Measuring preferences for analgesic treatment for cancer pain: how do African-Americans and Whites perform on choice-based conjoint (CBC) analysis experiments?

Salimah H Meghani,Joseph Curry,Alexandra L Hanlon,Jesse Chittams

doi:10.1186/1472-6947-13-118

Salimah H Meghani, Joseph Curry + Show 2 more

Open Access

https://doi.org/10.1186/1472-6947-13-118

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Abstract

BackgroundConjoint Analysis (CA) can serve as an important tool to study health disparities and unique factors underlying decision-making in diverse subgroups. However, methodological advancements are needed in exploiting this application of CA. We compared the internal and external predictive validity and inter-temporal stability of Choice-based-Conjoint (CBC) analysis between African-Americans and Whites in the clinical context of preferences for analgesic treatment for cancer pain.MethodsWe conducted a prospective study with repeated-measures at two time-points (T1 = baseline; T2 = 3-months). African-Americans (n = 102); and Whites (n = 139) with cancer-related pain were recruited from outpatient oncology clinics in Philadelphia. Informed by pilot work, a computer-assisted CBC experiment was developed using 5 attributes of analgesic treatment: type of analgesic; expected pain relief; type of side-effects; severity of side-effects; and out-of-pocket cost. The design included 2 choice alternatives, 12 random tasks, 2 holdout tasks, and maximum of 6 levels per attribute. The internal and external predictive validity of CBC was estimated using Root Likelihood (RLH) and Mean Absolute Error (MAE), respectively. Inter-temporal stability was assessed using Cohen’s kappa.ResultsWhites predominantly traded based on “pain relief” whereas African-Americans traded based on “type of side-effects”. At both time-points, the internal validity (RLH) was slightly higher for Whites than for African-Americans. The RLH for African-Americans improved at T2, possibly due to the learning effect. Lexicographic (dominant) behavior was observed in 29% of choice datasets; Whites were more likely than African-Americans to engage in a lexicographic behavior (60% vs. 40%). External validity (MAE) was slightly better for African-Americans than for Whites at both time-points (MAE: T1 = 3.04% for African-Americans and 4.02% for Whites; T2 = 8.04% for African-Americans; 10.24% for Whites). At T2, the MAE increased for both groups possibly reflecting an increase in the complexity of pain treatment decision-making based on expectations (T1) as opposed to reality (T2). The inter-temporal stability was fair for CBC attributes between T1 and T2 (kappa = 0.28, 95% CI: 0.24-0.32) and was not predicted by demographics including race.ConclusionsWhile we found slight group differences, overall the internal and external predictive validity of CBC was comparable between African-Americans and Whites. We discuss some areas to investigate and improve internal and external predictive validity of CBC experiments.

Highlights

Conjoint Analysis (CA) can serve as an important tool to study health disparities and unique factors underlying decision-making in diverse subgroups
Conjoint Analysis can serve as an important tool to understand what unique factors may underlie decision-making of diverse subgroups, methodological advancements are needed in exploiting this application of CA
External predictive validity Based on our findings on the external validity, we found that it was easier to predict the pain treatment decisions for African-Americans than for Whites as indicated by the smaller Mean Absolute Error (MAE) for African-Americans at both timepoints (Table 4)

Summary

Introduction

Conjoint Analysis (CA) can serve as an important tool to study health disparities and unique factors underlying decision-making in diverse subgroups. The healthcare and funding structures in the U.S have recently placed an unprecedented emphasis on the role of patients’ perspectives in healthcare outcomes [1] These directions necessitate understanding of techniques that improve assessment of patient-reported outcomes including the important intermediary outcomes of preferences and decision-making. By asking individuals to make trade-offs between an important but limited number of attributes, a unique set of values (“preference weights” or “part-worth utilities”) can be derived. These preference weights are results of modeling the underlying latent utility function such that a higher preference weight represents a higher value an individual assigns to that attribute [7]. The attributes can be compared to one another to ascertain the “relative importance” or the percentage of total variance in preferences that each attribute explains

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