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

Salimah H. Meghani, Christopher Lee, Alexandra L. Hanlon, Deborah W. Bruner

Research output: Contribution to journalArticle

14 Citations (Scopus)

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 X2 = 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.

Original languageEnglish (US)
Article number47
JournalBMC Medical Informatics and Decision Making
Volume9
Issue number1
DOIs
StatePublished - 2009
Externally publishedYes

Fingerprint

Cluster Analysis
Prostatic Neoplasms
Therapeutics
Sexual Behavior
Survival
Needs Assessment
Health
Urinary Incontinence
Erectile Dysfunction
Life Expectancy
Logistic Models
Demography

ASJC Scopus subject areas

  • Health Informatics
  • Health Policy

Cite this

Latent class cluster analysis to understand heterogeneity in prostate cancer treatment utilities. / Meghani, Salimah H.; Lee, Christopher; Hanlon, Alexandra L.; Bruner, Deborah W.

In: BMC Medical Informatics and Decision Making, Vol. 9, No. 1, 47, 2009.

Research output: Contribution to journalArticle

Meghani, Salimah H. ; Lee, Christopher ; Hanlon, Alexandra L. ; Bruner, Deborah W. / Latent class cluster analysis to understand heterogeneity in prostate cancer treatment utilities. In: BMC Medical Informatics and Decision Making. 2009 ; Vol. 9, No. 1.
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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 X2 = 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.",
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