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Understanding Patient Preferences in Individual Health Decisions: Conceptual and measurement considerations Sara J. Knight, PhD Interdisciplinary Program.

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Presentation on theme: "Understanding Patient Preferences in Individual Health Decisions: Conceptual and measurement considerations Sara J. Knight, PhD Interdisciplinary Program."— Presentation transcript:

1 Understanding Patient Preferences in Individual Health Decisions: Conceptual and measurement considerations Sara J. Knight, PhD Interdisciplinary Program to Improve Care for Veterans with Complex Comorbid Conditions

2 Goals Examine several conceptual models for understanding preferences
Review approaches to the measurement of patient treatment preferences Discuss qualitative strategies for refining conceptual models of patient treatment preferences and health outcomes Identify types of studies used to provide evidence for the construct validity of measures of patient values

3 Characteristics of a Strong Measure of Patient Preferences
Comprehensive in patient considerations Strong in psychometric properties Feasible in a busy clinic settings Appropriate for all educational backgrounds Related to health care recommendations and outcomes

4 Clarification of individual differences in decision making
What are possible contributions of improved patient preference assessment? Clarification of individual differences in decision making Understanding of how preferences may be constructed over time Improved patient-centered care Strengthened patient/physician relationship

5 What are preferences? Valuation of health care goods, services, and interventions Economic Theory Maximize subjective expected utility Utilities can be scaled in dollar-equivalence terms Based on assumptions of consistency and rationality with some limits

6 Preferences versus Values
Economic theory Utilities Values Psychological theory Attitudes, preferences, interests, goals, needs

7 Preferences versus Health Status
Patient experience, well-being, function Concurrent or retrospective Preferences Patient, consumer, or societal values and goals Prospective

8 Economic Model Preferences Information Utility of Choice Alternatives
U1 = u(Attrib11, Attrib12 , …, Attrib1n) Utility of Choice Alternatives U2 = u(Attrib21, Attrib22 , …, Attrib2n) U3 = u(Attrib31, Attrib32 , …, Attrib3n) Resources and Constraints Stated Choice Revealed Choice Demand Utilization

9 Another Economic Model: Adapted from McFadden
Experience Information Stated Perceptions Memory Perceptions/Beliefs Choice (Revealed Preferences) Motivation/Affect Process Time and Dollar Constraints Attitudes Preferences Attitude Scales Stated Preferences

10 Individual Decision Making
International Patient Decision Aid Standards (IPDAS) Collaboration Recommendations on Values Measurement Describe procedures and outcomes to help patients imagine what it is like to experience their physical, emotional, and social effects Ask patients to consider which positive and negative features matter most Suggest ways for patients to share what matters most with others

11 Recognize a decision needs to be made
IPDAS Effectiveness Recommendations: Does the patient decision aid ensure decision making is informed and values based? Recognize a decision needs to be made Know choice alternatives and their features Understand that values affect decisions Be clear about option features that matter most Discuss values with their practitioner Become involved in preferred ways

12 Individual Decision Making Model
Experience/Affect Information Attribute Value Attribute Value Attribute Value Attribute Value Attribute Value Patient Preferences Preference/Choice Concordance Physician Recommendation Treatment Choice Decision Quality Knowledge, Satisfaction, Conflict, Regret Treatment Outcomes Functional Status, Quality of Life, Symptoms

13 Methods of Measurement
Utilities Elicitation Standard Gamble Time Trade-Off Visual Analog Scale Rating Scales Attitude Questions Conjoint Analysis Combination Approaches

14 Utilities Value between 0 and 1, where 0 represents the value of being dead and 1 the value of living with perfect health Represents patient’s subjective value for choice attribute, such as a health state or a treatment characteristic

15 Example: Standard Gamble

16 Standard Gamble

17 Standard Gamble

18 Standard Gamble

19 Standard Gamble

20 Example: Time Trade-Off
Imagine that you have two friends, Mr. Smith and Mr. Jones. Imagine that Mr. Smith’s health fits the following description; that it will stay the same for the rest of his life; and that he will live about 10 more years.

