20 research outputs found
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Estimation and Validation of a Multiattribute Model of Alzheimer Disease Progression
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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Patient Dependence and Longitudinal Changes in Costs of Care in Alzheimer's Disease
BACKGROUND/AIMS: To examine the incremental effect of patients' dependence on others, on cost of medical and nonmedical care, and on informal caregiving hours over time. METHODS: Data are obtained from 172 patients from the Predictors Study, a large, multicenter cohort of patients with probable Alzheimer disease (AD) followed annually for 4 years in 3 University-based AD centers in the USA. Enrollment required a modified Mini-Mental State Examination score >or=30. We examined the effects of patient dependence (measured by the Dependence Scale, DS) and function (measured by the Blessed Dementia Rating Scale, BDRS) on medical care cost, nonmedical care cost, and informal caregiving time using random effects regression models. RESULTS: A one-point increase in DS score was associated with a 5.7% increase in medical cost, a 10.5% increase in nonmedical cost, and a 4.1% increase in caregiving time. A one-point increase in BDRS score was associated with a 7.6% increase in medical cost, a 3.9% increase in nonmedical cost and an 8.7% increase in caregiving time. CONCLUSIONS: Both functional impairment and patient dependence were associated with higher costs of care and caregiving time. Measures of functional impairment and patient dependence provide unique and incremental information on the overall impact of AD on patients and their caregivers
Validation of Electronic Systems to Collect Patient-Reported Outcome (PRO) Data - Recommendations for Clinical Trial Teams: Report of the ISPOR ePRO Systems Validation Good Research Practices Task Force
Validation of Electronic Systems to Collect Patient-Reported Outcome (PRO) Data - Recommendations for Clinical Trial Teams: Report of the ISPOR ePRO Systems Validation Good Research Practices Task Force Abstract. Outcomes research literature has many examples of high-quality, reliable patient-reported outcome (PRO) data entered directly by electronic means, ePRO, compared to data entered from original results on paper. Clinical trial managers are increasingly using ePRO data collection for PRO-based endpoints. Regulatory review dictates the rules to follow with ePRO data collection for medical label claims. A critical component for regulatory compliance is evidence of the validation of these electronic data collection systems. Validation of electronic systems is a process versus a focused activity that finishes at a single point in time. Eight steps need to be described and undertaken to qualify the validation of the data collection software in its target environment: requirements definition, design, coding, testing, tracing, user acceptance testing, installation and configuration, and decommissioning. These elements are consistent with recent regulatory guidance for systems validation. This report was written to explain how the validation process works for sponsors, trial teams, and other users of electronic data collection devices responsible for verifying the quality of the data entered into relational databases from such devices. It is a guide on the requirements and documentation needed from a data collection systems provider to demonstrate systems validation. It is a practical source of information for study teams to ensure that ePRO providers are using system validation and implementation processes that will ensure the systems and services: operate reliably when in practical use; produce accurate and complete data and data files; support management control and comply with any existing regulations. Furthermore, this short report will increase user understanding of the requirements for a technology review leading to more informed and balanced recommendations or decisions on electronic data collection methods