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Observational Medical Studies HRP 261 January 7, 2004

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  1. Observational Medical StudiesHRP 261 January 7, 2004

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

  29. 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) The Odds Ratio (OR)

  30. Properties of the OR (simulation)

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

  32. 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

  33. Odds Ratios in the literature

  34. 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?

  35. Advantages and Limitations: Case-Control Studies • Advantages: • Cheap and fast • Great for rare diseases • Disadvantages: • Exposure estimates are subject to recall bias (those with the disease are searching for reasons why they got sick and may be more likely to report an exposure) and interviewer bias (interviewer may prompt a positive response in cases). • Temporality is a problem (did exposure cause disease or disease cause exposure?)

  36. Final Note: controlling for confounders in observational studies • 1. Confounders can be controlled for in the design phase of a study (restriction or matching). • 2. Confounders can be controlled for in the analysis phase of a study (stratification or multivariate regression).