1. 31/7/20091 Summer Course: Introduction to Epidemiology August 21, 0900-1030 Confounding: control, standardization Dr. N. Birkett, Department of Epidemiology & Community Medicine, University of Ottawa

2. 31/7/20092 Session Overview Review methods used to control, prevent or deal with confounding Review matching methods Present standardization methods both direct and indirect (SMR).

3. 31/7/20093 Confounding (1) Consider a case-control study relating alcohol intake to mouth cancer risk. –Crude OR = 3.2 (95% CI: 2.1 to 4.9) –Stratify the data by smoking status (ever/never): Ever: OR = 1.2 (95% CI: 0.5 to 2.9) Never: OR = 1.2 (95% CI: 0.5 to 2.9) –Best estimate of the ‘true’ OR is 1.2 Adjusted OR (more complex methods used in the ‘real world’). –This is CONFOUNDING.

4. 31/7/20094 Confounding (2) Alcohol mouth cancer ??? Smoking

5. 31/7/20095 Confounding (3) Confounding requires three or more variables. –Two variables with multiple levels cannot produce confounding. Three requirements for confounding –Confounder relates to outcome –Confounder relates to exposure –Confounder is not part of causal pathway between exposure and outcome

6. 31/7/20096 Confounding (4) Confounding is a very serious problem in epidemiological research Potential confounders are often unknown –OR for leukemia in children living near high power hydro lines is about 1.3 –BUT, could be explained by unknown confounders (e.g. pesticide application to grass under hydro towers).

7. 31/7/20097 Confounding (5) How do we deal with confounding? –Prevention You need to ‘break’ one of the links between the confounder and the exposure or outcome –‘Treatment’ (analysis) Stratified analysis (like my simple example) Standardization (we’ll discuss this later) Regression modeling methods (covered in a different course )

8. 31/7/20098 Confounding (6) Prevention –Randomization One of the big advantages of an RCT –Restriction Limits the subject to one level of confounder (e.g. study effect of alcohol on mouth cancer ONLY in non-smokers) –Matching Ensures that the distribution of the exposure is the same for all levels of confounder

9. 31/7/20099 Confounding (7) Randomization –Exposure treatment –Subjects randomly assigned to each treatment without regard to other factors. –On average, distribution of other factors will be the same in each treatment group Implies no confounder/exposure correlation  no confounding. –Issues Small sample sizes Chance imbalances Infeasible in many situations Stratified allocation

10. 31/7/200910 Confounding (8) Restriction –Limit the study to people who have the same level of a potential confounder. Study alcohol and mouth cancer only in non- smokers. –Lack of variability in confounder means it can not ‘confound’ There is only one 2X2 table in the stratified analysis –Relatively cheap

11. 31/7/200911 Confounding (9) Restriction (cont) –ISSUES Limits generalizibility Cannot study effect of confounder on risk Limited value with multiple potential confounders Continuous variables? Can only study risk in one level of confounder –exposure X confounder interactions can’t be studied Impact on sample size and feasibility –Alternative: do a regular study with stratified analysis Report separate analyses in each stratum

12. 31/7/200912 Confounding (10) Matching –The process of making a study group and a comparison group comparable with respect to some extraneous factor. Breaks the confounder/exposure link –Most often used in case-control studies. –Usually can’t match on more than 3-4 factors in one study Minimum # of matching groups: 2x2x2x2 = 16 –Let’s talk more about matching

13. 31/7/200913 Matching (1) Example study (case-control) –Identify 200 cases of mouth cancer from a local hospital. –As each new case is found, do a preliminary interview to determine their smoking status. –Identify a non-case who has the same smoking status as the case If there are 150 cases who smoke, there will also be 150 controls who smoke.

14. 31/7/200914 Matching (1a) OR = Implies no smoking/outcome link and no confounding Case Control +ve 150 150 -ve 50 50 Outcome status Smoking

15. 31/7/200915 Matching (2) Two main types of matching –Individual (pair) Matches subjects as individuals Twins Right/left eye –Frequency Ensures that the distribution of the matching variable in cases and controls is similar but does not match individual people.

