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To Drink or Not to Drink? Volume 348:163-164 January 9, 2003 Ira J. Goldberg, M.D.

Introduction to observational medical studies and measures of association HRP 261 January 5, 2005 Read Chapter 1, Agresti. To Drink or Not to Drink? Volume 348:163-164 January 9, 2003 Ira J. Goldberg, M.D.

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To Drink or Not to Drink? Volume 348:163-164 January 9, 2003 Ira J. Goldberg, M.D.

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  1. Introduction to observational medical studies and measures of associationHRP 261 January 5, 2005 Read Chapter 1, Agresti

  2. To Drink or Not to Drink? Volume 348:163-164 January 9, 2003 Ira J. Goldberg, M.D. A number of epidemiologic studies have found an association of alcohol intake with a reduced risk of cardiovascular disease. These observations have been purported to explain the so-called French paradox: the lower rate of cardiovascular disease in…. …..With this in mind, is it time for a randomized clinical trial of alcohol?

  3. June 05, 2000 Coffee Chronicles BYMELISSA AUGUST, ANN MARIE BONARDI, VAL CASTRONOVO, MATTHEW JOE'S BLOWS Last week researchers reported that coffee might help prevent Parkinson's disease. So is the caffeine bean good for you or not? Over the years, studies haven't exactly been clear: • According to scientists, too much coffee may cause... • 1986 --phobias, --panic attacks • 1990 --heart attacks, --stress, --osteoporosis • 1991 -underweight babies, --hypertension • 1992 --higher cholesterol • 1993 --miscarriages • 1994 --intensified stress • 1995 --delayed conception But scientists say coffee also may help prevent... • 1988 --asthma • 1990 --colon and rectal cancer,... • 2004—Type II Diabetes (*6 cups per day!)

  4. February 14, 1996 Personal Health: Sorting out contradictory findings about fat and health. By Jane E. BrodyMANY health-conscious Americans are beginning to feel as if they are being tossed around like yo-yos by conflicting research findings. One day beta carotene is hailed as a life-saving antioxidant and the next it is stripped of health-promoting glory and even tainted by a brush of potential harm. Margarine, long hailed as a heart-saving alternative to butter, is suddenly found to contain a type of fat that could damage the heart. Now, after women have heard countless suggestions that a low-fat diet may reduce their breast cancer risk, Harvard researchers who analyzed data pooled from seven studies in four countries report that this advice may be based more on wishful thinking than fact. The researchers, whose review was published last week in The New England Journal of Medicine, found no evidence among a number of studies of more than 335,000 women that a diet with less than 20 percent of calories from fat reduced a woman's risk of developing breast cancer. Nor was risk related to the types of fats the women ate, the study reported. Is Fat Important? …….

  5. Statistics Humor • The Japanese eat very little fat and suffer fewer heart attacks than the British or the Americans. • On the other hand, the French eat a lot of fat and also suffer fewer heart attacks than the British or the Americans. • The Japanese drink very little red wine and suffer fewer heart attacks than the British or the Americans. • The Italians drink excessive amounts of red wine and also suffer fewer heart attacks than the British or the Americans. • Conclusion: Eat and drink whatever you like. It's speaking English that kills you.

  6. Assumptions and aims of medical studies • 1) Disease does not occur at random but is related to environmental and/or personal characteristics. • 2) Causal and preventive factors for disease can be identified. • 3) Knowledge of these factors can then be used to improve health of populations.

  7. ? Exposure Disease Medical Studies The General Idea… Evaluate whether a risk factor (or preventative factor) increases (or decreases) your risk for an outcome (usually disease, death or intermediary to disease).

  8. Observational vs. Experimental Studies Observational studies – the population is observed without any interference by the investigator Experimental studies – the investigator tries to control the environment in which the hypothesis is tested (the randomized, double-blind clinical trial is the gold standard)

  9. Confounding: A major problem for observational studies

  10. Confounding: Example

  11. Why Observational Studies? • Cheaper • Faster • Can examine long-term effects • Hypothesis-generating • Sometimes, experimental studies are not ethical (e.g., randomizing subjects to smoke)

  12. What is risk for a biostatistician? Risk = Probability of developing a disease or other adverse outcome (over a defined time period) In Symbols: P(D) Conditional Risk = Risk of developing a disease given a particular exposure In Symbols: P(D/E) Odds = Probability of developing a disease divided by the probability of not developing it In Symbols: P(D)/P(~D)

  13. Possible Observational Study Designs Cross-sectional studies Cohort studies Case-control studies

  14. Cross-Sectional (Prevalence) Studies Measure disease and exposure on a random sample of the population of interest. Are they associated? • Marginal probabilities of exposure AND disease are valid, but only measures association at a single time point.