21 Time Trade-Off: The Health State
Mr. Smith has mild difficulties or problems with urinating or bowel function. He is able to do most of his usual activities nearly all of the time. He is not overly tired and his energy level is pretty good. He usually has a good appetite. He has very little or no pain and it is easily controlled by medication. His ability to have sex and enjoy if has been mildly affected. He hardly ever feels tense, worried, irritable, sad, fearful or depressed.

22 Time Trade-Off: The Choice
Now imagine that your other friend, Mr. Jones, lives in perfect health, but will live somewhat less than ten years. If you had to be one of these 2 people, who would you rather be? Mark your answer below. 10 years as Mr. Smith in health state A or 5 years as Mr. Jones in perfect health?

23 Example: Visual Analog Scale

24 Advantages of Utilities
Based on economic and psychological theory; meets assumptions of normative decision making Experimental task involving a choice Scores comparable across methods Useful in studies of health economics and policy

25 Limitations of Utilities
Questionable reliability Logical inconsistencies within methods Limited evidence for validity Inconsistencies in scores across methods Not well accepted by patients Difficult for patients with low literacy and low numeracy Not related to treatment recommendations Souchek et al., 2000

26 Attitude Measures Attitudes are defined as a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor Usual measurement is through ratings or rankings Eagly and Chaiken, 1996

27 Example: Attitude Measure
If you were forced to choose between the term “faulty gene” and “altered gene”, which would you prefer? Please circle your response on the scale below: 1= I definitely prefer “altered gene” 2= I prefer “altered gene” 3= I am happy with either term 4= I prefer “faulty gene” 5= I definitely prefer “faulty gene” Wakefield et al., 2007

28 Evaluation of Attitude Measures
Requires few assumptions about underlying choice processes Easy for participants Holistic approach Valuation of an attribute as a whole Does not allow evaluation of levels or different categories of an attribute Vulnerable to “halo” effects Phillips, Johnson, Maddala, 2002

29 Conjoint Analysis Allows attributes to be evaluated in relation to each other, or “conjointly” Yields utilities Assumptions Each good or service is a bundle of potential attributes Each individual has set of unique relative utilities weights for attribute levels Combining utilities for difference attributes provides an individuals overall relative utility Phillips, Johnson, Maddala, 2002

30 Conjoint Analysis ATTRIBUTES TEST A TEST B Accuracy Privacy Cost
The diagnosis is correct 80% of the time, 20% of the time it is incorrect The diagnosis is correct almost 100% of the time Privacy You, your doctor, and your insurance company will receive information about your genetics Only you will receive information about your genetics Cost $0 $500 Would you choose Choice A or B?

31 Evaluation of Conjoint Analysis
Allows the evaluation of each attribute separately Provides estimation of willingness to pay; standardized approach to quantifying preferences for economic evaluation Complex and difficult for respondents Vulnerable to simplification of judgments Possible inconsistent responses Phillips, Johnson, Maddala, 2002

32 Integrative Approaches
Bockenholt has suggested that using a nested approach to understanding value judgments Comparative judgments are integrated with absolute judgments Scores contain information on both relative value and scale origin

33 Importance Ratings How important is… 
Extremely Important Very Moderately A Little Not Avoiding a treatment that causes problems for my relationship with my spouse/ partner

34 Best Worst Scaling Circle the concern that is most important to
you in making your decision about your prostate cancer treatment: Urinary Function Responsibilities Survival

35 Developing Measures of Preferences
Step 1 Identify a priori constructs Step 2 Establish comprehensive range of constructs and exemplars for each Step 3 Develop representative content for each construct Step 4 Generate items for each content area (items and subscales) Step 5 Select appropriate response choices Step 6 Refine measure based on pilot testing Step 7 Test measure properties

36 Construct Development
Ideally should be based on the purpose of the effort to understand preferences Economic analysis? Individual decision making? Cost analyses may require a different conceptual framework for understanding preferences and values and a different method for assessment than needed for the measurement of patient preferences in shared decision making

37 Literature Review Descriptions of treatment or test alternatives
Existing guidelines for care Patient education material Case studies Outcomes research Cost and utilization studies

