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Measurement issues

Measurement issues. Jean Bourbeau, MD Respiratory Epidemiology and Clinical Research Unit McGill University Clinical Epidemiology (679) June 8, 2005. Objectives. Define categorical and continuous variables Define 2 sources of variation: biological and measurement error (random and bias)

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Measurement issues

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  1. Measurement issues Jean Bourbeau, MD Respiratory Epidemiology and Clinical Research Unit McGill University Clinical Epidemiology (679) June 8, 2005

  2. Objectives • Define categorical and continuous variables • Define 2 sources of variation: biological and measurement error (random and bias) • Describe classification measures and their focus: functional, descriptive and methodological • Define and discuss the advantages and disadvantages of objective and subjective health measures • Define the psychometric properties of measurement instruments: reliability, validity, responsiveness • Discuss key questions and concerns about each of the psychometric properties of an instrument: reliability, validity and responsiveness • Define and discuss minimal clinically important difference

  3. Reading • Fletcher, Chapter 2

  4. Outlineof Measurement issues • 1. Measurements • 2. Source of variation • 3. Classification • 4. Health measurements • 5. Measurement properties

  5. Outlineof Measurement issues • 1. Measurements • 2. Source of variation • 3. Classification • 4. Health measurements • 5. Measurement properties

  6. Measurement We need to assign numbers to certain clinical phenomena to make them manageable and “scientific”

  7. Measurement • Measure: • A scale or test is an instrument to measure clinical phenomena; a score is a value on the scale in a given patient • If implemented in an objective and standardized manner, these measures can be used for program evaluation and research

  8. Measurement • The attributes or events that are measured in • a research study are called « variables » • Variables are measured according to 2 types: • Categorical • Continuous

  9. Categorical variables • Also called discrete variable • Dichotomous • or Polychotomous (multilevel): • - Nominal • - Ordinal

  10. Dichotomous categorical variables • Examples: • Vital status (alive vs dead) • Yes or no (response to a question) • Sex (male vs female)

  11. Polychotomous categorical variables • Nominal: • Named categories that bear no ordered relationship to one another • Example: • Hair, colour, race, or country of origin

  12. Nominal scale • Hierarchy of mathematical adequacy: • Lowest level (not a measurement but a classification) • Use numbers as a labels (such as male or female) • No inference can be drawn from the relative size of the numbers used

  13. Polychotomous categorical variables • Ordinal: • Named categories that bear an ordered relationship to one another • The intervals need not be equal • Example: • Ordinal pain scale that include « painseverity »: none, mild, moderate, and severe • Deep tendon reflex: absent, 1+,2+, 3+, or 4+

  14. Ordinal scale • Hierarchy of mathematical adequacy: • Numbers are again used as a labels for response categories • Numbers reflect the increasing order of the characteristics being measured (mild, moderate,severe) • Actual value of the numbers, and the numerical distance hold no intrinsic meaning

  15. Continuous variables • Also called dimensional, quantitative or interval variables • Expressed as integers, fractions, or decimals in which equal distances exist between successive internals • Examples: age, blood pressure, blood sugar

  16. Interval scale • Hierarchy of mathematical adequacy: • Numbers are assigned to the response categories in such a way that a unit change represents a constant change across the range of the scale (temperature in degrees Celsius)

  17. Ratio scale • Hierarchy of mathematical adequacy: • Possible to state how many times greater one score is that another • This improves on the interval scale by including a zero point

  18. Outlineof Measurement issues • 1. Measurements • 2. Source of variation • 3. Classification • 4. Health measurements • 5. Measurement properties

  19. Sources of variation • 2 sources of variation: • Biological variation • Measurement error

  20. Biological variation • Sources: • Dynamic nature of most biologic entities (difference age, sex, race, or disease status) • Temporal variation • (predictable sometime such as the clinical cycle of plasma cortisol)

  21. Measurement error • 2 different types: • Random (chance error) • Bias (systematic error)

  22. Measurement error • Can arise from: • The method (measuring instrument ) • Observer (the measurer)

  23. Measurement error • We can talk about the variability between methods of making the measurement or between the observers • Same method or observer to repeat the measurement • Intramethod or Intraobserver • Between two or more methods or observers • Intermethod or Interobserver

  24. Consequences of erroneous measurement • Individual • Makes no difference whether the error is systematic or random • Group • Variability in the absence of bias should not change the average group value • However, it can have deleterious consequences when one is seeking associations or correlations between 2 measures (analytic bias)

