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Mixed models incorporating intra‐familial correlation through spatial autoregression
Author(s) -
Knafl George J.,
Knafl Kathleen A.,
McCorkle Ruth
Publication year - 2005
Publication title -
research in nursing and health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 85
eISSN - 1098-240X
pISSN - 0160-6891
DOI - 10.1002/nur.20082
Subject(s) - autoregressive model , dimension (graph theory) , correlation , econometrics , random effects model , longitudinal data , spatial correlation , statistics , mathematics , computer science , data mining , medicine , meta analysis , geometry , pure mathematics
Family researchers are challenged by the need to account for the special forms of statistical dependence that can exist in family data. To address this issue, mixed modeling methods were adapted to account for dependence of continuous outcomes measured across multiple family members. This was accomplished using a spatial autoregressive approach that accounts for dependence on direction as well as on distance apart. For family data, the dimensions underlying direction can correspond to different family members, thereby accounting for different correlations between family members. When the data are also longitudinal, a dimension representing distance apart in time also can be included to account for temporal correlation. Fixed effects involving general linear models can be included as well. Example analyses were conducted to demonstrate the use of the spatial autoregressive approach for modeling intra‐familial correlation. © Wiley Periodicals, Inc. Res Nurs Health 28:348–356, 2005