Abstract
The multivariate normative comparison (MNC) method has been used for identifying cognitive impairment. When participants' cognitive brain domains are evaluated regularly, the longitudinal MNC (LMNC) has been introduced to correct for the intercorrelation among repeated assessments of multiple cognitive domains in the same participant. However, it may not be practical to wait until the end of study for diagnosis. For example, in participants of the Multicenter AIDS Cohort Study (MACS), cognitive functioning has been evaluated repeatedly for more than 35 years. Therefore, it is optimal to identify cognitive impairment at each assessment, while the family-wise error rate (FWER) is controlled with unknown number of assessments in future. In this work, we propose to use the difference of consecutive LMNC test statistics to construct independent tests. Frequency modeling can help predict how many assessments each participant will have, so Bonferroni-type correction can be easily adapted. A chi-squared test is used under the assumption of multivariate normality, and permutation test is proposed where this assumption is violated. We showed through simulation and the MACS data that our method controlled FWER below a predetermined level.
Original language | English (US) |
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Pages (from-to) | 52-67 |
Number of pages | 16 |
Journal | Statistics in Medicine |
Volume | 42 |
Issue number | 1 |
DOIs | |
State | Published - Jan 15 2023 |
Funding
information Directorate for Mathematical and Physical Sciences, Grant/Award Number: NSF DMS-1916001The authors thank the associate editor and two referees for their helpful comments which lead to an improved manuscript. The work was partially supported by the NSF DMS-1916001 to Cheng and the University of Pittsburgh Center for Research Computing through the resources provided. Data in this article were collected by the Multicenter AIDS Cohort Study (MACS), now the MACS/WIHS Combined Cohort Study (MWCCS). The contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health (NIH). MWCCS (Principal Investigators): Atlanta CRS (Ighovwerha Ofotokun, Anandi Sheth, and Gina Wingood), U01-HL146241; Baltimore CRS (Todd Brown and Joseph Margolick), U01-HL146201; Bronx CRS (Kathryn Anastos and Anjali Sharma), U01-HL146204; Brooklyn CRS (Deborah Gustafson and Tracey Wilson), U01-HL146202; Data Analysis and Coordination Center (Gypsyamber D'Souza, Stephen Gange and Elizabeth Golub), U01-HL146193; Chicago-Cook County CRS (Mardge Cohen and Audrey French), U01-HL146245; Chicago-Northwestern CRS (Steven Wolinsky), U01-HL146240; Northern California CRS (Bradley Aouizerat, Jennifer Price, and Phyllis Tien), U01-HL146242; Los Angeles CRS (Roger Detels and Matthew Mimiaga), U01-HL146333; Metropolitan Washington CRS (Seble Kassaye and Daniel Merenstein), U01-HL146205; Miami CRS (Maria Alcaide, Margaret Fischl, and Deborah Jones), U01-HL146203; Pittsburgh CRS (Jeremy Martinson and Charles Rinaldo), U01-HL146208; UAB-MS CRS (Mirjam-Colette Kempf, Jodie Dionne-Odom, and Deborah Konkle-Parker), U01-HL146192; UNC CRS (Adaora Adimora), U01-HL146194. The authors thank the associate editor and two referees for their helpful comments which lead to an improved manuscript. The work was partially supported by the NSF DMS‐1916001 to Cheng and the University of Pittsburgh Center for Research Computing through the resources provided. Data in this article were collected by the Multicenter AIDS Cohort Study (MACS), now the MACS/WIHS Combined Cohort Study (MWCCS). The contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health (NIH). MWCCS (Principal Investigators): Atlanta CRS (Ighovwerha Ofotokun, Anandi Sheth, and Gina Wingood), U01‐HL146241; Baltimore CRS (Todd Brown and Joseph Margolick), U01‐HL146201; Bronx CRS (Kathryn Anastos and Anjali Sharma), U01‐HL146204; Brooklyn CRS (Deborah Gustafson and Tracey Wilson), U01‐HL146202; Data Analysis and Coordination Center (Gypsyamber D'Souza, Stephen Gange and Elizabeth Golub), U01‐HL146193; Chicago‐Cook County CRS (Mardge Cohen and Audrey French), U01‐HL146245; Chicago‐Northwestern CRS (Steven Wolinsky), U01‐HL146240; Northern California CRS (Bradley Aouizerat, Jennifer Price, and Phyllis Tien), U01‐HL146242; Los Angeles CRS (Roger Detels and Matthew Mimiaga), U01‐HL146333; Metropolitan Washington CRS (Seble Kassaye and Daniel Merenstein), U01‐HL146205; Miami CRS (Maria Alcaide, Margaret Fischl, and Deborah Jones), U01‐HL146203; Pittsburgh CRS (Jeremy Martinson and Charles Rinaldo), U01‐HL146208; UAB‐MS CRS (Mirjam‐Colette Kempf, Jodie Dionne‐Odom, and Deborah Konkle‐Parker), U01‐HL146192; UNC CRS (Adaora Adimora), U01‐HL146194. The MWCCS is funded primarily by the National Heart, Lung, and Blood Institute (NHLBI), with additional co‐funding from the Eunice Kennedy Shriver National Institute Of Child Health & Human Development (NICHD), National Institute On Aging (NIA), National Institute Of Dental & Craniofacial Research (NIDCR), National Institute Of Allergy And Infectious Diseases (NIAID), National Institute Of Neurological Disorders And Stroke (NINDS), National Institute Of Mental Health (NIMH), National Institute On Drug Abuse (NIDA), National Institute Of Nursing Research (NINR), National Cancer Institute (NCI), National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Deafness and Other Communication Disorders (NIDCD), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute on Minority Health and Health Disparities (NIMHD), and in coordination and alignment with the research priorities of the National Institutes of Health, Office of AIDS Research (OAR). MWCCS data collection is also supported by UL1‐TR000004 (UCSF CTSA), UL1‐TR003098 (JHU ICTR), UL1‐TR001881 (UCLA CTSI), P30‐AI‐050409 (Atlanta CFAR), P30‐AI‐073961 (Miami CFAR), P30‐AI‐050410 (UNC CFAR), P30‐AI‐027767 (UAB CFAR), and P30‐MH‐116867 (Miami CHARM).
Keywords
- cognitive impairment
- dynamic classification
- family-wise error rate
- frequency modeling
- multivariate mixed-effect model
- sequential test
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability