Managerial Epidemiology: Assignment Week 4

Case Study: Chapters 8 to 10

Objective: The students will complete a Case study assignments that give the opportunity to synthesize and apply the thoughts learned in this and previous coursework to examine a real-world scenario. This scenario will illustrate through example the practical importance and implications of various roles and functions of a Health Care Administrator. The investigative trainings will advance students’ understanding and ability to contemplate critically about Experimental Study Designs, Measures of Effect, Data interpretation issues, and their problem-solving skills. As a result of this assignment, students will be better able to comprehend, scrutinize and assess respectable superiority and performance by all institutional employees.

ASSIGNMENT GUIDELINES (10%):

Students will critically measure the readings from Chapters 8 to 10 in your textbook. This assignment is planned to help you examination, evaluation, and apply the readings and strategies to your Health Care organization and Managerial Epidemiology.
You need to read the article (in the additional weekly reading resources localize in the Syllabus and also in the Lectures link) assigned for week 4 and develop a 3 page paper reproducing your understanding and capability to apply the readings to your Health Care organization and Epidemiology. Each paper must be typewritten with 12-point font and double-spaced with standard margins. Follow APA format when referring to the selected articles and include a reference page.

  

EACH   PAPER SHOULD INCLUDE THE FOLLOWING:

1. Introduction (25%) Provide a short-lived outline of the significance (not a description) of each Chapter and articles you read, in your own words that will apply to the case study presented. 

2. Your Critique (50%):  Case Study

“Gastroenteritis following a retirement party at the State Capitol” 1 Outbreak Summary Approximately 300 persons attended a retirement party at the Nebraska State Capitol held on May 27, 2017. Most of the attendees worked in the Capitol. A private caterer (Caterer A) prepared and served food for the reception. Based on initial telephone interviews of persons reporting illness, the predominant symptoms were nausea and diarrhea, and the incubation period was approximately 24-30 hours. The following foods were served at the retirement reception: Swedish meatballs, taco dip, crab dip, a vegetable tray and herbed ranch dip, cake, nuts and mints. The vegetable tray consisted of cucumbers, broccoli, cauliflower, carrots, celery, green peppers, and radishes. All foods were prepared onsite on the day of the reception with the exception of the nuts, which were purchased by a coworker, and the mints, which were made by a coworker. The Swedish meatballs consisted of ground beef, ground pork, sour cream and flour. The meatballs were cooked twice. The taco dip contained layers of cream cheese mixed with salsa, ground beef, tomatoes, lettuce, onion, cheese and salsa. The taco dip was prepared manually by Caterer A in the kitchen at the State Capitol, and was not cooked after assembly. The crab dip contained canned real crab, cream cheese and ketchup. The investigators received completed surveys from 227 attendees. Of those 227 attendees, 128 (56%) persons reported a gastrointestinal illness within 72 hours of the reception. The average interval between time of food consumption and onset of illness was 32.3 hours (range 6 to 67 hours). Table 1 shows the symptoms reported by ill attendees. The duration of symptoms generally lasted 24 to 36 hours. One person reported being symptomatic for five days. Eight persons sought medical treatment, mostly for re-hydration therapy. Persons working in the security office at the State Capitol ate samples of the items served at the reception except the taco dip. None of the people from this office reported illness.  

Table 1: Distribution of Symptoms Reported in Persons Meeting the Case-definition.

Symptom Number  (%) 

Nausea  117 (92.9%) 

Diarrhea  111 (88.1%) 

Abdominal Cramps  92 (74.2%) 

Vomiting  90 (72.0%) 

Headache  87 (70.7%) 

Chills  73 (59.8%) 

Muscle aches  72 (59.5%) 

Sweats  67 (55.4%) 

Bloody diarrhea  0 (0%)

CASE STUDY CHALLENGE 

1. What were the main finding in the case study?

2. What are the Conclusions from these findings? 

3. What are the increased risk for people to get gastroenteritis? 

3. Conclusion (15%)

Fleetingly recapitulate your thoughts & assumption to your critique of the case study and provide a possible outcome for the Managerial epidemiology and this case study. How did these articles and Chapters influence your opinions about study designs?

Evaluation will be based on how clearly you respond to the above, in particular:

a) The clarity with which you critique the case study;

b) The depth, scope, and organization of your paper; and,

c) Your conclusions, including a description of the impact of these Case study on any Health Care Setting.

