Aprèn - Klaus Langohr

Els assajos oncològics de fase I requereixen una selecció acurada del pacient, equilibrant els riscos potencials de toxicitat amb possibles beneficis. Aquest estudi té com a objectiu desenvolupar i validar models estadístics per identificar els pacients que més podrien beneficiar-se. Utilitzant dades de 921 pacients, els mètodes inclouen models estratificats de Cox, espines cúbiques restringides i tècniques d'aprenentatge automàtic com els boscos de supervivència aleatòria. Aquests models produeixen puntuacions de risc interpretables per a ús clínic pràctic. La validació de models inclou mètodes interns com el bootstrap i la validació creuada, i la validació externa amb dades independents. El rendiment s'avalua a través de la discriminació, la calibració, el rendiment general i la utilitat clínica. La validació interna va mostrar millors resultats que l'externa, amb boscos de supervivència aleatoris que aconsegueixen la major discriminació, mentre que els splines cúbics restringits ofereixen una millora limitada. L'estudi proporciona un marc per construir models pronòstics robustos per millorar la selecció del pacient en assajos de fase I, amb treballs futurs centrats en mètodes de calibració millorats i comparacions amb les eines existents.

Los ensayos oncológicos de fase I requieren una cuidadosa selección de pacientes, equilibrando los posibles riesgos de toxicidad con los posibles beneficios. El objetivo de este estudio es desarrollar y validar modelos estadísticos para identificar a los pacientes más beneficiados. Utilizando datos de 921 pacientes, los métodos incluyen modelos de Cox estratificados, splines cúbicos restringidos y técnicas de aprendizaje automático como los bosques de supervivencia aleatorios. Estos modelos producen puntuaciones de riesgo interpretables para su uso clínico práctico. La validación de los modelos incluye métodos internos como bootstrapping y validación cruzada, y validación externa con datos independientes. El rendimiento se evalua mediante la discriminación, la calibración, el rendimiento general y la utilidad clínica. La validación interna muestra mejores resultados que la externa, y los bosques de supervivencia aleatorios logran la mayor discriminación, mientras que los splines cúbicos restringidos ofrecen una mejora limitada. El estudio proporciona un marco para la construcción de modelos de pronóstico robustos para mejorar la selección de pacientes en ensayos de fase temprana, con el trabajo futuro centrado en la mejora de los métodos de calibración y las comparaciones con las herramientas existentes.

Phase I oncology trials require careful patient selection, balancing potential toxicity risks with possible benefits. This study aimed to develop and validate statistical models to identify patients who could benefit most. Using data from 921 patients, methods included stratified Cox models, restricted cubic splines, and machine learning techniques like random survival forests. These models produced interpretable risk scores for practical clinical use. Model validation included internal methods like bootstrapping and cross-validation, and external validation with independent data. Performance was assessed through discrimination, calibration, overall performance, and clinical utility. Internal validation showed better results than external, with random survival forests achieving the highest discrimination, while restricted cubic splines offered limited improvement. The study provides a framework for building robust prognostic models to enhance patient selection in early-phase trials, with future work focusing on improved calibration methods and comparisons with existing tools.

Statistical Methods in Epidemiology | Final Restricted access to the UPC academic community

Exam

Langohr, Klaus

Publication date: 2024

School of Mathematics and Statistics (FME)

STATISTICAL METHODS IN EPIDEMIOLOGY - 200654

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ADVANCED TOPICS IN SURVIVAL AANALYSIS | Final Restricted access to the UPC academic community

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Gómez Melis, Guadalupe; Langohr, Klaus

Publication date: 2024

School of Mathematics and Statistics (FME)

ADVANCED TOPICS IN SURVIVAL ANALYSIS - 200629

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ANÀLISI DE TEMPS DE VIDA | FINAL Restricted access to the UPC academic community

Exam

Gómez Melis, Guadalupe; Langohr, Klaus

Publication date: 2024

School of Mathematics and Statistics (FME)

LIFETIME DATA ANALYSIS - 200609

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Nonlinear growth models

Master thesis

Prior Rovira, Adrià

Tutor / Supervisor: Langohr, Klaus; Farre Cervelló, Maria Mercè

Publication date: 2024

School of Mathematics and Statistics (FME)

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SOFTWARE ESTADÍSTIC: R I SAS | FINAL Restricted access to the UPC academic community

