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Research Curriculum Session II –Study Subjects, Variables and Outcome Measures

Research Curriculum Session II –Study Subjects, Variables and Outcome Measures. Jim Quinn MD MS Research Director , Division of Emergency Medicine Stanford University. Overview. Study Subjects Sampling Recruitment Variables Types of outcome measures

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Research Curriculum Session II –Study Subjects, Variables and Outcome Measures

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  1. Research CurriculumSession II –Study Subjects, Variables and Outcome Measures Jim Quinn MD MS Research Director , Division of Emergency Medicine Stanford University

  2. Overview • Study Subjects • Sampling • Recruitment • Variables • Types of outcome measures • Precision, accuracy, validity, reliability

  3. Study SubjectsGeneralizing the Results “Research is only interesting to others if they can apply it to their practice”

  4. Study Subjects • Subjects in the study sample should be representative of the population of interest • Depending on study different populations may yield different results. • Examples: General population, ED patients, Clinic Patients, Attitudes of patients • Laceration studies, syncope study

  5. Study Subjects • Specify the best clinical and demographic characteristics of the study population to best answer question • Appropriate sampling from that target population • Results = truth in the study • Best possible chance to have the results generalizable.

  6. Selection CriteriaDefining the Target Population • Inclusion Criteria • defines the main characteristics of the target population – be specific

  7. Selection CriteriaDefining the Target Population • Exclusion Criteria • Individuals whose characteristics may interfere with the quality of the results E.g. – rare events, poor follow-up - May compromise generalizability of the study

  8. Sampling • Convenience Sample • Consecutive Sample Probability Samples - Simple Random Sample • Stratified Random Sample • Cluster Samples

  9. RecruitmentGoals • A sample that represents the target population - Non responders, lost follow-up • Enough subjects to meet sample size requirements - Play it safe, overestimate - There is always fewer patients than you think!

  10. “The best way to eliminate disease is to study it!”

  11. Outcome Measures Selection of Variables and Scales

  12. Selection of VariablesPractical Points/Precision/Accuracy • Continuous Variables • “discrete” variables • rich in information • Potential sample size “relief” • Categorical • Dichotomous • Nominal • Ordinal

  13. Measurement Scales • Categorical Variables • Phenomena often not suited for measurement (e.g. Death) • Dichotomous • Nominal • Ordinal – categories have order but no specific numerical or uniform difference

  14. Measurement Scales • Continuous (infinite values) • Ordered discrete (ordinal with numerical meaning) - Statistically handled very similarly

  15. Measurement ScalesSummary • Categorical • Scales may have more meaning and make more sense. • Less information, need large numbers • Continuous • some times hard to determine meaningful differences • sample size friendly

  16. Attributes of Outcome MeasuresPrecision • Is the measure “reproducible, reliable and consistent” • Subject to random error and variability • Observer variability • Instrument variability • Subject variability

  17. Assessing Precision • Inter and Intraobserver reproducibility • Within and between instrument reproducibility • Continuous variables – Coefficient of variation • Categorical – kappa statistic

  18. Enhancing Precision • Standardize measurement methods • Train and certify observers • Refining the instruments • Automating the instrument • Repetition (reduces random error)

  19. Accuracy “Does the variable actually measure or represent what it intends to” Assessed by comparison to a “Gold Standard” Different than precision, but many things that improve precision improve accuracy A function of systematic error • Observer bias • Subject Bias • Instrument Bias

  20. Enhancing Accuracy • Standardized measurement methods • Training observers • Refining instruments • Automating instruments • Making Unobtrusive measures • Blinding • Calibration of Instruments

  21. ValidityAccuracy when there is no “Gold Standard” • Measuring an abstract or subjective phenomena (e.g. – pain, quality of life) • Content Validity (Face, Inherent or sampling validity) • Construct Validity • Criterion Related Validity (Predictive Validity)

  22. Final Thoughts • An outcome measure should be sensitive enough to determine important clinical differences • It should be associated with only the characteristic of interest • Measurements should involve data collection that is efficient in time and cost • Efficiency is improved by increasing the quality of each item measured

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