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: 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?

According to scientists, too much coffee may cause phobias, --panic attacks heart attacks, --stress, --osteoporosis underweight babies, --hypertension higher cholesterol miscarriages intensified stress delayed conception But scientists say coffee also may help prevent asthma colon and rectal cancer, —Type II Diabetes (*6 cups per day!) Coffee Chronicles BY MELISSA 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: June 05, 2000

February 14, 1996 Personal Health: Sorting out contradictory findings about fat and health. By Jane E. Brody MANY 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? …….

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.

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.

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

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)

Confounding: A major problem for observational studies

Confounding: Example

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

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)

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

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.

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

Agresti Example: Belief in Afterlife YesNo or undecided Females Males

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

2. Cohort studies: 1. Sample on exposure status and track disease development (for rare exposures)  Marginal probabilities (and rates) of developing disease for exposure groups are valid.

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.

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

Exposure (E)No Exposure (~E) Disease (D)ab No Disease (~D)cd a+cb+d risk to the exposed risk to the unexposed The Risk Ratio, or Relative Risk (RR)

Hypothetical Data Normal BP Congestive Heart Failure No CHF High Systolic BP

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

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

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

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.

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.

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

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

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

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) Odds of disease in the exposed Odds of disease in the unexposed

Properties of the OR (simulation)

Properties of the lnOR Standard deviation =

Hypothetical Data Amyl Nitrite UseNo Amyl Nitrite AIDS2010 No AIDS Note that the size of the smallest 2x2 cell determines the magnitude of the variance

Odds Ratios in the literature

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

When can the OR mislead?

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

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

Volume 340: February 25, 1999 From: “The Effect of Race and Sex on Physicians' Recommendations for Cardiac Catheterization” Study overview: Researchers developed a computerized survey instrument to assess physicians' recommendations for managing chest pain. Actors portrayed patients with particular characteristics (race and sex) in scripted interviews about their symptoms. 720 Physicians at two national meetings viewed a recorded interview and was given other data about a hypothetical patient. He or she then made recommendations about that patient's care.

Media headlines on Feb 25 th, 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”

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

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.

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.

SAS code and output for generating OR/RR from 2x2 table CathNo Cath Female30555 Male

data cath_data; input IsFemale GotCath Freq; datalines; 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;

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) Cohort (Col1 Risk) Cohort (Col2 Risk) Sample Size = 720

Furthermore…stratification shows…

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?)

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).