Question

match the proper type of prevention strategy of confounding to its definition

Answer 1

Confounding variables or confounders are often defined as the variables correlate (positively or negatively) with both the dependent variable and the independent variable. A Confounder is an extraneous variable whose presence affects the variables being studied so that the results do not reflect the actual relationship between the variables under study.

The aim of major epidemiological studies is to search for the causes of diseases, based on associations with various risk factors. There may be also other factors that are associated with the exposure and affect the risk of developing the disease and they will distort the observed association between the disease and exposure under study. A hypothetical example would be a study of the relation between coffee drinking and lung cancer. If the person who entered in the study as a coffee drinker was also more likely to be a cigarette smoker, and the study only measured coffee drinking but not smoking, the results may seem to show that coffee drinking increases the risk of lung cancer, which may not be true. However, if a confounding factor (in this example, smoking) is recognized, adjustments can be made in the study design or data analysis so that the effects of confounder would be removed from the final results. Simpson's paradox too is another classic example of confounding. Simpson's paradox refers to the reversal of the direction of an association when data from several groups are combined to form a single group.

The researchers, therefore, need to account for these variables - either through experimental design and before the data gathering, or through statistical analysis after the data gathering process. In this case, the researchers are said to account for their effects to avoid a false positive (Type I) error (a false conclusion that the dependent variables are in a casual relationship with the independent variable). Thus, confounding is a major threat to the validity of inferences made about cause and effect (internal validity). There are various ways to modify a study design to actively exclude or control confounding variables including Randomization, Restriction, and Matching.

In randomization the random assignment of study subjects to exposure categories to breaking any links between exposure and confounders. This reduces the potential for confounding by generating groups that are fairly comparable with respect to known and unknown confounding variables.

Restriction eliminates variation in the confounder (for example if an investigator only selects subjects of the same age or same-sex then, the study will eliminate confounding by sex or age group). Matching which involves selection of a comparison group with respect to the distribution of one or more potential confounders.

Matching is commonly used in case-control studies (for example, if age and sex are the matching variables, then a 45-year-old male case is matched to a male control with the same age).

But all these methods mentioned above are applicable at the time of study design and before the process of data gathering. When experimental designs are premature, impractical, or impossible, researchers must rely on statistical methods to adjust for potentially confounding effects.

• Statistical Analysis to eliminate confounding effects:

Unlike selection or information bias, confounding is one type of bias that can be, adjusted after data gathering, using statistical models. To control for confounding in the analyses, investigators should measure the confounders in the study. Researchers usually do this by collecting data on all known, previously identified confounders. There are mostly two options to dealing with confounders in the analysis stage; Stratification and Multivariate methods.

1. Stratification :

The objective of stratification is to fix the level of the confounders and produce groups within which the confounder does not vary. Then evaluate the exposure-outcome association within each stratum of the confounder. So within each stratum, the confounder cannot confound because it does not vary across the exposure-outcome.

After stratification, Mantel-Haenszel (M-H) estimator can be employed to provide an adjusted result according to strata. If there is a difference between Crude result and the adjusted result (produced from strata) confounding is likely. But in the case that Crude result does not differ from the adjusted result, then confounding is unlikely.

2. Multivariate Models:

Stratified analysis works best in the way that there are not a lot of strata and if only 1 or 2 confounders have to be controlled. If the number of potential confounders or the level of their grouping is large, the multivariate analysis offers the only solution.

Multivariate models can handle large numbers of covariates (and also confounders) simultaneously. For example in a study that aimed to measure the relationship between body mass index and Dyspepsia, one could control for other covariates like as age, sex, smoking, alcohol, ethnicity, etc in the same model.

2.1. Logistic Regression:

Logistic regression is a mathematical process that produces results that can be interpreted as an odds ratio, and it is easy to use by any statistical package. The special thing about logistic regression is that it can control for numerous confounders (if there is a large enough sample size). Thus logistic regression is a mathematical model that can give an odds ratio which is controlled for multiple confounders. This odds ratio is known as the adjusted odds ratio because its value has been adjusted for the other covariates (including confounders).

2.2. Linear Regression:

The linear regression analysis is another statistical model that can be used to examine the association between multiple covariates and a numeric outcome. This model can be employed as a multiple linear regression to see through confounding and isolate the relationship of interest. For example, in research seeking for the relationship between LDL cholesterol level and age, the multiple linear regression lets you answer the question, How does LDL level vary with age, after accounting for blood sugar and lipid (as the confounding factors)? In multiple linear regression (as mentioned for logistic regression), investigators can include many covariates at one time. The process of accounting for covariates is also called adjustment (similar to logistic regression model) and comparing the results of simple and multiple linear regressions can clarify that how much the confounders in the model distort the relationship between exposure and outcome.

2.3. Analysis of Covariance:

The Analysis of Covariance (ANCOVA) is a type of Analysis of Variance (ANOVA) that is used to control for potential confounding variables. ANCOVA is a statistical linear model with a continuous outcome variable (quantitative, scaled) and two or more predictor variables where at least one is continuous (quantitative, scaled) and at least one is categorical (nominal, non-scaled). ANCOVA is a combination of ANOVA and linear regression. ANCOVA tests whether certain factors have an effect on the outcome variable after removing the variance for which quantitative covariates (confounders) account. The inclusion of this analysis can increase statistical power.

The Analysis of Covariance (ANCOVA) is a type of Analysis of Variance (ANOVA) that is used to control for potential confounding variables. ANCOVA is a statistical linear model with a continuous outcome variable (quantitative, scaled) and two or more predictor variables where at least one is continuous (quantitative, scaled) and at least one is categorical (nominal, non-scaled). ANCOVA is a combination of ANOVA and linear regression. ANCOVA tests whether certain factors have an effect on the outcome variable after removing the variance for which quantitative covariates (confounders) account. The inclusion of this analysis can increase statistical power.

The Analysis of Covariance (ANCOVA) is a type of Analysis of Variance (ANOVA) that is used to control for potential confounding variables. ANCOVA is a statistical linear model with a continuous outcome variable (quantitative, scaled) and two or more predictor variables where at least one is continuous (quantitative, scaled) and at least one is categorical (nominal, non-scaled). ANCOVA is a combination of ANOVA and linear regression. ANCOVA tests whether certain factors have an effect on the outcome variable after removing the variance for which quantitative covariates (confounders) account. The inclusion of this analysis can increase statistical power.