ASSIGNMENT DUE DATE:

The assignment is to be electronically posted no later than noon on Saturday, February 1st, 2020. 

Chapter 8

Experimental Study

Designs

Learning Objectives (abridged)

• State how study designs compare with respect

to validity of causal inference

• Distinguish between a controlled experiment

and a quasi-experiment

• Describe the scope of intervention studies

• Define the term controlled clinical trials and give

examples

• Explain the phases in testing a new drug or

vaccine

Learning Objectives (abridged)

• Discuss blinding and crossover in

clinical

trials.

• Define what is meant by community

trials.

• Discuss ethical aspects of

experimentation with human subjects.

True Experimental Studies

• Most convincing for conferring

evidence of associations between

risk factors and outcomes

• Manipulation of study factor and

randomization

of subjects

• An example is a randomized clinical

trial.

Women’s Health Initiative

• Hormone Replacement Therapy (HRT)

– Epidemiologic studies had shown that HRT

use had significant benefits against coronary

heart

disease.

– Clinical trials had failed to demonstrate any

benefit.

– Large body of epidemiologic research had

observed that women who took HRT had

elevated risks of breast cancer.

Women’s Health Initiative
• Hormone Replacement Therapy (HRT)

– To resolve the question of risks versus benefits of

HRT, a clinical trial was conducted.

– Demonstrated that:

• the epidemiologic findings on cancer were

generally accurate

• the benefits on cardiovascular disease had

been overestimated

– Results

• Use of HRT decreased 40%-80% after the trial

was stopped

Quasi-

Experiment/Community Trial

• Ranked immediately below

controlled experiments in rigor

• Investigator is unable to randomly

allocate subjects to the conditions.

• There may be contamination across

the conditions of the study.

Intervention Studies

• An investigation involving intentional

change in some aspect of the status

of subjects

• Used to test efficacy of preventive or

therapeutic measures

• Manipulation of the study factor and

randomization of study subjects

Intervention Studies

• Two categories:

– Clinical trials (focus on the individual)

– Community trial or community

intervention (focus on the group or

community.

• NOTE: Controlled clinical trials may

be conducted both at the individual

and community levels.

Clinical Trials: Definition

• A research activity that involves the

administration of a test regimen to

humans to evaluate its efficacy and safety

• Wide variation in usage:

– The first use of the term was for studies in

humans without any control treatment

– Now denotes a rigorously designed and

executed experiment involving RANDOM

ALLOCATION of test and control treatments

Characteristics of Clinical

Trials

• Carefully designed and rigidly enforced

protocol

• Tightly controlled in terms of eligibility,

delivery of the intervention, and monitoring

out outcomes

• Duration ranges from days to years

• Participation is generally restricted to a

highly selected group of individuals.

Characteristics of

Clinical Trials

• Once subjects agree to participate,

they are randomly assigned to one of

the study groups, e.g., intervention or

control (placebo)

History of Clinical Trials

• In 1537, Ambroise Paré applied

experimental treatment for battlefield

wounds.

• East India Shipping Company (1600)

found that lemon juice protected against

scurvy.

• James Lind (1747) used the concurrently

treated control group method.

History of Clinical Trials

• Edward Jenner’s efforts to develop a

smallpox vaccine in the late 18th century

• Most recent historical developments

include the use of multicenter trials.

– Instrumental in the development of

treatments for infectious diseases and

recently in chronic diseases that are of

noninfectious origin

Prophylactic and Therapeutic

Trials

• A prophylactic trial evaluates the

effectiveness of a substance that is used

to prevent disease; it can also involve a

prevention program.

• A therapeutic trial involves the study of

curative drugs or a new surgical procedure

to improve the patient’s health.

Outcomes of Clinical Trials

• Referred to as clinical end points

• May include rates of disease, death, or

recovery

• The outcome of interest is measured in

the intervention and control arms of the

trial to evaluate efficacy–these must be

measured in a comparable manner.

Examples of Clinical Trials

• Medical Research Council Vitamin

Study—studied role of folic acid in

preventing neural tube defects.

• South Bronx, NY, STD Program—

evaluated effectiveness of education

efforts to prevent spread of sexually

transmitted diseases (STDs).

