©2009 Mark Tuttle
Evidence-based Medicine Exam 1 Notes
Ethical Issues of Infectious Disease: Case Studies and Tips from the experts
10/19 Ethics
- Evidenced-based Medicine definition:
o “EBM de-emphasizes intuition, unsystematic clinical experience, and pathophysiologic
rationale as sufficient grounds for clinical decision making and stresses the examination
of evidence from clinical research” JAMA (1992)
o “The conscientious, explicit, and judicious use of current best evidence in making
decisions about the care of individual patients. It means integrating individual clinical
experience with the best available clinical evidence from systematic research.” David
Sackett M.D. (1996)
- BUT, Without clinical expertise, even excellent external evidence may be inapplicable to or
inappropriate for and individual patient
- 6-step EBM Process
1. The patient - Start with the patient - a clinical problem or question arises out of care of
the patient
2. The question - Construct a well built clinical question derived from the case.
3. The resource - Select the appropriate resources and conduct a search
4. The evaluation - Appraise that evidence for its validity (closeness to the truth) and
applicability (usefulness in clinical practice)
5. The patient - Return to the patient. Integrate that evidence with clinical expertise,
patient preferences and apply it to practice
6. Self-evaluation - Evaluate your performance with the patient.
- Construct a clinical question
o Patient or problem (demographics)
 How would you describe a group of patients similar to yours? This may include
primary problem, disease, co-morbidities, sex, age, race.
o Intervention, prognostic factor, or exposure
 What do you want to do for the patient? Meds? Tests? Surgery? What factors
may influence the prognosis? Age? Co morbidities? What was the patient
exposed to? Tobacco? Asbestos?
o Comparison
 What is the main alternative to compare with the intervention? Are you trying
to decide between two drugs, a drug and no medication, or two diagnostic
tests? The clinical question does not always need a specific comparison.
o Outcomes
 What do you wish to accomplish, measure, improve or affect? What are you
trying to do for the patient? Relieve symptoms? Reduce adverse events?
Improve function or scores?
o Ex. In elderly patients with congestive heart failure, is digoxin effective in reducing the
need for re-hospitalization?
- Types of questions:
o Diagnosis: How to select and interpret diagnostic tests
o Therapy: How to select treatments that do more good than harm and are worth the
efforts and costs of doing them.
o Prognosis: How to estimate a patients clinical course over time and anticipate likely
complications
©2009 Mark Tuttle
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o Harm/Etiology: How to identify causes of disease (including iatrogenic forms)
o Cost
Types of studies:
o Case Series/Case Reports
 Consists of collections of reports on the treatment of individual patients or a
report on a single patient.
 Because they are reports and there is no use of control groups they have no
statistical validity.
o Case Control Studies
 Patients who already have a certain condition are compared with people who
do not. They often rely on patient records or recall for data.
 Often less reliable than randomized controlled trials and cohort studies because
showing a statistical relationship does not mean that one factor necessarily
caused the other.
o Cohort Studies
 Follow patients who have a specific condition or receive a particular treatment
over time and compare them with another group that has not been affected by
the condition or treatment being studied.
 Cohort studies are observational and not as reliable as randomized controlled
studies, since the two groups may differ in ways other than in the variable under
study.
o Randomized Controlled Clinical Trials
 These are carefully planned projects that study the effect of a therapy on real
patients. They include methodologies that reduce the potential for bias
(randomization and blinding) and that allow for comparison between
intervention groups and control groups (no intervention)
o Prospective, Blind Comparison to a “Gold Standard”
 Study designed to show the efficacy of a diagnostic test or treatment.
 This is a controlled trial that looks at patients with varying degrees of an illness
and administers standard and investigational diagnostic test or treatment to
all of the patients in the study group.
o Systematic Reviews
 Reviews of the literature that are focused on a clinical topic to answer a specific
question.
 An extensive literature review is conducted to identify all studies with sound
methodology.
 The studies are reviewed, assessed, and the results are summarized according
to the predetermined criteria of the review question.
o Meta-analysis
 A study that thoroughly examines a number of valid studies on a topic and
combine the results using accepted statistical methodology as if they were from
one large study.
 Part of the methodology includes critical appraisal of the selected randomized
controlled trials selected for analysis.
The evaluation
o Validity: measuring what you think you are measuring?
o Reliability: can it be repeated?
