Browsing by Author "Stern, Yaakov"

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    A new algorithm for predicting time to disease endpoints in Alzheimer's disease patients.
    (J Alzheimers Dis, 2014) Razlighi, Qolamreza R; Stallard, Eric; Brandt, Jason; Blacker, Deborah; Albert, Marilyn; Scarmeas, Nikolaos; Kinosian, Bruce; Yashin, Anatoliy I; Stern, Yaakov
    BACKGROUND: The ability to predict the length of time to death and institutionalization has strong implications for Alzheimer's disease patients and caregivers, health policy, economics, and the design of intervention studies. OBJECTIVE: To develop and validate a prediction algorithm that uses data from a single visit to estimate time to important disease endpoints for individual Alzheimer's disease patients. METHOD: Two separate study cohorts (Predictors 1, N = 252; Predictors 2, N = 254), all initially with mild Alzheimer's disease, were followed for 10 years at three research centers with semiannual assessments that included cognition, functional capacity, and medical, psychiatric, and neurologic information. The prediction algorithm was based on a longitudinal Grade of Membership model developed using the complete series of semiannually-collected Predictors 1 data. The algorithm was validated on the Predictors 2 data using data only from the initial assessment to predict separate survival curves for three outcomes. RESULTS: For each of the three outcome measures, the predicted survival curves fell well within the 95% confidence intervals of the observed survival curves. Patients were also divided into quintiles for each endpoint to assess the calibration of the algorithm for extreme patient profiles. In all cases, the actual and predicted survival curves were statistically equivalent. Predictive accuracy was maintained even when key baseline variables were excluded, demonstrating the high resilience of the algorithm to missing data. CONCLUSION: The new prediction algorithm accurately predicts time to death, institutionalization, and need for full-time care in individual Alzheimer's disease patients; it can be readily adapted to predict other important disease endpoints. The algorithm will serve an unmet clinical, research, and public health need.
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    Estimation and validation of a multiattribute model of Alzheimer disease progression.
    (Med Decis Making, 2010-11) Stallard, Eric; Kinosian, Bruce; Zbrozek, Arthur S; Yashin, Anatoliy I; Glick, Henry A; Stern, Yaakov
    OBJECTIVES: To estimate and validate a multiattribute model of the clinical course of Alzheimer disease (AD) from mild AD to death in a high-quality prospective cohort study, and to estimate the impact of hypothetical modifications to AD progression rates on costs associated with Medicare and Medicaid services. DATA AND METHODS: The authors estimated sex-specific longitudinal Grade of Membership (GoM) models for AD patients (103 men, 149 women) in the initial cohort of the Predictors Study (1989-2001) based on 80 individual measures obtained every 6 mo for 10 y. These models were replicated for AD patients (106 men, 148 women) in the 2nd Predictors Study cohort (1997-2007). Model validation required that the disease-specific transition parameters be identical for both Predictors Study cohorts. Medicare costs were estimated from the National Long Term Care Survey. RESULTS: Sex-specific models were validated using the 2nd Predictors Study cohort with the GoM transition parameters constrained to the values estimated for the 1st Predictors Study cohort; 57 to 61 of the 80 individual measures contributed significantly to the GoM models. Simulated, cost-free interventions in the rate of progression of AD indicated that large potential cost offsets could occur for patients at the earliest stages of AD. CONCLUSIONS: AD progression is characterized by a small number of parameters governing changes in large numbers of correlated indicators of AD severity. The analysis confirmed that the progression of AD represents a complex multidimensional physiological process that is similar across different study cohorts. The estimates suggested that there could be large cost offsets to Medicare and Medicaid from the slowing of AD progression among patients with mild AD. The methodology appears generally applicable in AD modeling.
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    Finding Positive Meaning in Negative Experiences Engages Ventral Striatal and Ventromedial Prefrontal Regions Associated with Reward Valuation
    (Journal of Cognitive Neuroscience, 2017-02) Doré, Bruce P; Boccagno, Chelsea; Burr, Daisy; Hubbard, Alexa; Long, Kan; Weber, Jochen; Stern, Yaakov; Ochsner, Kevin N