Exam

Langohr, Klaus

Publication date: 2023

School of Mathematics and Statistics (FME)

STATISTICAL SOFTWARE: R AND SAS - 200648

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Estimating the probability of discharge among Covid-19 h hospitalizations using cure models

Master thesis

Höglund Persson, Ida Katarina

Tutor / Supervisor: Gómez Melis, Guadalupe; Langohr, Klaus

Publication date: 2023

School of Mathematics and Statistics (FME)

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Parametric survival models with interval-censored response and explanatory variables

Master thesis

Berche Barcelo, Roger

Tutor / Supervisor: Langohr, Klaus; Gómez Melis, Guadalupe

Publication date: 2023

School of Mathematics and Statistics (FME)

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Statistical models to analyze recurrent fragility fractures

Master thesis

García Lerma, Esther

Tutor / Supervisor: Tebé Cordomí, Cristian; Langohr, Klaus; Gómez Melis, Guadalupe

Publication date: 2023

School of Mathematics and Statistics (FME)

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ADVANCED TOPIS IN SURVIVAL ANALYSIS | FINAL Restricted access to the UPC academic community

Exam

Langohr, Klaus

Publication date: 2023

School of Mathematics and Statistics (FME)

ADVANCED TOPICS IN SURVIVAL ANALYSIS - 200629

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Una manera fàcil de reportar taules estadístiques amb R

Master thesis

Pecharroman Ravella, Emilio

Tutor / Supervisor: Langohr, Klaus

Publication date: 2023

School of Mathematics and Statistics (FME)

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SOFTWARE ESTADÍSTIC: R I SAS | PARCIAL 2 Restricted access to the UPC academic community

Exam

Langohr, Klaus

Publication date: 2022

School of Mathematics and Statistics (FME)

STATISTICAL SOFTWARE: R AND SAS - 200648

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EPIDEMIOLOGIA (Examen final, 2n quadrimestre)

Exam

Langohr, Klaus

Publication date: 2022

School of Mathematics and Statistics (FME)

EPIDEMIOLOGY - 200632

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Métodos estadísticos para identificar hospitales con exceso de mortalidad: caso aplicado al registro de pacientes ingresados por infarto en el sistema público de hospitales en Catalunya.

Master thesis

Munoa Urruticoechea, Virginia

Tutor / Supervisor: Langohr, Klaus; Subirana Cachinero, Isaac

Publication date: 2022

School of Mathematics and Statistics (FME)

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Asociación entre el cáncer de cuello uterino y las medidas antropométricas y la actividad física en el estudio EPIC

Master thesis

Panera Carracedo, Jon Aritz

Tutor / Supervisor: Langohr, Klaus; Roura Fornells, Esther

Publication date: 2022

School of Mathematics and Statistics (FME)

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Modelling survival data from hepatorenal syndrome acute kidney injury

Master thesis

Morató Catafal, Alba

Tutor / Supervisor: Langohr, Klaus; Aguilar Parera, Ferran

Publication date: 2022

School of Mathematics and Statistics (FME)

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Assessing the effect of weight fluctuation on competing death events using Joint Models. Application to the PREDIMED study

Master thesis

Toloba López-Egea, Andrea

Tutor / Supervisor: Subirana Cachinero, Isaac; Gómez Melis, Guadalupe; Langohr, Klaus

Publication date: 2022

School of Mathematics and Statistics (FME)

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Uso de modelos lineales generalizados para analizar los datos de la Macroencuesta de Violencia contra la Mujer 2019

Bachelor thesis

Freixedas Morató, Maria

Tutor / Supervisor: Langohr, Klaus

Publication date: 2021

School of Mathematics and Statistics (FME)

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SOFTWARE ESTADÍSTIC: R I SAS | PARCIAL 1 AMB R Restricted access to the UPC academic community

Exam

Langohr, Klaus

Publication date: 2021

School of Mathematics and Statistics (FME)

STATISTICAL SOFTWARE: R AND SAS - 200648

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Escalas pronóstico en pacientes con acceso al programa hospitalario de medicaciones en situaciones especiales: construcción del modelo de riesgo ICO-MEscore

Master thesis

Gonzalez Valencia, Andrea

Tutor / Supervisor: Langohr, Klaus; Esteve Gómez, Anna; esteve gomez

Publication date: 2021

School of Mathematics and Statistics (FME)

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Klaus Langohr

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