©2009 Mark Tuttle
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Study may be properly conducted (internally valid) but not be generalizable (externally
valid)
External validity: A research study or experiment has external validity if the results
obtained would apply to other similar programs or approaches. Can we generalize with
confidence that this is true for the target population?
 Because you are often using a restricted artificial population for your own study,
it may be hard to generalize to everyone.
 Generalizability may also be affected by those who volunteer for a study, as in
they may be inherently different from those who do not volunteer (participation
bias).
 Drop-ins may be when someone from the placebo group decides to change to
one of the treatment groups without anyone knowing, thus causing problems
with the results.
Validity criteria should be applied before an extensive analysis of the study data
(results or conclusions)
 Methodology, including potential bias, randomization, blinding, accounting for
all patients
Treatment Effect (Digoxin example)
 Relative Risk (RR) = Y/X= 0.64/0.67 = 0.96
 RR Reduction (RRR) = 1- X/Y x 100 = 1-0.96 = 4%
 Absolute Risk Reduction (ARR) = (X – Y) x 100 = (.67-.64) x 100=3%
 Number Needed to Treat (NNT) to prevent one adverse outcome
= 1/(X - Y) = 1/0.03 = 33 patients.
Orientation to Clinical Decision Making II
- This is just the syllabus of the class
11/30 EBM
©2009 Mark Tuttle
©2009 Mark Tuttle
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Searching PubMed for the Evidence
12/1 EBM
Background question: General knowledge about a condition
o Ex. What is the pathophysiology of myocardial infarction
Foreground question: Specific knowledge to inform clinical decisions in your clinical scenario
o Ex. How effective is computed tomographic colonography compared with standard
colonoscopy for detection of colorectal neoplasia?
PICO Format
o Patient/Population:
Disease or condition and the characteristics or subset
o Intervention:
Drug or treatment in question
o Comparison:
Alternative to selected treatment
o Outcome:
Improvement?
MeSH Terms: Medical Subject Headings
o Uniformity and consistency to the indexing of biomedical literature
(ex. Heart attack and myocardial infarction have a consolidated MeSH term)
Study Designs and Measures
12/2 EBM
- Epidemiology: The study of how disease is distributed in populations and the factors that
influence or determine this distribution
o The premise of epidemiology is that disease, illness, and ill health are not randomly
distributed in a population. We all have certain characteristics that predispose or
protect us from a variety of different diseases.
Learning Objectives
1. Identify risk factors of diseases
a. Risk factors increase a person’s likelihood of acquiring a disease, condition, or event.
For example, not wearing a seat belt increases the risk of injury or death in a car
accident. We are trying to identify causes, though this really is not possible. What
we really try to determine are RISK FACTORS. The goals being to know how a
disease/condition/event develops and how it might be transmitted (more related to
infectious diseases).
2. Determine the extent of disease found in a community
a. We want to know the burden (person, place, time) of disease as this helps for
planning health services, facilities, and training of health care providers.
Surveillance of populations (monitoring statistics of disease/events) is useful.
3. Study natural history and prognosis of disease.
a. We need to define the natural history of disease so we can create modes of
intervention, including treatment and prevention of complications.
4. Evaluate new preventive and therapeutic measures and new modes of health delivery.
a. Again, this works in the prevention and intervention areas of epidemiology.
5. Provide foundation for developing public policy and regulatory decisions
a. May deal with environmental problems such as radiation, radon in homes,
occupations associated with an increased risk of disease in workers. How could we
regulate conditions in the workplace? This is where policies are needed.
©2009 Mark Tuttle
Goals of Epidemiology
1. Identify subgroups in the population who are at high risk for disease
a. If we can identify these groups, we may be able to identify the risk factors that increase
their risk of disease and thus modify exposure to those risk factors.
2. Primary, secondary, and tertiary prevention
a. Primary prevention is preventing development of a disease in a person who is well.
Again, this does not have to be a disease, it can be an event such as preventing injuries
in the workplace (i.e., wearing hard hats in construction areas).
3. Reduce morbidity and mortality
4. Develop prevention and intervention programs
a. Direct preventive efforts, such as screening for early detection) to populations are useful
to those who can benefit from intervention.
5. Improve patient prognosis by discovering methods to enhance quantity and quality of life
Principles of conducting studies
- Causal inference: Causation cannot be observed. Causation is inferred.
