Browse > Article
http://dx.doi.org/10.5351/CSAM.2016.23.6.531

The effects of scanning position on evaluation of cerebral atrophy level: assessed by item response theory  

Mahsin, Md (Department of Mathematics and Statistics, University of Calgary)
Zhao, Yinshan (MS/MRI Research Group, Department of Medicine, University of British Columbia)
Publication Information
Communications for Statistical Applications and Methods / v.23, no.6, 2016 , pp. 531-541 More about this Journal
Abstract
Cerebral atrophy affects the brain and is a common feature of patients with mild cognitive impairment or Alzheimer's diseases. It is evaluated by the radiologist or reader based on patient's history, age and the space between the brain and the skull as indicated by magnetic resonance (MR) images. A total of 70 patients were scanned in the supine and prone positions before three radiologist assessed their atrophy level. This study examined the radiologist's assessment of the cerebral atrophy level using a graded response model of item response theory (IRT). A graded response model (GRM) is fitted to our data and then item-fit and person-fit statistics are evaluated to assess the fitted model. Our analysis found that the cerebral atrophy level is better discriminated by readers in the prone position because all item slopes were greater than 2 at this position, versus the supine position where all the slope parameters were less than 1. However, the thresholds are very similar for the first reader and are quite different for the second and third readers because the scanning position affects readers differently as the category threshold estimates vary considerably between the readers..
Keywords
cerebral atrophy; IRT; GRM; supine and prone; readers;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Hays RD, Morales LS, and Reise SP (2000). Item response theory and health outcomes measurement in the 21st century, Medical Care, 38, II28-II42.
2 Holman R, Glas CA, and de Haan RJ (2003). Power analysis in randomized clinical trials based on item response theory, Controlled Clinical Trials, 24, 390-410.   DOI
3 Kang T and Chen TT (2007). An Investigation of the Performance of the Generalized S - $X^2$ Item-Fit Index for Polytomous IRT Models, ACT Research, Columbus, IN.
4 Karas G, Scheltens P, Rombouts S, van Schijndel R, Klein M, Jones B, van der Flier W, Vrenken H, and Barkhof F (2007). Precuneus atrophy in early-onset Alzheimer's disease: a morphometric structural MRI study, Neuroradiology, 49, 967-976.   DOI
5 Loehlin JC (2004). Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis (4th ed), Lawrence Erlbaum Associates, Mahwah, NJ.
6 Masters GN (1982). A Rasch model for partial credit scoring, Psychometrika, 47, 149-174.   DOI
7 McCullough D, Levy L, DiChiro G, and Johnson D (1990). Toward the prediction of neurological injury from tethered spinal cord: investigation of cord motion with magnetic resonance, Pediatric Neurosurgery, 16, 3-7.   DOI
8 Muraki E (1992). A generalized partial credit model: application of an em algorithm, Applied Psychological Measurement, 16, 159-176.   DOI
9 Muraki E (1997). A generalized partial credit model. In van der Linden WJ and Hambleton RK (Eds), Handbook of Modern Item Response Theory (pp. 153-164), Springer, New York.
10 R Development Core Team (2011). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
11 Rasch G (1960). Probabilistic Models for Some Intelligence and Achievement Tests, Danish Institute for Educational Research, Copenhagen.
12 Rasch G (1961). On general laws and the meaning of measurement in psychology, In Proceedings of the 4th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, 321-333.
13 Reise SP (1990). A comparison of item-and person-fit methods of assessing model-data fit in IRT. Applied Psychological Measurement, 14, 127-137.   DOI
14 Reise SP (2016). The emergence of item response theory models and the patient reported outcomes measurement information systems, Austrian Journal of Statistics, 38, 211-220.   DOI
15 Reise SP and Flannery P (1996). Assessing person-fit on measures of typical performance, Applied Measurement in Education, 9, 9-26.   DOI
16 Reise SP and Waller NG (2009). Item response theory and clinical measurement, Annual Review of Clinical Psychology, 5, 27-48.   DOI
17 Reise SP,Widaman KF, and Pugh RH (1993). Confirmatory factor analysis and item response theory: two approaches for exploring measurement invariance, Psychological Bulletin, 114, 552-566.   DOI
18 McDonald RP (1981). The dimensionality of tests and items, British Journal of Mathematical and Statistical Psychology, 34, 100-117.   DOI
19 Samejima F (1969). Estimation of latent ability using a response pattern of graded scores, Psychometrika Monograph Supplement, 34, 100.
20 Samejima F (1972). A general model for free-response data, Psychometrika Monograph Supplement, 37, 68.
21 Samejima F (1997). Graded response model. In van der Linden WJ and Hambleton RK (Eds), Handbook of Modern Item Response Theory (pp. 85-100), Springer, New York.
22 Thissen D and Steinberg L (1986). A taxonomy of item response models, Psychometrika, 51, 567-577.   DOI
23 Witkamp TD, Vandertop WP, Beek FJ, Notermans NC, Gooskens RH, and van Waes PF (2001). Medullary cone movement in subjects with a normal spinal cord and in patients with a tethered spinal cord 1, Radiology, 220, 208-212.   DOI
24 Yen WM (1981). Using simulation results to choose a latent trait model, Applied Psychological Measurement, 5, 245-262.   DOI
25 Adams R, Rosier M, Campbell D, and Ruffin R (2005). Assessment of an asthma quality of life scale using item-response theory, Respirology, 10, 587-593.   DOI
26 Andrich D (1978). Application of a psychometric rating model to ordered categories which are scored with successive integers, Applied Psychological Measurement, 2, 581-594.   DOI
27 Bock RD (1972). Estimating item parameters and latent ability when responses are scored in two or more nominal categories, Psychometrika, 37, 29-51.   DOI
28 Cai L (2010). High-dimensional exploratory item factor analysis by a Metropolis-Hastings Robbins-Monro algorithm, Psychometrika, 75, 33-57.   DOI
29 Cattell RB (1966). The screen test for the number of factors, Multivariate Behavioral Research, 1, 245-276.   DOI
30 Cattell RB (1978). The Scientific Use of Factor Analysis in Behavioral and Life Sciences, Plenum, New York.
31 Chalmers RP (2012). mirt: a multidimensional item response theory package for the R environment, Journal of Statistical Software, 48, 1-29.
32 Coleman MJ, Cook S, Matthysse S, Barnard J, Lo Y, Levy DL, Rubin DB, and Holzman PS (2002). Spatial and object working memory impairments in schizophrenia patients: a Bayesian itemresponse theory analysis, Journal of Abnormal Psychology, 111, 425-435.   DOI
33 Drasgow F, Levine MV, andWilliams EA (1985). Appropriateness measurement with polychotomous item response models and standardized indices, British Journal of Mathematical and Statistical Psychology, 38, 67-86.   DOI
34 Edelen MO and Reeve BB (2007). Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement, Quality of Life Research, 16, 5-18.   DOI
35 Embretson, SE and Reise SP (2000). Item Response Theoryfor Psychologists, Lawrence Erlbaum Associates, Mahwah, NJ.
36 Hays RD, Liu H, Spritzer K, and Cella D (2007). Item response theory analyses of physical functioning items in the medical outcomes study, Medical Care, 45, S32-S38.   DOI