RESUMEN
To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.
Asunto(s)
COVID-19 , Humanos , Análisis Espacio-Temporal , Incidencia , Teorema de Bayes , Cuba/epidemiologíaRESUMEN
BACKGROUND: Immunosenescence biomarkers and peripheral blood parameters are evaluated separately as possible predictive markers of immunotherapy. Here, we illustrate the use of a causal inference model to identify predictive biomarkers of CIMAvaxEGF success in the treatment of Non-Small Cell Lung Cancer Patients. METHODS: Data from a controlled clinical trial evaluating the effect of CIMAvax-EGF were analyzed retrospectively, following a causal inference approach. Pre-treatment potential predictive biomarkers included basal serum EGF concentration, peripheral blood parameters and immunosenescence biomarkers. The proportion of CD8 + CD28- T cells, CD4+ and CD8+ T cells, CD4/CD8 ratio and CD19+ B cells. The 33 patients with complete information were included. The predictive causal information (PCI) was calculated for all possible models. The model with a minimum number of predictors, but with high prediction accuracy (PCI > 0.7) was selected. Good, rare and poor responder patients were identified using the predictive probability of treatment success. RESULTS: The mean of PCI increased from 0.486, when only one predictor is considered, to 0.98 using the multivariate approach with all predictors. The model considering the proportion of CD4+ T cell, basal Epidermal Growth Factor (EGF) concentration, neutrophil to lymphocyte ratio, Monocytes, and Neutrophils as predictors were selected (PCI > 0.74). Patients predicted as good responders according to the pre-treatment biomarkers values treated with CIMAvax-EGF had a significant higher observed survival compared with the control group (p = 0.03). No difference was observed for bad responders. CONCLUSIONS: Peripheral blood parameters and immunosenescence biomarkers together with basal EGF concentration in serum resulted in good predictors of the CIMAvax-EGF success in advanced NSCLC. Future research should explore molecular and genetic profile as biomarkers for CIMAvax-EGF and it combination with immune-checkpoint inhibitors. The study illustrates the application of a new methodology, based on causal inference, to evaluate multivariate pre-treatment predictors. The multivariate approach allows realistic predictions of the clinical benefit of patients and should be introduced in daily clinical practice.
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Protocolos de Quimioterapia Combinada Antineoplásica/administración & dosificación , Biomarcadores de Tumor/sangre , Vacunas contra el Cáncer/administración & dosificación , Carcinoma de Pulmón de Células no Pequeñas/terapia , Neoplasias Pulmonares/terapia , Modelos Estadísticos , Anciano , Biomarcadores de Tumor/inmunología , Recuento de Linfocito CD4 , Linfocitos T CD4-Positivos/inmunología , Carcinoma de Pulmón de Células no Pequeñas/sangre , Carcinoma de Pulmón de Células no Pequeñas/inmunología , Carcinoma de Pulmón de Células no Pequeñas/mortalidad , Ensayos Clínicos Fase III como Asunto , Terapia Combinada/métodos , Factor de Crecimiento Epidérmico/sangre , Factor de Crecimiento Epidérmico/inmunología , Femenino , Humanos , Inmunosenescencia , Neoplasias Pulmonares/sangre , Neoplasias Pulmonares/inmunología , Neoplasias Pulmonares/mortalidad , Masculino , Persona de Mediana Edad , Valor Predictivo de las Pruebas , Pronóstico , Ensayos Clínicos Controlados Aleatorios como Asunto , Estudios RetrospectivosRESUMEN
The intraclass correlation is commonly used with clustered data. It is often estimated based on fitting a model to hierarchical data and it leads, in turn, to several concepts such as reliability, heritability, inter-rater agreement, etc. For data where linear models can be used, such measures can be defined as ratios of variance components. Matters are more difficult for non-Gaussian outcomes. The focus here is on count and time-to-event outcomes where so-called combined models are used, extending generalized linear mixed models, to describe the data. These models combine normal and gamma random effects to allow for both correlation due to data hierarchies as well as for overdispersion. Furthermore, because the models admit closed-form expressions for the means, variances, higher moments, and even the joint marginal distribution, it is demonstrated that closed forms of intraclass correlations exist. The proposed methodology is illustrated using data from agricultural and livestock studies.
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Biometría/métodos , Modelos Lineales , Agricultura/estadística & datos numéricos , Animales , Ganado , Reproducibilidad de los Resultados , Estadística como AsuntoRESUMEN
In infectious diseases, it is important to predict the long-term persistence of vaccine-induced antibodies and to estimate the time points where the individual titers are below the threshold value for protection. This article focuses on HPV-16/18, and uses a so-called fractional-polynomial model to this effect, derived in a data-driven fashion. Initially, model selection was done from among the second- and first-order fractional polynomials on the one hand and from the linear mixed model on the other. According to a functional selection procedure, the first-order fractional polynomial was selected. Apart from the fractional polynomial model, we also fitted a power-law model, which is a special case of the fractional polynomial model. Both models were compared using Akaike's information criterion. Over the observation period, the fractional polynomials fitted the data better than the power-law model; this, of course, does not imply that it fits best over the long run, and hence, caution ought to be used when prediction is of interest. Therefore, we point out that the persistence of the anti-HPV responses induced by these vaccines can only be ascertained empirically by long-term follow-up analysis.
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Anticuerpos Antivirales/sangre , Ensayos Clínicos Controlados como Asunto/estadística & datos numéricos , Papillomavirus Humano 16/inmunología , Papillomavirus Humano 18/inmunología , Modelos Estadísticos , Estudios Multicéntricos como Asunto/estadística & datos numéricos , Vacunas contra Papillomavirus/inmunología , Adolescente , Adulto , Biomarcadores/sangre , Brasil , Femenino , Humanos , Esquemas de Inmunización , Estimación de Kaplan-Meier , Modelos Lineales , América del Norte , Vacunas contra Papillomavirus/administración & dosificación , Proyectos de Investigación/estadística & datos numéricos , Factores de Tiempo , Resultado del Tratamiento , Vacunación , Adulto JovenRESUMEN
According to the data from the National Cancer Registry, breast and cervical cancer are the two most common nonskin cancers in Cuban woman. This study was addressed to describe the geographical variation of their incidence at small area level over the period 1999-2003. For each municipality, standardized incidence ratios were calculated and smoothed using a Poisson-Gamma, Poisson-Lognormal and a Conditional Autoregressive (CAR) model. The covariate 'urbanization level' was included in the Poisson-Lognormal and CAR models. The posterior probability of each municipality's relative risk (RR) exceeding unity was computed. Clusters were confirmed using the spatial scan statistic of Kulldorff. The CAR model provided the best fit for the geographical distribution of breast and cervical cancer in Cuba. For breast cancer, a high-risk region was identified in municipalities of Ciudad de La Habana province (CAR-smoothed RR between 1.21 and 1.26). Cervical cancer exhibited two areas with excess risk in the east and extreme west of the island (CAR-smoothed RR range 1.2-2.01 both areas together). Clusters were confirmed only for cervical cancer (P = 0.001 for the most likely cluster and P = 0.003 for a secondary cluster). In conclusion, the study supports the hypothesis of a spatial variation in risk at small area level essentially for cervical cancer and also for breast cancer that probably reflects the territorial distribution of life style and socioeconomic factors. This is the first attempt to introduce this methodology in the framework of the National Cancer Registry of Cuba and we expect to extend its use to forthcoming analyses.