Accounting for Expected Adjusted Effect
Kimmo Sorjonen, Bo Melin, Michael Ingre
Abstract
The point that adjustment for confounders do not always guarantee protection against spurious findings and type 1-errors has been made before. The present simulation study indicates that for traditional regression methods, this risk is accentuated by a large sample size, low reliability in the measurement of the confounder, and high reliability in the measurement of the predictor and the outcome. However, this risk might be attenuated by calculating the expected adjusted effect, or the required reliability in the measurement of the possible confounder, with equations presented in the present paper.
Topics & Concepts
Spurious relationshipConfoundingReliability (semiconductor)StatisticsPsychologyEconometricsObservational errorSample size determinationSample (material)Reliability engineeringMathematicsEngineeringChemistryPower (physics)ChromatographyPhysicsQuantum mechanicsStatistical Methods in Clinical TrialsAdvanced Statistical Methods and ModelsReliability and Agreement in Measurement