The association of Adherence to the Mediterranean Diet with Mortality in the EPIC-Spain cohort using Flexible Parametric Survival Models

In this master thesis, we have analyzed the role of the Adherence to the Mediterranean Diet (mdscore) on global mortality and have performed a comparison of Cox's proportional hazards model and the Flexible Parametric PH model proposed by Royston and Parmar (2002). This study include individuals from five Spanish regions of the European Prospective cohort into Cancer and Nutrition (EPIC-Spain). Both fitted models consider the mdscore as the main variable, are stratified by sex, center and age at recruitment, and adjusted by potential confounders such as: smoke status, BMI, physical activity, energy intake, waist circumference and educational level

Multiple imputation approach for interval-censored time to HIV RNA viral rebound within a mixed effects Cox model

This is the peer reviewed version of the following article: “Alarcón-Soto, Y, Langohr K., Fehér, C., García, F., and Gómez, G. (2018) Multiple imputation approach for interval-censored time to HIV RNA viral rebound within a mixed effects Cox Model.Biometrical journal, December 13th ”which has been published in final form at [doi: 10.1002/bimj.201700291]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.We present a method to fit a mixed effects Cox model with interval-censored data. Our proposal is based on a multiple imputation approach that uses the truncated Weibull distribution to replace the interval-censored data by imputed survival times and then uses established mixed effects Cox methods for right-censored data. Interval-censored data were encountered in a database corresponding to a recompilation of retrospective data from eight analytical treatment interruption (ATI) studies in 158 human immunodeficiency virus (HIV) positive combination antiretroviral treatment (cART) suppressed individuals. The main variable of interest is the time to viral rebound, which is defined as the increase of serum viral load (VL) to detectable levels in a patient with previously undetectable VL, as a consequence of the interruption of cART. Another aspect of interest of the analysis is to consider the fact that the data come from different studies based on different grounds and that we have several assessments on the same patient. In order to handle this extra variability, we frame the problem into a mixed effects Cox model that considers a random intercept per subject as well as correlated random intercept and slope for pre-cART VL per study. Our procedure has been implemented in R using two packages: truncdist and coxme, and can be applied to any data set that presents both interval-censored survival times and a grouped data structure that could be treated as a random effect in a regression model. The properties of the parameter estimators obtained with our proposed method are addressed through a simulation study.Peer ReviewedPostprint (author's final draft

HIVconsv vaccines and romidepsin in early-treated HIV-1-infected individuals: safety, immunogenicity and effect on the viral reservoir (Study BCN02)

Kick&kill strategies combining drugs aiming to reactivate the viral reservoir with therapeutic vaccines to induce effective cytotoxic immune responses hold potential to achieve a functional cure for HIV-1 infection. Here, we report on an open-label, single-arm, phase I clinical trial, enrolling 15 early-treated HIV-1-infected individuals, testing the combination of the histone deacetylase inhibitor romidepsin as a latency-reversing agent and the MVA.HIVconsv vaccine. Romidepsin treatment resulted in increased histone acetylation, cell-associated HIV-1 RNA, and T-cell activation, which were associated with a marginally significant reduction of the viral reservoir. Vaccinations boosted robust and broad HIVconsv-specific T cells, which were strongly refocused toward conserved regions of the HIV-1 proteome. During a monitored ART interruption phase using plasma viral load over 2,000 copies/ml as a criterium for ART resumption, 23% of individuals showed sustained suppression of viremia up to 32 weeks without evidence for reseeding the viral reservoir. Results from this pilot study show that the combined kick&kill intervention was safe and suggest a role for this strategy in achieving an immune-driven durable viremic control.Peer ReviewedPostprint (published version

HIVconsv Vaccines and Romidepsin in Early-Treated HIV-1-Infected Individuals: Safety, Immunogenicity and Effect on the Viral Reservoir (Study BCN02)

Kick&kill strategies combining drugs aiming to reactivate the viral reservoir with therapeutic vaccines to induce effective cytotoxic immune responses hold potential to achieve a functional cure for HIV-1 infection. Here, we report on an open-label, single-arm, phase I clinical trial, enrolling 15 early-treated HIV-1-infected individuals, testing the combination of the histone deacetylase inhibitor romidepsin as a latency-reversing agent and the MVA.HIVconsv vaccine. Romidepsin treatment resulted in increased histone acetylation, cell-associated HIV-1 RNA, and T-cell activation, which were associated with a marginally significant reduction of the viral reservoir. Vaccinations boosted robust and broad HIVconsv-specific T cells, which were strongly refocused toward conserved regions of the HIV-1 proteome. During a monitored ART interruption phase using plasma viral load over 2,000 copies/ml as a criterium for ART resumption, 23% of individuals showed sustained suppression of viremia up to 32 weeks without evidence for reseeding the viral reservoir. Results from this pilot study show that the combined kick&kill intervention was safe and suggest a role for this strategy in achieving an immune-driven durable viremic control