  25. Regression toward the mean • Individual measurement is subject to both biologic variation and measurement error • An extremely high or low value obtained in an individual from a group is more likely to be an error than is an intermediate value • Tendency toward a less extreme value is greater than the tendency for an intermediate value to become more extreme

  26. Outlineof Measurement issues • 1. Measurements • 2. Source of variation • 3. Classification • 4. Health measurements • 5. Measurement properties

  27. Classification measures • Functional focus on: • Purpose of application of the measures • Descriptive focus on: • Their scope • Methodological focus on: • Technical aspects

  28. Functional classification • Measures have discriminative, evaluative or predictive properties • Choice of measure depend of the purpose(s) for which it will be used

  29. Functional classification • Discriminative instrument: • Can discriminate between people with different levels of a particular attribute or disease • For example: • NYHA scale • MRC dyspnea scale

  30. MRC Dyspnea Scale none Grade 1 Breathless with strenuous exercise Grade 2 Short of breath when hurrying on the level or walking up a slight hill Grade 3 Walks slower than people of the same age on the level or stops for breath while walking at own pace on the level Grade 4 Stops for breath after walking 100 yards Grade 5 Too breathless to leave the house or breathless when dressing severe

  31. Functional classification • Predictive instrument: • Can predictaclinical diagnosis (diagnostic test) or the likelihood of a future event (prognostic test)

  32. 5-year survival COPD FEV1 Dyspnea MRC scale ...according to the level of dyspnea as evaluated by the MRC Dyspnea Scale ...according to staging as defined by the ATS Guidelines (% predicted FEV1) Nishimura K, et al. Chest2002; 121: 1434-1440.

  33. Functional classification • Evaluative instrument: • Can measure change over time in the same person • For example: • Dyspnea subscale of the Chronic Respiratory Questionnaires (CRQ) (COPD disease specific quality of life questionnaire)

  34. Descriptive classification • Large number of possible categories • Can categorize by: • Domain (dyspnea, fatigue, emotion) • Generic or specific

  35. COPD Questionnaires Disease-Specific General • used in any population • cross-condition comparison • co-morbid conditions and effects to treatment covered • do not focus on HRQL/ COPD • irrelevant items • insensitive to small changes • focus on relevant aspects of HRQL • greater sensitivity for disease changes • increased responsiveness • no comparisons

  36. Methodological classification • Large number of possible categories • Can categorize by: • Interviewer versus self-administered • Objective versus subjective

  37. Outlineof Measurement issues • 1. Measurements • 2. Source of variation • 3. Classification • 4. Health measurements • 5. Measurement properties

  38. Health measurements • Measurements may be based on: • Laboratory ordiagnostic tests (objective) • Indicatorsinwhich the patient ortheclinician makes a judgement(subjective)

  39. Health measurements • Unfortunately subjective is also used in other ways: • To indicate if the variable is observable or not • Examples: • Objective indicator such as « The ability to climb stairs » • Subjective indicators such as « pain or feelings »

  40. Objective vs Subjective • Objective: • More often continuous (lab data) • Few categorical (vital status, sex and race) • Subjective: • Greater potential, for bias or variability on the part of the observer • Many variables that are most important in caring for patients are « soft » and subjective • For example: pain, mood, dyspnea, ability to work

  41. The example of CABG • Why is quality of life important in studies • of CABG patients? • Survival with surgery > medical treatment for patients with left main and triple vessels • Survival similar in patients with less severe disease • CASS NEJM 1984; European cooperative study Lancet 1982.

  42. As Feinstein has emphasized The tendency of clinical investigators to focus on “objective” rather that “subjective” measurements, can result in research that is both dehumanizing and irrelevant

  43. Subjective vs Objective measurement

  44. Objective vs Subjective • Data traditionally considered objective “hard” can be seen, to have feet of softer clay • Example: • X-ray or cytopathologic diagnosis have been shown to be subject to considerable intra- and interobserver variability

  45. Subjective health measurements • May be grouped into 3 main categories: • General feelings of well-being • Symptoms of illness • Adequacy of a person’s functioning

  46. Subjective health measurements • Advantages: • Amplify the data obtainable from morbidity and mortality statistics • Give insights into matters of human concern such as pain suffering or depression • Offer a systematic way to record the « voiceof the patient » • Do not require expensive or invasive procedures

  47. Subjective health measurements • Disadvantages: • For example, they contrast sharply with the inherent reliability of mortality rate • Seem more susceptible to bias • Applying these measures to an entire population more difficult or impossible

  48. Subjectivehealth measurements The use of rating methods suitable for statistical analysis permit subjective health measurements to rival the quantitative strengths of the traditional “objective” indicators 

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