- Counterfactual ideal: perfect comparison group for a group of exposed individuals consists of
exactly the same individuals had they not been exposed.
o This isn’t practical, so you find a “substitute population” for control
- Types of studies
o Interventional (Trials)
 The active manipulation of the agent by the investigator.
 Adding OR removing a factor.
 May use a surrogate outcome such as lower cholesterol (instead of CAD)
 Randomized Clinical Trial
 The goal is to randomize everything possible except what you are trying
to study, thus removing as much potential as possible for confounding.
 Randomization helps us control confounders that we cannot measure
 Stratification helps ensure that key confounding variables are equally
distributed between the treatment and comparison groups. Individuals
are first stratified or separated according to the confounding
characteristic (i.e., sex, age).
 The “gold standard” with large enough sample size
 Intent-to-Treat Principle: look at who you began the study and didn’t
finish because there may be confounding variables
o Non-compliance can be for reasons such as adverse reactions to
the treatment, waning interest, and seeking other therapies
 Make the intervention as different as possible from non-intervention
 Equipoise: The ethical principle that it is truly uncertain whether the
treatment is better than control
 Statistical significance tests to evaluate baseline imbalances in a clinical
trial is improper
 Run-in phase: all potential subjects are given a placebo. Their
compliance with the regimen is determined and for those in whom
compliance is considered acceptable  entrance into trial
©2009 Mark Tuttle
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Observational (Case-Control, Cohort, Cross-sectional, Ecological)
 Cohort Study
 An existing group divided into exposed/unexposed  measure whether
they get the disease
 Ex. Smokers (exposed) and non-smokers (unexposed) getting lung
cancer
 Prospective studies are stronger and less biased but more expensive
 Can measure disease incidence (risk)
 Inefficient for rare, latent outcomes. Attrition can be poor
 Case-control Study
 Locate people with the disease first then see if they have been exposed
to a given variable
 Also locate people without the disease (also first) and then see if they
were exposed the variable
 The goal of case identification is to identify as many TRUE cases
(prevalent and incident cases) of disease quickly and inexpensively.
 BETTER for rare, latent outcomes.
 For example, cancers involving solid tumors may have a latency period
(time between point of pre-clinical detection to disease diagnosis) of 15
years or more. It is hard to follow people for that much time and is very
expensive.
 Selecting controls: make them as similar as possible to the case group in
extraneous factors to make sure there is less chance of confounding
 Limitations
o Limitations in recall may lead to misclassification
o Problems in control selection
o Only estimates RR and IRR, and only under certain conditions
o Inefficient for rare exposures
©2009 Mark Tuttle
Risk (Cumulative Incidence)
o # of new cases/# population at risk  for a given period of time
o Ex. 14 new lung cancer cases out of 100 smokers in one year would be the 1-year risk of
lung cancer
o # population at risk (denominator) must be free of disease at beginning
o CI1 = # new cases in exposed / # total exposed
o CI2 = # new cases in unexposed / #total unexposed
- Relative risk
o CI1 / CI2
- Incidence Rate (Incidence Density)
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Odds
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Once you get disease, you are no longer included in the denominator
If you have 5 marbles in a jar (1 blue and 4 red) the odds of selecting a blue marble are
1:4.
o NOT a proportion
o Also, Odds = Probability / (1-Probability)
Probability
o This IS a proportion
o Probability of selecting that blue marble is 25%
Exposure odds ratio
o The odds of being exposed among the cases
Case Control TOTAL
divided by the odds of being exposed among
Exposed
a
b
a+b
the controls.
Unexposed c
d
c+d
o For example if the OR = 1.50 in a study of HRT
TOTAL
a+c b+d
N
use and breast cancer, the odds of being
exposed to HRT among breast cancer cases are 1.50 times the odds of being exposed to
HRT among controls.
o
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# 𝑛𝑒𝑤 𝑐𝑎𝑠𝑒𝑠
Σ 𝑜𝑓 𝑝𝑒𝑟𝑠𝑜𝑛−𝑡𝑖𝑚𝑒 𝑠𝑝𝑒𝑛𝑡 𝑎𝑠 𝑎𝑡−𝑟𝑖𝑠𝑘
Exposure odds ratio =
𝑎 𝑏
𝑎𝑑
𝑐 𝑑
𝑐𝑏
⁄ =
Disease odds ratio
o The odds of developing the disease among the exposed divided by the odds of
developing the disease among the unexposed.
o For example, for the OR = 1.50, the odds of developing breast cancer in those taking
HRT are 1.50 times those for those not taking HRT.