Blinding (Masking)

• To maintain the integrity of a study and

reduce the potential for bias, the

investigator may utilize one of two popular

approaches:

–Single-blind design: subject unaware of

group

assignment

–Double-blind design: Neither subject nor

experimenter is aware of group

assignment

Phases of Clinical Trials

• Before a vaccine, drug, or treatment can

be licensed for general use, it must go

through several stages of development.

• This lengthy process requires balance to:

– protect the public from a potentially

deleterious vaccine

– satisfy the urgent needs for new vaccines

Stages in the Development of A

Vaccination Program
• Pre-licensing evaluation of vaccine

– Phase I trials: Safety of adult volunteers

– Phase II trials: Immunogenicity and reactogenicity in
the target population.

– Phase III trials: protective efficacy

• Post-licensing evaluation

– Safety and efficacy of vaccine

– Disease surveillance

– Serologic surveillance

– Measurement of vaccine coverage

Phase IV Trials

• There can be more than three phases in a

clinical trial.

• Phase IV trials involve post-marketing

research to gather more information about

risks and benefits of a drug.

Randomization

• Method of choice for assigning subjects to

the treatment or control conditions of a

clinical trial.

• Non-random assignment may cause

mixing of the effects of the intervention

with differences (e.g., demographic)

among the participants of the trial.

Crossover Designs
• Any change of treatment for a patient in a

clinical trial involving a switch of study

treatments

• In planned crossovers a protocol is

developed in advance, and the patient

may serve as his or her own control.

• Unplanned crossovers exist for various

reasons, such as patient’s request to
change treatment.

Ethical Aspects of Human

Experimentation
• Benefits must outweigh risks.

• Ethical issues:
– Informed consent

– Withholding treatment known to be effective

– Protective the interests of the individual
patient

– Monitoring for side effects

– Deciding when to withdraw a patient

Reporting the Results of

Clinical Trials

• The CONSORT Statement is a

protocol that guides the reporting of

randomized trials by providing a 22-

item checklist and a flowchart.

Summary of Clinical Trials

• Strengths:

– Provide the greatest control over:

• the amount of exposure

• the timing and frequency of exposure

• the period of observation

– Ability to randomize reduces the likelihood

that groups will differ significantly.

Summary of Clinical Trials

(cont’d)

• Limitations:

– Artificial setting

– Limited scope of potential impact

– Adherence to protocol is difficult to

enforce

– Ethical dilemmas

Community Trials

• Community intervention trials determine the

potential benefit of new policies and programs

• Intervention: Any program or other planned

effort designed to produce changes in a target

population

• Community refers to a defined unit, e.g., a

county, state, or school district

Community Trials (cont’d)

• Start by determining eligible communities and

their willingness to participate

• Collect baseline measures of the problem to be

addressed in the intervention and control

communities

• Use a variety of measures, e.g., disease rates,

knowledge, attitudes, and practices

Community Trials (cont’d)

• Communities are randomized and followed over

time

• Outcomes of interest are measured

Examples of Community Trials

• North Karelia Project

• Minnesota Heart Health Program

• Stanford Five-City Project

• Pawtucket Heart Health Program

• Community Intervention Trial for Smoking

Cessation (COMMIT)

• Project Respect

Summary of Community Trials:

Advantages

• They represent the only way to estimate

directly the impact of change in behavior

or modifiable exposure on the incidence of

disease.

Summary of Community Trials:

Disadvantages

• They are inferior to clinical trials with respect to
ability to control entrance into study, delivery of
the intervention, and monitoring of outcomes.

• Fewer study units are capable of being
randomized, which affects comparability.

• They are affected by population dynamics,
secular trends, and nonintervention influences.

Four Stages of Evaluation

• Formative: Will all plans and procedures

work as conceived?

• Process: Is the program serving the

target group as planned?

• Impact: Has the program produced any

changes among the target group?

• Outcome: Did the program accomplish

its ultimate goal?

Overview of Quasi-Experimental

Study Designs

Type of Study Design Group(s) Pretest Intervention Posttest

Posttest only Intervention O X X

(has only one group)

Pretest/Posttest Intervention X X X

(has only one group)

Pretest/Posttest/Control Intervention X X X

(has two groups) Control X O X

Solomon Four-Group Intervention 1 X X X

(has four groups) Intervention 2 O X X

Control 1 X O X

Control 2 O O X

Note. O = not used; X = used.

Quasi-Experimental Designs

• Posttest only–observations are made only

after the program has been delivered.

• Pretest/Posttest–baseline and follow-up

observations are made.

