PubMed · 11238367
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M B Nashelsky. 2001. Call for help.. https://doi.org/10.1136/ewjm.174.3.214
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A common class of models for longitudinal data are random effects (mixed) models. In these models, the random effects covariance matrix is typically assumed constant across subject. However, in many situations this matrix may differ by measured covariates. In this paper, we propose an approach to model the random effects covariance matrix by using a special Cholesky decomposition of the matrix. In particular, we will allow the parameters that result from this decomposition to depend on subject-specific covariates and also explore ways to parsimoniously model these parameters. An advantage of this parameterization is that there is no concern about the positive definiteness of the resulting estimator of the covariance matrix. In addition, the parameters resulting from this decomposition have a sensible interpretation. We propose fully Bayesian modelling for which a simple Gibbs sampler can be implemented to sample from the posterior distribution of the parameters. We illustrate these models on data from depression studies and examine the impact of heterogeneity in the covariance matrix on estimation of both fixed and random effects.
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Patients who fail to respond adequately to pharmacological treatment present an ongoing therapeutic challenge. The term "incomplete recovery" (IR) is preferred to the current term "treatment resistance" to describe these patients. IR should be considered from a multidimensional perspective that includes a broad range of symptoms and functional disabilities that are relevant to schizophrenia. The approach to the incompletely recovered patient needs to be systematic, with consideration given to the factors that may hamper recovery. "Atypical" (second-generation) antipsychotic drugs target various domains of symptoms relevant to IR. Adjunctive treatment strategies (eg, mood stabilisers, antidepressants, combinations of antipsychotics) may be useful, but should be undertaken in specialist psychiatric settings. Although pharmacological treatment is a necessary first step in managing incompletely recovered patients, adjunctive psychosocial interventions are needed to optimise treatment success.