OBJECTIVES: Prior research has identified numerous genetic (including sex), education, health, and lifestyle factors that predict cognitive decline. Traditional model selection approaches (e.g., backward or stepwise selection) attempt to find one model that best fits the observed data, risking interpretations that only the selected predictors are important. In reality, several predictor combinations may fit similarly well but result in different conclusions (e.g., about size and significance of parameter estimates). In this study, we describe an alternative method, Information-Theoretic (IT) model averaging, and apply it to characterize a set of complex interactions in a longitudinal study on cognitive decline. METHODS: Here, we used longitudinal cognitive data from 1256 late-middle aged adults from the Wisconsin Registry for Alzheimer's Prevention study to examine the effects of sex, apolipoprotein E (APOE) ɛ4 allele (non-modifiable factors), and literacy achievement (modifiable) on cognitive decline. For each outcome, we applied IT model averaging to a set of models with different combinations of interactions among sex, APOE, literacy, and age. RESULTS: For a list-learning test, model-averaged results showed better performance for women versus men, with faster decline among men; increased literacy was associated with better performance, particularly among men. APOE had less of an association with cognitive performance in this age range (∼40-70 years). CONCLUSIONS: These results illustrate the utility of the IT approach and point to literacy as a potential modifier of cognitive decline. Whether the protective effect of literacy is due to educational attainment or intrinsic verbal intellectual ability is the topic of ongoing work. (JINS, 2019, 25, 119-133).
OBJECTIVES: Prior research has identified numerous genetic (including sex), education, health, and lifestyle factors that predict cognitive decline. Traditional model selection approaches (e.g., backward or stepwise selection) attempt to find one model that best fits the observed data, risking interpretations that only the selected predictors are important. In reality, several predictor combinations may fit similarly well but result in different conclusions (e.g., about size and significance of parameter estimates). In this study, we describe an alternative method, Information-Theoretic (IT) model averaging, and apply it to characterize a set of complex interactions in a longitudinal study on cognitive decline. METHODS: Here, we used longitudinal cognitive data from 1256 late-middle aged adults from the Wisconsin Registry for Alzheimer's Prevention study to examine the effects of sex, apolipoprotein E (APOE) ɛ4 allele (non-modifiable factors), and literacy achievement (modifiable) on cognitive decline. For each outcome, we applied IT model averaging to a set of models with different combinations of interactions among sex, APOE, literacy, and age. RESULTS: For a list-learning test, model-averaged results showed better performance for women versus men, with faster decline among men; increased literacy was associated with better performance, particularly among men. APOE had less of an association with cognitive performance in this age range (∼40-70 years). CONCLUSIONS: These results illustrate the utility of the IT approach and point to literacy as a potential modifier of cognitive decline. Whether the protective effect of literacy is due to educational attainment or intrinsic verbal intellectual ability is the topic of ongoing work. (JINS, 2019, 25, 119-133).
Entities:
Keywords:
Alzheimer’s disease; Cognitive decline; Kullback-Leibler divergence; Model averaging; Model likelihoods; Model selection
Authors: James P Olsen; Robert P Fellows; Monica Rivera-Mindt; Susan Morgello; Desiree A Byrd Journal: Clin Neuropsychol Date: 2015-12-21 Impact factor: 3.535
Authors: Scott C Neu; Judy Pa; Walter Kukull; Duane Beekly; Amanda Kuzma; Prabhakaran Gangadharan; Li-San Wang; Klaus Romero; Stephen P Arneric; Alberto Redolfi; Daniele Orlandi; Giovanni B Frisoni; Rhoda Au; Sherral Devine; Sanford Auerbach; Ana Espinosa; Mercè Boada; Agustín Ruiz; Sterling C Johnson; Rebecca Koscik; Jiun-Jie Wang; Wen-Chuin Hsu; Yao-Liang Chen; Arthur W Toga Journal: JAMA Neurol Date: 2017-10-01 Impact factor: 18.302
Authors: M X Tang; Y Stern; K Marder; K Bell; B Gurland; R Lantigua; H Andrews; L Feng; B Tycko; R Mayeux Journal: JAMA Date: 1998-03-11 Impact factor: 56.272
Authors: Megan Elizabeth Lenehan; Mathew James Summers; Nichole Louise Saunders; Jeffery Joseph Summers; James C Vickers Journal: Psychogeriatrics Date: 2014-12-17 Impact factor: 2.440
Authors: Erin E Sundermann; Pauline M Maki; Leah H Rubin; Richard B Lipton; Susan Landau; Anat Biegon Journal: Neurology Date: 2016-10-05 Impact factor: 9.910
Authors: Eric D Anderson; Michelle Wahoske; Mary Huber; Derek Norton; Zhanhai Li; Rebecca L Koscik; Emre Umucu; Sterling C Johnson; Jana Jones; Sanjay Asthana; Carey E Gleason Journal: Alzheimers Dement (Amst) Date: 2016-05-26
Authors: Kimberly D Mueller; Derek Norton; Rebecca L Koscik; Martha C Morris; Erin M Jonaitis; Lindsay R Clark; Taylor Fields; Samantha Allison; Sara Berman; Sarah Kraning; Megan Zuelsdorff; Ozioma Okonkwo; Nathaniel Chin; Cynthia M Carlsson; Barbara B Bendlin; Bruce P Hermann; Sterling C Johnson Journal: PLoS One Date: 2020-04-23 Impact factor: 3.240
Authors: Erin M Jonaitis; Rebecca L Koscik; Lindsay R Clark; Yue Ma; Tobey J Betthauser; Sara E Berman; Samantha L Allison; Kimberly D Mueller; Bruce P Hermann; Carol A Van Hulle; Bradley T Christian; Barbara B Bendlin; Kaj Blennow; Henrik Zetterberg; Cynthia M Carlsson; Sanjay Asthana; Sterling C Johnson Journal: Alzheimers Dement (Amst) Date: 2019-01-11