©2009 Mark Tuttle
Basics of Confidence Intervals p-values Power and Screening
12/4 EBM
Screening Measures
- Lead time bias: Appearance of increasing survival time when the disease was just detected
earlier than if it were just detected by regular medical testing.
o Avoid this by using mortality rates instead of survival rates
- Length-time bias: Since screening is more likely to detect slow-growing lesions it makes it seem
like screening/early treatment are more effective than usual care when they may not be (Since
fast-growing lesions would probably be detected in normal medical setting rather than in
screening.)
- Compliance bias: Better results for the volunteers in a screening may be due to other factors
related to compliance, i.e. a self-selective pool
Calculations
Diagnostic Result
Screening Result Disease
No Disease
TOTAL
Positive
True Positives (TP)
False Positives (FP) TP + FP
Negative
False Negatives (FN) True Negatives (TN) FN + TN
TOTAL
TP + FN
FP + TN
TP + FP + FN + TN
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Sensitivity = True Positives / (True Positives + False Negatives)
o Ex. The probability of a positive mammogram given a woman who has breast cancer.
o With poor sensitivity there will be a lot of false negatives.
o 1-α (α=Type I Error)
Specificity = True Negatives / (True Negatives + False Positives)
o Ex. The probability of a negative mammogram given a woman who does NOT have
breast cancer.
o With poor specificity there will be a lot of false positives.
o 1-β (β=Type II Error) = Power
Predictive Value Positive = True Positives / (True Positives + False Positives)
o Ex. The probability of breast cancer given a woman with a positive mammogram.
Predictive Value Negative = True Negatives / (True Negatives + False Negatives)
o Ex. The probability of NO breast cancer given a woman with a negative mammogram.
Reliability/Repeatability (precision): Regardless of other metrics, test is useless if it cannot be
repeated with consistent results
o Sources: Biological variation of disease, test method variation, intra-observer variability,
inter-observer variability
Validity (accuracy): Are you measuring what you think you are measuring?
Test cut-points and their effects on sensitivity, specificity, predictive value (Ex: Hypertension)
o Moving the point to the left (decreasing threshold) will
decrease false negatives but increase false positives
 In other words, it increases sensitivity but
decreases specificity.
o Moving the point to the right (increasing threshold) will
increase false negatives, but decrease false positives
 Increases specificity, but decreases sensitivity
©2009 Mark Tuttle
Confidence Intervals, p-values, and Statistical Tests, and More
- Types of variables
o Ordinal:
Order rank (Ex. Low, medium, high)
o Nominal:
Name (Ex. Red, green, blue)
- Correlation (r)
o Range from -1.0 to 1.0
o Measure strength and direction of relationship
- Confidence Interval (CI): Method used to quantify random error surrounding a point estimate
o p value is significant when less than the α. If interval is 95%, α=0.05.
o Point estimate +/- (Constant) * (Standard Error)
 Point estimate is a relative risk, odds ratio, mean value, etc.
 The value of the constant is related to α (Z score), usually set at 1.96
 For a given 95% interval, your point estimate contains the true value 95% of the
time
o CI is narrower for a larger sample size
- p-Values
o The probability, assuming that the null hypothesis is true, of observing a value as
extreme or more extreme than the one you obtained by chance.
o Traditionally, a value less than 0.05 is significant.
o Drawback: don’t tell anything about relative risk
Types of Distributions
- The normal curve
o Location:
Mean ("average", μ)
o Scale:
Variance (standard deviation2, σ2)
o Z-scores: # of SDs from the mean
 CIs: 90% = 1.645, 95% = 1.96, 98% = 2.236, 99% = 2.576
o Bimodal distribution: Ex. Heights of males & females in a population
- Skewness
o Right or left determined by where the
tail of the distribution is
o Ex. Positive-skewed has
Mode<Median<Mean
o  Which side of the mean the median
is a rule of thumb for skewedness
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Analysis of Variance (ANOVA): Analysis of 2 sample means from a population with a 1 mean
o Ex. Is there a statistically significant difference in the starting salaries of education, arts
& sciences, and business majors? All come from a population with one true mean.
T-test: Determine if the difference between two groups is statistically significant.
o Test hypothesis about the population mean when our sample size is small and/or when
we do not know the variance of the sampled population.
o In so-called one-sample t-tests, the observed mean (from a single sample) is compared
to an expected (or reference) mean of the population (e.g., some theoretical mean), and
the variation in the population is estimated based on the variation in the observed
sample.
