Slide 1

Designs vary based on the following key elements:
Things to consider in choosing a study design:
The prevalence of the EXPOSURE (Frequent vs. Rare)
The prevalence of the OUTCOME (Frequent vs. Rare)
How much is known about the relationship?
How strong you want the evidence to be?
How are subjects sampled (on disease status or exposure status)
Data collection methods (survey, observations, experimental)
Timing of data collection (when)
Unit of observation (Individuals or groups)
Number of observations made (data collection points)
Availability of subjects
$$$$$

Choosing a good study design is essentialin epidemiology
The general common choices are:
Case reports and Case series
Ecologic
Cross-sectional
Case-control
Cohort
Randomized-controlled Trial (experimental design)
Observational vs. Experimental Approaches
Manipulation of study factor
Was exposure of interest controlled by the investigator? (No=observational; Yes=Experimental)
Randomization of study subjects
Was there use of a random process to determine exposure of study subjects?
(No=observational; Yes=Experimental)

(Observational/ descriptive Studies =
the investigator observes the natural occurrence of the events)
These studies help us generate hypotheses

I have a headache and fever, and purple polka dots on my feet.
Case Report
Physician or group of physicians seeing a handful of cases of a very rare disease, or an unusual presentation of a disease
They are useful as a starting point for thinking about etiology (cause).
Has no comparison group
Without any comparison group, we CANNOT assume any associations.
Case Series
A case series is an extension of a case report. You now have several cases of the disease
You can start looking for commonalitiesWhich may help you develop a hypothesis.
You cannot make ANY argument about an exposure being associated with disease.
For that, we need a comparison group.
Selection bias (major disadvantage)
Error due to systematic differences in characteristics between those who take part in a study and those who do not (nonresponse problems).

Ecologic Studies (group level analysis)
Aggregates of individuals defined by units: geographic region, school, health care facility.
Does the overall occurrence of disease in a population correlate with occurrence of the exposure.
No individual level data
People argue that a high-fat diet is associated with breast cancer since countries with high-fat diets have higher breast cancer incidence.

Ecologic Study Drawbacks
Can we be sure that the pattern we are seeing is reflecting the real cause?
No, not with this design.
We are looking at data on groups, not individuals.
Tendency to apply the relationship seen at the group level to an individual == ecologic fallacy
Tendency to think what is meaningful for the group is meaningful to the individual
Likely to be many confounding factors we haven’t considered
So, to look at causal relationships, I should have a comparison group…
…and I should have data on individuals, not groups

Cross-Sectional Studies (Surveys)
Collect data (single point in TIME) on individuals and allows for comparison between groups.
Sampling is done on a single group of people of interest (e.g. college students and drinking)
The main tool is a survey.
After collecting data,yougroup them by exposure and an outcome status to everyone (drinkers vs. nondrinkers)
Compute a measure of association to compare by either exposure or disease status
Considers prevalenceof disease or condition

Cross-Sectional 2 x 2
Analysis starts here, since you sampled one whole group.
Now, you distribute your total N based on E and D status
e.g., obesity and diabetes

Example: Cross-Sectional Study
Total subjects surveyed
Prevalence of exposurecomparison in diseased and nondiseased:
OR = a/c = ad
b/d bc
= (150 x 125) / (50 x 75) = 18750 / 3750 = 5.0

Cross-Sectional Studies
Pros
Quick &Inexpensive
Usually easy to conduct
Representative sample
Describe patterns of disease occurrence
Can study several diseases or exposures
Cons
We have prevalence data only, no incidence data.
We collect exposure and outcome information at the same time, so we cannot argue any temporal relationship between the two (exposure must precede disease in order to be causal)
Selection bias (non-response)

So what is our next step?
So far we discussed at, descriptive designs that allow us to look at correlations and associations, and provide knowledge for hypothesis development
But, we need to consider incident cases and time sequence if we want to look at cause-effect relationships (temporality)
For that, we must turn to the analytic study designs.
case-control, and cohort (hypothesis testing)

