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BMC Med Inform Decis Mak. 2009 Nov 27;9:47. doi: 10.1186/1472-6947-9-47.

Latent class cluster analysis to understand heterogeneity in prostate cancer treatment utilities.

Author information

1
Biobehavioral and Health Sciences Division, NewCourtland Center for Transitions and Health, University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania 19104, USA. meghanis@nursing.upenn.edu

Abstract

BACKGROUND:

Men with prostate cancer are often challenged to choose between conservative management and a range of available treatment options each carrying varying risks and benefits. The trade-offs are between an improved life-expectancy with treatment accompanied by important risks such as urinary incontinence and erectile dysfunction. Previous studies of preference elicitation for prostate cancer treatment have found considerable heterogeneity in individuals' preferences for health states given similar treatments and clinical risks.

METHODS:

Using latent class mixture model (LCA), we first sought to understand if there are unique patterns of heterogeneity or subgroups of individuals based on their prostate cancer treatment utilities (calculated time trade-off utilities for various health states) and if such unique subgroups exist, what demographic and urological variables may predict membership in these subgroups.

RESULTS:

The sample (N=244) included men with prostate cancer (n=188) and men at-risk for disease (n=56). The sample was predominantly white (77%), with mean age of 60 years (SD+/-9.5). Most (85.9%) were married or living with a significant other. Using LCA, a three class solution yielded the best model evidenced by the smallest Bayesian Information Criterion (BIC), substantial reduction in BIC from a 2-class solution, and Lo-Mendell-Rubin significance of <.001. The three identified clusters were named high-traders (n=31), low-traders (n=116), and no-traders (n=97). High-traders were more likely to trade survival time associated with treatment to avoid potential risks of treatment. Low-traders were less likely to trade survival time and accepted risks of treatment. The no-traders were likely to make no trade-offs in any direction favouring the status quo. There was significant difference among the clusters in the importance of sexual activity (Pearson's chi2=16.55, P=0.002; Goodman and Kruskal tau=0.039, P<0.001). In multinomial logistic regression, the level of importance assigned to sexual activity remained an independent predictor of class membership. Age and prostate cancer/at-risk status were not significant factors in the multinomial model.

CONCLUSION:

Most existing utility work is undertaken focusing on how people choose on average. Distinct clusters of prostate cancer treatment utilities in our sample point to the need for further understanding of subgroups and need for tailored assessment and interventions.

PMID:
19941668
PMCID:
PMC2789058
DOI:
10.1186/1472-6947-9-47
[Indexed for MEDLINE]
Free PMC Article

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