The association of Adherence to the Mediterranean Diet with Mortality in the EPIC-Spain cohort using Flexible Parametric Survival Models

In this master thesis, we have analyzed the role of the Adherence to the Mediterranean Diet (mdscore) on global mortality and have performed a comparison of Cox's proportional hazards model and the Flexible Parametric PH model proposed by Royston and Parmar (2002). This study include individuals from five Spanish regions of the European Prospective cohort into Cancer and Nutrition (EPIC-Spain). Both fitted models consider the mdscore as the main variable, are stratified by sex, center and age at recruitment, and adjusted by potential confounders such as: smoke status, BMI, physical activity, energy intake, waist circumference and educational level

Data science in HIV : statistical approaches for therapeutic HIV vaccine data

The present dissertation contributes to Data Science in the Human lmmunodeficiency Virus (HIV) field, addressing specific issues related to the modelling of data coming from three different clinical trials based on the development of HIV therapeutic vaccines. The biological questions that these studies raise are identify biomarkers that predict HIV viral rebound; explain the time to viral rebound as a consequence of antiretroviral therapy (cART) stop considering the variability of data sources; and find the relationship between spot size and spot count from Enzyme-Linked lmmunosorbent spot (ELISpot) assays data. To handle these problems from a statistical perspective, in this thesis we: adapt the elastic net penalization to the accelerated failure time model with interval-censored data, fit a mixed effects Cox model with interval-censored data, and improve statistical methodologies to deal with ELISpot assays data and a binary response, respectively. In order to address the variable selection among a vast number of predictors to explain the time to viral rebound, we consideran elastic-net penalization approach within the accelerated failure time model. Elastic-net regularization considers a possible correlation structure among covariates, which is the case of messenger RNA (mRNA) data. For this purpose, we derive the expression of the penalized log-likelihood function for the special case of the interval-censored response (time to viral rebound). Following, we maximize this function using distinct approaches and optimization methods. Finally, we apply these approaches to the Dendritic Cell-Based Vaccine clinical trial, and we discuss different numerical methods for the maximization of the log-likelihood. To explain the time to viral rebound in the context of another study with data from several clinical trials, we use a mixed effects Cox model to account for the data heterogeneity. This model allows us to handle the heterogeneity between the Analytical Treatment lnterruption (ATI) studies and the fact that the patients had different number of ATI episodes. Our method proposes the use of a multiple imputation approach based on a truncated Weibull distribution to replace the interval-censored by imputed survival times. Our simulation studies show that our method has desirable properties in terms of accuracy and precision of the estimators of the fixed effects parameters. Concerning the clinical results, the higher the pre-cART VL, the larger the instantaneous risk of a viral rebound. Our method could be applied to any data set that presents both interval-censored survival times and a grouped data structure that could be treated as a random effect. We finally address two different issues that have arisen when analyzing the BCN02 clinical trial. On one hand, we fit univariate log-binomial models as an alternative to the usual logistic regression. On the other hand, we use one/two- way unbalanced ANOVA to analyze the variability of the main outcomes from the ELISpot assays across time. Although these assays are widely used in the context of the HIV study, the relationship between spot size or spot count and other variables has not been studied until now. In this thesis, we propose, develop, and apply different statistical approaches that contributes to answer diverse clinical questions that are relevant in several clinical trials. We have tried to highlight that to be able to choose the appropriate methodology, make correct clinical interpretations and contribute to a meaningful scientific progress, a narrow collaboration with scientists is necessary. We expect that the original results from this thesis will contribute to the path of development and evaluation of a therapeutic HIV vaccine, helping to improve the way of living of HIV-infected people.La presente tesis contribuye a la ciencia de datos abordando problemas biológicos relevantes en el desarrollo de vacunas terapéuticas para el Virus de Inmunodeficiencia Humana (VIH) mediante la modelización de datos procedentes de tres ensayos clínicos diferentes. Algunas de las cuestiones suscitadas en estos estudios y que esta tesis aborda son: identificar biomarcadores para estudiar los factores de riesgo del rebote viral del VIH, explicar el tiempo transcurrido hasta el rebote viral como consecuencia del cese de la terapia antirretroviral (cART) considerando la variabilidad de las fuentes de datos y estudiar la relación entre las variables spot size y spot count en ensayos inmunoabsorbentes (ELISpot). Para abordar cada uno de estos interrogantes desde una perspectiva estadística, en esta tesis hemos adaptado una penalización de red elástica para el modelo de vida acelerada (AFT) con datos censurados en un intervalo, ajustado un modelo de Cox de efectos mixtos con datos censurados en un intervalo y mejorado las metodologías estadísticas existentes para tratar los datos de los ensayos ELISpot y de respuesta binaria, respectivamente. En primer lugar, hemos abordado el problema de tener más de cinco mil ARN mensajeros (ARNm) para explicar el tiempo hasta el rebote viral. Para ello, hemos considerado un enfoque de penalización de red elástica para el modelo de vida acelerada. Esta regularización considera una posible estructura de correlación entre las covariables, como sucede con los ARNm. Para este objetivo, primero derivamos la expresión de la función de verosimilitud penalizada considerando una respuesta censurada en un intervalo (tiempo hasta el rebote viral). A continuación, maximizamos esta función utilizando distintos enfoques y métodos de optimización. Finalmente, aplicamos estos métodos al ensayo clínico DCV2 y discutimos sobre diferentes enfoques numéricos para la maximización de la verosimilitud. En segundo lugar, para explicar el tiempo hasta el rebote viral proponemos ajustar un modelo de Cox de efectos mixtos. Dado que el tiempo hasta el rebote viral está censurado en un intervalo utilizamos imputación múltiple basada en una distribución de Weibull truncada. Este modelo nos permite controlar la heterogeneidad entre los estudios de interrupción analítica del tratamiento (ATI) y el hecho de que los pacientes tengan diferente número de episodios ATI. Según el estudio de simulación que realizamos, nuestro método tiene propiedades deseables en términos de exactitud y precisión de los estimadores de los parámetros de efectos fijos. Finalmente abordamos dos problemas diferentes dentro del ensayo clínico BCN02. Por un lado, ajustamos modelos log-binomiales univariados como alternativa a la clásica regresión logística. Por otro lado, utilizamos un modelo ANOVA no balanceado para analizar la variabilidad de los resultados principales de los ensayos ELISpot a lo largo del tiempo. Aunque los ensayos ELISpot se usan a menudo en el estudio del VIH, la relación entre variables como el spot size, spot count y otras no se había estudiado hasta ahora. En esta tesis hemos propuesto y desarrollado diferentes enfoques estadísticos que han dado respuesta a preguntas biológicas planteadas en tres ensayos clínicos. En este trabajo se destaca la importancia de que los distintos miembros de un equipo científico-multidisciplinar colaboren estrechamente, para así poder determinar la metodología apropiada, hacer correctas interpretaciones clínicas de los resultados de éste y, de esta forma, contribuir a un progreso científico significativo. Esperamos que los resultados originales de esta tesis contribuyan al desarrollo y la evaluación de una vacuna terapéutica del VIH, lo cual ayudaría notablemente a mejorar la calidad de vida de las personas infectadas por VIH.Postprint (published version

