Case control study

Analytical epidemiology

1. Analytical Epidemiology CASE CONTROL STUDY By Dr nayana m 2ND year post graduate Department of public health dentistry 1

2. Contents Introduction Design of case control study Basic steps Selection of cases and controls Matching Measurement of exposure Analysis of exposure Potential biases in case control studies Confounding and bias 2

3. contents 3 Advantages and disadvantages of case control study Conclusions References

4. Introduction to Analytical study Subject of interest is the individual within the population ANALYTICAL EPIDEMIOLOGY 4

5. CASE CONTROL STUDY Schematic diagram on design of Case control study Cases (people with disease) Controls (people without disease) 5 TIME Direction of inquiry Exposed Not exposed Exposed Not exposed Population

6. CASE CONTROL STUDY Also called the ‘ Retrospective study’ 3 distinct features: Both exposure and outcome (disease) have occurred before the start of the study Study proceeds backwards from effect to cause Uses control or the comparison group to support/refute an inference Hence they are b asically comparison studies , wherein cases and controls must be comparable with respect to known ‘confounding factors’ like age, sex, occupation, social status etc 6

7. Basic design of a case control study 7

8. 8 BASIC STEPS

9. 1a. SELECTION OF CASES Identification of cases is relatively easy but selection of suitable controls may pose difficulties Group of individuals with the disease are cases Definition of case : Prior definition to what constitutes a case is important Involves two specifications: Diagnostic criteria Diagnostic criteria of the disease and the stage(eg: Stage 1 cancer) of disease if any must be specified before the study is undertaken Once the criteria is established , they should not be changed or altered till the study is over 9

10. Eligibility criteria: Incident cases are considered more eligible than prevalent cases The reason is that any risk factors we may identify in a study using prevalent cases may be related more to survival with disease than to development of disease (incidence) Sources of cases: Cases may be drawn from hospitals , or general population Hospitals: Often convenient Recruitment of only newly diagnosed (incident) cases within a specified period of time are eligible than old cases or cases in the advanced stages ( prevalent) May be drawn from a single hospital or a network of hospitals, admitted at a specific period of time Entire case series or a random sample is drawn from it 10

11. 11 General population: In population based studies, all cases with study disease within a defined geographical area at a specified period of time are selected Cases are ascertained through a survey, a disease registry or hospital network Either entire case series or a random sample is drawn from it Cases must fairly represent all the cases in community

12. 1b. Selection OF CONTROLS 12 Controls must be free from disease under study Must be as similar as possible to cases except the absence of disease Difficulties in selection of controls may arise when the disease under investigation occur in subclinical forms whose diagnosis is difficult

13. HOSPITAL CONTROLS 13 Controls may be selected from the same hospital as cases but should be with different illness than the disease under study Relatively more economical Disadvantages Source of “selection bias” May have diseases also influenced by the factor under study Eg: Relationship of tobacco and oral cancer is been studied and cases with bladder cancer is chosen as controls , the relationship may not be demonstrated

14. Non Hospitalized controls 14 Relatives Controls from siblings and spouses Siblings are unsuitable for studies undertaking genetic conditions Neighborhood controls Drawn from persons living in the same locality as cases Same factory or children attending same schools General population Obtained from defined geographic areas by taking a random sample of individuals free of disease

15. How many controls are needed? 15 If many cases are available, a large study is contemplated If the cost to collect case and control is about equal , then one tends to use one control for each case If study group is small (eg: under 50 ) , 2, 3 or even 4 controls may be selected for each study subject

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17. 2. Matching 17 Ensures comparability between cases and controls DEFINITION : Process by which we select controls in such a way that they are similar to cases with regard to certain pertinent variables (age,sex,occupation etc) which are known to influence the outcome of the disease and which if not adequately matched for comparability could distort or confound the results Protects against an unexpected strong association between the matched factor and the disease TWO TYPES: Group matching/ frequency matching Individual matching/ matched pairs

18. Group matching/Frequency matching 18 S electing the controls in such a manner that the proportion of controls with a certain characteristic(age,occupation,social status etc) is identical to the proportion of cases with the same characteristic For example if 25% of the cases are married, the controls will be selected so that 25% of that group is also married After calculations are made of the proportions of certain characteristics in the group of cases, then a control group, in which the same characteristics occur in the same proportions, is selected Frequency distribution of the matched variable must be similar in study and control group

19. Individual matching/matched pairs 19 F or each case selected for the study, a control is selected who is similar to the case in terms of the specific variable or variables of concern Eg: 50 year old mason with a particular disease will have a 50 year old mason without the disease as control One can obtain pairs of patients and controls of same sex,age,duration, severity of illness etc O ften used in case-control studies that use hospital controls

20. Problems with matching 20 Practical Problems : If an attempt is made to match according to too many characteristics, it may prove difficult or impossible to identify an appropriate control Overmatching also leads to an inability to statistically analyze variables used in matching Conceptual problems : Once we have matched controls to cases according to a given characteristic, we cannot study that characteristic/risk factor For example, suppose we are interested in studying age as a risk factor for periodontitis. If we match the cases (with periodontitis) and the controls (no periodontitis) for age, we can no longer study whether or not age is a risk factor for periodontitis