16. 31/7/200916 Matching (3) Matching by itself does not fully eliminate confounding in a case-control study! –You must use analytic methods as well Matched OR Stratified analyses Logistic regression models In a cohort study, you don’t have to use these methods although they can help. –But, matching in cohort studies is uncommon

17. 31/7/200917 Matching (4) Advantages –Strengthens statistical analysis, especially when the number of cases is small. –Increases study credibility for ‘naive’ readers. –Useful when confounder is a complex, nominal variable (e.g. occupation). Standard statistical methods can be problematic, especially if many levels have very few subjects.

18. 31/7/200918 Matching (5) Disadvantages –You can not study the relationship of matched variable to outcome. –Can be costly and time consuming to find matches, especially if you have many matching factors. –Often, some important predictors can not be matched since you have no information on their level in potential controls before doing interview/lab tests Genotype Depression/stress –If matching factor is not a confounder, can reduce precision and power.

19. 31/7/200919 Matching (6) Individual matching –My personal view: this method is over-used and misrepresented In many apparent cases of individual matching, that isn’t what is going on. –Most useful when there is a strong ‘natural’ pairing. Twins Body parts –Analysis uses McNemar method to estimate OR (and to do a chi-square test). Unit of analysis is the pair.

20. 31/7/200920 Matching (7) 625 pairs of subjects –201 pairs where both case and control were exposed –80 pairs where only case was exposed –43 pairs where only control was exposed –201 pairs where neither case or control were exposed +ve - ve +ve 201 80 -ve 43 302 Control member Case Member

21. 31/7/200921 Matching (8) If exposure causes disease, there should be more pairs with only the case exposed then pairs with only the control exposed. McNemar OR = 80/43 = 1.86 Ignoring matching would give OR=1.28 Chi-square = +ve - ve +ve 201 80 -ve 43 302 Control member Case Member

22. 31/7/200922 Matching (9) McNemar OR = b/c ‘a’ and ‘d’ pairs contribute no information on OR (wasteful of interviews). Make sure table is set-up correctly!! More sophisticated analysis uses conditional logistic regression modeling (another course). +ve - ve +ve a b -ve c d Control member Case Member

23. 31/7/200923 Matching (10) Frequency matching –Most commonly used method –Many ways to implement this. Here’s one: Case-control study of prostate cancer. Cases will include all new cases in Ottawa in one year. –Based on cancer registry data, we know what the age distribution of cases will be. Controls selected at random from the population. We use the projected distribution of age in the cases to describe how many controls we need in each age group.

24. 31/7/2009 Matching (11) 400 cases & 400 conts 5% of cases are under age 60 I want 5% of my controls to be under 60 –400 * 0.05 = 20 Similar for other age groups 26065%>70 400 # 12030%60-70 205%<60 ContCase

25. 31/7/200925 Matching (12) Frequency matching (cont) –Do you distribute the control recruitment through-out the case recruitment period? –Analysis must stratify by matching groups or strata –Having too many matching groups is a problem –How do I find the matching controls? Only 4% of the population is age 75-84 but about 30% of my cases are in this group. How do I efficiently over-sample this age group? Lack of control selection lists in Canada –Mandates use of Random Digit Dialing (RDD) methods.

26. 31/7/200926 Confounding (11) Analysis options –Stratified analysis Divide study into strata based on levels of potential confounding variable(s). Do analysis within each strata to give strata- specific OR or RR. If the strata-specific values are ‘close’, produce an adjusted estimate as some type of average of the strata-specific values. Many methods of adjustment of available. Mantel- Haenzel is most commonly used.

27. 31/7/200927 Confounding (12) Stratified analysis (cont) –Strata specific OR’s are: 2.3, 2.6, 3.4 –A ‘credible’ adjusted estimate should be between 2.3 and 3.4. Simple average is: 2.8 –Ignores the number of subjects in the strata. If one group has very few subjects, its estimate is less ‘valuable’. Weight by # of subjects in each group, e.g.: Mantel-Haenzel does the same thing with different weights

28. 31/7/200928 Confounding (13) Stratified analysis (cont) –This approach limits the number of variables which can be controlled or adjusted. –Also hard to apply it to continuous confounders –But, gives information about strata-specific effects and can help identify effect modification. –Used to be very common. Now, no longer widely used in research with case-control studies. –Stratified analysis methods can be applied to cohort studies with person-time. This is still commonly used

29. 31/7/200929 Confounding (14) Analysis options –Regression modeling Beyond the scope of this course The most common approach to confounding Can control multiple factors (often 10-20 or more) Can control for continuous variables Logistic regression is most popular method for case-control studies Cox models (proportional hazard models) are often used in cohort studies.