Analytic study designs

Design of a Case-Control Study
e.g., lung cancer patients and controls are asked how much they smoked in the past
Source:www.socialresearchmethods.net/.../cohort.html

Case-control Study Analytic study
The study subjects are selected on the basis of whether they have the disease (cases) or not (controls) and then
We determine how many in each group had the risk factor (exposed).
A case definition is a statement of the characteristics a subject must have to be considered a “case” of the disease to be studied.
Usually confirmed by clinical signs or laboratory test
Signs must distinguish the specific disease from similar conditions that might be caused by different exposures, genetic traits, interactions, etc.
May consist of combinations of symptoms. (e.g., flu vs. cold)

Case-control Study
Exposure collection is retrospective
You will ask cases and controls about their exposures before they became a case (or before the date they were enrolled as a control)
Need to identify a comparison group - controls
These could be people in the hospital with other conditions, people from the neighborhood where the case lives, people identified from voter registration
The goal is to identify a control group that represents the source population of the cases
Population-based controls
Patients from the same hospital as the cases
Relatives of cases
Friends of cases--SES control
Any exclusion or inclusion criteria applied to the selection of cases must also be applied in the selection of controls.

Case-control 2 x 2 Odds Ratio
Definition of odds: the ratio of the probability of an event occurring (disease) to that of it not occurring = P /1-P
Your total group was sampled here.
You assign exposure status for the case and control groups, and this completes the table

Calculating Odds Ratio
CasesControls
Exposed a b
Not Exposedc d
a+cb+d
Odds of exposure among cases:
Proportion of exposed among cases
Proportion of non-exposed among cases
= (a/a+c) / (c/a+c) = a/c
OR = a/c = ad
b/d bc
Odds of exposure among controls:
Proportion of exposed among controls
Proportion of non-exposed among controls
= (b/b+d)/(d/b+d) = b/d

Myocardial infarction
Total
No
Yes
327
2949
3276
304
2816
3120
23
133
156
Current OC use
Yes
No
Total
a = 23 b = 304
c = 133 d = 2816
Data from L. Rosenberg et al., Oral contraceptive use in relation to non-fatal
myocardial infarction. Am J. Epidemiol. 111:59, 1980.
a/d 23 x 2816
OR = = = OR = 1.6
b/c 304 x 133
Data from a case-control study of current oral contraceptive (OC) use and myocardial infarction in premenopausal female nurses
Those who used OC had a 60% increased risk of having MI
compared to women who did not use. Or
the relative odds are nearly 2 times higher among those who used OC

Case-control studies
Pros
Relatively inexpensive, small sample sizes, fast study technique
Great for rare diseases (sampling on disease status lets you guarantee enough cases to make comparisons)
Can study multiple exposures
Cons
Hard to find an appropriate control group (should match the cases in other characteristics)
Potential for recall bias – biggest problem (limited recall or selective memory)
Can assume, but cannot confirm the temporal sequence of exposure and disease

Cohort Design
Disease
Exposed
No disease
People without disease
A cohort is defined as a population group, or subset thereof, that is followed over a period of time.
Population
Disease
Not exposed
No disease
Instead of sampling on disease status, we sample on exposure(Subjects are defined on the basis of presence or absence of exposure to a risk factor; but you are not assigning an exposure to occur)
Follow people prospectivelyuntil disease occurs or the study ends (thus, you can demonstrate that exposure precedes disease)
At the time exposure status is defined subjects are outcome negative.

Cohort Design
The purpose of following a cohort is to measure the occurrence of one or more specific diseases/outcomes during the period of follow-up, usually with the aim of comparing the disease rates for two or more cohorts.
What are the requirements for the Cohort Population
All members of the cohort population must:
• Be free of the disease at the start of the study period.
• Be at risk of developing the disease
– Must be alive (exclude dead people).
– Must not be immune (exclude people who have been immunized or have had the disease before, if the first episode confers immunity). Example: measles
– Must not be in a non-susceptible group (men don’t get cervical cancer)

