understanding study designs through examples manish chaudhary mph (bpkihs) [email protected]

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Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) [email protected]

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Page 1: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

Understanding study designs through examples

Manish ChaudharyMPH (BPKIHS)

[email protected]

Page 2: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

Framework of Cohort StudyExposed Disease Total

Present Absent

Yes a b a+b

No c d c+d

Total a+c b+d a+b+c+d

Incidence of disease among exposed( I1)=a/(a+b)Incidence of disease among non-exposed( I0)=c/(c+d)

Relative Risk= {a/(a+b)}/{c/(c+d)}Attributable Risk/Risk Difference= {a/(a+b)-c/(c+d)}

Attributable Risk percent= {a/(a+b)-c/(c+d)}/ {a/(a+b)}Population attributable risk= incidence of disease in total

population minus incidence of disease among unexposed.

Page 3: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

The Concept of Relative Risk

Both case-control and cohort studies are designed to determine whether there is an association between exposure to a factor and development of a disease. If an association exists, how strong is it? If we carry out a cohort study, we can put the question another way: “What is the ratio of the risk of disease in exposed individuals to the risk of disease in non-exposed individuals?” This ratio is called the relative risk.

Page 4: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

Relative risk (RR)measures the strength of association

1. If RR=1, the numerator equals the denominator, and the risk in exposed persons equals the risk in non-exposed persons. So, no evidence exists for any increased risk in exposed individuals or for any association of the disease with the exposure in question.

2. If RR>1, the numerator is greater than the denominator, and the risk in exposed persons is greater than the risk in non-exposed persons. This is evidence of a positive association, and may be causal.

3. If RR <1, the numerator is less than the denominator, and the risk in exposed persons is less than the risk in non-exposed persons. This is evidence of a negative association, and it may be indicative of a protective effect. Such a finding can be observed in people who are given an effective vaccine (“exposed” to the vaccine).

Page 5: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

• RR=2 means the incidence rate of disease is 2 times higher in the exposed group as compared with unexposed. Similarly there is 100% increase in risk.

• RR=0.25 means a 75% reduction in the incidence rate in exposed individuals as compared to the unexposed.

• Absolute effect is calculated as (AR) = I1- I0• Relative effect is calculated as = (I1-I0)/I0 =RR-1• Subtracting 1 from relative risk the measure of

absolute effective to the baseline(I0)

Page 6: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

Attributable Risk (AR)

• AR is the risk difference in Incidence rates of disease (or death) between exposed groups and non-exposed. It is also called Risk Difference. Often expressed in percentage.

Page 7: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

Relative VS. Attributable Risk

• RR important for etiological inquiries.

• Better index than AR in identifying cause.

• Larger the RR, stronger the association.

• AR gives better idea than does RR of the impact of successful preventive or public health programme might have in reducing the problem.

Page 8: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

POPULATION ATTRIBUTABLE RISK

• It is the difference between the incidence of disease (or death) in the total population and the incidence of disease (or death) among those who were not exposed to the suspected causal factors.

Page 9: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

Framework of Case- Control Study

Exposed Disease TotalPresent Absent

Yes a b a+b

No c d c+d

Total a+c b+d a+b+c+d

Odds Ratio: ad/bc

Page 10: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

Odds Ratio

• The odds ratio is a way of comparing whether the probability of a certain event is the same for two groups.

• An odds ratio of 1 implies that the event is equally likely in both groups. An odds ratio greater than one implies that the event is more likely in the first group. An odds ratio less than one implies that the event is less likely in the first group.

• Shown is the typical 2 by 2 table.

Page 11: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

• The odds ratio (OR) is simply the ratio of the two odds

• Odds: ratio of probability of an event occurring to that of not occurring

• In order to calculate odds-ratio, calculate the odds of disease in the exposed to odds of disease to unexposed to the risk factor.

