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Statistical Fridays

Statistical Fridays. J C Horrow, MD, MS STAT Clinical Professor, Anesthesiology Drexel University College of Medicine. Session Review. Sensitivity / specificity Predictive value Effect of disease prevalence The ROC curve. Session Overview. Learn new concepts: Observations vary

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Statistical Fridays

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  1. Statistical Fridays J C Horrow, MD, MSSTAT Clinical Professor, Anesthesiology Drexel University College of Medicine

  2. Session Review • Sensitivity / specificity • Predictive value • Effect of disease prevalence • The ROC curve

  3. Session Overview • Learn new concepts: • Observations vary • Observational vs. experimental data • Graphing your data • Example: • 50 patients induced with TPL or PPF

  4. Observations Vary Subject JH’s systolic BP is 151 mmHg. He applies 1 mL of 5% minoxidil to his scalp in a vain attempt to keep his remaining hair. 15 min later, his systolic BP is 148 mm Hg. Q: Did his BP decrease ? Q: May we state “BP decreased” ?

  5. Observations Vary

  6. Observations Vary When do things “differ”?

  7. When do things differ? When statistical tests indicate so. Data nearly always “differ numerically” Only statistics tells us when numbers differ. There is no such thing as a difference that is not a statistical difference.

  8. When do things differ? Only when they differ statistically.

  9. Population v. Sample POPULATION : The theoretical cohort about which we wish to draw conclusions…………….. Examples: • Patients with heart disease • Pregnant women with hypertension • Patients with antithrombin deficiency 1

  10. Population v. Sample • SAMPLE: • The specific subjects for whom we have measurements………………… • Patients with heart disease • 25 patients with classic angina • Pregnant women with hypertension • 45,420 pregnant women taking Atacand • Patients with antithrombin deficiency • The patient seen in clinic yesterday

  11. Population v. Sample: Models • Statistical Model : • Device by which we infer properties about a population based on information obtained in a sample • ALL MODELS ARE WRONG.Some are useful.

  12. Statistical Model Example: Let x = TPL dose (mg/kg) at induction Let y = decrease in SPB at induction y = b0 + b1x+ e

  13. Retrospective • Survey data • Lack intervention • Lack active randomization • Prospective • Active randomization • Intervention • Controls / blinding Types of Data • Observational • Experimental

  14. Types of Data • Observational • Experimental ASSOCIATION Fifty (50) patients receive TPL or Propofol for induction. The SPB for each is recorded. CAUSATION Fifty (50) patients are randomly assigned to receive either TPL or Propofol for induction. The SBP for each isrecorded.

  15. Graphing Data • Histograms • Scatterplots • Boxplots

  16. Example: SBP data • 100 measurements total: • 25 SBP before TPL • 25 SBP after TPL • 25 SBP before PPF • 25 SBP after PPF • Calculate DSBP for each

  17. HISTOGRAM

  18. Histogram by Treatment

  19. Scatterplot: before v. after

  20. Scatterplot by treatment group

  21. Lower Quartile Upper Quartile Median Max Min TPL PPF 0 1 2 3 4 5 6 7 8 910 12 14 16 18 20 Boxplot of DSBP by treatment

  22. Max Min Q1 Q3 Median

  23. Max Min Q1 Q3 Median

  24. Session Review • New concepts: • Observations vary • Observational vs. experimental data • Graphing your data • Example: • 50 patients induced with TPL or PPF • Homework: • 20 patients given spinals for C-section

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