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Measurement issues

Measurement issues. Jean Bourbeau, MD Respiratory Epidemiology and Clinical Research Unit McGill University Clinical Epidemiology (679) June 19, 2006. 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 19, 2006

  2. Objectives • Define categorical and continuous variables • Define 2 sources of variation: biological and measurement error (random and bias) • Describe the 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. Sources of variation • 3. Classification • 4. Health measurements • 5. Measurement properties

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

  6. Examples • In a 60-year-old patient after right hemicolectomy, the DUKE stage is a widely accepted, indispensable descriptive tool for planning further treatment. • Adjuvant post operative chemotherapy is currently the recommended treatment for resected Duke C colon cancer.

  7. Examples • In a 20-year-old woman with right lower quadrant pain and vomiting, the likely diagnosis is an appendicitis or a gynecological infection. • After excluding pelvic inflammatory disease, an experienced surgeon or gastroenterologist will diagnose appendicitis based on history, clinical findings and ultrasound.

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

  9. Measurement • Measure: • A scale or test is an instrument to measure a clinical phenomenon; a score is a value on the scale in a given patient

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

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

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

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

  14. 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

  15. 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+

  16. 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) • The numeric values, and the differences between them, hold no intrinsic meaning

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

  18. 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)

  19. Ratio scale • Hierarchy of mathematical adequacy: • With a ratio scale, it becomes possible to state how many times greater one score is than another • This improves on the interval scale by including a zero point

  20. Scales Binary Rank order (small to large) Continuous (0 to∞) Ratios

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

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

  23. Biological variation • Sources: • Dynamic nature of most biologic entities (differences in age, sex, race, or disease status) • Temporal variation • (sometimes predictable, such as the diurnal cycle of plasma cortisol)

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

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

  26. Measurement error • We can talk about the variability between methods of making the measurement or between the observers • Repeated measurements by the same method or observer • Intramethod or Intraobserver • Between two or more methods or observers • Intermethod or Interobserver

  27. 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)

  28. 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

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

  30. Classificationsof measures • Functional classifications focus on: • Purpose of application of the measures • Descriptive classifications focus on: • Their scope • Methodological classifications focus on: • Technical aspects

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

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

  33. 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

  34. Functional classification • Predictive instrument: • Can predict the probability of aclinical diagnosis (diagnostic test) or the likelihood of a future event (prognostic test)

  35. 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.

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

  37. Descriptive classification • Large number of possible categories • Can categorize instruments by: • Content: domains of interest (dyspnea, fatigue, emotion) • Generic or disease-specific

  38. 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

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

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

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

  42. 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 »

  43. 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, HRQL

  44. 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.

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

  46. Subjective vs Objective measurement

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

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

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