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Difficulty: MediumBias and Confounding in Research

A prospective cohort study is conducted to evaluate the association between the daily consumption of artificially sweetened beverages and the incidence of type 2 diabetes mellitus in adults over a 10-year period. The investigators hypothesize that artificial sweeteners increase the risk of developing diabetes. The initial analysis reveals a statistically significant association.

The researchers subsequently decide to stratify the data by baseline body mass index (BMI) to account for baseline obesity, which is known to be strongly associated with both the choice to consume artificially sweetened beverages and the development of type 2 diabetes. The results of the analysis are presented in the table below:

AnalysisRelative Risk (95% CI)
Crude (Unadjusted)1.9 (1.5 - 2.4)
Stratified by BMI ≥ 30 kg/m²1.05 (0.8 - 1.3)
Stratified by BMI < 30 kg/m²1.03 (0.7 - 1.4)

Based on these findings, which of the following best describes the epidemiological role of BMI in this study?

  1. Confounding variableAnswer
  2. B
    Effect modifier
  3. C
    Mediating variable
  4. D
    Selection bias
  5. E
    Information bias

Answer

Confounding variable
The correct answer is a confounding variable. A confounder is an extraneous factor that correlates with both the exposure and the outcome, creating a false association or masking a true one. In statistical analysis, confounding is classically identified using stratification. When a dataset is stratified by a confounding variable, the stratum-specific relative risks (or odds ratios) will be similar to each other but significantly different from the crude (unadjusted) relative risk. In this scenario, the crude relative risk of 1.9 suggested a strong association. However, after stratifying by BMI, the relative risks in both the obese and non-obese groups fell to approximately 1.0 (indicating no actual association). This demonstrates that baseline BMI was driving the apparent initial association, confirming its role as a confounder.

Step-by-Step Solution

1
Analyze the crude (unadjusted) relative risk.
The crude relative risk is 1.9, suggesting a significant positive association between artificial sweeteners and type 2 diabetes.
Establishing the baseline finding before stratification is necessary to compare against adjusted results.
2
Examine the stratum-specific relative risks.
The relative risk is 1.05 for obese individuals and 1.03 for non-obese individuals.
Stratification is the primary method for distinguishing between confounding and effect modification during data analysis.
3
Compare the stratum-specific relative risks to each other and to the crude relative risk.
The stratum-specific relative risks are approximately equal to each other but differ significantly from the crude relative risk.
This pattern indicates that the initial association was driven by the stratifying variable, defining it as a confounder.

Key Concept

Confounding in research is identified when stratified analysis reveals stratum-specific measures of association that are similar to each other but significantly different from the crude, unadjusted measure.
Estimated Time:1m 15s
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Bias and Confounding in Research

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