This document discusses various study designs used in epidemiology, including measures of disease occurrence such as prevalence and incidence. It defines prevalence as the total number of cases of a disease at a specified time, while incidence refers to the number of new cases that occur over a period of time. Cohort studies are described as following groups over time to compare rates of an outcome between those exposed and unexposed to a factor. Case-control studies select groups based on having or not having an outcome and look back to compare exposures. Biases such as selection, information and confounding are also outlined.

1. STUDY DESIGNS IN EPIDEMIOLOGY
ARAVIND L R

2. Measures of events
• Occurrence of disease can be measured using rates or proportions
• Proportions indicates the fraction of population affected by disease
• Rates inform how fast the disease occurs in a population

3. Measures of events
Prevalence – Total number of persons with specified diseases/condition
in a particular population
No. of cases of disease (new+ old) present in the population at a
specified time x 1000
No. of persons in the population at that specified time

4. Measures of events
• Incidence – Number of new cases of a disease that occur during a
specified period of time in a population at risk for developing the
disease
No. of new cases of disease occurring in the population
during a specified period of time x 1000
No. of persons who are at risk of developing the disease
during that period of time

5. • X is a ward with population of 1000.Among these 30 had tuberculosis.
What is the prevalence of tb in this population ?
Prevalence of tb = 30/1000 = 0.03 or 3%
• X is a ward with population of 1000 all free of disease. In this
community new tb cases diagnosed during the last 1 year period was
25.What is the incidence of tb in this population?
Incidence of tb = 25/975 = 0.025 or 2.5%

6. Probability and odds
• Chance of occurrence of an event. (range 0-1 or 0% to 100%)
Eg: probability of obtaining heads in a coin toss = ½ = 0.5 (50%)
• Odds are simply a different expression of probability (range 0- infinity)
• Odds = probability/(1-probability)
• Eg: Odds = 0.5/(1-0.5) = 1

7. Metrics of comparison
• Rate ratio, Rate difference, Risk ratio, Risk difference and Odds ratio are
the metrics used for comparing groups
• These are called ‘measures of association’ because they probe
whether there is any association between the disease under study and the
factor/ factors on which the grouping for comparison is done
• They are also called ‘effect measures’

8. Operational definitions
• Process of strictly defining variables into measurable factors.
• Difficult concepts eg: Health are defined in such a way that they can
be measured and scientifically and quantitatively eg: BMI or scales
• Lays down strict definitions for each variable including exposure and
outcome.

9. BIAS
Systematic, non-random deviation of results and 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 which are systematically different from the
truth. (Dictionary of Epidemiology, 3rd ed.)

10. Common types of bias:
Information bias
• Systematic difference in the content/ quality of information
between groups
• Recall bias: refers to bias introduced by differential memory
• Interviewer bias: differential response to interviewers
• Loss information due to differential response or follow up rate

11. Examples of selection bias
• People who chew tobacco may be alert to any oral lesions, and
report to dentist early
• Women on estrogen may have spotting as a symptom and report to
the doctor, prompting detection of early stages of cervical cancer
• People exposed to loud noise in their working environment may
lose their hearing; but they may not notice their loss of hearing
sensitivity since in their working environment, everybody tends to
talk loud.

12. Bias in studies comparing two groups
• Bias arises out of flawed design
• Best avoided, by carefully choosing methods
• Difficult to adjust in analysis

13. What is a potential
confounding factor?
EXPOSURE OUTCOME
CONFOUNDING
FACTOR

14. What is a potential
confounding factor?
This should be associated with the disease without being associated with
the exposure, AND associated with the exposure without being
associated with disease
Observed
Association
Coffee drinking
Smoking
Pancreatic cancer
Coffee drinking
Pancreatic cancer

15. THE DIFFERENCE BETWEEN BIAS AND
CONFOUNDING
Bias creates an association that is not true, but
confounding describes an association that is true, but
potentially misleading.