38 Expert Judgments Identify key stakeholders Methods Clinicians
Investigators Patient advocates Health administrators Methods Key informant interviews Panel discussions Delphi process Conceptual mapping

39 Focus Groups Used to generate broad range of constructs
Often 8 to10 participants; fewer can be justified Experienced moderator needed Stratify on key variables: treatment choice, ethnicity, gender Structured exercises Differences among constructs Rankings among constructs Characteristics that influence choice

40 Analytic Approach Content analysis using a priori constructs as a starting point Grounded theory approach may be used where an a priori model does not exist Software NVivo Ethnograph

41 Cognitive Interviews Think aloud
Well, it depends on what you mean by family responsibilities. Do you mean money? Helping my wife with my grandkids? My wife says she’s OK with any treatment as long as I’m still alive.

42 Structured Cognitive Interviews
Pretesting of measures Administration Item stems Response scales Order Methods Thinkalouds Retrospective protocols Behavioral observations

43 Psychometric measures versus utilities?
Psychometric measures of attitudes yield scores interpreted from a population reference point and utility elicitation yields judgments scaled in terms of an absolute reference point (death, perfect health) Psychometric concepts of content validity and predictive validity may not be applicable to utilities where construct-irrelevant variance and construct under representation may be more relevant concepts Lenert and Kaplan, 2000

44 Evaluating Measurement Properties: Accumulating Evidence
Reliability Internal Consistency Test-retest Validity Face Content Criterion Construct Construct irrelevant variance Construct under-representation Consistency within Consistency over time Correct concept Adequate coverage of concept External criterion Construct being measured Minimal spurious influence Representative constructs

45 Reliability: Internal Consistency
Interpretation The consistency of responses within a scale or within a subscale Statistical Method Cronbach’s alpha (α) is the most common method. It is based on the average correlation over items weighted by variances It is the percent of variance that the current scale explains of the unidimensional underlying construct being measured Acceptable values Values range from with higher scores reflecting more accurate measure of the underlying trait. Values below .70 suggest a need for evaluation of each item within the scale, values above .90 for use of a measure with individuals.

46 Reliability: Test-Retest
Interpretation The stability in responses across assessment occasions Experimental Method The same instrument is administered twice to the same sample on two occasions usually 1-2 weeks or up to one month apart Statistical Method Intraclass reliability coefficient for continuous valued measures, kappa for discrete valued measures Acceptable values Values range from with higher scores reflecting more accurate measure of the underlying trait. Values below .60 suggest low consistency and potential future difficulty in detecting differences due to real effects

47 Validity: Face Interpretation
Determination that the instrument is measuring the construct of interest Assessment Method Prior to development, focus groups can inform the content. After the development of the instrument, cognitive interviews can be used to assess understanding of the content of the questionnaire Statistical Method No specific statistical method is associated with documenting face validity Population considerations The face validity of an instrument may vary across patient populations

48 Validity: Content Interpretation
Determining the adequacy of coverage of the instrument Experimental Method Experts in the area judge whether the instrument captures all domains of the construct of interest Statistical Method Though rarely reported, the Content Validity Index or the Content Validity Ratio indicates the extent of expert agreement. Determination of Experts Choose a panel of experts that informs rather than limits your interpretation of the breadth of your construct.

49 Validity: Criterion Interpretation
The association between the construct and some external criterion Experimental Methods Concurrent validity: both validation and construct tools are administered on one occasion. Predictive validity: construct is measured first; criterion is measured later Statistical Method Tests of association are used to assess construct validity such as a Pearson r for continuous valued scale scores, or Chi-square for categorical scale scores. Acceptable values coefficients of .70 or greater

50 Validity: Construct Interpretation
Establishes the ability of the instrument to measure the construct and to distinguish varying levels of the presence of that construct Experimental Methods Collection of data from many subjects from the patient population of interest to determine latent trait(s) underlying the assessment instrument. Collection of data from two groups known to vary on the construct (known-groups technique). Multi-Trait Multi-Method—measure should be more strongly related to other measures of similar constructs and to measures of dissimilar constructs Statistical Methods A statistical method to explain variability in responses (e.g. factor analysis, component analysis). A statistical method to detect differences between groups (e.g. t-test)


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