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Maximum likelihood estimation of models for binary longitudinal data is typically inconsistent if the dependence structure is misspecified. Unfortunately, diagnostics specifically designed for detecting such misspecifications are scant. We develop residuals and diagnostic tests based on comparing observed and expected frequencies of response patterns over time in the presence of arbitrary time‐varying and time‐invariant covariates. To overcome the sparseness problem, we use lower‐order marginal tables, such as two‐way tables for pairs of time‐points, aggregated over covariate patterns. Our proposed pairwise concordance residuals are valuable for exploratory diagnostics and for constructing both generic tests for misspecified dependence structure as well as targeted adjacent pair concordance tests for excess serial dependence. The proposed methods are straightforward to implement and work well for general situations, regardless of the number of time‐points and the number and types of covariates. [ABSTRACT FROM AUTHOR]

Generalized linear mixed models for longitudinal data assume that responses at different occasions are conditionally independent, given the random effects and covariates. Although this assumption is pivotal for consistent estimation, violation due to serial dependence is hard to assess by model elaboration. We therefore propose a targeted diagnostic test for serial dependence, called the transition model test (TMT), that is straightforward and computationally efficient to implement in standard software. The TMT is shown to have larger power than general misspecification tests. We also propose the targeted root mean squared error of approximation (TRSMEA) as a measure of the population misfit due to serial dependence. [ABSTRACT FROM AUTHOR]

Conclusion: The present study demonstrates a very strong effect of the parental communication mode on the auditory capabilities and speech/language outcome for cochlear implanted children. The children exposed to spoken language had higher odds of scoring high in all tests applied and the findings suggest a very clear benefit of spoken language communication with a cochlear implanted child. Objectives: The aim of the study was to identify factors associated with speech and language outcomes for cochlear implanted children and also to estimate the effect-related odds ratio for each factor in relation to the children's speech and language performances. Methods: Data relate to 155 prelingually deafened children with cochlear implant (CI). A test battery consisting of six different speech and language tests/assessments was used. Seven different factors were considered, i.e. hearing age, implantation age, gender, educational placement, ear of implantation, CI center, and communication mode. Logistic regression models and proportional odds models were used to analyze the relationship between the considered factors and test responses. Results: The communication mode at home proved essential to speech and language outcome, as children exposed to spoken language had markedly better odds of performing well in all tests, compared with children exposed to a mixture of spoken language and sign support, or sign language. [ABSTRACT FROM AUTHOR]