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Using Logistic Regression to Predict Onset and Recovery With Tau Equivalency
Kimmo Sorjonen1, Michael Lundberg2, Bo Melin1
1Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Frontiers in Psychology
|October 18, 2018
Summary
Statistical analysis can detect predictor effects on health outcomes even without true score changes. This occurs with outcome test-retest correlation and baseline predictor-outcome correlation, cautioning researchers against hasty conclusions.
Area of Science:
- Psychometrics
- Biostatistics
- Health Outcomes Research
Background:
- Studies often examine predictor effects on health outcome onset or recovery.
- Understanding potential biases in detecting these effects is crucial for accurate interpretation.
Purpose of the Study:
- To investigate if predictor effects on outcomes can be detected without changes in true outcome scores.
- To identify conditions under which spurious effects might be observed in onset/recovery research.
Main Methods:
- Simulation study analyzing the relationship between a predictor (X) and an outcome (Y).
- Examined scenarios with tau equivalency (no true score change in Y).
- Incorporated varying degrees of outcome test-retest correlation and baseline predictor-outcome correlation.
Main Results:
- Detected predictor effects on outcome onset/recovery even with tau equivalency.
- The presence of positive test-retest correlation for the outcome and baseline correlation between predictor and outcome are key.
- These correlations can create apparent effects without true changes in the outcome.
Conclusions:
- Researchers must be cautious about interpreting predictor effects on health outcomes.
- Control for expected correlations between predictor and outcome is necessary to avoid drawing hasty conclusions.
- Findings highlight the importance of considering measurement properties in longitudinal health studies.
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