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Estimates of intra-cluster correlation coefficients from 2018 USA Medicare data to inform the design of cluster randomized trials in Alzheimer’s and related dementias
by
Li, Fan
, Ouyang, Yongdong
, Li, Xiaojuan
, Mor, Vincent
, Bynum, Julie
, Taljaard, Monica
in
Age
/ Aged
/ Aged, 80 and over
/ Alzheimer Disease - diagnosis
/ Alzheimer Disease - therapy
/ Alzheimer's disease
/ Beneficiaries
/ Biomedicine
/ Clinical outcomes
/ Clinical trials
/ Cluster Analysis
/ Cluster randomized trials
/ Datasets
/ Dementia
/ Dementia - diagnosis
/ Dementia - therapy
/ Disease
/ Drug therapy
/ Emergency Service, Hospital - statistics & numerical data
/ Estimates
/ Female
/ Health Sciences
/ Hospitalization
/ Hospitalization - statistics & numerical data
/ Hospitals
/ Humans
/ Interactive R shiny app
/ Intra-cluster correlation coefficient
/ Male
/ Medicare
/ Medicine
/ Medicine & Public Health
/ Methodology
/ Older people
/ Postal codes
/ Power
/ Randomized Controlled Trials as Topic
/ Research Design
/ Sample Size
/ Sample size calculation
/ Statistics for Life Sciences
/ Treatment effect heterogeneity
/ United States
2024
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Estimates of intra-cluster correlation coefficients from 2018 USA Medicare data to inform the design of cluster randomized trials in Alzheimer’s and related dementias
by
Li, Fan
, Ouyang, Yongdong
, Li, Xiaojuan
, Mor, Vincent
, Bynum, Julie
, Taljaard, Monica
in
Age
/ Aged
/ Aged, 80 and over
/ Alzheimer Disease - diagnosis
/ Alzheimer Disease - therapy
/ Alzheimer's disease
/ Beneficiaries
/ Biomedicine
/ Clinical outcomes
/ Clinical trials
/ Cluster Analysis
/ Cluster randomized trials
/ Datasets
/ Dementia
/ Dementia - diagnosis
/ Dementia - therapy
/ Disease
/ Drug therapy
/ Emergency Service, Hospital - statistics & numerical data
/ Estimates
/ Female
/ Health Sciences
/ Hospitalization
/ Hospitalization - statistics & numerical data
/ Hospitals
/ Humans
/ Interactive R shiny app
/ Intra-cluster correlation coefficient
/ Male
/ Medicare
/ Medicine
/ Medicine & Public Health
/ Methodology
/ Older people
/ Postal codes
/ Power
/ Randomized Controlled Trials as Topic
/ Research Design
/ Sample Size
/ Sample size calculation
/ Statistics for Life Sciences
/ Treatment effect heterogeneity
/ United States
2024
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Estimates of intra-cluster correlation coefficients from 2018 USA Medicare data to inform the design of cluster randomized trials in Alzheimer’s and related dementias
by
Li, Fan
, Ouyang, Yongdong
, Li, Xiaojuan
, Mor, Vincent
, Bynum, Julie
, Taljaard, Monica
in
Age
/ Aged
/ Aged, 80 and over
/ Alzheimer Disease - diagnosis
/ Alzheimer Disease - therapy
/ Alzheimer's disease
/ Beneficiaries
/ Biomedicine
/ Clinical outcomes
/ Clinical trials
/ Cluster Analysis
/ Cluster randomized trials
/ Datasets
/ Dementia
/ Dementia - diagnosis
/ Dementia - therapy
/ Disease
/ Drug therapy
/ Emergency Service, Hospital - statistics & numerical data
/ Estimates
/ Female
/ Health Sciences
/ Hospitalization
/ Hospitalization - statistics & numerical data
/ Hospitals
/ Humans
/ Interactive R shiny app
/ Intra-cluster correlation coefficient
/ Male
/ Medicare
/ Medicine
/ Medicine & Public Health
/ Methodology
/ Older people
/ Postal codes
/ Power
/ Randomized Controlled Trials as Topic
/ Research Design
/ Sample Size
/ Sample size calculation
/ Statistics for Life Sciences
/ Treatment effect heterogeneity
/ United States
2024
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Estimates of intra-cluster correlation coefficients from 2018 USA Medicare data to inform the design of cluster randomized trials in Alzheimer’s and related dementias
Journal Article
Estimates of intra-cluster correlation coefficients from 2018 USA Medicare data to inform the design of cluster randomized trials in Alzheimer’s and related dementias
2024
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Overview
Background
Cluster randomized trials (CRTs) are increasingly important for evaluating interventions embedded in health care systems. An essential parameter in sample size calculation to detect both overall and heterogeneous treatment effects for CRTs is the intra-cluster correlation coefficient (ICC) of both outcome and covariates of interest. However, obtaining advance estimates for the ICC can be challenging. When trial outcomes will be obtained from routinely collected data sources, there is an opportunity to obtain reliable ICC estimates in advance of the trial. Using USA national Medicare data, we estimated ICCs for a range of outcomes to inform the design of CRTs for people living with Alzheimer’s and related dementias (ADRD).
Method
Data from 2018 Medicare Fee-for-Service beneficiaries, specifically, 1,898,812 individuals (≥ 65 years) with diagnosis of ADRD within 3436 hospital service areas (treated as clusters) and 306 hospital referral regions (treated as fixed strata), were used to calculate unadjusted and adjusted ICC estimates for three outcomes: death, any hospitalizations, and any emergency department (ED) visits and three covariates: age, race and sex. We present both overall and stratum-specific ICC estimates. We illustrate their use in sample size calculations for overall treatment effects as well as detecting treatment effect heterogeneity.
Results
The unadjusted overall ICCs for death, hospitalizations, and ED visits were 0.001, 0.010, and 0.017 respectively. Stratum-specific ICCs varied widely across the 306 HRRs: median 0.001, 0.010 and 0.025 for death, hospitalizations, and ED visits respectively and 0.007, 0.001, and 0.080 for age, sex and race. An interactive R Shiny app is provided that allows users to retrieve estimates overlayed on a map of the USA.
Conclusions
We presented both adjusted and unadjusted ICCs for outcomes as well as unadjusted ICCs for covariates of potential interest from population-level data in the USA and demonstrated how the estimates may be used in sample size calculations for CRTs in ADRD.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
/ Aged
/ Alzheimer Disease - diagnosis
/ Datasets
/ Dementia
/ Disease
/ Emergency Service, Hospital - statistics & numerical data
/ Female
/ Hospitalization - statistics & numerical data
/ Humans
/ Intra-cluster correlation coefficient
/ Male
/ Medicare
/ Medicine
/ Power
/ Randomized Controlled Trials as Topic
/ Statistics for Life Sciences
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