©2009 Mark Tuttle
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Chi-squared test (χ2)
o Ex. If the null hypothesis were true, what is the chance of randomly selecting subjects
with this large a discrepancy between observed and expected counts? We can combine
the observed and expected counts into a variable, the chi-square.
o The chi-square statistic is calculated by finding the difference between each observed
and theoretical frequency for each possible outcome, squaring them, dividing each by
the theoretical frequency, and taking the sum of the results.
o Pearson Chi-squared:
 The events considered must be mutually exclusive and have total probability 1.
Power & Sample Size
Reality
Decision
Treatment not different Treatment is different
Conclude treatment not different
Correct Decision
Type II Error (β)
Conclude treatment is different
Type I Error (α)
Correct Decision
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Type 1 Error: Stating there is a difference when none exists (Accept Ha and reject H0)
Type 2 Error: Stating there is no difference when one exists (Fail to reject H0 when H0 is false)
Power (1-β): ability of a study to demonstrate an association if one exists.
o Equivalent to sensitivity
o Example: Let’s say the electricity goes out and you are in a dark family room looking for
a tiny paper clip with a flashlight. If you have a weak flashlight, you will have
tremendous difficulty finding it. This would be low power. We might conclude the
paper clip is not in the room when in reality, the paper clip is in the room. In any case,
the ability to make the correct decision that the paper clip is indeed in the room would
be severely reduced (low power)
Specificity (1-α)
 You need a larger sample size to detect smaller differences
©2009 Mark Tuttle
©2009 Mark Tuttle
Research Ethics
- Subjective elements of research:
o Research design
o Participant selection
o Data analysis
o Data reporting
o Conclusions
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Pitfalls of clinical research
o Uncontrolled patient
population
o Uncontrolled environment
o Confounding variables
o Patient factors: literacy,
education, comprehension
o Difficulty enrolling subjects
o Difficulty getting informed
consent
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12/7 Ethics
Where research can go wrong
o Study design
o IRB approval
o Selection of participants
o Informed Consent
o Data Collection
o Data management
o Statistical analysis
o Publication
Causes of misconduct
o Competition in the research
environment
o “Publish or perish”
environment
o Publication pressures
o Time constraints,
inattentiveness
o Prestige
o Reputation
Research Misconduct Definition
o “Significant misbehavior that appropriates the intellectual property or contributions of
others, that intentionally impedes the progress of research, or that risks corrupting the
scientific record or compromising the integrity of scientific practices”
o “Misconduct or Misconduct in Science means fabrication, falsification, plagiarism, or
other practices that seriously deviate from those that are commonly accepted within
the scientific community for proposing, conducting, or reporting research. It does not
include honest error or honest differences in interpretations or judgments of data.”
Errors in Data reporting
o Failure to include number of eligible participants
 Could be poorly compliant for a reason
o Inaccurate reporting of missing data points
o Failing to report all pertinent data
o Failing to report negative results
o Allowing research sponsors to influence reporting of results
o Formatting errors: Inappropriate graph labels
o Reporting results of inappropriately applied statistical tests
 Example: One-tailed t-test to compare two treatment groups may show
statistical significance, but two-tailed t-test may not.
o Reporting differences, when statistical significance is not reached
o Reporting no difference, when power is inadequate
o Data “Dredging”.
 Corrections for multiple statistical tests: Bonferroni or Scheffe correction
o Inappropriate use of terminology without precise definitions
 Ex. “Where you satisfied with your ED visit today?”
o Reporting conclusions that are not supported by data
©2009 Mark Tuttle
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Prevention of Misconduct
o Integrity of investigators and authors
o Education and supervision
o Mentoring and modeling
o Awareness of laws and policies
o Retention of raw data for 5 years
o Verification of research findings by independent researchers
o Journal and peer review policies
o Regulatory agencies:
o Department of Health and Human Services
o COPE: Committee on Publication Ethics
Conclusions
o Researchers should conduct research to increase the body of scientific knowledge, and
ultimately, to improve patient care
o Individual researchers must adhere to high standards of ethical conduct of research
o Oversight bodies should assist in maintaining an environment of research integrity
 Ex. NIH, FDA
©2009 Mark Tuttle
USMLE Type Questions
12/8 EBM
1. An epidemiology wants to use Kaplan-Meier method to construct a life table for workers with
lead exposure. Which of the following data is necessary for this endeavor?
