Statistical methods for dementia risk prediction and recommendations for future work: A systematic review

Numerous dementia risk prediction models have been developed in the past decade. However, methodological limitations of the analytical tools used may hamper their ability to generate reliable dementia risk scores. We aim to review the used methodologies.

[1]  P. Peduzzi,et al.  Comparison of the logistic and Cox regression models when outcome is determined in all patients after a fixed period of time. , 1987, Journal of chronic diseases.

[2]  G. Collins,et al.  Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement , 2015, BMC Medicine.

[3]  Louise Robinson,et al.  Dementia: timely diagnosis and early intervention , 2015, BMJ : British Medical Journal.

[4]  Shai Ben-David,et al.  Understanding Machine Learning: From Theory to Algorithms , 2014 .

[5]  J. Hilbe Logistic Regression Models , 2009 .

[6]  Quincy M. Samus,et al.  Dementia prevention, intervention, and care , 2017, The Lancet.

[7]  C. Brayne,et al.  Dementia risk prediction in the population: are screening models accurate? , 2010, Nature Reviews Neurology.

[8]  Kristin L. Sainani,et al.  Logistic Regression , 2014, PM & R : the journal of injury, function, and rehabilitation.

[9]  P. Visser,et al.  Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review , 2015, PloS one.

[10]  Kipp W. Johnson,et al.  Machine learning in cardiovascular medicine: are we there yet? , 2018, Heart.

[11]  Can Zhang,et al.  Models for predicting risk of dementia: a systematic review , 2018, Journal of Neurology, Neurosurgery, and Psychiatry.

[12]  Daniel B. Mark,et al.  TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS , 1996 .

[13]  Michael J Pencina,et al.  Choice of time scale and its effect on significance of predictors in longitudinal studies , 2007, Statistics in medicine.

[14]  M. Woodward,et al.  Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker , 2012, Heart.

[15]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. , 2010, International journal of surgery.

[16]  P. Anderberg,et al.  Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review , 2017, PloS one.

[17]  P. Mock Empirical comparisons of proportional hazards and logistic regression models. , 1990, Statistics in medicine.

[18]  M. Woodward,et al.  Risk prediction models: II. External validation, model updating, and impact assessment , 2012, Heart.

[19]  R du Berger,et al.  Flexible modeling of the effects of serum cholesterol on coronary heart disease mortality. , 1997, American journal of epidemiology.

[20]  Geert Jan Biessels,et al.  Midlife risk score for the prediction of dementia four decades later , 2014, Alzheimer's & Dementia.

[21]  D. D. Ingram,et al.  Empirical comparisons of proportional hazards and logistic regression models. , 1989, Statistics in medicine.

[22]  Dimitris Rizopoulos,et al.  Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time‐to‐Event Data , 2011, Biometrics.

[23]  Enrico Pellegrini,et al.  Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review , 2018, Alzheimer's & dementia.

[24]  L. Fisher,et al.  Time-dependent covariates in the Cox proportional-hazards regression model. , 1999, Annual review of public health.

[25]  Ameet Talwalkar,et al.  Foundations of Machine Learning , 2012, Adaptive computation and machine learning.

[26]  F. Harrell,et al.  Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors , 2005 .