Sequential Allocation to Balance Prognostic Factors in a Psychiatric Clinical Trial

OBJECTIVE: This paper aims to describe and discuss a minimization procedure specifically designed for a clinical trial that evaluates treatment efficacy for OCD patients. METHOD: Aitchison's compositional distance was used to calculate vectors for each possibility of allocation in a covariate adaptive method. Two different procedures were designed to allocate patients in small blocks or sequentially one-by-one. RESULTS: We present partial results of this allocation procedure as well as simulated data. In the clinical trial for which this procedure was developed, successful balancing between treatment arms was achieved. Separately, in an exploratory analysis, we found that if the arrival order of patients was altered, most patients were allocated to a different treatment arm than their original assignment. CONCLUSION: Our results show that the random arrival order of patients determine different assignments and therefore maintains the unpredictability of the allocation method. We conclude that our proposed procedure allows for the use of a large number of prognostic factors in a given allocation decision. Our method seems adequate for the design of the psychiatric trials used as models. Trial registrations are available at clinicaltrials.gov NCT00466609 and NCT00680602

Survival Probability in Patients with Sickle Cell Anemia Using the Competitive Risk Statistical Model.

The clinical picture of patients with sickle cell anemia (SCA) is associated with several complications some of which could be fatal. The objective of this study is to analyze the causes of death and the effect of sex and age on survival of Brazilian patients with SCA. Data of patients with SCA who were seen and followed at HEMORIO for 15 years were retrospectively collected and analyzed. Statistical modeling was performed using survival analysis in the presence of competing risks estimating the covariate effects on a sub-distribution hazard function. Eight models were implemented, one for each cause of death. The cause-specific cumulative incidence function was also estimated. Males were most vulnerable for death from chronic organ damage (p = 0.0005) while females were most vulnerable for infection (p=0.03). Age was significantly associated (p ≤ 0.05) with death due to acute chest syndrome (ACS), infection, and death during crisis. The lower survival was related to death from infection, followed by death due to ACS. The independent variables age and sex were significantly associated with ACS, infection, chronic organ damage and death during crisis. These data could help Brazilian authorities strengthen public policies to protect this vulnerable population

In-hospital outcomes of Infective Endocarditis from 1978 to 2015: analysis through machine-learning techniques

© 2021 The Authors. Published by Elsevier Inc. on behalf of the Canadian Cardiovascular Society. This is an open access article under the CC BY-NC- ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Background: Early identification of patients with infective endocarditis (IE) at higher risk for in-hospital mortality is essential to guide management and improve prognosis. Methods: A retrospective analysis was conducted of a cohort of patients followed up from 1978 to 2015, classified according to the modified Duke criteria. Clinical parameters, echocardiographic data, and blood cultures were assessed. Techniques of machine learning, such as the classification tree, were used to explain the association between clinical characteristics and in-hospital mortality. Additionally, the log-linear model and graphical random forests (GRaFo) representation were used to assess the degree of dependence among in-hospital outcomes of IE. Results: This study analyzed 653 patients: 449 (69.0%) with definite IE; 204 (31.0%) with possible IE; mean age, 41.3 ± 19.2 years; 420 (64%) men. Mode of IE acquisition: community-acquired (67.6%), nosocomial (17.0%), undetermined (15.4%). Complications occurred in 547 patients (83.7%), the most frequent being heart failure (47.0%), neurologic complications (30.7%), and dialysis-dependent renal failure (6.5%). In-hospital mortality was 36.0%. The classification tree analysis identified subgroups with higher in-hospital mortality: patients with community-acquired IE and peripheral stigmata on admission; and patients with nosocomial IE. The log-linear model showed that surgical treatment was related to higher in-hospital mortality in patients with neurologic complications. Conclusions: The use of a machine-learning model allowed identification of subgroups of patients at higher risk for in-hospital mortality. Peripheral stigmata, nosocomial IE, absence of vegetation, and surgery in the presence of neurologic complications are predictors of fatal outcomes in machine learning-based analysis.info:eu-repo/semantics/publishedVersio

Estatística em psiquiatria

Este artigo apresenta os paradigmas do processo de pesquisa que utilizam a análise de dados. Indica como os avanços da computação influíram no processo, permitindo a análise de sistemas complexos. Na psiquiatria, são indicadas algumas áreas onde a estatística tem papel importante e como a colaboração entre psiquiatras e estatísticos pode ser implementada

Model choice in nonnested families

This book discusses the problem of model choice when the statistical models are separate, also called nonnested. Chapter 1 provides an introduction, motivating examples and a general overview of the problem. Chapter 2 presents the classical or frequentist approach to the problem as well as several alternative procedures and their properties. Chapter 3 explores the Bayesian approach, the limitations of the classical Bayes factors and the proposed alternative Bayes factors to overcome these limitations. It also discusses a significance Bayesian procedure. Lastly, Chapter 4 examines the pure likelihood approach. Various real-data examples and computer simulations are provided throughout the text

A screening system for smear-negative pulmonary tuberculosis using artificial neural networks

Objectives: Molecular tests show low sensitivity for smear-negative pulmonary tuberculosis (PTB). A screening and risk assessment system for smear-negative PTB using artificial neural networks (ANNs) based on patient signs and symptoms is proposed. Methods: The prognostic and risk assessment models exploit a multilayer perceptron (MLP) and inspired adaptive resonance theory (iART) network. Model development considered data from 136 patients with suspected smear-negative PTB in a general hospital. Results: MLP showed higher sensitivity (100%, 95% confidence interval (CI) 78–100%) than the other techniques, such as support vector machine (SVM) linear (86%; 95% CI 60–96%), multivariate logistic regression (MLR) (79%; 95% CI 53–93%), and classification and regression tree (CART) (71%; 95% CI 45–88%). MLR showed a slightly higher specificity (85%; 95% CI 59–96%) than MLP (80%; 95% CI 54–93%), SVM linear (75%, 95% CI 49–90%), and CART (65%; 95% CI 39–84%). In terms of the area under the receiver operating characteristic curve (AUC), the MLP model exhibited a higher value (0.918, 95% CI 0.824–1.000) than the SVM linear (0.796, 95% CI 0.651–0.970) and MLR (0.782, 95% CI 0.663–0.960) models. The significant signs and symptoms identified in risk groups are coherent with clinical practice. Conclusions: In settings with a high prevalence of smear-negative PTB, the system can be useful for screening and also to aid clinical practice in expediting complementary tests for higher risk patients