30. 31/7/200930 Standardization (1) Crude prostate cancer incidence rates (fictional): –Canada (2000): 100/100,000 –Canada (1940):50/100,000 Does this mean that prostate cancer is twice as common in 2000 (RR = 2.0)? –Yes, the rate is twice as high –BUT: answer is too simplistic if it is taken to mean that people in 2000 are at higher risk of developing prostate cancer.

31. 31/7/200931 Standardization (2) Concern is that the population in Canada is older in 2000 than in 1950. Also, prostate cancer incidence increases with age. Sound familiar? Age Calendar time Prostate cancer

32. 31/7/200932 Standardization (3) Changes in the age distribution of the Canadian population could confound any change in incidence over time. –Will make it appear that the population is at higher risk when it really isn’t This is really a type of confounding. –For historical reasons, this issue is usually taught as a separate topic, often before confounding is introduced. Approached through direct standardization or age adjustment.

33. 31/7/200933 Standardization (4) Remember stratified analysis? –Divide the sample into strata –Within each stratum, compute the OR/RR/etc –Produce an average of the strata-specific estimates to adjust for the confounder. Roughly, the same process is used for direct standardization.

34. 31/7/200934 Standardization (5) Direct Standardization Select a reference population (can be anything) Compute age-specific incidence in each study group. Multiply the age-specific incidence by the # of people in the reference population in that age stratum  ‘expected’ number of cases Add up the ‘expected’ number and divide by the total size of the reference population. Age-adjusted rate for the study group. Let’s look at an example

35. 31/7/200935 Standardization (6) # casesPop sizeIncidence Area ‘A’38016,0000.0238 Area ‘B’82516,0000.0516 RR = 2.35 Mean age:Area A = 49.7 yrs Area B = 63.4 yrs

36. Standardization (7) Age group casespopincidcasesPopincid 35-50100100000.011520000.0075 50-658040000.026040000.015 >6520020000.10750100000.075 3801600082516000 Area A Area B RR in each age stratum (B vs A) = 0.75 not 2.35

37. 31/7/200937 Standardization (8) Why the difference? –Area ‘A’ is a lot younger than area ‘B’. –Incidence increases with age. –  confounding by age. Direct standardization First, select reference population –We will take combined population of area ‘A’ and area ‘B’.

38. Standardization (9) 1110148032000 9000.07512000.1012000>65 1200.0151600.02800050-65 900.00751200.011200035-50 exprateexprateRef Pop Age group Area AArea B

39. 31/7/200939 Standardization (10) Ref popExp 3200014801100 Area AArea B Area A adjusted incidence = 1480/32000 = 0.0463 Area B adjusted incidence = 1100/32000 = 0.0347 The adjusted RR (area B to A) = 0.75

40. 31/7/200940 Standardization (11) Adjustment has rendered the rates comparable by eliminating the confounding due to age. There are more complex ways of doing this but this approach gives the basic ideas.

41. 31/7/200941 Standardization (11) Does it always work? –If the rate is higher in one area for younger age groups but lower for higher ones, adjustment can give a misleading picture. Do NOT treat adjusted rates as ‘real’ rates. –To estimate the burden of illness, you must use unadjusted rates. What if the group has very few events? –SMR & indirect standardization. NO!!

42. 31/7/200942 Standardization (12) Indirect Standardization Used when the study group has few cases so the age-specific rates will be unstable (subject to wide chance variation). Does not produce adjusted estimates. Is used to compare study population to rates expected based on a large general population or reference population. Rate taken from reference pop (unlike direct standardization). Main statistic produced is the SMR (standardized mortality rate)

43. Standardization (13) 3876 204020000.01> 65 81640000.00250-65 1020100000.00135-50 Exp events Obs events # peopleRef rateAge group SMR = # obs cases/# exp cases = 76/38 = 2.0 (or 200)

44. Standardization (14) Indirect Standardization (cont) SMR does not depend on the number of observed events in each age stratum. That is why it is useful when the number of cases is small. Interpret an SMR similar to an RR or OR: –< 1.0  protection – 1.0  null value (no effect) –> 1.0  increased risk

45. 31/7/200945 Summary Confounding is a very common problem Try to prevent it through: –Restriction –Matching Use statistical methods to adjust for it: –Stratified analysis –Matched analysis –Regression modeling