Pooled Rate of Pseudoprogression, Patterns of Response, and Tumor Burden Analysis in Patients Undergoing Immunotherapy Oncologic Trials for Different Malignancies.

AIMS: Assess rates of true pseudoprogression in unconfirmed progressive disease (iUPD) in a pool of immunotherapy clinical trials for different cancers, analyze tumor characteristics that drive iUPD classification, and investigate potentials predictors of pseudoprogression. MATERIALS AND METHODS: Retrospective interpretation of prospectively acquired data. Patients from 18 immunotherapy clinical trials with two arms (RECIST 1.1, iRECIST), of 10 cancer types were selected. Pooled rate of true pseudoprogression among iUPD was estimated using a common effect meta-analysis. Target, Non-target, and new lesions as the trigger of confirmed-vs pseudo-progression were compared using Chi-Square and Fisher exact tests. Conditional logistic regression was used to investigate the association between age, sex, tumor burden at baseline, and number of follow ups and pseudoprogression. RESULTS: 60/287 (21%) patients (17 women) were classified as iUPD with at least one subsequent confirmatory timepoint. The overall pooled estimate of pseudoprogression was 15% (95%CI: 8%--26%). Nontarget lesions were significantly more frequent the cause of iUPD than change in Target lesions size (p< 0.001). Most observations of true pseudoprogression occurred in the first follow-up (77%), whereas confirmed progression occurred in later time points during the trial. Pseudoprogression was not significantly associated with age, sex, tumor burden at baseline, or number of timepoints. CONCLUSION: In a pool of immunotherapy trials, the rate of true pseudoprogression was 15%, most often in the first timepoint after baseline than later in treatment. iUPD categorization was mostly driven by changes in NT lesions rather than objective changes in measurements of target lesions.

Path analysis and multi-criteria decision making: an approach for multivariate model selection and analysis in health.

PURPOSE: This paper introduces an approach that includes non-quantitative factors for the selection and assessment of multivariate complex models in health. METHODS: A goodness-of-fit based methodology combined with fuzzy multi-criteria decision-making approach is proposed for model selection. Models were obtained using the Path Analysis (PA) methodology in order to explain the interrelationship between health determinants and the post-neonatal component of infant mortality in 59 municipalities of Brazil in the year 1991. Socioeconomic and demographic factors were used as exogenous variables, and environmental, health service and agglomeration as endogenous variables. Five PA models were developed and accepted by statistical criteria of goodness-of fit. These models were then submitted to a group of experts, seeking to characterize their preferences, according to predefined criteria that tried to evaluate model relevance and plausibility. Fuzzy set techniques were used to rank the alternative models according to the number of times a model was superior to ("dominated") the others. RESULTS: The best-ranked model explained above 90% of the endogenous variables variation, and showed the favorable influences of income and education levels on post-neonatal mortality. It also showed the unfavorable effect on mortality of fast population growth, through precarious dwelling conditions and decreased access to sanitation. CONCLUSIONS: It was possible to aggregate expert opinions in model evaluation. The proposed procedure for model selection allowed the inclusion of subjective information in a clear and systematic manner.

The path analysis approach for the multivariate analysis of infant mortality data.

PURPOSE: This paper reviews the use of the Path Analysis (PA) methodology in health determinants modeling, with special reference to infant mortality modeling. METHODS: A review of the literature on PA applications in the modeling of infant mortality and similar problems is presented, together with a discussion of the conceptual basis of PA and its relation to other multivariate statistical techniques. Important aspects of the technique are discussed: 1) criteria for path formulation; 2) parameter estimation methods; 3) direct, indirect, spurious, and joint effects; and 4) goodness-of-fit and modification indices. RESULTS AND CONCLUSION: The review of the literature suggests that PA represents a methodological improvement regarding multivariate techniques used in modeling some health-related issues. PA allows investigation of more complex models, providing information that could have been previously overlooked, such as how the interrelations among independent variables in a model affect the dependent ones.

Risk factors for hospitalization and death from diarrhea in a public pediatric hospital in Rio de Janeiro, Brazil.

OBJECTIVE: This study was carried out in a public pediatric hospital located in the city of Rio de Janeiro, Brazil, with the aim of identifying risk factors for hospitalization and/or death due to diarrhea in children. MATERIAL AND METHODS: The study included 406 children under three years of age who were seen or admitted for diarrhea from January 1987 to February 1988. The main variable of interest was the outcome of clinical evaluation and subsequent hospitalization, which was classified as follows: 1) outpatient treatment; 2) hospitalization and survival; and 3) death during hospitalization. The chi-square test was used to identify variables (p = < 0.05) that were significantly related to the treatment outcome. RESULTS: The group was composed by 60.6% males and 39.4% females. A proportion of 26.8% of children was under two months of age, 24.9% was 3-5 months old, 25.9% was 6-11 months old, and 22.4% was 12 months or older. The variables most significantly related to the risk of hospitalization from diarrhea were age, current nutritional status (weight-for-age percentile), and concomitant illness. Variables most significantly associated with risk of death from diarrhea were low birth weight and past hospitalization. CONCLUSIONS: Use of this study's results by health services could make a substantial contribution to reducing children's hospitalization and death due to diarrhea in the city of Rio de Janeiro.

[Risk factors for death caused by pneumonia in children younger than 1 year old in a metropolitan region of southeastern Brazil. A case- control study]. / Fatores de risco para morte por pneumonia em menores de um ano em uma região metropolitana do sudeste do Brasil. Um estudo tipo caso-controle.

In a case-control study, a sample of post-neonatal deaths from pneumonia occurring in the Metropolitan Area of Rio de Janeiro, Brazil (1986-1987) were compared with healthy controls who lived in the same neighborhood. Risk factors investigated were variables related to the mother's pregnancy history and the child's birth, to the family's social condition and to the use of health services. Using the univariate logistic regression model, the coefficients of each independent variable, the relative risk and its confidence limits were first estimated. Birth weight and age of weaning were strongly associated with the dependent variable. After adjustment by means of the multiple logistic regression model, only 4 variables remained statistically associated with mortality: age of weaning, birth weight, over crowding, and BCG vaccination. Based on the available data, it was concluded that mortality from pneumonia in children under 1 year of age is significantly related to the social condition of the family, particularly to that of the mother.