21. 21 By using matching to impose comparability for a certain factor, we ensure the same prevalence of that factor in the cases and the controls Clearly we will not be able to ask whether cases differ from controls in the prevalence of that factor U nplanned matching may inadvertently occur in case-control studies Eg: I f we use neighborhood controls, we are in effect matching for socioeconomic status as well as for cultural and other characteristics of a neighborhood Unplanned matching on a variable that is strongly related to the exposure being investigated in the study is called overmatching Overmatching reduce odds ratio

22. Use of multiple controls 22 Matching 2 : 1, 3 : 1 or 4 : 1 will increase the statistical power of the study . Therefore many case-control studies will have more controls than cases These controls may be either : (1) controls of the same type or (2) controls of different types , such as hospital and neighborhood controls or controls with different diseases

23. Multiple controls of same type 23 Multiple controls of the same type , such as two controls or three controls for each case, are used to increase the power of the study A noticeable increase in power is gained only up to a ratio of about 1 case to 4 controls Why not keep the ratio of controls to cases at 1 : 1 and just increase the number of cases? F or many of the relatively infrequent diseases we study (which are best studied using case-control designs), there may be a limit to the number of potential cases available for study The number of cases cannot be increased without either extending the study in time to enroll more cases or developing a collaborative multicenter study, the option of increasing the number of controls per case is often chosen These controls are of the same type (e.g., neighborhood controls); only the ratio of controls to cases has changed

24. Multiple controls of different type 24 T he exposure of the hospital controls used in a study may not represent the rate of exposure that is “expected” in a population of non diseased persons—that is, the controls may be a highly selected subset of non diseased individuals and may have a different exposure experience To address this problem, we may choose to use an additional control group, such as neighborhood controls

25. 3. Measurement of exposure 25 Information about exposure must be precisely obtained Obtained by interviews, questionnaires, or studying past records of cases like hospital records, employment records etc.

26. 4. analysis 26 Final step to find out: Exposure rates among cases and controls to suspected factor Estimation of disease risk associated with exposure (odds ratio) Exposure rates Case control studies give direct estimation of the exposure rates (frequency of exposure) to a suspected factor in disease and non disease groups

27. 27 Cases (with disease) Controls (without disease) Total Tobacco users a b a + b Non users c d c + d Total a + c b + d a + b + c + d Cases = a/a+c Controls = b/b+d Example : Tobacco use and oral cancer

28. 28 The particular test of significance will depend upon the variables under investigation According to convention, if P is less than or equal to 0.05 , regarded as statistically significant Smaller the P value , the greater the statistical significance or probability that the association is not due to chance alone P value does not imply causation

29. 1. Estimation of risks 29 Estimates the disease risk associated with exposure Obtained by an index called “relative risk” (RR) or the “risk ratio” Its defined as the ratio between the incidence of disease among exposed persons and incidence among non- exposed Formula Relative risk = Incidence among exposed Incidence among non exposed (a/a+b divided by c/c+d) Cases (with disease) Controls (without disease) Total Tobacco users a b a + b Non users c d c + d Total a + c b + d a + b + c + d

30. 30 A typical case control study does not provide incidence rates from which RR can be calculated directly As there is no appropriate denominator or population at risk to calculate these risks RR is exactly determined in cohort studies

31. 31 Interpretation of relative risk Value ranges from 0 to infinity RR equal to 1 indicates no association between the exposure and the health related event RR greater than 1 indicates positive association and RR less than 1 indicates negative association Taking the previous example , if the relative risk value equals to 2.5 , then it indicates that the smokers are 2.5 times more likely to develop oral cancer than non smokers

32. 2.Odds ratio/relative odds 32 Also called the “cross product ratio” Measure of strength of association between risk factor and outcome Closely related to RR Derivation is based on 3 assumptions: Disease being investigated must be relatively rare Cases must represent those with the disease Controls must represent those without the disease

33. 33 DISEASES Yes No Exposed a b Not exposed c d Total a+b+c+d Odds ratio = a/b = ad c/d bc Key parameter in the analysis of case control studies

34. Example for odds ratio 34

35. 35 Interpretation of Odds ratio Value ranges from 0 to infinity Source: www.cdc.gov

36. potential Biases in case control studies 36 Bias is any systematic error in the determination of the association between the exposure and the disease Can increase or decrease the relative risk estimate Selection bias : Occur due to ill defined population, during sampling etc There may be systematic differences in characteristics between cases and controls Can be controlled by its prevention Examples : Health care access bias : When cases admitted to certain facility do not represent cases in the community

