The document discusses various study designs used in epidemiology and statistics, including observational and experimental designs. It provides details on descriptive and analytical observational studies. Descriptive studies generate hypotheses, while analytical studies allow determination of causal associations by including a comparison or control group. Experimental designs are randomized studies that can establish causal relationships. The document also covers topics like odds ratios, relative risks, attributable risks, chi-square tests, sensitivity and specificity in diagnostic testing.

1. Epidemiology & Statistics

2. Study Designs
Observational
Descriptive
Analytical
Experimental / Interventional
NonRandomized
•Cohort Studies
•Case Report
•Case Control(Trohoc)
•Case Series
•Cross – Sectional D. •Cross –Sectional Analytical
•Ecological
•Prevalence
/Surveillance
Randomized

3. LANCET
2002:359:57 - 61

4. LANCET
2002:359:57 - 61

5. Observational - Descriptive
• Frequency, Natural History, Possible
•
•
•
determinants
No comparisons groups
Useful for hypothesis generation about causal
associations
E.g.:
o
o
o
o
Case Reports (SLJOP) –www.sljol.info
Case series
Descriptive Cross Sectional
Ecological/Population – Correlations

6. Observational - Descriptive
• Data
Analysis / Presentation
o Rates
o Proportions
o Percentages
with 95% Confidence Interval

7. Observational - Analytical
• Always have a comparison/control group
• Allows determination of causal association
• Hypothesis testing
• E.g.
o Cohort study
o Case Control Study (Trohoc Study)
o Analytical Cross Sectional Study

8. Observational - Analytical
LANCET
2002:359:57 - 61

9. Observational - Analytical
• Data
o
o
o
o
o
o
o
o
o
Analysis / Presentation - Basic
Rates
Proportions
with 95% Confidence Interval
Percentages
Odds Ratio (95% CI) – Case control & Cross sectional
Relative Risk (95% CI)
Attributable Risk/ Risk difference
Attributable Risk ratio / Aetiologic Fraction
Cohort
Population Attributable risk
Number Needed to Treat /Harm

10. Observational - Analytical
• Data
o
o
o
o
o
o
o
o
Analysis / Presentation – Advanced
Comparison of 2 proportions – Z test
Comparison of 2 Means - t test , Z test
Comparison of >2 Means - ANOVA
Comparison of 2 Medians – M- W U test /Rank Sum Test
Comparison of >2 Medians – Kruskal Wallis Test
Comparison of Categories (counts) – Chi Square Test
Correlation – Pearson /Spearman
Linear Association bet 2 variables – Simple Linear
regression
Exceptions and more classifications are there !

11. 2 x 2 table
Disease
Factor
Total
Positive
Negative
Positive
a
b
a+b
Negative
c
d
c+d
Total
a+c
b+d
a+b+c+d

12. 2 x 2 table
• Odds
o Odds
o Odds ratio (OR)
• Risk
o
o
o
o
o
Risk
Relative risk (RR) / Risk ratio
Attributable risk (AR)/Risk difference
Attributable risk ratio (AR %) /Aetiologic fraction
Number Needed to Treat /Harm (NNT/NNH)
• Chi square test (
• Screening
o
o
o
o
2)
Sensitivity
Specificity
Positive predicative value (PPV)
Negative predictive value (NPV)

13. Odds
•
Odds = Part /Non part
Disease
Factor
Total
Positive
Negative
Positive
a
b
a+b
Negative
c
d
c+d
Total
a+c
b+d
a+b+c+
d
•Odds of disease among exposed (factor positive) = a / b
•Odds of disease among non exposed (factor negative) = c / d

14. Odds ratio (OR)
• Ratio between odds of exposure and
•
Non
exposure odds
OR = (a / b) / (c / d ) = (a x d ) / (b x c)
Disease
Factor
Total
Positive
Negative
Positive
a
b
a+b
Negative
c
d
c+d
Total
a+c
b+d
a+b+c+d
Cross product ratio

15. Odds ratio (OR)…
• CI does not include 1 then its significant
•
o OR =2.6 ,CI 1.9 – 3.3
o OR = 1.1,CI 0.4 – 2.3 Not significant
IF OR bet. 0 -1 with CI not crosses 1
o OR is significant
o Factor is protective
o Eg
 OR 0. 4, CI 0.36 - 0.48
• Small CI – higher precision

16. Risk
• Risk ( proportion ) =
Diseased / Pop. at risk(Total
exposed) [also known as (Cumulative) Incidence]
Disease
Factor
Total
Positive
Negative
Positive
a
b
a+b
Negative
c
d
c+d
Total
a+c
b+d
a+b+c+d
•Risk of disease among exposed (factor positive) = a / a+b
•Risk of disease among non exposed (factor negative) = c / c+d
Estimated risk/ Average risk

17. Risk Ratio / Relative Risk - RR
• Ratio between risks of exposure and
•
Non
exposure risks for the disease
RR = [a /(a+b) ] / [c /(c+d) ]
Disease
Factor
Total
Positive
Negative
Positive
a
b
a+b
Negative
c
d
c+d
Total
a+c
b+d
a+b+c+d
Remember OR ??

