Transcript Evidence-Based Medicine 3 More Knowledge and Skills for Critical Reading Karen E. Schetzina, MD, MPH.

Evidence-Based
Medicine 3
More Knowledge and Skills for Critical
Reading
Karen E. Schetzina, MD, MPH
Epidemiology
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Definition - the study of the distribution and
determinants of health-related states or events
in specified populations, and the application of
this study to the control of health problems.
Epidemiologists and clinical researchers study
samples of populations, collect information on
variables of interest from persons in the
samples, and then look for associations between
the variables of interest.
Through this process, hypotheses are generated,
causes of disease are identified, treatments are
discovered, etc.
Critical Reading - Review
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The Effect of Race and Sex on Physicians’
Recommendations for Cardiac Catheterization
Design: Computerized Survey
 Surveyed 720 primary care physicians attending two
national meetings
 Physicians viewed video recorded interviews of
black and white male and female patients ages 55 –
70 with chest pain, as well as results of their
electrocardiography and thallium stress tests
 The physicians then were asked whether they wished
to refer the patient for cardiac catheterization
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Critical Reading - Review
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What is a variable?
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What were the main predictor variables in this
study?
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What was the main outcome variable in this
study?
Critical Reading – Review
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Results – Table 4: Referral for Cardiac Catheterization
According to Experimental Factor
Factor
Mean
Referral
Rate (%)
Odds Ratio
(95% CI)
Male
Female
90.6
84.5
1.0
0.6 (0.4-0.9)
White
Black
90.6
84.7
1.0
0.6 (0.4-0.9)
P Value
0.02
0.02
Critical Reading – Review
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Odds Ratio – The ratio of two odds. For rare
diseases, this approximates relative risk.
Commonly calculated in cross-sectional studies
and case control studies, and from logistic
regression.
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Interpretation:
>1 suggests positive association
 <1 suggests negative association
 =1 suggests no difference between groups
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Odds Ratio – Cardiac
Catheterization Article
Referred
Not Referred
Female
A
B
Male
C
D
OR =odds of referral for females = A/B=AD
odds of referral for males = C/D BC
Odds Ratio – General Definition
D+
D-
E+
A
B
E-
C
D
OR =odds of disease for E+ = A/B=AD
odds of disease for E- = C/D BC
Exposure Odds Ratio – Case Control
Study
D+
D-
E+
A
B
E-
C
D
OR =odds of exposure for D+ = A/C=AD
odds of exposure for D- = B/D BC
Relative Risk
D+
D-
E+
A
B
E-
C
D
RR = Risk of disease for E+ = A/(A + B)
Risk of disease for EC/(C + D)
Absolute Risk
D+
E+
D-
A
B
C
D
E
AR = (Risk for E+) - (Risk for E-) =
A/(A + B) - C/(C + D)
Critical Reading - Review
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“In univariate analysis, the race and sex of the
patient were significantly associated with the
physicians’ decisions about whether to make
referrals for cardiac catheterization, with men
and whites more likely to be referred than
women and blacks, respectively.”
Critical Reading - Review
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Results – Table 4: Referral for Cardiac Catheterization
According to Experimental Factor
Factor
Mean
Referral
Rate (%)
Odds Ratio
(95% CI)
Male
Female
90.6
84.5
1.0
0.6 (0.4-0.9)
White
Black
90.6
84.7
1.0
0.6 (0.4-0.9)
P Value
0.02
0.02
Hypothesis Testing
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Random sampling error exists in all
epidemiological studies. Hypothesis testing
allows us to account for this random error and
to determine whether a result is “statistically
significant.”
Hypothesis Testing – Statistically test the
study hypothesis against the null hypothesis
(the null hypothesis is the nothing hypothesis says there is no association between two
variables – i.e. between risk factor and disease).
Study Hypothesis – i.e. - There is an
association between sex & race and physicians’
recommendations for cardiac catheterization.
p-Value
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Test statistic – A value quantifying the degree
of association between two variables that is
calculated from the statistical test procedure.
