Comparison of Modern Methods for Analyzing Repeated Measures Data with Missing Values

Autor/inn/en	Vallejo, G.; Fernandez, M. P.; Livacic-Rojas, P. E.; Tuero-Herrero, E.
Titel	Comparison of Modern Methods for Analyzing Repeated Measures Data with Missing Values
Quelle	In: Multivariate Behavioral Research, 46 (2011) 6, S.900-937 (38 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0027-3171
DOI	10.1080/00273171.2011.625320
Schlagwörter	Personality Problems; Psychosis; Prevention; Patients; Equations (Mathematics); Computation; Comparative Analysis; Computer Software; Models; Error Patterns; Data Analysis; Statistical Analysis; Multivariate Analysis; Behavioral Science Research; Acquired Immunodeficiency Syndrome (AIDS) + Suchen Sie Ihr Suchwort? Psychose; Prävention; Vorbeugung; Patient; Equations; Mathematics; Gleichungslehre; Analogiemodell; Fehlertyp; Auswertung; Statistische Analyse; Multivariate Analyse
Abstract	Missing data are a pervasive problem in many psychological applications in the real world. In this article we study the impact of dropout on the operational characteristics of several approaches that can be easily implemented with commercially available software. These approaches include the covariance pattern model based on an unstructured covariance matrix (CPM-U) and the true covariance matrix (CPM-T), multiple imputation-based generalized estimating equations (MI-GEE), and weighted generalized estimating equations (WGEE). Under the missing at random mechanism, the MI-GEE approach was always robust. The CPM-T and CPM-U methods were also able to control the error rates provided that certain minimum sample size requirements were met, whereas the WGEE was more prone to inflated error rates. In contrast, under the missing not at random mechanism, all evaluated approaches were generally invalid. Our results also indicate that the CPM methods were more powerful than the MI-GEE and WGEE methods and their superiority was often substantial. Furthermore, we note that little or no power was sacrificed by using CPM-U method in place of CPM-T, although both methods have less power in situations where some participants have incomplete data. Some aspects of the CPM-U and MI-GEE methods are illustrated using real data from 2 previously published data sets. The first data set comes from a randomized study of AIDS patients with advanced immune suppression, the second from a cohort of patients with schizotypal personality disorder enrolled in a prevention program for psychosis. (Contains 9 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10