37. 37 Popularity bias : When admissions are based on interest of the investigator Neyman bias/ Selective survivor bias/ Late look bias/Prevalence-incidence bias : When exposure of interest is a prognostic determinant by under evaluating association between disease and risk factor ,ie: mild, clinically resolved or fatal cases are excluded from case group Berkesonian bias : Occur due to different rates of admission to hospitals for people with different diseases Inclusion bias : Controls with one /more conditions related to exposure are selected Exclusion bias : Controls with conditions related to exposure are excluded Mimicry bias : Conditions clinically close to disease may be diagnosed as disease itself

38. 38 Non-response Bias : When participants differ from nonparticipants. The healthy worker effect is a particular case when the participants are healthier than the general population Information bias / Measurement bias : Occur due to systematic measurement error, misclassification of subjects in one / more variables Examples Observer bias : When different observers may get different measurement for a case Interviewer bias : When the interviewer knows the hypothesis and also who the cases are can introduce errors in questioning the cases more thoroughly ,by asking leading questions , emphasizing on some questions and helping with responses Can be eliminated by checking the length of time taken for interviewing the average cases and average controls and by double blinding

39. 39 Memory/Recall bias : When asked about past history , the cases are more likely to recall certain events or factors than controls Reporting bias : Participants help the researchers by giving answers in direction of interest or do not answer sensitive questions Hawthorne effect : When participants are aware that they are being observed there can be an increase in productivity or outcome Surrogate bias : When case himself id not available for giving information, somebody other than case is interviewed

40. 40 Confounding and bias Bias that occur due to a confounding factor Confounding factor is a third factor associated with both exposure and disease , distributed unequally in control and study group C onfounding occurs because of non-random distribution of risk factors in the study population Be associated with exposure without being the consequence of exposure Be associated with outcome independently of exposure (not an intermediary ) Example Study of role of smoking in oral cancer , alcohol is a confounding factor Confounding can be eliminated by matching

41. 41 By convention, when a third variable masks or weakens a true association between two variables, this is negative confounding (observed association is biased towards the null) When a third variable produces an association that does not actually exist, this is positive confounding (observed association is biased away from the null) To be clear, neither type of confounding is a “ good thing ” (i.e., neither is a positive factor); both are “bad ” ( i.e., negative in terms of effect)

42. Synergism and effect modification 42 Synergism (from Greek roots meaning “work together”) is the interaction of two or more presumably causal variables, so that the combined effect is clearly greater than the sum of the individual effects Eg: Combined effect of uncontrolled diabetes and bad oral hygiene will have greater risk of periodontitis than the individual factors alone When an association between an exposure and disease outcome is modified by the level of an extrinsic risk factor , that extrinsic variable is called an effect modifier This association is usually called effect modification by epidemiologists and interaction by biostatisticians

43. Confounding v/s effect modification 43 CONFOUNDING An effect or association between an exposure and outcome is distorted by the presence of another variable If an observed association is not correct because a different variable is associated with both the potential risk factor and the outcome, but it is not a causal factor itself EFFECT MODIFICATION A variable that differentially (positively and negatively) modifies the observed effect of a risk factor on disease status.  Different groups have different risk estimates when effect modification is present If an effect is real  but the magnitude of the effect is different for different groups of individuals (e.g., males vs. females or blacks vs. whites)

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45. conclusion 45 In recent years, considerable attention has focused on whether it is possible to take advantage of the benefits of both case control and cohort studies into a single study Resulting combination lead to HYBRID DESIGN in which a case control study is initiated within a cohort study Here the population is identified and followed over time Based on approaches used in the selection of controls there are two types: Nested case control study Case-cohort study

46. 46 Initial data and / or serum, urine, or other specimens obtained YEARS CASE CONTROL STUDIES

47. Design of case cohort study 47 C ases develop at the same times that were seen in the nested case-control design just discussed, but the controls are randomly chosen from the defined cohort with which the study began This subset of the full cohort is called the sub cohort advantage of this design is that because controls are not individually matched to each case, it is possible to study different diseases (different sets of cases) in the same case-cohort study using the same cohort for controls

48. References 48 Park K. Health care of the community. Textbook of Preventive and Social Medicine. 24th ed. Jabalpur: Banarsidas Bhanot Publishers. 2018;75-85 Celentano D David.Szklo Moyses.Gordis epidemiology.6 th ed.Canada.Elsevier.2019;157-174,187 Merrill RM. Introduction to epidemiology. 5 th edition. Jones & Bartlett Learning; 2010;186-196 Bonita R, Beaglehole R, Kjellström T. Basic epidemiology. World Health Organization; 2006.

49. 49 Kumar G, Acharya AS. Biases in epidemiological studies: How far are we from the truth?. Indian Journal of Medical Specialities. 2014 Jan 1;5(1):29-35 . Katz DL, Elmore JG, Wild D, Lucan SC. Jekel's Epidemiology, Biostatistics, Preventive Medicine, and Public Health: With STUDENT CONSULT Online Access. Elsevier Health Sciences; 2013 Feb 11; 64-65 Interpreting results of case control studies. Centre for Disease Control.[Accessed on 6 th December 2019] Url at : www.cdc.gov/training/SIC_CaseStudy/Interpreting_Odds_ptversion

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