16. STUDY DESIGN

17. Why study design is important in epidemiology?
• Meeting the objectives of the study
• Arriving at correct study conclusions1
• Determine data analysis methods and summary measures
• Minimizes bias in the study
• Ensures comparability between population
• Generalizability of results
• Poorly designed study cannot be retrieved unlike analysis issues

18. Design selection
Choice of a particular study design depends upon various factors1
• Objectives
• Ethical issues
• Previous research studies
• Availability of study participants
• Time constraints
• Availability of resources
• Each design has its own inherent strengths and weakness

19. Major classification
Ecological
Descriptive studies Disease surveillance
Cross sectional
Observational study
Cohort
Analytical studies
Case Control
Randomized control Trials
Experimental study Field Trials
Community Intervention Trials

20. Evidence pyramid
Animal Research/Laboratory Studies
Ideas,Editorials,Opinions
Case Series/Case Reports
Cross sectional Studies
Case Control Studies
Cohort Studies
Randomized Controlled Trials
Systematic Reviews
Meta-analyses

21. • Descriptive studies describe events in terms of place, person and time
• Considered to be less rigorous than analytical design due to large number of confounders
• In contrast to descriptive studies, in analytical studies, the investigator proceeds with a
‘preformed hypothesis’ regarding a “causal exposure”. A large number of epidemiological
and health research questions are answered by undertaking analytical studies

22. Ecological study
• Basic observational study
• Studies populations/ groups/cluster rather than individuals
• At least one variable is measured at the group (not individual) level
• Suitable for hypothesis generation on cause and effect

23. Prostate mortality versus sugar consumption in 71 countries
Colli JL, Colli A. International comparisons of prostate cancer mortality rates with dietary practices
and sunlight levels. Urol Oncol. 2006;24:184–94.

24. Situations warranting an ecological design14
• New hypothesis
• Lack of information on individual level variables
• Individual level study cannot be performed due to ethical issues
• Effect of interest is from grouped data
• Time and resource constraints

25. Case series and case reports11
• Case series and case reports consist either of collections of reports on
the treatment of individual patients, or of reports on a single patient.
• No control group to assess outcomes
• Hence no statistical validity Hospital

26. Cross sectional design
• Snap shot: Exposure and disease related information measured at the
same time period
• Usually used to determine prevalence (number of cases in a population
at given point of time)
• Odds ratio is used for analysis

27. Advantages &disadvantages of cross sectional design4
Pros Cons
Comparatively quick and easy to conduct Relation between cause and effect cannot
be inferred
Identifying associations between variables Do not provide an explanation for findings
Useful to study multiple outcomes Cannot study rare diseases efficiently
Relatively less expensive ‘Telescoping’
Less ethical issues Selective Memory, Interviewer Bias

28. The key word in analytical studies is “COMPARISON”
Exposure (E)
Outcome (O)
Total
Present (O+) Absent (O -)
Present (E+) E+O+ (a) E+O - (b) All E+ (a+b)
Absent (E -) E - O+ (c) E - O - (d) All E - (c+d)
Total All O+ (a+c) All O - (b+d) a+b+c+d

29. How all can we compare?
• we can collect two groups of subjects, all free of CA Colon one group
eating low fibre diet (E+) and another eating high fibre diet (E - );
follow up both these groups for 15 - 20 years and see how many in
each group develop colonic CA.
• The comparison would then be made between “those who develop
the outcome out of total exposed” (i.e. a / (a+b)) and “those who
develop the outcome out of the total not exposed”, (i.e. c / (c+d)).
• we may take a group of patients already suffering from CA Colon (O+)
and another not suffering (O - ) and take the history of low dietary
fibre intake during last 15 - 20 years from all of them.
• the comparison would be made between “those with the outcome
having the exposure” (i.e. a / (a+c)) and those without the outcome
but having the exposure (i.e. b / (b+d)).

30. Cohort study
• In epidemiology, cohort refers to a group with common characteristics
and which is followed up over time
• Refers to “source population” or population from which cases arise

31. Cohort study
• Cohort or the group is usually arbitrarily decided by the researcher
• Rate of outcome determines the sample size13
• Expected drop out rates needs to be accounted in design
• Relative Risk is the effect measure

32. Cohort study design 11

33. Cohort based on direction
Grimes DA, Schulz KF. Cohort studies : marching towards outcomes. Lancet. 2002;359:341–5.

34. Smoking
(Exposure)
IHD (Outcome)
TotalDeveloped
(O +)
Did not
Develop (O - )
Present (E+)
150 (10%)
a
1350 (90%)
b
1500 (100%)
a+ b
Absent (E - )
175 (5%)
c
3325 (95%)
d
3500 (100%)
c+ d
Total
325
a+ c
4675
b+ d
5000
a + b + c+ d
Relative Risk = 0.10/0.05 =2.0

35. Risk ratio
• If we compute incidence (group 1)/ incidence (group 2), it gives
us the risk ratio
• If risk ratio =1, the two groups have comparable risk
• If the risk ratio > 1 or <1, one group has a higher risk ( or the
other group has a lower risk)
• RR>1 implies association between exposure and outcome

