Context: Dropout rates in randomized clinical trials of antipsychotic drugs have consistently been reported to be high, and the use of a placebo-controlled design is hypothesized to be one of the reasons for this.
Objective: To investigate this hypothesis in a meta-analysis of available data from pertinent clinical trials.
Data Sources: Comprehensive search of PubMed- and MEDLINE-listed journals.
Study Selection: Double-blind randomized controlled clinical trials of the second-generation antipsychotics risperidone, olanzapine, quetiapine, amisulpride, ziprasidone, and aripiprazole meeting the following criteria: unselected patient population with a diagnosis of schizophrenia or schizoaffective disorder, change in psychopathologic symptoms as the primary end point, and trial duration of 12 weeks or less.
Data Extraction: Sample size, mean age, baseline disease severity, dropout rate, trial design, trial duration, and publication year.
Data Synthesis: Thirty-one trials meeting the inclusion criteria were found, comprising 10 058 subjects. Weighted mean dropout rates in the active treatment arms were significantly higher in placebo-controlled trials (PCTs) than in active-control trials: 48.1% (PCTs) vs 28.3% (active-control trials) for second-generation antipsychotics (odds ratio, 2.34; 95% confidence interval, 1.58-3.47) and 55.4% (PCTs) vs 37.2% (active-control trials) for classical antipsychotics (odds ratio, 2.10; 95% confidence interval, 1.29-3.40). Within PCTs, attrition rates were significantly higher in the placebo arms than with second-generation antipsychotics (60.2% vs 48.1%; odds ratio, 1.63; 95% confidence interval, 1.37-1.94). Within the subset of trials in which both second-generation and classical antipsychotics were used, dropout rates were significantly higher with classical antipsychotics.
Conclusions: Use of a placebo-controlled design had a major effect on the dropout rates observed. Because high dropout rates affect the generalizability of such studies, it is suggested that, in addition to the PCTs, studies with alternative designs need to be considered when evaluating an antipsychotic's clinical profile.
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http://dx.doi.org/10.1001/archpsyc.62.12.1305 | DOI Listing |
Asian Pac J Cancer Prev
January 2025
National School of Public Health, Rabat, Morocco.
Objective: This study aimed to investigate loss to follow-up (LFU) rates within breast and cervical cancer screening programs in Kenitra-Morocco, identifying contributing factors from both patient and healthcare worker perspectives to enhance care continuity.
Methods: The study was a non-experimental, mixed-methods design conducted in three-phases. We started by identifying LFU women and their characteristics from medical records, interviewing LFU women to ascertain reasons for discontinuation, and surveying healthcare workers for perceived determinants of LFU through semi-structured questionnaires.
Genetics
January 2025
Institute of Forest Sciences (ICIFOR-INIA), CSIC, Ctra. De la Coruña km 7.5, 28040 Madrid, Spain.
We present a new hierarchical Bayesian method using multilocus genotypes to estimate recent seed and pollen migration rates in a spatially explicit framework that incorporates distance effects separately for each type of dispersal. The method additionally estimates population allelic frequencies, population divergence values, individual inbreeding coefficients, individual maternal and paternal ancestries, and allelic dropout rates. We conduct a numerical simulation analysis that indicates that the method can provide reliable estimates of seed and pollen migration rates and allow accurate inference of spatial effects on migration, at affordable sample sizes (25-50 individuals/population) when population genetic divergence is not low (FST≥0.
View Article and Find Full Text PDFCJC Open
January 2025
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Background: Supervised exercise programs improve walking impairment and quality of life (QoL) in patients with peripheral artery disease (PAD). However, such programs are underutilized, due to their limited accessibility. A feasible and effective exercise program is needed.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Psychology, Faculty of Behavioural and Social Sciences, University of Groningen, Grote Kruisstraat 2/1, 9712TS, Groningen, The Netherlands.
Recruits are exposed to high levels of psychological and physical stress during the special forces selection period, resulting in dropout rates of up to 80%. To identify who likely drops out, we assessed a group of 249 recruits, every week of the selection program, on their self-efficacy, motivation, experienced psychological and physical stress, and recovery. Using linear regression as well as state-of-the-art machine learning techniques, we aimed to build a model that could meaningfully predict dropout while remaining interpretable.
View Article and Find Full Text PDFSci Rep
January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
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