PubMed · 12668398
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Karen P Hayhurst, Petra Brown, Shon W Lewis. 2003. Postcode prescribing for schizophrenia.. https://doi.org/10.1192/bjp.182.4.281
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In this study, the foundations of long-acting antipsychotics development, the adherence to treatment, the factors that make difficult the following of the prescription and their consequences, the benefits and acceptance of long-acting antipsychotics are revised. Based on the data obtained from the first part of this study, the following issues must be highlighted: the non-adherence treatment level in schizophrenia is about 60%. Considering schizophrenia a chronic disease with high relapse risk, the maintenance treatment helps its control. The long-acting antipsychotics were developed exactly for the purpose of guaranteeing the administration of the medication and its regularity, which are essential requirements in preventing a relapse. In spite of the immensurable benefits of the long-acting antipsychotics, several factors have contributed to their usage below their potential.
This review examines the question--Do atypical antipsychotic medications improve psychosocial outcomes?--by examining studies that compared the effects of atypicals to conventional antipsychotics or to other atypical medications. The authors reviewed randomized clinical trials of atypical antipsychotic medication that included psychosocial variables as outcomes. Findings from 31 published studies on more than 12,000 individuals generally showed that atypical medications led to significant improvements in negative symptoms compared to conventional antipsychotics. Effects on global assessment of psychosocial functioning and on the quality of life were mixed with only about half the studies reporting significant improvements. Olanzapine yielded the best results on psychosocial functioning; remoxipride was found to yield few significant changes on these variables. This review provides evidence that some atypical antipsychotics may have direct effects on some of the psychosocial disabilities that result from serious mental illness.
Random-coefficient pattern-mixture models (RCPMMs) have been proposed for longitudinal data when drop-out is thought to be non-ignorable. An RCPMM is a random-effects model with summaries of drop-out time included among the regressors. The basis of every RCPMM is extrapolation. We review RCPMMs, describe various extrapolation strategies, and show how analyses may be simplified through multiple imputation. Using simulated and real data, we show that alternative RCPMMs that fit equally well may lead to very different estimates for parameters of interest. We also show that minor model misspecification can introduce biases that are quite large relative to standard errors, even in fairly small samples. For many scientific applications, where the form of the population model and nature of the drop-out are unknown, interval estimates from any single RCPMM may suffer from undercoverage because uncertainty about model specification is not taken into account.