adt_package: The 'ADTool' package.
In olssol/ADTool: Alzheimer Disease Data Tools
Background
Data Sources
Objectives
Background
Sharp increases in Alzheimer's disease (AD) cases, deaths, and costs are
stressing the U.S. health care system and caregivers. Without effective
interventions, the costs and unpaid care are projected to increase 4-fold by
2050 [1]. The current scientific consensus indicated that the preclinical
stage, when patients are still cognitively normal, is the crucial window of
opportunity for early interventions before irreversible brain structure
changes occur [2, 3]. However, because the preclinical stage is typically
asymptomatic, it is important to focus on AD biomarkers, quantify their
ordering in time and magnitude of the signal, and estimate their evolution
during the preclinical progression of AD.
Data Sources
There are several major AD data sources exist which allows researchers to
conduct their research.
The BIOCARD study is a longitudinal, observational study initiated in 1995,
and designed to identify biomarkers associated with progression from
cognitively normal (CN) to mild cognitive impairment (MCI) or dementia. It
enrolled 349 cognitively normal individuals who were primarily middle-aged
(mean age=57.1) at enrollment. Most participants by design had a first-degree
relative with dementia. Clinical assessments and cognitive testing were
completed annually; MRI scans, cerebrospinal fluid, and blood specimens were
collected approximately every 2 years. The cohort remains being followed
currently with an administrative gap between 2005 and 2009. As of May 31,
2018, 80 participants have cognitive symptom onset. Study characteristics are
shown in table 1. This study provides rare and valuable information of
longitudinal biomarker measurements over 20 years (median follow up 13.7
years) and mostly before subjects have cognitive decline – the crucial
preclinical time window for understanding the AD biomarker cascade.
The ADNI study is a multicenter observation study launched in 2004, to
collect clinical, imaging, genetic and biospecimen biomarkers from cohorts of
different clinical states at baseline. It could be characterized as a
cross-sectional study with longitudinal follow-up in that participants in
diagnostic groups were very different from each other at baseline. The
initial phase of ADNI (2004, 5 years) enrolled 200 CN elderly, 400 MCI, and
200 AD. This cohort was followed and augmented in three later phases, ADNI-GO
(2009, 2 years), ADNI-2 (2011, 5 years) and ADNI-3 (2016, 5 years), with over
1000 additional participants enrolled in existing cohorts as well as in new
cohorts of non-MCI but with Significant Memory Concern (SMC), early MCI, and
late MCI. We will only use ADNI subjects that are not demented at baseline in
our analysis. This study uses a similar protocol for biomarker measurements
as the BIOCARD study. It complements the BIOCARD data on a larger cohort and
a wider spread of the AD progression continuum beyond the preclinical phase.
The NACC UDS data is a collection of data reflecting the total enrollment
since 2005 across 34 Alzheimer’s Disease Centers and includes subjects with a
range of cognitive status. The majority of these subjects are followed
longitudinally with assessments obtained annually. A standard protocol is
used to collect clinical information, neuropsychological test results, CSF
markers, and MRI markers when available. This protocol will use a sub-cohort
of UDS data that included subjects who had at least one MRI evaluation and
had volumetric MRI biomarkers calculated, N= 1317 as of May 2018. This cohort
mostly does not have CSF or PET imaging information, but provides information
on a large population with intensive cognitive and MRI measures.
Objectives
In this package, we establish AD data standards and data dictionaries in this
package that define the formats and organization structures of the AD data
across multiple data sources. R Functions are provided for data analysts to
integrate data from multiple data sources and create their analysis dataset.
olssol/ADTool documentation built on Feb. 12, 2021, 3:49 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Background Data Sources Objectives
Background
Sharp increases in Alzheimer's disease (AD) cases, deaths, and costs are stressing the U.S. health care system and caregivers. Without effective interventions, the costs and unpaid care are projected to increase 4-fold by 2050 [1]. The current scientific consensus indicated that the preclinical stage, when patients are still cognitively normal, is the crucial window of opportunity for early interventions before irreversible brain structure changes occur [2, 3]. However, because the preclinical stage is typically asymptomatic, it is important to focus on AD biomarkers, quantify their ordering in time and magnitude of the signal, and estimate their evolution during the preclinical progression of AD.
Data Sources
There are several major AD data sources exist which allows researchers to conduct their research.
The BIOCARD study is a longitudinal, observational study initiated in 1995, and designed to identify biomarkers associated with progression from cognitively normal (CN) to mild cognitive impairment (MCI) or dementia. It enrolled 349 cognitively normal individuals who were primarily middle-aged (mean age=57.1) at enrollment. Most participants by design had a first-degree relative with dementia. Clinical assessments and cognitive testing were completed annually; MRI scans, cerebrospinal fluid, and blood specimens were collected approximately every 2 years. The cohort remains being followed currently with an administrative gap between 2005 and 2009. As of May 31, 2018, 80 participants have cognitive symptom onset. Study characteristics are shown in table 1. This study provides rare and valuable information of longitudinal biomarker measurements over 20 years (median follow up 13.7 years) and mostly before subjects have cognitive decline – the crucial preclinical time window for understanding the AD biomarker cascade.
The ADNI study is a multicenter observation study launched in 2004, to collect clinical, imaging, genetic and biospecimen biomarkers from cohorts of different clinical states at baseline. It could be characterized as a cross-sectional study with longitudinal follow-up in that participants in diagnostic groups were very different from each other at baseline. The initial phase of ADNI (2004, 5 years) enrolled 200 CN elderly, 400 MCI, and 200 AD. This cohort was followed and augmented in three later phases, ADNI-GO (2009, 2 years), ADNI-2 (2011, 5 years) and ADNI-3 (2016, 5 years), with over 1000 additional participants enrolled in existing cohorts as well as in new cohorts of non-MCI but with Significant Memory Concern (SMC), early MCI, and late MCI. We will only use ADNI subjects that are not demented at baseline in our analysis. This study uses a similar protocol for biomarker measurements as the BIOCARD study. It complements the BIOCARD data on a larger cohort and a wider spread of the AD progression continuum beyond the preclinical phase.
The NACC UDS data is a collection of data reflecting the total enrollment since 2005 across 34 Alzheimer’s Disease Centers and includes subjects with a range of cognitive status. The majority of these subjects are followed longitudinally with assessments obtained annually. A standard protocol is used to collect clinical information, neuropsychological test results, CSF markers, and MRI markers when available. This protocol will use a sub-cohort of UDS data that included subjects who had at least one MRI evaluation and had volumetric MRI biomarkers calculated, N= 1317 as of May 2018. This cohort mostly does not have CSF or PET imaging information, but provides information on a large population with intensive cognitive and MRI measures.
Objectives
In this package, we establish AD data standards and data dictionaries in this package that define the formats and organization structures of the AD data across multiple data sources. R Functions are provided for data analysts to integrate data from multiple data sources and create their analysis dataset.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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