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Confounding: An Introduction

Confounding: An Introduction. Philip la Fleur, RPh MSc( Epidem ) Deputy Director, Center for Life Sciences plafleur@kazcan.com. Epidemiology Supercourse Astana, July 2012. Objectives. Review why randomization is used and how it can minimize confounding

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Confounding: An Introduction

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

  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: • The factor must be associated with the exposure being investigated • Must be independently associated with the outcome being investigated • Not be in the causal pathway between exposure and outcome.

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

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

  8. The Truth: RR of Treatment = 1.0 Risk of Death in Young = 10% Risk of Death in Old = 20% • Treatment Group • 80 young • 20 old • Control Group • 20 young • 80 old Overall Analysis (all patients) 8+4 = 12 88 2+16 = 18 82 30 170

  9. Calculate Relative Risk 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

  10. How do we solve this problem? • Young Patients • Treatment • Control • Old Patients • Treatment • Control

  11. All Subjects Young Subjects Old Subjects Risk in Treatment Group: 8/80 = 0.1 Risk in Treatment Group: 4/20 = 0.2 Risk in Control Group: 10/100 = 0.1 Risk in Control Group: 16/80 = 0.2 Relative Risk = 1.0 Relative Risk = 1.0

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

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

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

  15. Example: Do we have a confounder?

  16. Example: Do we have a confounder?

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

  18. Is it a Confounder? Test #1 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

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

  20. Is it a Confounder? Test #2

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

  22. Confounding

  23. End The End

  24. References/Bibliography • LastJM. A Dictionaryof Epidemiology, 4th ed. Oxford UniversityPress, 2001 • Guyatt G et al. Users’ Guides tothe Medical Literature, 2nd ed. McGraw Hill, 2008 • Kennedy CC et al TipsforTeachers of EBM: AdjustingforPrognosticImbalances (Confounding variables) in studies of therapyorharm. J Gen IntMed 23(3):337-43 (and associatedlectureby G. Guyatt) • Streiner GR, Norman DL, PDQ Epidemiology. 2nd Ed. BC Decker Inc. 1998

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