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Outcomes Research

Outcomes Research. Chapter 5 Cummings 5 th ed. Darshni Vira. AKA clinical epidemiology Study of the effectiveness of treatment in a nonrandomized, real-world setting (observational data) Outcome measures - survival, costs, physiologic measures, QOL. Study Outline.

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Outcomes Research

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  1. Outcomes Research Chapter 5 Cummings 5th ed. Darshni Vira

  2. AKA clinical epidemiology • Study of the effectiveness of treatment in a nonrandomized, real-world setting (observational data) • Outcome measures - survival, costs, physiologic measures, QOL

  3. Study Outline • Pt presents at baseline with a condition • Receives treatment for that condition • Experiences a response to treatment

  4. Bias and Confounders • Bias - “Compared components are not sufficiently similar” • Selection bias • Treatment bias • Confounders – “Variable thought to cause an outcome is actually not responsible because of the unseen effects of another variable • age, gender, ethnicity, race, comorbidities

  5. Assessment of Baseline Condition • Definition of disease • Inclusion criteria • Disease severity • TNM • Sinusitis – Lund-Mackay, Harvard, etc  reproducible results • Comorbidity • Adult Comorbidity Evaluation 27 (ACE-27) is a validated instrument for evaluating comorbidity in cancer patients

  6. Assessment of Treatment • Control Groups

  7. Assessment of Outcomes • Efficacy • Health intervention, in a controlled environment, achieves better outcomes than does placebo • Effectiveness • Retains its value under usual clinical circumstances

  8. Study Design

  9. Measurement of Clinical Outcomes • Psychometric Validation (questionnaires) • Reliability • Validation • Responsiveness • Burden

  10. Categories of Outcomes • Health Status • Individual’s physical, emotional, and social capabilities and limitations • Function • How well an individual is able to perform important roles, tasks, or activities • QOL • Central focus is on the value that individuals place on their health status and function

  11. Examples of Outcome Measures • Medical Outcomes Study Short Form-36 (SF-36) • European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC-HN35) • Hearing Handicap Inventory in the Elderly (HHIE) • Sinonasal Outcome Test (SNOT-20) • Child Health Questionnaire (CHQ) • Voice Handicap Index • Functional Outcomes of Sleep Questionnaire (FOSQ)

  12. Interpreting Medical Data Chapter 6 Cummings 5th ed.

  13. 1. Check quality before quantity 2. Describe before you analyze 3. Accept the uncertainty of all data 4. Measure error with the right statistical test 5. Put clinical importance before statistical significance 6. Seek the sample source 7. View science as a cumulative process Habits of a Highly Effective Data User

  14. 1. Check Quality before Quantity • Experimental vs observational study • Bias • Confounders • Control group • Placebo response • Prospective studies measure incidence (new events) whereas retrospective studies measure prevalence (existing events)

  15. 2. Describe Before You Analyze • Begins by defining the measurement scale that best suits the observations • Categorical (qualitative) • Numerical (quantitative) • Bell-shaped curve with standard deviation • Median • Survival curve

  16. Categorical

  17. Odds ratio with retrospective review • Relative risk with prospective review • Rate difference with prospective trials • Correlation coefficient with ordinal or numerical data • Coefficient (r) from 0 to 0.25 indicates little or no relationship, from 0.25 to 0.49 a fair relationship, from 0.50 to 0.74 a moderate to good relationship, and greater than 0.75 a good to excellent relationship. A perfect linear relationship would yield a coefficient of 1.00

  18. 3. Accept the Uncertainty in All Data • Precision (repeatability) • Should be reported with a 95% confidence interval • Precision may be increased by using a more reproducible measure, by increasing the number of observations (sample size), or by decreasing the variability among the observations • Accuracymeasures nearness to the truth • measured in an unbiased manner and reflect what is truly purported to be measured

  19. 4. Measure Error with the Right Statistical Test • All statistical tests measure error • Choosing the right test is determined by (1) whether the observations come from independent or related samples, (2) whether the purpose is to compare groups or to associate an outcome with one or more predictor variables, and (3) the measurement scale of the variables

  20. 5. Putting Clinical Importance Before Statistical Significance • The next logical question after “Is there a difference?” (statistical significance) is “How big a difference is there?” (clinical importance) • Effect size • reflects the magnitude of difference between groups • Measured by correlation coefficient • Confidence intervals (CI) are more appropriate measures of clinical importance than P values, because they reflect both magnitude and precision • If “significant” results, the lower limit of the 95% CI should be scrutinized; a value of minimal clinical importance suggests low precision (inadequate sample size) • If “nonsignificant” results, the upper limit of the 95% CI should be scrutinized; a value indicating a potentially important clinical effect suggests low statistical power (false-negative finding)

  21. 6. Seek the Sample Source • A statistical test is valid only when the study sample is random and representative • Identifying the sampling method and selection criteria (inclusion and exclusion criteria) that were applied to the target population to obtain the study sample • When the process appears sound, one concludes that the results are generalizable

  22. 7. View Science as a Cumulative Process • Process of Integration  • Systemic Reviews (meta-analysis)  • Clinical practice guidelines

  23. Popular Statistical Tests • T-test - comparing the means of two independent or matched (related) samples of numerical data • ANOVA - three or more independent groups of continuous data differ significantly with regard to a single factor (oneway ANOVA) or two factors (two-way ANOVA) • Contingency tables - association between two categorical variables by using the chi-square statistic • Survival analysis (Kaplan-Meier) - estimates the probability of an event based on the total period of observation • Multivariate (regression) - Examines the simultaneous effect of multiple predictor variables (generally three or more) on an outcome of interest

  24. Standard error is used instead of standard deviation Small sample study results are taken at face value Post hoc P values are used for statistical inference Some results are “significant” but there are a large number of P values Subgroups are compared until statistically significant results are found No significant difference is found between groups in a small sample study Significant P values are crafted through improper use of hypothesis tests Statistical Deceptions

  25. The End

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