Zusammenfassung
Purpose: To investigate (1) the bias in effect estimation due to heaping or digit preference, (2) the as-sociation between age at hypertension diagnosis and risk of cardiovascular comorbidities, and (3) the influence of heaping on risk estimates.Methods: We performed a simulation study with various scenarios, binary outcome, and normal or log-normal distributed covariables. We calculated mean ...
Zusammenfassung
Purpose: To investigate (1) the bias in effect estimation due to heaping or digit preference, (2) the as-sociation between age at hypertension diagnosis and risk of cardiovascular comorbidities, and (3) the influence of heaping on risk estimates.Methods: We performed a simulation study with various scenarios, binary outcome, and normal or log-normal distributed covariables. We calculated mean logistic coefficients under the original and heaped data and their relative deviation. The association of age at hypertension diagnosis and risk of >= 1 cardio-vascular comorbidity was investigated using logistic regression among 50,858 participants in the NAKO Gesundheitsstudie (German National Cohort) who reported such diagnosis. We assessed the proportion of heaped observations and to what extent heaping may have influenced risk estimates.Results: Based on the simulation study and assuming 50% of observations in the variable of interest to be heaped, relative bias was < 6%. In NAKO, a 5-year younger age at hypertension diagnosis was asso-ciated with a 15% increased risk of having >= 1 cardiovascular comorbidity. Observed heaping in age at hypertension diagnosis was 12.6%, and bias of the risk estimate was 0.14%.Conclusions: Bias in effect estimation due to heaping is low in most common scenarios. Younger age at hypertension diagnosis is associated with a higher risk of cardiovascular comorbidities.(c) 2022 Elsevier Inc. All rights reserved.