Data science in hiv. Statistical approaches for therapeutic hiv vaccine data

• Autores: Yovaninna del Carmen Alarcón Soto
• Directores de la Tesis: Klaus Langohr (dir. tes.) , Guadalupe Gómez Melis (codir. tes.)
• Lectura: En la Universitat Politècnica de Catalunya (UPC) ( España ) en 2021
• Idioma: español
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• Resumen

  • 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.