36. Advantages &disadvantages of Cohort study15
Pros Cons
Calculate incidence rate, risk and
relative risk
Time –specific biases
Multiple outcomes Repeated data collection
Less potential for recall bias Expensive
Facilitates causal inference Long follow up period
Widely used for research on risk
factors
Large sample size
Natural course of disease, survival Attrition bias or Loss to follow up

37. Case control studies
• Case – refers to a group with outcome of interest
(with diseases/poor outcome to a treatment)
• Control – refers to group without the outcome of interest
(no diseases/good outcome to a treatment)
• Retrospective in direction ie from outcome to exposure
• Higher exposure in cases compared to control implies association between
exposure and disease
• Prevalence of exposure determines the sample size13

38. Design of Case control studies

39. Selection of controls7
• Most difficult aspect of a case control study
• Controls should be comparable to cases in all aspects except disease status
• Sampled independent of exposure status (exposed/unexposed)
• Controls need to be sampled from same population as cases

40. Selection of controls 7
• Controls should reflect exposure distribution in source population
• Selecting multiple controls per case can increase likelihood of obtaining
significant associations
• The ratio can be 1:1, 1:2 or even up to 1:4
• Beyond this ratio recruiting further controls does not increase
likelihood

41. Source of controls
• Hospital controls – exclude controls related to disease as well as related risk
factors
• Friend Controls – similar socio demographic characteristics
• Relative controls
• Neighborhood or community controls – to counter Berksons bias
• Sibling controls – genetic diseases

42. Types of case control study
1)Unmatched
2)Matched :
Individual (matched pair) or Frequency matching (Group matching)
• Matching is defined as the process of selecting controls so that they are
similar to cases in certain characteristics such as age, race, socioeconomic
status and occupation.18

43. Calculating Odds ratio in case control study
Cases Controls
112 (a) 176 (b)
88 (c) 224 (d)
Smokers
Non Smokers
Odds ratio = ad/bc = 112*224/176*88 =1.62

44. Advantages &disadvantages of case control design 15
Pros Cons
Appropriate for rare diseases Recall bias
Multiple exposures can be studied Cannot compute incidence
Understanding etiology of novel diseases Provides only an estimate of relative risk ie. Odds
ratio
Useful for outbreak investigations Natural course of disease, survival cannot be
studied
Diseases with long induction period Assess only one outcome variable in a study
Comparatively less expensive Availability of quality records
Quick and easy to conduct, few subjects Difficulty in selection of controls
Less potential for loss to follow up Subject to various Bias

45. Systematic reviews
State objectives and eligibility criterion explicitly
Comprehensive search for related literature
Examine thoroughly and assess for methodological
rigor
Apply Eligibility criterion and justify exclusions
Prepare a critical summary of the review, stating
aims, describing materials, and reporting results

46. Meta analysis
Study 2 Study 4Study 3 Study 5 Study 6Study 1
Combined Estimate
Statistical Pooling

47. references
1. Thiese MS. Observational and interventional study design types; an
overview. Biochem Medica. 2014;24(2):199–210.
2. WHO
http://www.who.int/ipcs/publications/ehc/216_disinfectants_part_4.pdf
3. IARC http://www.iarc.fr/en/publications/pdfs-
online/epi/cancerepi/CancerEpi-5.pdf
4. Mann CJ. Observational research methods . Research design II :cohort,
cross sectional, and case-control studies. Emerg Med J. 2003;20:54–60.
5. IARC http://www.iarc.fr/en/publications/pdfs-
online/epi/cancerepi/CancerEpi-5.pdf
6. Nelson A, Brunette K. Case-Control Studies for Outbreak Investigations.
Focus F Epidemiol. 3(2).

48. references
11. EBM Tutorial http://library.downstate.edu/EBM2/research.htm
12. https://onlinecourses.science.psu.edu/stat507/node/46
13. https://onlinecourses.science.psu.edu/stat507/node/68
14. https://onlinecourses.science.psu.edu/stat507/node/45
15. https://onlinecourses.science.psu.edu/stat507/node/62
16. Kleinbaum DG, Sullivan KM, Barker ND. A Pocket Guide to Epidemiology. 2007.
17.Levin KA. Study Design VI - Ecological Studies. Evid Based Dent. 2003;7:60–1.
18.Rothman KJ. Epidemiology An Introduction Oxford University Press 2012,1-268

49. Thank you