Examkin

Question

Difficulty: MediumBias and Confounding in Research

A team of epidemiologists has recently concluded data collection for a large retrospective case-control study investigating the potential association between chronic proton pump inhibitor use and the development of chronic kidney disease. Before conducting the final statistical evaluation, the investigators identify that a history of type 2 diabetes mellitus is highly prevalent among proton pump inhibitor users and is also a well-established independent risk factor for chronic kidney disease. The investigators must now ensure that the reported association is not spuriously driven by this factor. Given that the study is currently strictly in the data analysis phase, which of the following methodological strategies are appropriate to control for or assess this identified variable? (Select all that apply)

  1. Calculating the odds ratio of chronic kidney disease separately for participants with and without type 2 diabetes mellitus.Answer
  2. Including type 2 diabetes mellitus as an independent covariate in a multivariable logistic regression model.Answer
  3. C
    Selecting control subjects during the initial enrollment period to perfectly mirror the type 2 diabetes mellitus status of the cases.
  4. D
    Restricting the initial study recruitment exclusively to individuals who have no prior diagnosis of type 2 diabetes mellitus.
  5. E
    Pooling the data to calculate a single adjusted odds ratio if the stratum-specific odds ratios are found to be significantly different from each other.

Answer

The correct strategies for the analysis phase are calculating the odds ratio separately for participants with and without the variable (stratified analysis) and including the variable as an independent covariate in a multivariable logistic regression model.
Because the study has completed data collection and is strictly in the data analysis phase, the researchers must use statistical techniques to control for confounding. The appropriate techniques for this phase are stratification (calculating the outcome measure separately for different levels of the confounding variable) and multivariable regression (incorporating the confounder as a covariate in a statistical model to adjust for its effect). Both methods allow for the evaluation of the true association between the exposure and outcome while neutralizing the impact of the confounding variable.

Step-by-Step Solution

1
Identify the phase of the study and the goal.
The study is strictly in the data analysis phase (data collection is complete), and the goal is to control for type 2 diabetes mellitus, a potential confounder.
Recognizing the study phase is critical because it eliminates any methods that must be implemented during study design or participant enrollment.
2
Evaluate which methods are applicable to the analysis phase.
Stratified analysis and multivariable regression are appropriate statistical methods to adjust for confounding after data collection is complete.
These statistical methods can mathematically separate or adjust for the confounder's effect on the exposure-outcome relationship using the existing dataset.
3
Evaluate and eliminate the incorrect methodological strategies.
Matching and restriction are design-phase methods and thus incorrect. Pooling significantly different strata is mathematically incorrect because different strata indicate effect modification, not confounding.
Applying design-phase methods is impossible during analysis, and pooling data in the presence of effect modification obscures true biological interactions.

Key Concept

Controlling for confounding can be achieved in the design phase (randomization, matching, restriction) or the analysis phase (stratification, multivariable regression).
Rate this question

Topics

Bias and Confounding in Research

More questions from this topic