Introduction to observational medical studies and measures of association HRP 261 January 5, 2005 Re

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Introduction to observational medical studies
and measures of associationHRP 261 January 5,
2005 Read Chapter 1, Agresti
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(No Transcript)
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To Drink or Not to Drink? Volume 348163-164
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?
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• June 05, 2000

• 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

• 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,...
• 2004Type II Diabetes (6 cups per day!)

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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? .
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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.

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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.

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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).
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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)
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Confounding A major problem for observational
studies
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Confounding Example
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Why Observational Studies?

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

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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)

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Possible Observational Study Designs

• Cross-sectional studies
• Cohort studies
• Case-control studies

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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.

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Introduction to the 2x2 Table
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Agresti Example Belief in Afterlife
582
509
810
281
1091
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Cross-Sectional Studies

• Advantages
• Cheap and easy
• generalizable
• good for characteristics that (generally) dont
  change like genes or gender
• Disadvantages
• difficult to determine cause and effect

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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.

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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.

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Cohort Studies
Disease
Disease-free
Target population
Disease
Disease-free
TIME
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The Risk Ratio, or Relative Risk (RR)
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Hypothetical Data

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Advantages/LimitationsCohort 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

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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.
• Doesnt require knowledge of the absolute risks
  of disease
• For rare diseases, can approximate relative risk

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Case-Control Studies
Exposed in past

• Disease
• (Cases)

Not exposed
Target population
Exposed
No Disease (Controls)
Not Exposed
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Example the AIDS epidemic in the early 1980s

• 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.

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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 1950s.

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The Odds Ratio (OR)
abcases
cdcontrols
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The Odds Ratio (OR)
abcases
cdcontrols
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The Odds Ratio
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The Odds Ratio (OR)
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Properties of the OR (simulation)
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Properties of the lnOR
Standard deviation
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Hypothetical Data
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Odds Ratios in the literature
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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 47is this misleading?

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When can the OR mislead?
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When is the OR is a good approximation of the RR?
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Volume 340618-626
February 25, 1999
From The Effect of Race and Sex on Physicians'
Recommendations for Cardiac Catheterization
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Volume 340618-626
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.

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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

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Their results
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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.
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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 doesnt
  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.

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SAS code and outputfor generating OR/RR from 2x2
table
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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 IsFemale1-IsFema
le GotCath1-GotCath run proc freq
datareversed tables IsFemaleGotCath
/measures weight freq run
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SAS output
Statistics for Table of IsFemale by GotCath
Estimates of the Relative
Risk (Row1/Row2) Type of Study
Value 95 Confidence
Limits

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
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Furthermorestratification shows
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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?)

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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).