• Pretest/Postest/Control–observations are

made in both intervention and control

groups before and after the program.

Quasi-Experimental Designs

(cont’d)

• Solomon Four-Group assignment:

– Used to overcome the Hawthorne Effect.

– Uses four equivalent groups, two

intervention and two control:

• Two are observed before and after intervention.

• Two are observed only after intervention.

Chapter 9

Measures of Effect

Learning Objectives

• Explain the meeting of absolute and relative

effects

• Calculate and interpret the following

measures: risk difference, population risk

difference, etiologic fraction, and population

etiologic fraction

• Discuss the role of statistical tests in

epidemiologic research

• Apply Hill’s criteria for evaluation of

epidemiologic associations

Effect Measure

• A quantity that measures the effect of

a factor on the frequency or risk of a

health outcome

Three Effect Measures

• Attributable Fractions

– Measure the fraction of cases due to a

factor.

• Risk and Rate Differences

– Measure the amount a factor adds to

the risk or rate of a disease.

• Risk and Rate Ratio

– Measure the amount by which a factor

multiplies the risk or rate of disease.

Absolute vs. Relative Effects

• Absolute

– Attributable risk is also known as a rate

difference or risk difference.

– Population risk difference

• Relative

– Relative risk

– Etiologic fraction

– Population etiologic fraction

Risk Difference (Attributable

Risk)

• Risk difference–the difference

between the incidence rate of

disease in the exposed group (Ie)

and the incidence rate of disease in

the nonexposed group (Ine).

• Risk difference = Ie – Ine

Calculation of Risk Difference

• For women younger than age 75, the
incidence (Ie) of hip fractures per 100,000
person-days was highest in the winter
(0.41), and the incidence (Ine) was lowest
in the summer (0.29). The risk difference
between the two seasons (Ie – Ine) was 0.41
– 0.29, or 0.12 per 100,000 person-days.

Population Risk Difference

• Measures the benefit to the

population derived by modifying a

risk factor.

Etiologic Fraction

• Defined as the proportion of the rate

in the exposed group that is due to

the exposure.

• Also termed attributable proportion or

attributable fraction.

Population Etiologic Fraction

• Provides an indication of the effect of

removing a particular exposure on the

burden of disease in the

population.

• Also termed attributable fraction in the

population.

Statistical Measures of Effect

• Significance tests

• The P value

• Confidence interval

Null Hypothesis

• Underlying all statistical tests is a null

hypothesis, which states that there is

no difference among the groups being

compared.

• The parameters may consist of the

prevalence or incidence of disease in

the population.

Significance Tests

• Used to decide whether to reject or fail to reject

a null hypothesis.

• Involves computation of a test statistic, which is

compared with a critical value obtained from

statistical tables.

• The critical value is set by the significance level

of the test.

• The significance level is the chance of rejecting
the null hypothesis when, in fact, it is true.

The P Value

• Indicates the probability that the

findings observed could have

occurred by

chance alone.

• However, a nonsignificant difference

is not necessarily attributable to

chance alone.

The P Value (cont’d)

• Possible meaning of nonsignificant

differences: For studies with a small

sample size the sampling error may

be large, which can lead to a

nonsignificant test even if the

observed difference is caused by a

real effect.

Confidence Interval (CI)

• A computed interval of values that, with a

given probability, contains the true value

of the population parameter.

• The degree of confidence is usually stated

as a percentage; commonly the 95% CI is

used.

• Influenced by variability of the data and

sample size.

Clinical vs. Statistical

Significance

• While small differences in disease frequency or

low magnitudes of relative risk (RR) may be

significant, they may have no clinical

significance.

• Conversely, with small sample sizes, large

differences or measures of effect may be

clinically important and worthy of additional

study.

Statistical Power

• The ability of a study to demonstrate

an association if one exists.

• Determined by:

– Frequency of the condition under study.

– Magnitude of the effect.

– Study design.

– Sample size.

Evaluating Epidemiologic

Associations

• Five key questions to be asked:

– Could the association have been observed by

chance?

• Determined through the use of statistical tests.

– Could the association be due to bias?

• Bias refers to systematic errors, i.e., how samples

were selected or how data was analyzed.

Evaluating Epidemiologic

Associations (cont’d)

• Could other confounding variables have

accounted for the observed relationship?

• To whom does this association apply?

