Confounders

The objective of this lecture is to know the role of confounders in epidemiological studies and how to evaluate it

A mixing of the effect of the exposure under study on the disease with that of a third factor which must be associated with the exposure & independent of that exposure be a risk factor for the dis., so the observed relationship between the exposure & disease can be attributed totally, or in part, to the effect of the conf. (ex.MI, exercise & age) so conf. can lead to an under estimation or over estimation of the true relationship between exposure & disease. 1- if there is no association between the exposure & the potential confounder or conversely, if the potential confounder has no relation with the risk of the disease, there can be no conf. (ex.MF,exercise & fluid intake)

2- The potential conf. factor must be predictive of disease independently of its association with the exposure under study i.e, conf. factor can not be related to risk of disease only through it's association with the exposure. This means that there must be an association between the conf. & disease even among non exposed individuals (previous ex.) 3- The potential conf. Cannot be an intermediate link in the causal chain between the exposure & dis. Under study Alcohol HDL MI HBV Chr. Hepatitis CA liver

4- Adjustment for the variable should result in a change in the estimate of the association between the exposure & disease to consider this conf. as an actual one. 5- In evaluating the effect of potential conf., it is important not merely to evaluate it’s presence or absence , but also to identify the direction & quantify the magnitude of it's effect on the estimate of the association between exposure & disease.

Control of conf. 1- Randomization: to ensure that all-potential confounding factors (those known to the investigator & even [more important] those currently unknown or unsuspected) are evenly distributed among the groups. 2-Restriction: restrict the admissibility criteria for subjects & limit entrance into the study to individuals who fall within a specified category or categories of the conf.

Advantages: straightforward, Convenient & inexpensive means of controlling conf. Disadvantages: -May decrease the No. of subjects eligible to participate in the study leading to problems in achieving the sample size which is necessary for adequate statistical power in a reasonable period of time -The potentials for residual confounding if the criteria are not sufficiently narrow. (ex. MI & exercise, restricted age 40-65 still there’s a difference in the ability for exercise within this broad age) -Eliminate the evaluation of the effect of confounding factors on the disease.

3. Matching: (either in the design or the analysis) Not as restriction (where control of conf. achieved by selection into the study only individuals with homogenous level of the potential conf.) While in matching, all levels of these factors are allowable for inclusion in the study but the particular subjects are distributed in an identical manner among each of the study groups, ex.: (case-control study of exercise & MI in which age & smoking are potential conf., for each case of M.I a control would be selected of the same ; age , sex smoking status,...

Disadvantages: - Difficult, expensive & time consuming to find such comparison subjects thus, matching is primarily utilized in case-control studies which in general tend to be smaller in sample size. Inability to evaluate the effect of a factor that has been matched on the risk of the outcome. A & D are (concordant pairs), & as there is no difference in the exposure status bet-case & control, it provides no information about the magnitude of association between factor & disease. B & C (non concordant) are important because there is a difference in the exposure status between cases and controls so that we can get the RR from them : RR = B/C When the RR is small (about 1) then matching has no much effect so we can use non-matched pairs in that study.

Methods to control conf.in the analysis: 1.Matching 2.Stratified analysis: To classify the study groups into strata according to their exposure level to the conf. & each stratum specific estimate is un confounded by the factor-since there is no variability of the conf. variable within the stratum. 3.Multivariate analysis: A fundamental problem with stratified analysis is it`s inability to control simultaneously for even a moderate No. of potential conf. for ex. if in MI & exerc. the conf factors were: sex, age(<50,50-59,60-69,70+),smoking status (never smoke, past smoker, current smoker) obesity Wt/Ht (lowest, 2nd, 3rd, highest quartiles) there variables would require a total of 2*4*3*4=96 strata to represent the possible combination of sex, age, smoking, and obesity