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. 2023 May;37(4):463-499.
doi: 10.1037/neu0000888.

Linking self-perceived cognitive functioning questionnaires using item response theory: The subjective cognitive decline initiative

Laura A Rabin  1 , Sietske A M Sikkes  2 , Douglas Tommet  3 , Richard N Jones  3 , Paul K Crane  4 , Milushka M Elbulok-Charcape  1 , Mark A Dubbelman  5 , Rebecca Koscik  6 , Rebecca E Amariglio  7 , Rachel F Buckley  7 , Mercè Boada  8 , Gaël Chételat  9 , Bruno Dubois  10 , Kathryn A Ellis  11 , Katherine A Gifford  12 , Angela L Jefferson  12 , Frank Jessen  13 , Sterling Johnson  14 , Mindy J Katz  15 , Richard B Lipton  15 , Tobias Luck  16 , Eleni Margioti  17 , Paul Maruff  18 , Jose Luis Molinuevo  19 , Audrey Perrotin  9 , Ronald C Petersen  20 , Lorena Rami  19 , Barry Reisberg  21 , Dorene M Rentz  7 , Steffi G Riedel-Heller  22 , Shannon L Risacher  23 , Octavio Rodriguez-Gomez  8 , Perminder S Sachdev  24 , Andrew J Saykin  23 , Nikolaos Scarmeas  25 , Colette Smart  26 , Beth E Snitz  27 , Reisa A Sperling  7 , Vanessa Taler  28 , Wiesje M van der Flier  2 , Argonde C van Harten  5 , Michael Wagner  29 , Steffen Wolfsgruber  29 ; Alzheimer’s Disease Neuroimaging Initiative; Canadian Longitudinal Study on Aging; Health and Aging Brain Study: Health Disparities (HABS-HD) Study Team
Affiliations

Linking self-perceived cognitive functioning questionnaires using item response theory: The subjective cognitive decline initiative

Laura A Rabin et al. Neuropsychology. 2023 May.

Abstract

Objective: Self-perceived cognitive functioning, considered highly relevant in the context of aging and dementia, is assessed in numerous ways-hindering the comparison of findings across studies and settings. Therefore, the present study aimed to link item-level self-report questionnaire data from international aging studies.

Method: We harmonized secondary data from 24 studies and 40 different questionnaires with item response theory (IRT) techniques using a graded response model with a Bayesian estimator. We compared item information curves to identify items with high measurement precision at different levels of the self-perceived cognitive functioning latent trait. Data from 53,030 neuropsychologically intact older adults were included, from 13 English language and 11 non-English (or mixed) language studies.

Results: We successfully linked all questionnaires and demonstrated that a single-factor structure was reasonable for the latent trait. Items that made the greatest contribution to measurement precision (i.e., "top items") assessed general and specific memory problems and aspects of executive functioning, attention, language, calculation, and visuospatial skills. These top items originated from distinct questionnaires and varied in format, range, time frames, response options, and whether they captured ability and/or change.

Conclusions: This was the first study to calibrate self-perceived cognitive functioning data of geographically diverse older adults. The resulting item scores are on the same metric, facilitating joint or pooled analyses across international studies. Results may lead to the development of new self-perceived cognitive functioning questionnaires guided by psychometric properties, content, and other important features of items in our item bank. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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Conflict of interest statement

Conflict of Interest

SCJ serves on an advisory boards to Roche Diagnostics, Prothena, and Eisai in the past two years and has received research support from Cerveau Technologies. JLM is a full time employee at H. Lundbeck A/S. RCP is a consultant for Roche, Inc., Merck, Inc., Biogen, Inc., Genentech, Inc., Eisai, Inc., and Nestle, Inc. PSS was a Member of Advisory Committees for Biogen Australia and Roche Australia in 2020 and 2021. AJS receives support from multiple NIH grants (P30 AG010133, P30 AG072976, R01 AG019771, R01 AG057739, U19 AG024904, R01 LM013463, R01 AG068193, T32 AG071444, and U01 AG068057 and U01 AG072177). He has also received support from Avid Radiopharmaceuticals, a subsidiary of Eli Lilly (in kind contribution of PET tracer precursor); Bayer Oncology (Scientific Advisory Board); Eisai (Scientific Advisory Board); Siemens Medical Solutions USA, Inc. (Dementia Advisory Board); NIH NHLBI (MESA Observational Study Monitoring Board); Springer-Nature Publishing (Editorial Office Support as Editor-in-Chief, Brain Imaging and Behavior). NS is Chair of the Data Safety Monitoring Board for an NIH funded study at The Albert Einstein College of Medicine. NS is also a local site PI of a phase III industry sponsored study (Semaglutide - Novonordisc), with funding to the Institution.

Figures

Figure 1
Figure 1
Raincloud Plot of Posterior Predictive P-Values (PPP) for Items the Mayo Clinic Study of Aging (MCSA) from a Unidimensional and Bifactor Model
Figure 2
Figure 2
Left. Scatterplot of Factor Score Estimates Under Bifactor (y-axis) and Unidimensional Model (x-axis) Right. Scatterplot of Differences in Factor Score Estimates as a Function of the Average of the Two Factor Scores
Figure 3
Figure 3
Raincloud Plot of Posterior Predictive P-Values (PPP) for All Items from All Studies from a Unidimensional and Bifactor Model
None
Number of Studies in which Each of 40 Study Questionnaires was Utilized
Note. The total number of studies = 24 (substudies are not included); please refer to Table 2 for the definition of questionnaire abbreviations.
Figure 1
Figure 1
Sequence of Data Coding and Analytic Procedures
Figure 2
Figure 2. Overlap Among Self-Perceived Cognitive Functioning Items Across Studies
Figure 2 displays the overlap between self-perceived cognitive functioning items in the studies (including substudies) and shows how the different SCD-I working group studies and items are linked together. In the top panel, the bars/dots represent items administered within a study. The bottom panel displays additional detail for the portion of the top panel enclosed in a gray rectangle. Overall, the figure reveals very little item overlap across studies and no single item represented across all studies. Note. The figure displays 27 instead of 24 studies because the EAS Substudy 1, EAS Substudy 2 and Pitt Substudy were linked separately from EAS and University of Pittsburgh/MYHAT studies. Please refer to Table 1 for the definitions of the study abbreviations. Please refer to Table 2 for information about study items and questionnaires.
Figure 2
Figure 2. Overlap Among Self-Perceived Cognitive Functioning Items Across Studies
Figure 2 displays the overlap between self-perceived cognitive functioning items in the studies (including substudies) and shows how the different SCD-I working group studies and items are linked together. In the top panel, the bars/dots represent items administered within a study. The bottom panel displays additional detail for the portion of the top panel enclosed in a gray rectangle. Overall, the figure reveals very little item overlap across studies and no single item represented across all studies. Note. The figure displays 27 instead of 24 studies because the EAS Substudy 1, EAS Substudy 2 and Pitt Substudy were linked separately from EAS and University of Pittsburgh/MYHAT studies. Please refer to Table 1 for the definitions of the study abbreviations. Please refer to Table 2 for information about study items and questionnaires.
Figure 3
Figure 3. Item Information Plots
Figure 3 presents the item information plots for the 601 items included in the analysis. Items presented in blue have the highest information in the ranges of interest (-2 to -1 SDs and 0 to +1 SDs), and these are the items presented in Tables 4a and 4b. All remaining items are illustrated with gray lines.
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