– Representativeness of sample

– Participation rates

• Does the association represent a cause-

and-effect relationship?

– Considers criteria of causality.

Types of Associations between

Factors and Outcomes

• Not statistically associated

(independent)

• Statistically associated

Statistical Association

• When a factor and outcome are

statistically associated, the

relationship can be:

– Non-causal

– Causal

• Indirect

• Direct

Multiple Causality

• Also referred to as multifactorial

etiology.

• “…requirement that more than one
factor be present for disease to

develop…”

Models of Multiple Causality

• Epidemiologic triangle

• Web of causation, e.g., in avian

influenza

• Wheel model, e.g., childhood lead

poisoning

• Pie model, e.g., lung cancer

Chapter 10

Data Interpretation Issues

Learning Objectives

• Distinguish between random and

systematic errors

• State and describe sources of bias

• Identify techniques to reduce bias at the

design and analysis phases of a study

• Define what is meant by the term

confounding and provide three examples

• Describe methods to control confounding

Validity of Study Designs

• The degree to which the inference drawn

from a study, is warranted when account it

taken of the study, methods, the

representativeness of the study sample,

and the nature of the population from

which it is drawn.

Validity of Study Designs

• Two components of validity:

– Internal validity

– External validity

Internal Validity

• A study is said to have internal validity

when there have been proper selection of

study groups and a lack of error in

measurement.

• Concerned with the appropriate

measurement of exposure, outcome, and

association between exposure and

disease.

External Validity

• External validity implies the ability to

generalize beyond a set of observations to

some universal statement.

• A study is externally valid, or

generalizable, if it allows unbiased

inferences regarding some other target

population beyond the subjects in the

study.

Sources of Error in

Epidemiologic Research

• Random errors

• Systematic errors (bias)

Random Errors

• Reflect fluctuations around a true value of

a parameter because of sampling

variability.

Factors That Contribute to

Random Error

• Poor precision

• Sampling error

• Variability in measurement

Poor Precision

• Occurs when the factor being measured is

not measured sharply.

• Analogous to aiming a rifle at a target that

is not in focus.

• Precision can be increased by increasing

sample size or the number of

measurements.

• Example: Bogalusa Heart Study

Sampling Error

• Arises when obtained sample values

(statistics) differ from the values

(parameters) of the parent population.

• Although there is no way to prevent a

non-representative sample from

occurring, increasing the sample size

can reduce the likelihood of its

happening.

Variability in Measurement

• The lack of agreement in results from

time to time reflects random error

inherent in the type of measurement

procedure employed.

Bias (Systematic Errors)

• “Deviation of results or inferences
from the truth, or processes leading to
such deviation. Any trend in the
collection, analysis, interpretation,
publication, or review of data that can
lead to conclusions that are
systematically different from the
truth.”

Factors That Contribute to

Systematic Errors

• Selection bias

• Information bias

• Confounding

Selection Bias

• Refers to distortions that result from procedures
used to select subjects and from factors that
influence participation in the study.

• Arises when the relation between exposure and
disease is different for those who participate and
those who theoretically would be eligible for study
but do not participate.

• Example: Respondents to the Iowa Women’s
Health Study were younger, weighed less, and were
more likely to live in rural, less affluent counties than
nonrespondents.

Information Bias

• Can be introduced as a result of

measurement error in assessment of

both exposure and disease.

• Types of information bias:

– Recall bias: better recall among cases

than among controls.

• Example: Family recall bias

Information Bias (cont’d)

– Interviewer/abstractor bias–occurs

when interviewers probe more

thoroughly for an exposure in a case

than in a control.

– Prevarication (lying) bias–occurs when

participants have ulterior motives for

answering a question and thus may

underestimate or exaggerate an

exposure.

Confounding

• The distortion of the estimate of the
effect of an exposure of interest
because it is mixed with the effect of
an extraneous factor.

• Occurs when the crude and
adjusted measures of effect are not
equal (difference of at least 10%).

• Can be controlled for in the data
analysis.

Criteria of Confounders

• To be a confounder, an extraneous

factor must satisfy the following

criteria:

– Be a risk factor for the disease.

– Be associated with the exposure.

– Not be an intermediate step in the

causal path between exposure and

disease.

Simpson’s Paradox as an
Example of Confounding

• Simpson’s paradox means that an

association in observed subgroups of a

population may be reversed in the entire

population.

• Illustrated by examining the data (% of

black and gray hats) first according to two

individual tables and then by combining all

the hats on a single table.

Simpson’s Paradox (cont’d)

• When the hats are on separate tables, a
greater proportion of black hats than gray
hats on each table fit.

– On table 1:
• 90% of black hats fit

• 85% of gray hats fit

– On table 2:
• 15% of black hats fit

• 10% of gray hats fit

Simpson’s Paradox (cont’d)

Simpson’s Paradox (cont’d)

• When the man returns the next day

and all of the hats are on one table:

– 60% of gray hats fit (18 of 30)

– 40% of black hats fit (12 of 30)

Note that combining all of the hats on

one table is analogous to

confounding.

Examples of Confounding

• Air pollution and bronchitis are positively

associated. Both are influenced by

crowding, a confounding variable.

• The association between high altitude and

lower heart disease mortality also may be

linked to the ethnic composition of the

people in these regions.

Techniques to Reduce

Selection Bias

• Develop an explicit (objective) case

definition.

• Enroll all cases in a defined time and

region.

• Strive for high participation rates.

• Take precautions to ensure

representativeness.

Reducing Selection Bias Among

Cases

• Ensure that all medical facilities are thoroughly

canvassed.

• Develop an effective system for case

ascertainment.

• Consider whether all cases require medical

attention; consider possible strategies to

identify where else the cases might be

ascertained.

Reducing Selection Bias

Among Controls

• Compare the prevalence of the exposure

with other sources to evaluate credibility.

• Attempt to draw controls from a variety of

sources.

Techniques to Reduce

Information Bias
• Use memory aids; validate exposures.

• Blind interviewers as to subjects’ study status.

• Provide standardized training sessions and
protocols.

• Use standardized data collection forms.

• Blind participants as to study goals and
classification status.

• Try to ensure that questions are clearly
understood through careful wording and
pretesting.

Methods to Control

Confounding

• Prevention strategies–attempt to control confounding

through the study design itself.

• Three types of prevention strategies:

–

Randomization

–

Restriction

– Matching

• Two types of analysis strategies:

– Stratification

– Multivariate techniques

Randomization

• Attempts to ensure equal distributions of the
confounding variable in each exposure
category.

• Advantages:

– Convenient, inexpensive; permits straightforward
data analysis.

• Disadvantages:

– Need control over the exposure and the ability to
assign subjects to study groups.

– Need large sample sizes.

Restriction

• May prohibit variation of the confounder in the
study groups.

– For example, restricting participants to a
narrow age category can eliminate age as a
confounder.

• Provides complete control of known
confounders.

• Unlike randomization, cannot control for
unknown confounders.

Matching
• Matches subjects in the study groups according

to the value of the suspected or known
confounding variable to ensure equal
distributions.

• Frequency matching–the number of cases with
particular match characteristics is tabulated.

• Individual matching–the pairing of one or more

controls to each case based on similarity in sex,

race, or other variables.

Matching (cont’d)

• Advantages:

– Fewer subjects are required than in

unmatched studies of the same hypothesis.

– May enhance the validity of a follow-up study.

• Disadvantages:

– Costly because extensive searching and

recordkeeping are required to find matches.

Two Analysis Strategies to

Control Confounding

• Stratification–analyses performed to evaluate

the effect of an exposure within strata (levels) of

the confounder.

• Multivariate techniques–use computers to

construct mathematical models that describe

simultaneously the influence of exposure and

other factors that may be confounding the

effect.

Advantages of Stratification

• Performing analyses within strata is a

direct and logical strategy.

• Minimum assumptions must be

satisfied for the analysis to be

appropriate.

• The computational procedure is

straightforward.

Disadvantages of Stratification

• Small numbers of observations in some

strata.

• A variety of ways to form strata with

continuous variables.

• Difficulty in interpretation when several

confounding factors must be evaluated.

• Categorization results in loss of

information.

Multivariate Techniques

• Advantages:

– Continuous variables do not need to be

converted to categorical variables.

– Allow for simultaneous control of several

exposure variables in a single analysis.

• Disadvantages:

– Potential for misuse.

Publication Bias

• Occurs because of the influence of

study results on the chance of

publication.

– Studies with positive results are more

likely to be published than studies with

negative results.

Publication Bias (cont’d)

• May result in a preponderance of

false-positive results in the

literature.

• Bias is compounded when

published studies are subjected to

meta-analysis.