For example, a chi-square statistic.
p-Value - The probability of obtaining a value
for the test statistic as extreme or more extreme
as that observed if the null hypothesis were true
(also calculated from the statistical test
procedure). A p-Value quantifies the degree of
random variability in the sampling process.
p-Value
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Statistical Significance – Most researchers are
willing to declare that a relationship is
statistically significant if the chances of
observing the relationship in the sample when
nothing is going on in the population are less
than 5%. This is why the commonly accepted
cut point for calling a result “statistically
significant is p<0.05.
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Confidence Intervals
Another value that can be calculated from
statistical test procedures that accounts for
random sampling error.
95% Confidence Intervals (95% CI) are
commonly reported.
95% CI – A range of values computed from the
sample that should contain the true population
parameter with 95% probability in repeated
collections of the data (i.e. a range of values that
is almost sure to contain the true population
parameter).
Confidence Intervals
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The width of a confidence interval is inversely
proportionate to the sample size of the study.
For risk ratios and odds ratios, if the confidence
interval includes the value “1,” the association is
not “statistically significant.”
If the confidence intervals for measures in two
groups overlaps, the two groups do not differ
“significantly” with respect to that measure.
Important!
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p-Values and Confidence Intervals assume that
there is no bias, or systematic error, in the
study - i.e., they do not account for bias in the
study. They do not assure that the association is
real. They do not quantify clinical
significance. It is important not to completely
discount values that are not statistically
significant. One must also look at trends and
how the results compare to previous studies.
Hill’s Causal Criteria
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Strength
Consistency
Specificity
Temporality
Biologic gradient
Plausibility
Coherence
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Experimental evidence
Analogy
Test Your Knowledge
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From Table 3 in “Factors Associated with
Hypertension Control in the General Population
of the United States”
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Age- and sex- adjusted odds ratios and 95%
confidence intervals for the association between
hypertension control and having private health
insurance (compared to no insurance):
 NHW:
1.64 (0.99-2.70)
 NHB: 2.62 (1.62-4.26)
 MA: 1.16 (0.52-2.60)
Test Your Knowledge
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From Table 4 in “Factors Associated with
Hypertension Control in the General Population
of the United States”
 Multivariate
Adjusted Odds Ratio and 95%
Confidence Intervals and p-Values for the
association between hypertension control and
marital status:
 Currently married (compared to never
married):
 OR=2.39 (1.52-3.71) p-Value<0.001
Next Lecture
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We will discuss sources of systematic error (bias) and
confounding. Some examples are:
1. Selection-bias (people who volunteer for studies may be
different, "healthy-worker effect"). From the study: "Physicians
who attend professional meeting may be better informed than
those who do not attend . . . may have a greater interest than
others in coronary heart disease." How might findings differ if
they sampled all practicing physicians?
2. Non-response bias (How do respondents and nonrespondents differ in regard to the study question?). This study
does not give response rates - only says that 720 physicians
participated - at least they did not know that it was a study of the
effects of race and sex.
3. Measurement bias (How accurately were the predictor and
outcome variables measured?)
Next Lecture
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Confounding may be considered "a confusion of effects" - attributing a
result or disease to a specific risk factor when it is in fact due to another
factor It can lead to over- or under-estimation of an effect or can even
change the direction of the effect.
Researchers may attempt to control confounding in several difference ways.
From the study: these authors reported that they clothed patients identically
and listed them as having the same type of insurance and occupations to help
to remove the potential confounding effects of SES and insurance.
Another way the authors attempted to control for confounding was by using a
"multivariate logistic regression analysis." From the study: Are the differences
between rates of referral by race and sex due to other factors besides just race
and sex? Physicians are aware of many different risk factors for coronary
heart disease (several are reported in the other article you read). Perhaps they
referred the white males in the study more often because they thought that
they were at higher risk for coronary artery disease based on their clinical
presentation? Well, the authors attempted to account for this possibility in
the analysis as well as for other factors (age, level of risk, type of chest pain,
results of thallium test) in an attempt to determine the differences in referral
rates just based on sex and race as independent factors.