06. Analytical study design methods.pptx

Analytical study design methodologies

1. HEALTH RESEARCH FUNDAMENTALS

2. A n a l y t i ca l s t ud ies Investigator does not assign the exposure Makes careful measurement of patterns of exposure and disease in populations Comparison group Make inferences about exposure and disease

3. C o h o r t s tud y Cohort 300 to 600 man unit in Roman Army Cohort Group of people sharing some common characteristics (ex. Birth cohort)

4. Develop Disease Remain non-diseased Develop Disease Remain non-diseased Exposed Non- E x p os ed Incidence Incidence Relative Risk Population D e si g n o f coho r t s tu d y Exposure (eg. smoker) Ou t c o m e (CVD) Ti me

5. T yp e o f coho r t s t udi e s E xpo s u r e Study starts D i s e as e E xpo s u r e t i me D i s e as e Study starts E xpo s u r e Study starts D i s e as e P r o s p ec t i v e time Retrospective time Ambispective

6. Fr a m i n g ha m h e ar t s t u d y Objectives Identify Risk factors for cardio-vascular diseases Framingham town (population: 28,000) Have risk factor Don’t have risk factor Start of study H y pe r t e n si v e Normotensive Co m p a r e incidence of CVD

7. A n ili n e d y e s an d u ri na r y b l add e r canc er Case R et al, Brit J Industr Med, 1954 Exposure to aniline dyes Employees working in dye industry (n=4622) Start of study Death due to bladder tumors Expected cases of bladder cancer using national statistics 1920 1950 1930 1940

8. Cl os e d ( fi x e d ) an d o p e n coho rt Closed cohort Once cohort is enrolled and follow-up begins, no one can be added Cohort size always get smaller over time Ex: Victims of Bhopal gas tragedy Open cohort Members can leave, or added in the cohort over time Ex: Framingham study

9. E l e m e n t s o f coho r t s t u d y Selection of study populations Gathering baseline information Follow-up Analysis

10. S e lec t i o n o f s t u d y p o p u l a t i on General population cohorts or a sub-set Framingham heart study Nurses health study Special exposure cohorts Occupational groups

11. G a ther i n g b a s e li n e i n f o r m a t i on Objective Valid assessment of exposure status of members of cohort Identification data Exclude individuals having disease at baseline Define individuals at risk Obtain data on co-variables (other exposure variables)

12. Sou r c e s o f b as elin e i n f o rm a t i o n Existing records Hospital records, employment records Interviews Personal interviews/mailed questionnaires etc. Examinations Medical and other special examination Measurement of environment E.g. air pollution, exposure to radiation

13. C h o i c e o f c o mpa r i s o n g r o u p Internal comparison group Unexposed persons in the population External comparison group When internal comparison group not available Ex: Observed number of bladder cancer deaths in aniline dye industry compared with expected cases

14. F oll o w -up Objectives Uniform and complete follow-up of all cohort members Uniform surveillance in exposed and unexposed groups Complete ascertainment of exposures and outcome/s Standardized diagnosis of outcome events

15. P r e s e n t a tio n o f t h e d a t a i n a c o h o r t s t ud y i n a 2 x 2 t a b l e Diseased Non-diseased Total Exposed a b a+b Unexposed c d c+d a+c b+d a+b+c+d Known at the start of the study

16. R e l a t i v e r i s k a / a + b Incidence of disease in exposed = Incidence of disease in unexposed = c / c +d Diseased Non-diseased Total Exposed a b a+b Unexposed c d c+d a+c b+d a+b+c+d

17. I n t erp r e t in g R el a t i v e ri sk RR=1 Incidence in exposed and unexposed is same Exposure is not associated with disease RR > 1 Incidence in exposed is higher than unexposed Exposure is positively associated with disease RR < 1 Incidence in exposed is lower than unexposed Exposure is negatively associated with disease

18. Cohort study – Strengths and weaknesses Strengths Allows calculation of incidence Examine multiple outcomes for a given exposure Clarity of temporal sequence Good for investigating rare exposures Weakness May have to follow large numbers of subjects for a long time. Expensive and time consuming. Not good for rare diseases. Not good for diseases with a long latency. Differential loss to follow up can introduce bias.

19. C a s e c o n t r o l s tud y

20. D e s i g n o f c a s e - c o n t r o l s t ud y Cases (Ca. lung) O u t c o m e Exposure Controls (non cancer patients) Ti me Exposure odds Exposure odds Odds Ratio Objective: Test association between cigarette smoking and lung cancer (Doll and Hill, 1952) Exposed (smoker) Un-exposed (Non-smoker)

21. E l e m e n t s o f cas e con t r o l s t u d y Selection of cases Selection of controls Information on exposure Analysis

22. S e lec t i o n o f c as e s All people in source population who develop the disease of interest Sample of cases Independent of the exposure under study Clear definition of outcome studied Prevalent vs. incident cases Prevalent cases may be related more to survival with disease than to development of disease

23. Sou r ce s o f c as e s Hospital/clinic based cases Easier to find May represent severe cases Population based (cancer registry) not biased by factors drawing a patient to a particular hospital

24. S e lec t i o n o f con t r ols Represent the distribution of exposure in the source population of cases Selected from the same source population that gives rise to the cases Selected independently of their exposure status

25. S e lec t i o n o f c o n t r o l s Population based Sampling of the general population Health care facility based Patients with other diseases Case-based Friends, Neighbourhood

26. C o ll e c t i n g g oo d da t a o n e xp osu r e Objectively Reproducibility of exposure measurement Accurately Information reflecting as closely as possible the effect of exposure Precisely Quality management in exposure measurement

27. P r e s entat i o n o f t h e d at a o f a ca s e -cont r ol s t u d y i n a 2 x 2 ta b l e Cases Controls Total Exposed a b a+b Unexposed c d c+d a+c b+d a+b+c+d Known at the start of the study

28. O dd s r a t io Odds that case was exposed = Probability that case was exposed Probability that case was not exposed Cases Controls Total Exposed a b a+b Unexposed c d c+d a+c b+d a+b+c+d =[(a/a+c)] =[(c/a+c)] = a / c Odds that control was exposed = Probability that control was exposed Probability that control was unexposed =[(b/b+d)] = [ ( d/b+d)] = b/ d Odds ratio=[a/c]/[b/d] = ad/bc

29. I n t e r p r et i n g O dd s R a t i o OR=1 Odds of exposure among cases and controls are same Exposure is not associated with disease OR > 1 Odds of exposure among cases are higher than controls Exposure is positively associated with disease OR < 1 Odds of exposure among cases are lower than controls Exposure is negatively associated with disease

30. C a s e con t r o l s t u d y : S t r e n g t h s an d w e akn e ss e s Strengths Good for examining rare outcomes or outcomes with long latency Relatively quick to conduct, inexpensive Requires comparatively few subjects Multiple exposures or risk factors can be examined Weaknesses Susceptible to recall bias Selection of an appropriate comparison group may be difficult Rates of disease in exposed and unexposed individuals cannot be determined