of main cancer practitioner” (1.1 vs 1.7, p<0.04). Pilot data showed a non-significant trend towards better scores in other items related to decision quality, such as decisional conflict, disease-specific knowledge, and measures of shared decision making. Conclusion: Individual-level, conjoint analysis-based preference assessment is feasible in the clinic setting. Pilot data indicate that prostate cancer patients who undergo values clarification with conjoint analysis felt more satisfied with their carel and that they perceived that the cancer practitioner counseling them was more thorough than men who did not. The latter finding may be explained by the use of the preference report that was used as a discussion point post values clarification

Preference markets address the need for scalable, fast and engaging market research in new product development. The Web 2.0 paradigm, in which users contribute numerous ideas that may lead to new products, requires new methods of screening those ideas for their marketability and preference markets offer just such a mechanism. For faster new product development decisions, a flexible prioritization methodology for product features and concepts is tested. It scales up in the number of testable alternatives, limited only by the number of participants. New product preferences for concepts, attributes and attribute levels are measured by trading stocks whose prices are based upon share of choice of new products and features. Benefits of preference markets include speed, scalability, flexibility, and respondent enthusiasm for the method.

Whitaker Foundation. The authors also wish to thank Rob Hardy of MIT and Limor Weisberg for their efforts in programming and designing many of the web sites that comprise this research. Market prices are well known to efficiently collect and aggregate diverse information regarding the value of commodities and assets. The role of markets has been particularly suitable to pricing financial securities. This article provides an alternative application of the pricing mechanism to marketing research- using pseudo-securities markets to measure preferences over