Literature DB >> 9708581

Medical decision-making and the patient: understanding preference patterns for growth hormone therapy using conjoint analysis.

J Singh1, L Cuttler, M Shin, J B Silvers, D Neuhauser.   

Abstract

OBJECTIVES: This study examines two questions that relate to patients' role in medical decision making: (1) Do patients utilize multiple attributes in evaluating different treatment options?, and (2) Do patient treatment preferences evidence heterogeneity and disparate patterns? Although research has examined these questions by using either individual- or aggregate-level approaches, the authors demonstrate an intermediate level approach (ie, relating to patient subgroups).
METHODS: The authors utilize growth augmentation therapy (GAT) as a context for analyzing these questions because GAT reflects a class of nonemergency treatments that (1) are based on genetic technology, (2) aim to improve the quality (rather than quantity) of life, and (3) offer useful insights for the patient's role in medical decision making. Using conjoint analysis, a methodology especially suited for the study of patient-consumer preferences but largely unexplored in the medical field, data were obtained from 154 parents for their decision to pursue GAT for their child.
RESULTS: In all, six attributes were utilized to study GAT, including risk of long-term side effects (1:10,000 or 1:100,000), certainty of effect (50% or 100% of cases), amount of effect (1-2 inches or 4-5 inches in adult height), out-of-pocket cost ($100, $2,000, or $10,000/year) and child's attitude (likes or not likes therapy). An experimental design using conjoint analysis procedures revealed five preference patterns that reflect clear disparities in the importance that parents attach to the different attributes of growth therapy. These preference patterns are (1) child-focused (23%), (2) risk-conscious (36%), (3) balanced (23%), (4) cost-conscious (14%), and (5) ease-of-use (4%) oriented. Additional tests provided evidence for the validity of these preference patterns. Finally, this preference heterogeneity related systematically to parental characteristics (eg, demographic, psychologic).
CONCLUSIONS: The study results offer additional insights into medical decision making with the consumer as the focal point and extend previous work that has tended to emphasize either an individual- or aggregate-based analysis. Implications for researchers and health care delivery in general and growth hormone management in particular are provided.

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Year:  1998        PMID: 9708581     DOI: 10.1097/00005650-199808001-00005

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  19 in total

1.  Measuring preferences for health care interventions using conjoint analysis: an application to HIV testing.

Authors:  Kathryn A Phillips; Tara Maddala; F Reed Johnson
Journal:  Health Serv Res       Date:  2002-12       Impact factor: 3.402

2.  Measuring what people value: a comparison of "attitude" and "preference" surveys.

Authors:  Kathryn A Phillips; F Reed Johnson; Tara Maddala
Journal:  Health Serv Res       Date:  2002-12       Impact factor: 3.402

3.  Patients' Preferences for Generic and Branded Over-the-Counter Medicines: An Adaptive Conjoint Analysis Approach.

Authors:  Merja Halme; Kari Linden; Kimmo Kääriä
Journal:  Patient       Date:  2009-12-01       Impact factor: 3.883

4.  Quality of life and relative importance: a comparison of time trade-off and conjoint analysis methods in patients with age-related macular degeneration.

Authors:  P A Aspinall; A R Hill; B Dhillon; A M Armbrecht; P Nelson; C Lumsden; E Farini-Hudson; R Brice; A Vickers; P Buchholz
Journal:  Br J Ophthalmol       Date:  2007-01-17       Impact factor: 4.638

5.  Effects of simplifying choice tasks on estimates of taste heterogeneity in stated-choice surveys.

Authors:  F Reed Johnson; Semra Ozdemir; Kathryn A Phillips
Journal:  Soc Sci Med       Date:  2009-10-31       Impact factor: 4.634

6.  Patient weighting of importance of asthma symptoms.

Authors:  L M Osman; L McKenzie; J Cairns; J A Friend; D J Godden; J S Legge; J G Douglas
Journal:  Thorax       Date:  2001-02       Impact factor: 9.139

7.  Using Latent Class Analysis to Model Preference Heterogeneity in Health: A Systematic Review.

Authors:  Mo Zhou; Winter Maxwell Thayer; John F P Bridges
Journal:  Pharmacoeconomics       Date:  2018-02       Impact factor: 4.981

8.  Towards personalizing treatment for depression : developing treatment values markers.

Authors:  Marsha N Wittink; Knashawn H Morales; Mark Cary; Joseph J Gallo; Stephen J Bartels
Journal:  Patient       Date:  2013       Impact factor: 3.883

9.  Views of older people on cataract surgery options: an assessment of preferences by conjoint analysis.

Authors:  M-A Ross; A J Avery; A J E Foss
Journal:  Qual Saf Health Care       Date:  2003-02

10.  Developing emergency department-based education about emergency contraception: adolescent preferences.

Authors:  Cynthia J Mollen; Melissa K Miller; Katie L Hayes; Marsha N Wittink; Frances K Barg
Journal:  Acad Emerg Med       Date:  2013-11       Impact factor: 3.451

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