a. Number of workers lost to follow up
b. Causes of death of workers during study period
c. Exposures of the workers at beginning of the study period
d. Specific diseases contracted by workers during the study period
e. Health statuses of the workers at the beginning of the study period
2. As a physician you are asked to assist in recruitment of some of your cancer patients for a study
trying to establish a link between prior pesticide exposure and cancer. What study design best
describes this research?
a. Cohort
b. Randomized trial
c. Cross-sectional
d. Case-control
e. Ecological
3. A new screening test has been recommended for a disease. A small study of 5000 patients is
used to determine its sensitivity. What is the sensitivity of the test?
a. .32
b. .68
c. .80
d. .25
e. .77
4. A new screening test for colorectal cancer has been developed. An oncologist would like to see
suspected cases screened with this test. Which test parameter best describes the ability of the
test to correctly identify a cancer free patient?
a. Sensitivity
b. Pos predictive value
c. Specificity
d. Neg predictive value (prob of cancer-free pt given that they have a negative test – as
opposed to this one where we already know the pt is disease-free)
e. Positive likelihood
5. What type of study would be best to evaluate a new drug proposed to lower cholesterol versus
a standard drug, Lipitor?
a. Cohort study
b. Randomized trial
c. Case-control study
d. Cross-sectional study
e. Ecological study
6. A study was conducted to look at the cases of hypertension in relation to their fiber
intake. What is the odd ratio (OR) for hypertension in this case-control study, comparing high
fiber consumers to low fiber consumers?
a. 1.38
b. 0.4
c. 0.55
d. 0.73
e. 1.22
©2009 Mark Tuttle
7. Z-scores (values) are used incomputing confidence limits. They correspond to the standard
normal distribution expressed in standard deviations. Which z-score is used for computing 95%
confidence intervals?
a. 1.64
b. 1.96
c. 2.58
d. 2.76 (99% CI)
e. 3.00
8. In a case control study of alcohol consumption and liver cancer, cases over-report alcohol
consumption, attributing this to their cancer. What type of bias is this?
a. Selection bias
b. Confounding (would require third variable that would alter relationship between alcohol
and cancer)
c. Random bias
d. Recall bias (does not involve forgetting info)
e. Lead-time bias
9. In a cohort study of obesity and risk of type 2 diabetes, the loss to follow-up is 30% among the
obese subjects and 10% among the non-obese subjects. What effect will this most likely have
on the calculated relative risk (RR)?
a. Overestimation
b. Underestimation
c. No change
d. Cannot determine
10. The incidence rate of myocardial infarction over the observation period was:
a. 3/6 = 0.5 cases/person year
b. 3/10
c. 2/15
d. 3/15
e. 3/5
11. The statistical method most appropriate to analyze the means of the four groups is
a. T-test (2 groups)
b. ANOVA (used for more than two groups)
c. Linear regression
d. Correlation
e. Logistic regression
12. A cohort study is performed to evaluate the relationship between inflammation as measured by
a high C-reactive protein and the occurrence of myocardial infarction among women. In the
study, 500 subjects with High CRP and 500 with normal CRP are studied over 20 year
period. During study, 50 of the women with high CRP and 15 with normal develop a newly
diagnosed myocardial infarction.Incidence rate (per 10000 erson years) for a myocardial
infarction for a persion with normal CRP is
a. 15 .