18. Risk Ratio / Relative Risk
• CI does not include 1 then its significant
•
o RR =2.6 ,CI 1.9 – 3.3
o RR = 1.1,CI 0.4 – 2.3 Not significant
IF RR bet. 0 -1 with CI not crosses 1
o RR is significant
o Factor is protective
o Eg
 RR 0. 4, CI 0.36 - 0.48
• Higher the
RR – Higher the Risk

19. Attributable Risk (AR) / Risk Difference
• Excess risk of disease among exposed,
compared to non exposed
• AR = (Risk exposed) – (Risk unexposed)
= (a / a+b) – (c / c+d )
• Simple difference of risks

20. Attributable risk ratio (AR %)/Aetiologic fraction
• Estimate of the proportion of disease that can be
•
attributed to the exposure
Proportion of disease that can be eliminated if the
exposure is removed
• AR %
= (Risk exposed) – (Risk unexposed) x 100
(Risk exposed)
= Attributable fraction x 100

21. Number Needed to Treat /Harm (NNT/NNH)
• NNH
o Number of subjects, if given a harmful exposure (Risk
exposed > Risk unexposed) would cause the one case of
disease
• NNT
o Number of subjects, if given a treatment /Protective
exposure (Risk exposed < Risk unexposed) would
prevent the one case of disease
• NNT/NNH = 1/(Risk exposed)–(Risk unexposed)
= 1 / AR

22. Variable 2
Chi Square ( 2) Test – for counts
Variable 1
Total
Category 1
(Level 1)
Category 2
(Level 2)
Category 1
(Level 1)
a
b
a+b
Category 2
(Level 2)
c
d
c+d
Total
a+c
b+d
a+b+c+d

23. Chi Square ( 2) Test (2x2 table)
• Check the association of 2 categorical variables
• There is an association of that variables
o If p value is less than 0.05 (or significant level )
and / or
o If Chi Square value (or Test Statistic ) Is > 3.86
• Degree of freedom(df) is 2
• Need to interpret according to the variables
concerned

24. Screening
Test
Disease
(Gold standard)
Total
Positive
Negative
Positive
a
b
a+b
Negative
c
d
c+d
Total
a+c
b+d
a+b+c+d
• Sensitivity
• Specificity
• Positive Predictive Value (PPV)
• Negative Predictive Value (NPV)

25. Sensitivity
Test
Disease
(Gold standard)
Total
Positive
Negative
Positive
a
b
a+b
Negative
c
d
c+d
Total
a+c
b+d
a+b+c+d
• Sensitivity
= Test Positive X 100 % = a/(a+c) %
True Positive

26. Specificity
Test
Disease
(Gold standard)
Total
Positive
Negative
Positive
a
b
a+b
Negative
c
d
c+d
Total
a+c
b+d
a+b+c+d
• Specificity
= Test Negative X 100 % = d/(b+d) %
True Negative

27. Positive Predictive Value (PPV)
Test
Disease
(Gold standard)
Total
Positive
Negative
Positive
a
b
a+b
Negative
c
d
c+d
Total
a+c
b+d
a+b+c+d
• PPV =Test Positives among true positivesX100%
Test Positive

28. Negative Predictive Value (NPV)
Test
Disease
(Gold standard)
Total
Positive
Negative
Positive
a
b
a+b
Negative
c
d
c+d
Total
a+c
b+d
a+b+c+d
• NPV =Test Negatives among true negativesX100%
Test Negatives

29. Thank You

30. Questions and Answers

31. Sensitivity / Specificity
1.Comment briefly on the following.
o
o
o
o
Sensitivity
Specificity
Positive predictive value
Negative predictive Value

32. Sensitivity / Specificity
2. A new screening test for Chlamydia was
administered to 500 patients attending the
STD clinic and 100 0f them tested positive.
Out of these 100 patients 90 gave positive
results on the confirmatory test (gold
standard). Among those who gave negative
results for the new screening test 20 gave
positive results on the confirmatory test.
1. Present the above data as a 2 by 2 table
2. Calculate Sensitivity, Specificity, Positive predictive
value ,Negative predictive value

33. Diagnostic test
Disease
Positive
Negative
Positive
90
10
100
Negative
20
380
400
110
390
500
•
Sensitivity = (90 /110)*100 = 81.818 %
•
Specificity= (380/390)*100 = 97.4358%
•
PPV = 90 / 100 *100 = 90%
•
NPV = 380/400 * 100 = 95 %

34. • P value ??
• OR = 20/20 80 /180 = 2.25
• Interpretation : odds of having a breast cancer
is 2.25 time is higher if life time breast feeding
duration less than 24months

38. 2.8 times higher the risk for hospitalization with diarrhea during infancy in
children who were Introduced complementary feeding before 6 months.
If we take 100 samples of similar children from the population 95 from
them have the calculated risk between 1.9 to 4.2
•
•
6.1 8 times higher the risk for hospitalization with diarrhea during infancy
in children who have Illiterate mothers. But . If we take 100 samples of
similar children from the population 95 from them have the calculated
risk between 0.9 to 143.2. Its shows huge variation and interval includes
1. So this factor is less important in making conclusions
Protective factor - The odds of the Water on tap inside the house group
experiencing hospitalization with diarrhea during infancy is less than the
odds of the Water on tap not inside the house group experiencing the
hospitalization with diarrhea during infancy. In other words its 0.5 times
less than Water on tap not inside the house group. The range not cross
1 so this is significant.

43. • Risk (x) = 48 / 1002 = 0.0479
• Risk (Placebo) = 53/500 = 0.106
• NNT
• NNT
= 1/ 0.0479 - 0.106
= 17.211
≈ 18

44. Thank You