Does One Size Fit All? Investigating Heterogeneity in Men’s Preferences for Benign Prostatic Hyperplasia Treatment Using Mixed Logit Analysis

Abstract

In this study, the authors demonstrate how mixed logit analysis of discrete choice experiment (DCE) data can provide information about unobserved preference heterogeneity. Their application investigates unobserved heterogeneity in men's preferences for benign prostatic hyperplasia (BPH) treatment. They use a DCE to elicit preferences for seven characteristics of BPH treatment: time to symptom improvement, sexual and nonsexual treatment side effects, risks of acute urinary retention and surgery, cost of treatment, and reduction in prostate size. They investigate the importance of these characteristics and the trade-offs men are willing to make between them. Preferences are elicited from a sample of 100 men attending an outpatient clinic in Ireland. The authors find all treatment characteristics are significant determinants of treatment choice. There is significant preference heterogeneity in the population for four treatment characteristics: time to symptom improvement, treatment reducing prostate size, risk of surgery, and sexual side effects. The importance of preference heterogeneity at the policy level within the context of shared decision making is discussed.

Men’s preferences for the treatment of lower urinary tract symptoms associated with benign prostatic hyperplasia: a discrete choice experiment

“…This is consistent with previous DCEs, which reported erectile dysfunction was the least desirable sexual side effect and confirmed the undesirability of decreased sexual desire and no fluid during ejaculation (dry orgasm) 21,35. Our results suggest that restrictions on sexual activity, specifically described as an inability to be sexually active, have a large negative effect on men’s quality of life.…”

“…As such, this DCE did not consider markers of treatment failure (eg, acute urinary retention or surgery) or treatment outcomes (eg, time to achieve improved symptoms or reduced prostate size), endpoints specifically associated with α 1 -blockers and 5-ARIs, which have been reported to be important to men with BPH40,41 and included in the previously published DCEs of BPH 21,35. The attribute levels for nonsexual side effects were expanded versus the previously published DCEs to include dry mouth, headache, and dizziness.…”

“…This attribute was framed as the total cost for men, including any prescription fees, travel expenses and absenteeism. The range of levels for the cost attribute was derived from earlier related studies21,35 and adjusted for inflation using the consumer price index. This range was tested in the pilot study in two ways: by examining the proportion of men never selecting a treatment in the DCE choice sets and including a payment card contingent valuation instrument.…”

What sort of follow-up services would Australian breast cancer survivors prefer if we could no longer offer long-term specialist-based care? A discrete choice experiment

“…The empirical model to be estimated is specified as: , where U itj is the utility individual i derives from choosing alternative j in choice scenario t , x itj is a vector of observed attributes of alternative j , β i is a vector of individual specific coefficients reflecting the desirability of the attributes and ɛ itj is a random error term. In order to investigate the potential for preference heterogeneity, the mixed logit model, also known as the random parameter logit model, was utilised (McFadden and Train, 2000; Hole, 2007; Eberth et al , 2009). Within the mixed logit model, the β i is expressed as β + η i , where β constitutes the vector of average preferences of the population for each attribute and η i is the individual's specific preference components.…”

The Variability of Patient Preferences

“…Based on these studies and others, I suspected there would be high variation among patients in preferences for the possible outcome states after orthopaedic surgery, and that there would be highly variable treatment thresholds and highly variable avidity for surgery, and that such variability in preferences would be found even if the clinical problem was defined without variability [10]. I also suspected that patients do not reliably estimate their own treatment thresholds.…”