13. A study was undertaken to evaluate the use of computed tomography in the diagnosis of lumbar
disk herniation. 80 pts with lumbar disk herniation confirmed by surgery were evaluated, as
were 50 without. CT results positive in 56 of pts with herniation and 10 without. Sensitivity =
a. 56/80
©2009 Mark Tuttle
14. MESSED UP QUESTION If the relationship between two measures is linear and the coefficient of
determination has a value near 1, a scatterplot of the observations
a. Is horizontal straight line
b. Is vertical straight line
c. Has positive slope
d. Has negative slope
e. Is a straight line that is neither horizontal nor vertical
15. In a placebo-controlled trial of the use of oral aspirin-dipyridamole to prevent arterial restenosis
after coronary angioplasty, 45% of pts receiving the drug has restenosis, 46% receiving placebo
had restenosis. In reporting this finding, the authors stated p > 0.05 which means
a. Probability is greater than 1 in 20 that a difference this large could occur by chance
alone
16. A study was undertaken to evaluate any increased risk of breast cancer among women who use
birth control pills. The RR was calculated. A type I error in this study consists of concluding
a. A significant increase in RR when RR is actually > 1
b. Significant increase in RR when RR is actually < 1
c. Significant increase in RR when RR is actually 1
d. No significant increase in RR when RR is actually 1
e. No significant increase in RR when the RR is actually > 1
17. Supposed the confidence limits for the mean of a variable are 8.55 and 8.65. These limits are
a. Less precise but have a higher confidence than 8.1 and 9.5
b. More precise but have a higher confidence than 8.1 and 9.5
c. More precise but have a lower confidence than 8.1 and 9.5
d. Less precise but have a lower confidence than 8.1 and 9.5
e. Cannot be determined
18. A study was undertaken to compare treatment options in black and white patients who are
diagnosed as having breast cancer. The 95% CI for the odds ratio for blacks being more likely to
be untreated than whites was 1.4 to 3. The statement that most accurately describes the
meaning of these limits is that
a. 95% of the odds radios fall within these limits
b. 95% of the time blacks are more likely than whites to be untreated
c. No difference exists in the treatment of black and white women
d. Black women are up to 3 times more likely than whites to be untreated
e. White women are up to 3 times more likely than black to be untreated
19. The ability to assign by chance the type of antihypertensive agent used reduces the likelihood of
confounding of diet and physical activity in a study of treatment of BP elevation. What is the
most appropriate study design for this research?
a. Case control
b. Cohort
c. Ecological
d. Cross-sectional
e. Randomized clinical
©2009 Mark Tuttle
20. An eval of an antibiotic in the tx of possible occult baacteremia was undertaken. 500 children
with fever but no focal infection were randomly assigned to the abx or to a placebo. A blood
sample for culture was obtained prior to beginning therapy and all pateitns were reevaluated
after 48 hours. The authors reported the proportion of children with major infectious morbidity
among those with bacteremia was 13% in the placebo group and 10% in the antibiotic
group. The 95% CI for the difference was proportions was -2/6% to +8.6%. Thus, the most
important conclusion is that
a. The diff in major infectious morbidity between plcebo and antibiotic is statistically
significant
b. The proportion of children with major infectious morbidity is the same with placebo and
antibiotic
c. No statistically significant difference exists in the proportions that received placebo and
antibiotic
d. The study has low power to detect a difference owing to the small sample sze and no
conclusions should be drawn until a larger study is done
e. Using a chi-square test to determine significance is preferable to determining a CI for
the difference
21. In a sample of 49 people, the mean total leukocute count id found to be 7600 cells/mm3, with
SD of 1400. Assuming a normal distribution of counts, a randomly selected individual has a total
leukocyte count lower than 4800
a. 1% of the time
b. 2.5 % of the time (2 SD = 95%, divide by 2 for just those on the L side of the curve)
c. 5% of the time
d. 10% of the time
e. 15% of the time
22. In an epidemiologic study of carbon-black workers, 500 workers with respiratory disease and
200 without were selected for study. The investigators obtained a history of exposure to carbonblack dust in both groups. Among workers with disease, 250 had history of exposure, 50 of
those without had a history. What type of study?
a. Cohort (classify based on exposure status, then follow forward to find those that
develop the outcome)
b. Case-control (identify people with a disease and without, then ask about prior exposure
that may have precipitated)
c. Cross-sectional
d. Randomized clinical
e. Ecological
23. A study undertaken to evaluate the use of CT in the diagnosis of lumbar disk herniation. 80 with
lumbar disk herniation confirmed by surgery were evaluated with Ct, as were 50 pts without
herniation. The CT results were positive in 56 of the pts with herniation and in 10 of the pts
without. What is the false-positie rate?
a. 10/50 = 20%
b. 25/80 = 30%
c. 56/80
d. 40/50
e. 56/66
©2009 Mark Tuttle
24. Scale used in measuring cholesterol is
a. Ordinal (ranked values – like strongly agree, agree, neutral, disagree, etc.)
b. Discrete (rather continuous range of values)
c. Nominal
d. Qualitative
e. Interval
25. Which of the following sources is most likely to provide an accurate estimate of prevalence of
MS in a community?