TYPES OF COHORT STUDIES
A. TIMING
PROSPECTIVE (OR CONCURRENT)
RETROSPECTIVE (OR NON-CONCURRENT)
B. SAMPLING
cohort studies with sampling unrelated to exposure (common)
cohort studies with exposure-based sampling (rare exposure)
C. POPULATION BASE
Population-based
not-population based
D. TYPE OF COHORTS
• OPEN - people moving in and out
• CLOSED - fixed population

Types of Cohorts
Closed Cohort:
cohort is defined at the start of the study.
No subjects are added after the start,
some cohort members may drop out or die before the end of the study.
Investigators try to follow all cohort members to the study end.
Examples: Clinical trials, Framingham study, Nurses, Health Study
Open Cohort = The cohort takes on new members and may lose members as the study progresses. Also called a Dynamic Cohort or Dynamic Population.
Examples: Cancer registries, school studies, hospital
infection surveillance studies

Study starts
Study starts
Study starts
Disease
occurrence
Exposure
time
Prospective cohort study
Retrospective cohort study
Disease
occurrence
Exposure
Disease
occurrence
Exposure
time
time

Prospective vs. Retrospective studies
Prospective Study: Observing a cohort of subjects and over a long periodfor an event occur (e.g., disease or death, or cure) during the study period and take a note of suspected risk or protective factor(s).
The outcome of interest should be clear and defined.
Use incidenceof an outcome or the relative risk of an outcome based on exposure.
Retrospective Study: Looks backwards and examines exposures to suspected risk or protection factors in relation to an outcome. E.g., cancer
Many valuable case-control studies, are retrospective and ask for patient histories.
the odds ratio provides an estimate of relative risk (in Rare disease).

Examples of Prospective Cohort Studies
Framingham Heart Study(http://www.framinghamheartstudy.org/about/index.html
Study began in 1948 by recruiting an Original Cohort of 5,209 men and women between the ages of 30 and 62 from the town of Framingham, Massachusetts, who had not yet developed overt symptoms of cardiovascular disease or suffered a heart attack or stroke
. Since that time the Study has added an Offspring Cohort in 1971, the Omni Cohort in 1994, a Third Generation Cohort in 2002, a New Offspring Spouse Cohort in 2003, and a Second Generation Omni Cohort in 2003.
identification of major CVD risk factors

CARDIOVASCULAR DISEASE
Framingham, MA
Tecumseh, MI
Evans county, GA (biracial)
Muscatine, IA
Bogalusa, LA (children)
OCCUPATION BASED TO STUDY EXPOSURES
Benzene-workers (leukemia) Coke-oven workers (lung cancer) Asbestos workers (lung cancer) Radium dial painters (oral cancer)
VETERANS
Mustard-gas poisoning from WW I (lung disease)
Vietnam Veterans (post-traumatic stress disorder, agent orange effects)
Gulf War Veterans (Gulf war syndrome)

http://www.nationalchildrensstudy.gov/Pages/default.aspx
The National Children’s Studywill examine the effects of environmental influences on the health and development of 100,000 children across the United States, following them from before birth until age 21.
105 Study locations (counties or groups of counties) across the United States.
79 metropolitan areas (urban, suburban, and small cities), as well as 26 rural communities.
All locations were selected using a probability-based sampling method to ensure that children and families across the nation—from diverse ethnic, racial, economic, religious, geographic, and social groups—are represented in the Study.
Enroll women who are either pregnant or likely to have a child during the recruitment period.
Each Study location will recruit enough women for 250 infant births per year during the four-year enrollment period.

Outcomes
http://www.nationalchildrensstudy.gov/research/hypotheses/Pages/hypotheses_list.aspx
Research efforts geared toward studying children’s health and development and will form the basis of child health guidance, interventions, and policy for generations to come.
The NCS will examine important health issues to establish links between children’s environments and their health, including:
birth defects and pregnancy-related problems
behavior, learning, and mental health disorders
injuries
asthma
obesity
diabetes
Study Locations State Map
* To become active 2009 - 2010Families will join the Study, or enroll, beginning in some communities in the winter of 2009. Other communities will begin enrolling families over the next couple of years.