  15. Exposure (E) No Exposure (~E) Disease (D) a b a+b = P(D) No Disease (~D) c d c+d = P(~D) a+c = P(E) b+d = P(~E) Marginal probability of exposure Marginal probability of disease Introduction to the 2x2 Table

  16. Yes No or undecided Females 435 147 Males 375 134 Agresti Example: Belief in Afterlife 582 509 810 281 1091

  17. Cross-Sectional Studies • Advantages: • Cheap and easy • generalizable • good for characteristics that (generally) don’t change like genes or gender • Disadvantages • difficult to determine cause and effect

  18. 2. Cohort studies: • Sample on exposure status and track disease development (for rare exposures) • Marginal probabilities (and rates) of developing disease for exposure groups are valid.

  19. Example: The Framingham Heart Study • The Framingham Heart Study was established in 1948, when 5209 residents of Framingham, Mass, aged 28 to 62 years, were enrolled in a prospective epidemiologic cohort study. • Health and lifestyle factors were measured (blood pressure, weight, exercise, etc.). • Interim cardiovascular events were ascertained from medical histories, physical examinations, ECGs, and review of interim medical record.

  20. Exposed Disease-free cohort Not Exposed Cohort Studies Disease Disease-free Target population Disease Disease-free TIME

  21. Exposure (E) No Exposure (~E) Disease (D) a b No Disease (~D) c d a+c b+d risk to the exposed risk to the unexposed The Risk Ratio, or Relative Risk (RR)

  22. Normal BP Congestive Heart Failure High Systolic BP No CHF 400 400 1500 3000 1100 2600 Hypothetical Data

  23. Advantages/Limitations:Cohort Studies • Advantages: • Allows you to measure true rates and risks of disease for the exposed and the unexposed groups. • Temporality is correct (easier to infer cause and effect). • Can be used to study multiple outcomes. • Prevents bias in the ascertainment of exposure that may occur after a person develops a disease. • Disadvantages: • Can be lengthy and costly! More than 50 years for Framingham. • Loss to follow-up is a problem (especially if non-random). • Selection Bias: Participation may be associated with exposure status for some exposures

  24. Case-Control Studies Sample on disease status and ask retrospectively about exposures (for rare diseases) • Marginal probabilities of exposure for cases and controls are valid. • Doesn’t require knowledge of the absolute risks of disease • For rare diseases, can approximate relative risk

  25. Case-Control Studies Disease (Cases) Exposed in past Not exposed Target population Exposed No Disease (Controls) Not Exposed

  26. Example: the AIDS epidemic in the early 1980’s • Early, case-control studies among AIDS cases and matched controls indicated that AIDS was transmitted by sexual contact or blood products. • In 1982, an early case-control study matched AIDS cases to controls and found a positive association between amyl nitrites (“poppers”) and AIDS; odds ratio of 8.6 (Marmor et al. 1982). This is an example of confounding.

  27. Case-Control Studies in History • In 1843, Guy compared occupations of men with pulmonary consumption to those of men with other diseases (Lilienfeld and Lilienfeld 1979). • Case-control studies identified associations between lip cancer and pipe smoking (Broders 1920), breast cancer and reproductive history (Lane-Claypon 1926) and between oral cancer and pipe smoking (Lombard and Doering 1928). All rare diseases. • Case-control studies identified an association between smoking and lung cancer in the 1950’s.