a. Survey of practicing physicians to ask how many MS patients they are currently treating
b. Info from hospital discharge summaries
c. Data from autopsy reports
d. Telephone survey of a sample of randomly selected homes asking how many people in
home have disease
e. Examination of medical records of a representative sample of people living in the
community
26. A significant positive correlation has been observed between alcohol consumption and the level
of systolic BP in men. From this correlation, we may conclude that:
a. No association exists between alcohol and systolic pressure
b. Men who consume less alcohol are at lower risk for increased systolic
c. Men who consume less are at higher risk for increased systolic
d. High alcohol can cause increased systolic pressure (correlation, not causation)
e. Low alcohol can cause increased systolic pressure
27. A population distribution plot of cholesterol levels among healthy and diseased individuals is
shown. In defining a cut-off value for disease screening, which of the following characteristics
best describes line 2?
a. High Se, high PPV
b. High Se, low PPV
c. High Se, high NPV
d. High Se, high PPV
e. High accuracy
28. A researcher develops a new test to detect lung cancer at an earlier stage. The test picks up the
lung cancer in 85% of pts with lung cancer diagnosed by the gold standard and is negative in
90% of pts without lung cancer. The prevalence of lung cancer in pop is 3%. What is the
sensitivity
a. 15%
b. 60%
c. 85%
d. 87.5%
e. 90%
29. An investigator wnts to study the effects of smoking on subsequent development of lung
cancer. She gathers a group of 100 smokers and 100 non-smokers and foloows them for 20
years to compare rates of development of lung cancer. What type of study?
a. Cohort study
©2009 Mark Tuttle
30. The ELISA for HIV virus has a sensitivity and specificity of 99.9%. Therefore, in a pop of 100,000
people where the prevalence is 1%, there will be 1 false negatives and 99 false
positives. Likewise, there will be 999 true positives and 98,901 true negatives.
a. 1000 HIV+: 999 true +, 1 true –
b. 99000 HIV-: 98901 true+, 99 true –
c. If an analysis was made of a blood donor pool in which blood had already been screened
for HIV and now the prevalence of HIV was 0.1%, how would the sensitivity, specificity,
PPV and NPV change?
d. Decreased Se and Sp, same PPV and NPV
e. Decreased Se, Sp, PPV, NPV
f. Same Se, Sp, PPV, and NPV
g. Same Se and Sp, decreased PPV and NPV
h. Same Se and Sp, decreased PPV, increased NPV (fewer positive cases, so less likely you’ll
have positive test; more negative cases, so more likely you’ll get a negative test –
prevalence only chances predictive vlues)
31. Chicken pox is slowly taking over the entire med student community (3000 students). In 2008,
the prevalence of this disease within this community was 10%. In 2009, 270 new cases were
reported. What is the incidence of this chicken pox within this student population in 2009?
a. 5%
b. 9%
c. 10% (at risk population is 3000-300 for those that already got it last year)
d. 15%
e. 19%
32. At UT, a study of pulse rates was conducted on 1000 students. The mean pulse rate was 75 and
the standard deviation was 10. Which is true?
a. Approx 99.7% had pulse between 75 and 85
b. Approx 95% had pulse rate between 65 and 85 (95% = 2 SD above and below)
c. Approx 68% between 65 and 85
d. Approx 95% between 65 and 75
e. Approx 99.7% between 65 and 85
33. A researcher is using the 15-point Borg Scale of Pereived Exertion in an exervise study. The
minimum score of 6 indicates 20% effort and the maximum score of 20 indicates
exhaustion. What type of variable?
a. Nominal
b. Interval
c. Discrete
d. Ratio
e. Ordinal (ranked scale ranking how tired you are)
34. A one year study was conducted to look at risk of developing CHD in relation to their BP
status. What is the RR for CHD inthis cohort study, comparing hypertensives to normotensives?
a. 9.33
b. 0.11
c. 2.33
d. 0.29
e. 3.50
©2009 Mark Tuttle
35. A researcher develops a new test to detect lung cancer at an earlier stage. The test picks up the
lung cancer in 80% with lung cancer, and is negative in 90% of those without. What is
sensitivity?
a. 5%
b. 10%
c. 80%
d. 85%
e. 90%
©2009 Mark Tuttle
Answers:
1. A
2. D
3. E
4. C
5. B
6. C
7. B
8. D
9. B
10. D
11. B
12. A
Practice with EBM Skills
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
A
C, E
A
C
C
D
E
C
B
B
A
B
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
E
B
B, C
C
A
H
C
C
E
E
C
12/10 EBM