1. Confounding: An Introduction Epidemiology Supercourse Astana, July 2012 Philip la Fleur, RPh MSc(Epidem) Deputy Director, Center for Life Sciences plafleur@kazcan.com

2. Objectives Review why randomization is used and how it can minimize confounding Understand how to identify a confounder Understand the fundamental logic underlying adjusted analyses

3. Review: Why Randomize? Emerg Med J 2003;20:164-168

4. Tadalafil Therapy for Pulmonary Arterial Hypertension (PAH). Circul 2009;119:2894

5. Definition of a Confounder For a variable to be a confounder it should meet three conditions: 1.The factor must be associated with the exposure being investigated 2.Must be independently associated with the outcome being investigated 3.Not be in the causal pathway between exposure and outcome.

6. Higher versus Lower Positive End-Expiratory Pressures in Patients with the Acute Respiratory Distress Syndrome NEJM 2004;351:327-36

9. Understanding Confounding and Adjusting for Confounding; Qualitative Demonstration Treatment Group (N=100) – 80 young – 20 old Result = Control Group N=100 – 20 young – 80 old Treatment (apparently) Worked!

10. Treatment Group – 80 young – 20 old Control Group – 20 young – 80 old The Truth: RR of Treatment = 1.0 Risk of Death in Young = 10% Risk of Death in Old = 20% DeadAliveTotal Treated100 Control100 Total200 8+4 = 1288 2+16 = 18 82 30170 Overall Analysis (all patients)

11. Calculate Relative Risk DeadAliveTotal Treated1288100 Control1882100 Total30170200 Risk of Dying in Treated: 12/100 = 0.12 Risk of Dying in Control: 18/100 = 0.18 Relative Risk of Dying in Treated Compared to Control = 0.12/0.18 = 0.67

12. How do we solve this problem? Young Patients – Treatment – Control Old Patients – Treatment – Control

13. DeadAliveTotal Treated87280 Control21820 Total1090100 DeadAliveTotal Treated41620 Control166480 Total2080100 Young Subjects Old Subjects Risk in Treatment Group: 8/80 = 0.1Risk in Treatment Group: 4/20 = 0.2 DeadAliveTotal Treated1218100 Control1882100 Total30170200 All Subjects Risk in Control Group: 10/100 = 0.1 Relative Risk = 1.0 Risk in Control Group: 16/80 = 0.2 Relative Risk = 1.0

14. Higher versus Lower Positive End-Expiratory Pressures in Patients with Acute Respiratory Distress Syndrome NEJM 2004;351:327-36

15. Definition of a Confounder For a variable to be a confounder it should meet three conditions: 1.The factor must be associated with the exposure being investigated 2.Must be independently associated with the outcome being investigated 3.Not be in the causal pathway between exposure and outcome. EXPOSURE (Truck Driving) OUTCOME (Lung Cancer) CONFOUNDER (Smoking)

16. Example: Do we have a confounder? Oral Contraceptive Use Cervical Cancer Age at first intercourse = CONFOUNDER?

17. Example: Do we have a confounder? Used OCNever used OC Cases450300 Controls200250 Odds Ratio = 1.9

18. Example: Do we have a confounder? Age at first intercourse was < 20 years Age at first intercourse was 20+ years Used OCNever Used OCUsed OCNever Used OC Cases40020050100 Controls10050100200 Estimated Odds Ratio = 1.0

19. Is it a Confounder? Test #1 1.The factor must be associated with the exposure being investigated 2.Must be independently associated with the outcome being investigated 3.Not be in the causal pathway between exposure and outcome. Oral Contraceptive Use Cervical Cancer Age at First Intercourse ?

20. Is it a Confounder? Test #1 Exposure Confounder Used OCNever Used OC Age at first intercourse <20 years100 (50%)50 (20%) Age at first intercourse 20+ years100 (50%)200 (80%) Total200 (100%)250 (100%) 20% of those who never used OC had an early age of intercourse 50% of those who used OC had an early age of intercourse

21. Is it a Confounder? Test #2 1.The factor must be associated with the exposure being investigated 2.Must be independently associated with the outcome being investigated 3.Not be in the causal pathway between exposure and outcome. Oral Contraceptive Use Cervical Cancer Age at First Intercourse ?

22. Is it a Confounder? Test #2 Confounder Age at first intercourse <20 years Age at first intercourse 20+ years Cases600150 Controls150300 Odds Ratio= 8.0

23. Is it a Confounder? Test #3 1.The factor must be associated with the exposure being investigated 2.Must be independently associated with the outcome being investigated 3.Not be in the causal pathway between exposure and outcome. Oral Contraceptive Use Cervical Cancer Age at First Intercourse

24. Confounding Relative Risk in the entire population Relative Risk in young people Relative Risk in old people Adjusted Relative Risk Scenario 1No confounding3.0 Scenario 2Confounding3.02.0 Scenario 3Confounding1.91.0

25. End The End

26. References/Bibliography 1.Last JM. A Dictionary of Epidemiology, 4th ed. Oxford University Press, 2001 2.Guyatt G et al. Users’ Guides to the Medical Literature, 2nd ed. McGraw Hill, 2008 3.Kennedy CC et al Tips for Teachers of EBM: Adjusting for Prognostic Imbalances (Confounding variables) in studies of therapy or harm. J Gen Int Med 23(3):337- 43 (and associated lecture by G. Guyatt) 4.Streiner GR, Norman DL, PDQ Epidemiology. 2nd Ed. BC Decker Inc. 1998