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Difficulty: MediumBias and Confounding in Research

Researchers are analyzing data from a large retrospective electronic health record database to investigate the association between long-term proton pump inhibitor (PPI) therapy and the subsequent development of chronic kidney disease (CKD). An initial unadjusted analysis indicates a statistically significant increased risk of CKD among PPI users. However, reviewers point out that patients prescribed PPIs often have a higher prevalence of concurrent hypertension. Hypertension is a known independent risk factor for CKD and is not an intermediate step in the pathway between PPI use and CKD. Since the raw data has already been collected, which of the following analytical techniques can the researchers employ to adjust for the influence of hypertension? (Select all that apply)

  1. Dividing the study population into hypertensive and normotensive strata before calculating the adjusted measure of associationAnswer
  2. Incorporating hypertension status as an independent variable within a multivariable regression modelAnswer
  3. C
    Evaluating if the odds ratio in the hypertensive group is significantly different from the odds ratio in the normotensive group
  4. D
    Calculating the crude odds ratio while blinding the data analysts to the hypertension status of the participants
  5. E
    Randomly reassigning the database subjects into balanced hypertension categories to distribute the baseline risk equally

Answer

The correct analytical methods to control for confounding after data collection are dividing the study population into separate strata based on the confounder and incorporating the confounding variable into a multivariable regression model.
Hypertension acts as a confounder in this study because it is associated with both the exposure (PPI use) and the outcome (CKD), but is not an intermediate step in the causal pathway. Because the data has already been collected, the investigators must use analysis-phase strategies to control for this confounding. Stratification involves dividing the data into subgroups (hypertensive vs. normotensive) and calculating the association within those groups, which removes the confounding effect of hypertension. Multivariable regression is an advanced statistical technique that builds a mathematical model to estimate the independent effect of the exposure while simultaneously adjusting for the effects of one or more confounders, such as hypertension.

Step-by-Step Solution

1
Identify the third variable's epidemiological role based on the vignette.
Hypertension is associated with both the exposure (PPI use) and the outcome (CKD), and is not on the causal pathway, defining it as a confounder.
Accurate identification of the variable's role dictates the appropriate statistical management.
2
Determine the phase of the study.
The vignette explicitly states that the raw data has already been collected, meaning researchers must use analysis-phase techniques rather than design-phase techniques.
Certain confounding control methods (like randomization, restriction, and matching) are typically implemented during study design, whereas others are used during data analysis.
3
Select the appropriate analysis-phase methods to control for confounding.
Stratification and multivariable regression modeling are standard statistical techniques used to adjust for confounders during the analysis phase.
Stratification evaluates the exposure-outcome relationship within homogenous categories of the confounder, while regression mathematically adjusts for the confounder's independent effect.

Key Concept

Confounding variables can be controlled during the data analysis phase primarily through stratification (analyzing data in subgroups of the confounder) or multivariable regression (statistically adjusting for the confounder).
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Topics

Bias and Confounding in Research

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