Cohort Data Analysis
The tabulation and analysis of morbidity or mortality rates
The study base is the person-time experience of the individuals in whom the outcome is ascertained.
Calculation of person-years at risk is the means of achieving equivalence of study base in cohort studies.
The denominator depends on whether all subjects were followed till the end of the study or not.

Measuring Exposure: The Scale
• There are different ways to measure exposure, and
their association with outcomes will vary.
• For example,
measuring smoking exposure:
– Smoker
• Yes
• No
• Never
• Past but quit (pack-years, type of smoking)
• Current (pack-years, type of smoking)
– Pack-years of smoking
How many cigarettes do you currently smoke per day?
Then we can categorize accordingly.

Cohort Study 2 x 2
We can calculate either the Relative Risk as a valid measure of association,
You selected study subjects by exposure status, so you know these row totals.
(a+b) and (c+d)
Now you separate your two exposure groups by disease status

Risk ratios or Relative Risk
What if we started with exposure instead of disease status? We could compute risk directly, so the measure of association is the risk ratio.
Essentially, you compute the risk in the exposed group and divide by the risk in the unexposed group
Risk in exposed=a/(a+b)
Risk in unexposed=c/(c+d)

Example
Imaging that the data from Example 1 were actually from a cohort (meaning we followed OJ drinkers and non-drinkers over time)
The relative risk would be 2.5, meaning orange juice drinkers had a 150% increase (2.5-1) in the risk of stomach ulcer as compared to non-drinkers.

Life Table Methods
Give estimates for survival during time intervals and present the cumulative survival probability at the end of the interval.
Example: Life tables can be constructed to portray the survival times of patients in clinical trials.
There are two life table methods:
Cohort life table:
Shows the mortality experience of all persons born during a particular year.
Period life table:
Enables us to project the future life expectancy of persons born during the year as well as the remaining life expectancy of persons who have attained a certain age.

Survival Curves
A method for portraying survival times
In order to construct a survival curve, the following information is required:
Time of entry into the study
Time of death or other outcome
Status of patient at time of outcome, e.g., dead or censored (patient is lost to follow-up)
Example
15 subjects followed over 36 months; all entered the study at the same time.
Nine died at different points of the study.
Deaths of two patients caused a steep drop at 19 months.
Each step indicates the death(s) of one or more patients.

Advantages of Cohort Studies
- Can examine rare exposures (asbestos = lung cancer)
Temporal relationship can be inferred (prospective design); temporal order between exposure and outcome (Temporality)
Time-to-event analysis is possible - Multiple outcomes of a single exposure can be studied
(smoking = lung cancer, COPD, larynx cancer); may uncover unanticipated associations with outcome
Cohort studies are usually but not exclusively prospective,
- outcome is measured after exposure and can determine multiple effects of a single exposure.

Lengthy/ Time consuming and expensive (if prospective study)
If retrospective, requires availability of adequate records
- May require very large samples
- Not suitable for rare diseases: When outcomes are rare, large populations need to be followed for outcomes.
- Not suitable for diseases with long-latency
- Unexpected environmental changes may influence the association
Nonresponse, migration and loss-to-follow-up biases (Tracking for a long time)
Potential for bias from loss to follow up affecting the Validity
Sampling, ascertainment and observer biases are still possible
Inefficient for study of rare disease
Disadvantages of Cohort Studies

Bias or issues in Interpretation of the results
1. Loss to Follow Up
Large loss to follow up (more than 30%) will raise issues about validity.
Obtain as much data as possible.
Loss to follow up analysis using baseline data to compare those interviewed and those lost to follow up.
Loss to follow up is the major source of bias in cohort studies.
Retention rate related to length of follow up.
2. Non-response
Those who agree to participate may differ from non-participants.
Non-response affects generalizability and MAY affect validity.
Non-response affects validity if it is associated to both exposure and other risk factors.

Slide 1

by Medresearch

Accessibility

Categories

Upload Details

Usage Rights

Statistics

Slide 1 Presentation Transcript