The Aga Khan University Confounding Dr. Sunita Dodani Assistant Professor Family Medicine, CHS The Aga Khan University Pakistan 2/23/2019

Learning objectives To understand the role of confounders in a study To learn relationship between an exposure, disease and potential confounding factors To understand difference between confounding and effect modification To learn methods to control confounding in study designs and in data analysis 2/23/2019

Performance objectives After this lecture the student will be able to: Differentiate the role of a confounder and a exposure in a study Use methods to control effects of confounders in research projects 2/23/2019

Confounding Confounding occurs when two factors are associated with each other, or “travel together” and the effect of one is confused with or distorted by the effect of the other. A confounder is a variable which is associated with the exposure, and independent of that exposure is a risk factor of the disease 2/23/2019

Confounding Examples: Study one: found an association with smoking and loss of hairs. The study was confounded by age Study two: found improved outcome for maternal centers when compared to hospitals Study might be confounded by highly motivated volunteers that may have selected these centers as an option 2/23/2019

Confounding Confounders are generally correlates of other causal factors HSV-2 Sexual activity HPV Cervical cancer A confounder cannot be an intermediate link in the causal pathway between exposure and disease 2/23/2019

Confounding Delete sample document icons and replace with working document icons as follows: From Insert Menu, select Object... Click “Create from File” Locate File name in “File” box Make sure “Display as Icon” is checked Click OK Select icon From Slide Show Menu, Select “Action Settings” Click “Object Action” and select “Edit” In other words, confounding is a variable that is associated with the predictor variable and is a cause of the outcome variable Aside from bias, confounding is often the likely alternative explanation to cause-effect and the most important one to try to rule out. In contrast to bias, confounding can be controlled at several levels of a study 2/23/2019

Effect modification Effect modification is a type of interaction When the strength of the relationship between two variables is different with respect to some third variable called effect modifier. 2/23/2019

Effect modification EXAMPLES 1 relationship between dose of thiazide and risk of sudden death.addition of K sparing drug modifies the effect at several doses. effect modifier…….. K sparing drug 2/23/2019

Effect modification Example 2 People who take monoamine oxidase inhibitors (MAOI) are at risk of stroke if they eat certain foods such as cheese. effect modifier………. MAOI MAOI is not associated with eating cheese. This is not a confounder 2/23/2019

Coping with confounders In the design phase Investigators should be aware of confounders and able to control them First list the variables (like age & sex) that may be associated with the predictor variable of interest as well as cause of the outcome 2/23/2019

Coping with confounders Two design phase strategies Specification Matching Both sampling strategies Specification: Design inclusion criteria that specify a value of the potential confounder and exclude everyone with a different value e.g In coffee and MI , only non smokers could be included in the study.if an association observed b/w coffee and MI, it obviously could not be due to smoking 2/23/2019

Coping with confounders Specification: Advantages Easily understood Focuses only on subjects for the research question at hand Disadvantages Limits generalizability May make it difficult to acquire adequate sample size 2/23/2019

Coping with confounders Matching (mostly in case control studies) Selection of cases and controls with matching values of the confounding variable Pair wise matching e.g in coffee drinking study as a predictor of MI, each case (a patient with MI) could be matched with one or more controls that smoked roughly the same amount as the case (10-20 cigarettes/day) 2/23/2019

Coping with confounders Matching Advantages: Can eliminate influence of strong confounders Can increase precision (power) by balancing the number of cases and controls in each stratum May be sampling convenience making it easier to select controls 2/23/2019

Coping with confounders Matching Disadvantages Time consuming Requires early decision as to which variables are predictors and which are confounders Requires matched analysis Creates the danger of over matching( matching on a factor which is not a founder, thereby reducing power) 2/23/2019

Coping with confounders In the Analysis Stratification Adjustment Ensures that only cases and controls with similar level of a potential confounding variable are compared. It involves segregating the subjects into strata. 2/23/2019

Coping with confounders Stratification Advantages Easily understood Flexible and reversible Can choose which variable to stratify upon after data collection 2/23/2019

Coping with confounders Stratification Disadvantages Number of strata limited by sample size needed for each stratum Few co variables can be considered Few strata per co variable leads to less complete control of confounding 2/23/2019

Coping with confounders Statistical Adjustment Several statistical techniques are available to adjust for confounders. These techniques model the nature of the associations among the variable to isolate the effects of predictor variables and confounders This require software for multivariate analysis 2/23/2019

Coping with confounders Statistical Adjustment Advantages Multiple confounders can be controlled simultaneously Information in continuous variables can be fully used Flexible and reversible 2/23/2019

Coping with confounders Statistical Adjustment Disadvantages Model may not fit Inaccurate estimates of strength of effect (if model does not fit predictor-outcome relationship) Results may be hard to understand Relevant co variables must have been measured 2/23/2019