Page 12: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

• Odds of disease in the exposed– Probability of disease among exposed {a/(a+b)} to

probability of no disease among exposed {b/(a+b)}– a/b

• Odds of disease in the no exposure – Probability of disease among unexposed {c/(c+d)} to

Probability of no disease among unexposed{d/(c+d)}– c/d

• Odds Ratio– Ratio of Odds of exposure risk factors– ad/bc

Page 13: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

• Odds ratio is the approximation to risk ratio when incidence of the disease is low(<10%)

• Risk ratio= {a/(a+b)}/{c/(c+d)}• Odds ratio=(a/b)/(c/d)• In this situation the odds ratio is equivalent to risk

ratio only when b = a+b and c = c+d

Exposed Disease Total

Present Absent

Yes a b a+b

No c d c+d

Total a+c b+d a+b+c+d

Page 14: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

• Q1 At the start of the study, 374 laryngeal cancer patients were identified and 381 were taken as comparison group. 331 were found to be smokers in patients and 218 were found smokers in comparison group. Find the appropriate strength of association.

Page 15: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

• Suppose we are trying to study whether eating tasty pork dish in a particular dinner. 40 people assumed dinner party, 20 were Muslims. 16 out of pork eaters had diarrhea after returning from party and 2 out of non pork eater develop diarrhea. Find

• Risk ratio• Attributable risk• Attributable risk percentage• Population attributable risk• Population attributable risk percentage

Page 16: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

The RRs and ARs of Cardiovascular complications in women taking oral contraceptives

CVD risk 100,000 Patients years

Ages

30-39 40-44

RR 2.8 2.8

AR 3.5 20

Page 17: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

Risk Assessment, Smokers Vs Non Smokers

Cause of Death

Death Rate/1000 RR AR %

Smokers Non-smokers

Lung Cancer

0.90 0.07 12.86 92.2

CHD 4.87 4.22 1.15 13.3

Page 18: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

Examples from the literature

• Framingham Heart Study initiated in 1948 by US Public Health Services:

to study the relationship of a variety of factors to the subsequent development of heart disease

Group of persons30 – 62yrs

6,500Both sexes

20 years follow up

Information:S. cholest.levelBl.pressure , weightCig. Smoking

outcome

Page 19: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

Children (<12 yrs)

1000

Family smoker500 childrenExposed

Family non-smoker500 childrenNot exposed

1 year

Diseased 300

Not diseased 200

Diseased 120

Not diseased 380

OutcomeStart

Page 20: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

Rate: Incidence rate

•Incidence of Resp. Infection among exposed children: 300

500 = 60%

•Incidence of Resp. Infect. Among non exposed children: 120

500 = 24%

Page 21: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

Cohort Study (cont.)Relative Risk: Incidence rate among exposed Risk Ratio Incidence rate in non exposed.

60 24 = 2.5

Relative Risk is a direct measure of risk (to assess the etiologic role of a factor in disease occurrence).

300 x 500 500 120

Page 22: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

Cohort Study (cont.)Relative Risk:Smoking- Lung Cancer mortality: RR=18.57- Myocardial infarction mortality: RR=1.35

It measures the strength of association

Page 23: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

Cohort Study (cont.)Attributable Risk: The absolute difference in

Incidence rates among groups. “Risk Difference” RD

60 - 24 = 36%The extent to which the incidence of disease can

be attributed to the risk factor

Smoking-Lung cancer mortality: RD=1.23-Myocardial infarction mortality RD=1.25

Page 24: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

Annual Death Rates / 100,000 persons Exposure Category

Lung Cancer Coronary Heart D. 166 599

7 422

166 / 7 = 23.7 599 / 422 = 1.4

166 – 7 =159 599 – 422 = 177

Heavy smokersNonsmokers

Measures of Excess Risk

Relative Risk:

Attributable risk:

Doll and Hill study : Mortality of British doctors cited from Mausner, 1985

Page 25: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

• The previous table suggests that prevention of coronary heart disease would require alteration of other factors in addition to smoking.

• The population attributable risk: relates both relative risk and frequency of the factor in the population

• i.e. a large proportion of the deaths from lung cancer in the total population are due to smoking not only because of the high RR associated with smoking, but also bec large proportion of the pop that smoke.

Page 26: Understanding study designs through examples Manish Chaudhary MPH (BPKIHS) manish264@gmail.com

THANK YOU!!!