Multiple imputation approach for interval-censored time to HIV RNA viral rebound within a mixed effects Cox model

This is the peer reviewed version of the following article: “Alarcón-Soto, Y, Langohr K., Fehér, C., García, F., and Gómez, G. (2018) Multiple imputation approach for interval-censored time to HIV RNA viral rebound within a mixed effects Cox Model.Biometrical journal, December 13th ”which has been published in final form at [doi: 10.1002/bimj.201700291]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.We present a method to fit a mixed effects Cox model with interval-censored data. Our proposal is based on a multiple imputation approach that uses the truncated Weibull distribution to replace the interval-censored data by imputed survival times and then uses established mixed effects Cox methods for right-censored data. Interval-censored data were encountered in a database corresponding to a recompilation of retrospective data from eight analytical treatment interruption (ATI) studies in 158 human immunodeficiency virus (HIV) positive combination antiretroviral treatment (cART) suppressed individuals. The main variable of interest is the time to viral rebound, which is defined as the increase of serum viral load (VL) to detectable levels in a patient with previously undetectable VL, as a consequence of the interruption of cART. Another aspect of interest of the analysis is to consider the fact that the data come from different studies based on different grounds and that we have several assessments on the same patient. In order to handle this extra variability, we frame the problem into a mixed effects Cox model that considers a random intercept per subject as well as correlated random intercept and slope for pre-cART VL per study. Our procedure has been implemented in R using two packages: truncdist and coxme, and can be applied to any data set that presents both interval-censored survival times and a grouped data structure that could be treated as a random effect in a regression model. The properties of the parameter estimators obtained with our proposed method are addressed through a simulation study.Peer Reviewe