Analyzing temporal patterns of infant sleep and negative affective behavior: a comparison between different statistical models |
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| Authors: | Hemmi Mirja H Schneider Silvia Müller Silvana Meyer Andrea H Wilhelm Frank H |
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| Affiliation: | aSwiss Etiological Study of Adjustment and Mental Health (sesam), Institute of Psychology, University of Basel, Switzerland;bDepartment of Clinical Child and Adolescent Psychology, Institute of Psychology, University of Bochum, Germany;cDepartment of Clinical Psychology, Division of Clinical Psychology and Psychotherapy, Section of Applied Statistics in Life Sciences, University of Basel, Switzerland;dDepartment of Clinical Psychology, Psychotherapy, and Health Psychology, University of Salzburg, Austria |
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| Abstract: | ObjectiveVariability in infant sleep and negative affective behavior (NAB) is a developmental phenomenon that has long been of interest to researchers and clinicians. However, analyses and delineation of such temporal patterns were often limited to basic statistical approaches, which may prevent adequate identification of meaningful variation within these patterns. Modern statistical procedures such as additive models may detect specific patterns of temporal variation in infant behavior more effectively.MethodHundred and twenty-one mothers were asked to record different behaviors of their 4–44 weeks old healthy infants by diaries for three days consecutively. Circadian patterns as well as individual trajectories and day-to-day variability of infant sleep and NAB were modeled with generalized linear models (GLMs) including a linear and quadratic polynomial for time, a GLM with a polynomial of the 8th order, a GLM with a harmonic function, a generalized linear mixed model (GLMM) with a polynomial of the 8th order, a generalized additive model, and a generalized additive mixed model (GAMM).ResultsThe semi-parametric model GAMM was found to fit the data of infant sleep better than any other parametric model used. GLMM with a polynomial of the 8th order and GAMM modeled temporal patterns of infant NAB equally well, although the GLMM exhibited a slightly better model fit while GAMM was easier to interpret. Besides the well-known evening clustering in infant NAB we found a significant second peak in NAB around midday that was not affected by the constant decline in the amounts of NAB across the 3-day study period.ConclusionUsing advanced statistical procedures (GAMM and GLMM) even small variations and phenomena in infant behavior can be reliably detected. Future studies investigating variability and temporal patterns in infant variables may benefit from these statistical approaches. |
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| Keywords: | Abbreviations: NAB, negative affective behavior GLM, generalized linear models GLMM, generalized linear mixed models GAM, generalized additive models GAMM, generalized additive mixed models |
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