Lauren K Dillard1,2, Amy L Cochran1,3, Alex Pinto4, Cynthia G Fowler2, Mary E Fischer4, Ted S Tweed2,4, Karen J Cruickshanks1,4. 1. Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA. 2. Department of Communication Sciences and Disorders, University of Wisconsin-Madison, Madison, WI, USA. 3. Department of Mathematics, University of Wisconsin-Madison, Madison, WI, USA. 4. Department of Ophthalmology and Visual Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
Abstract
OBJECTIVE: The purpose of this study was to (i) develop a model that predicts hearing aid (HA) use and (ii) determine if model fit is improved by adding factors not typically collected in audiological evaluations. DESIGN: Two models were created and evaluated. The "clinical" model used factors typically collected during audiologic clinical evaluations. The "expanded" model considered additional clinical, health and lifestyle factors to determine if the model fit could be improved (compared to clinical model). Models were created with least absolute shrinkage and selection operator (LASSO) logistic regression with 10-fold cross validation. Predictive ability was evaluated via receiver operating characteristic curves and concordance statistics (c-statistics). STUDY SAMPLE: This study included 275 participants from the Beaver Dam Offspring Study, a prospective longitudinal cohort study of aging, with a treatable level of hearing loss and no HA use at baseline. RESULTS: The clinical and expanded models report predictors important for HA use. The c-statistics of the clinical (0.80) and expanded (0.79) models were not significantly different (p = 0.41). CONCLUSIONS: Similar predictive abilities of models suggest audiological evaluations perform well in predicting HA use.
OBJECTIVE: The purpose of this study was to (i) develop a model that predicts hearing aid (HA) use and (ii) determine if model fit is improved by adding factors not typically collected in audiological evaluations. DESIGN: Two models were created and evaluated. The "clinical" model used factors typically collected during audiologic clinical evaluations. The "expanded" model considered additional clinical, health and lifestyle factors to determine if the model fit could be improved (compared to clinical model). Models were created with least absolute shrinkage and selection operator (LASSO) logistic regression with 10-fold cross validation. Predictive ability was evaluated via receiver operating characteristic curves and concordance statistics (c-statistics). STUDY SAMPLE: This study included 275 participants from the Beaver Dam Offspring Study, a prospective longitudinal cohort study of aging, with a treatable level of hearing loss and no HA use at baseline. RESULTS: The clinical and expanded models report predictors important for HA use. The c-statistics of the clinical (0.80) and expanded (0.79) models were not significantly different (p = 0.41). CONCLUSIONS: Similar predictive abilities of models suggest audiological evaluations perform well in predicting HA use.
Entities:
Keywords:
Demographics/epidemiology; hearing aids; hearing loss; predictive model
Authors: Sibylle Bertoli; Katharina Staehelin; Elisabeth Zemp; Christian Schindler; Daniel Bodmer; Rudolf Probst Journal: Int J Audiol Date: 2009-04 Impact factor: 2.117
Authors: Karen J Cruickshanks; Ted S Tweed; Terry L Wiley; Barbara E K Klein; Ronald Klein; Rick Chappell; David M Nondahl; Dayna S Dalton Journal: Arch Otolaryngol Head Neck Surg Date: 2003-10
Authors: Jacqueline M Weycker; Lauren K Dillard; Alex Pinto; Mary E Fischer; Karen J Cruickshanks; Ted S Tweed Journal: Am J Audiol Date: 2021-11-03 Impact factor: 1.636