  28. Exposure (E) No Exposure (~E) Disease (D) a b No Disease (~D) c d Odds of exposure in the cases The proportion of cases and controls are set by the investigator; therefore, they do not represent the risk (probability) of developing disease. Odds of exposure in the controls The Odds Ratio (OR) a+b=cases c+d=controls

  29. Exposure (E) No Exposure (~E) Disease (D) a b No Disease (~D) c d The Odds Ratio (OR) a+b=cases c+d=controls

  30. 1 Via Bayes’ Rule 1 When disease is rare: P(~D)  1 “The Rare Disease Assumption” The Odds Ratio

  31. Exposure (E) No Exposure (~E) Disease (D) a = P (D& E) b = P(D& ~E) No Disease (~D) c = P (~D&E) d = P (~D&~E) Odds of disease in the exposed Odds of disease in the unexposed The Odds Ratio (OR)

  32. Properties of the OR (simulation)

  33. Standard deviation = Properties of the lnOR Standard deviation =

  34. Amyl Nitrite Use No Amyl Nitrite AIDS 20 10 No AIDS 6 24 Note that the size of the smallest 2x2 cell determines the magnitude of the variance Hypothetical Data 30 30

  35. Odds Ratios in the literature

  36. Highest Quintile of Mercury (in toenails) and Risk of Heart Attacks (NEJM Nov 02) OR= 1.47 (.99-2.14) • Things to think about: • What does an Odds Ratio of 1.47 mean? • “An increased risk of 47%”—is this misleading?

  37. When can the OR mislead?

  38. General Rule of Thumb: “OR is a good approximation as long as the probability of the outcome in the unexposed is less than 10%” When is the OR is a good approximation of the RR?

  39. February 25, 1999 Volume 340:618-626 From: “The Effect of Race and Sex on Physicians' Recommendations for Cardiac Catheterization”

  40. February 25, 1999 Volume 340:618-626 From: “The Effect of Race and Sex on Physicians' Recommendations for Cardiac Catheterization” • Study overview: • Researchers developed a computerized survey instrument to assessphysicians' recommendations for managing chest pain. • Actorsportrayed patients with particular characteristics (race and sex) in scriptedinterviews about their symptoms. • 720 Physicians attwo national meetings viewed a recordedinterview and was given other data about a hypothetical patient.He or she then made recommendations about that patient's care.

  41. Media headlines on Feb 25th, 1999… Wall Street Journal: “Study suggests race, sex influence physicians' care.” New York Times: Doctor bias may affect heart care, study finds.” Los Angeles Times: “Heart study points to race, sex bias.” Washington Post: “Georgetown University study finds disparity in heart care; doctors less likely to refer blacks, women for cardiac test.” USA Today: “Heart care reflects race and sex, not symptoms.” ABC News: “Health care and race”

  42. The Media Reports: “Doctors were only 60 percent as likely to order cardiac catheterization for women and blacks as for men and whites.” Their results…

  43. A closer look at the data… The authors failed to report the risk ratios: RR for women: .847/.906=.93 RR for black race: .847/.906=.93 Correct conclusion: Only a 7% decrease in chance of being offered correct treatment.

  44. Lessons learned: • 90% outcome is not rare! • OR is a poor approximation of the RR here, magnifying the observed effect almost 6-fold. • Beware! Even the New England Journal doesn’t always get it right! • SAS automatically calculates both, so check how different the two values are even if the RR is not appropriate. If they are very different, you have to be very cautious in how you interpret the OR.

  45. Cath No Cath Female 305 55 Male 326 34 360 360 SAS code and outputfor generating OR/RR from 2x2 table

  46. data cath_data; input IsFemale GotCath Freq; datalines; 1 1 305 1 0 55 0 1 326 0 0 34 run; data reversed; *Fix quirky reversal of SAS 2x2 tables; set cath_data; IsFemale=1-IsFemale; GotCath=1-GotCath; run; proc freq data=reversed; tables IsFemale*GotCath /measures; weight freq; run;

  47. SAS output Statistics for Table of IsFemale by GotCath Estimates of the Relative Risk (Row1/Row2) Type of Study Value 95% Confidence Limits ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Case-Control (Odds Ratio) 0.5784 0.3669 0.9118 Cohort (Col1 Risk) 0.9356 0.8854 0.9886 Cohort (Col2 Risk) 1.6176 1.0823 2.4177 Sample Size = 720

  48. Furthermore…stratification shows…

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