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Method issues in dietary data analyses in the Multiple Risk Factor Intervention Trial

G A Grandits1, G E Bartsch, J Stamler

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis 55414, USA.

The American Journal of Clinical Nutrition
|January 1, 1997
PubMed
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Study selection in the Multiple Risk Factor Intervention Trial (MRFIT) altered risk factor relationships. Analyses adjusted for selection bias and regression to the mean, focusing on changes over time.

Area of Science:

  • Cardiovascular disease research
  • Clinical trial methodology
  • Nutritional epidemiology

Background:

  • Participant selection in clinical trials can introduce bias, affecting observed relationships between risk factors.
  • The Multiple Risk Factor Intervention Trial (MRFIT) experienced selection-related differences in risk factor correlations and initial serum cholesterol levels.

Purpose of the Study:

  • To analyze the impact of participant selection on risk factor relationships within the MRFIT.
  • To address and account for biases, including regression to the mean and regression-dilution bias, in the trial's data.
  • To emphasize analytical methods that mitigate these biases for more accurate results.

Main Methods:

  • Covariate adjustment was used to address differing risk factor relationships between selected and unselected populations.

Related Experiment Videos

  • Emphasis was placed on analyzing changes in plasma cholesterol over time, using the second screening measurement to account for initial artificial elevations.
  • Regression-dilution bias in nutrition data was examined using 24-hour dietary recalls, with analyses focusing on averaged multiple follow-up measures.

    • Main Results:

    • Selection into the MRFIT created inverse relationships between smoking, blood pressure, and cholesterol in assigned participants, contrasting with direct relationships in the unselected group.
    • An artificial increase in initial mean serum cholesterol was observed, largely due to regression to the mean.
    • Nutrient-biochemical relationships showed considerable regression-dilution bias, with within- to between-subject variability ratios typically between one and four.

    Conclusions:

    • Participant selection significantly impacts observed risk factor associations in intervention trials.
    • Methodological adjustments, such as covariate adjustment and focusing on change over time, are crucial for valid analysis.
    • Underreporting of dietary intake can affect nutritional assessments, necessitating methods like expressing nutrient data as densities.