Multistate Models: Accurate and Dynamic Methods to Improve Predictions of Thrombotic Risk in Patients with Cancer

Alberto Carmona-Bayonas; Paula Jimenez-Fonseca; Marcelo Garrido; Ana Custodio; Raquel Hernandez; Alejandra Lacalle; Juana María Cano; Gema Aguado; Eva Martínez de Castro; Felipe Alvarez Manceñido; Ismael Macias; Laura Visa; Marta Martín Richard; Monserrat Mangas; Manuel Sánchez Cánovas; Federico Longo; Leticia Iglesias Rey; Nieves Martínez Lago; Alfonso Martín Carnicero; Ana Sánchez; Aitor Azkárate; María Luisa Limón; Carolina Hernández Pérez; Avinash Ramchandani; Paola Pimentel; Paula Cerdá; Raquel Serrano; Aitziber Gil-Negrete; Miguel Marín; Alicia Hurtado; Rodrigo Sánchez Bayona; Javier Gallego

doi:10.1055/s-0039-1694012

Thrombosis and Haemostasis, Table of Contents

Thromb Haemost 2019; 119(11): 1849-1859
DOI: 10.1055/s-0039-1694012

Stroke, Systemic or Venous Thromboembolism

Georg Thieme Verlag KG Stuttgart · New York

Multistate Models: Accurate and Dynamic Methods to Improve Predictions of Thrombotic Risk in Patients with Cancer

Alberto Carmona-Bayonas

¹Hematology and Medical Oncology Department, Hospital General Universitario Morales Meseguer, University of Murcia, IMIB, Murcia, Spain

,

Paula Jimenez-Fonseca

²Medical Oncology Department, Hospital Universitario Central de Asturias, Oviedo, Spain

,

Marcelo Garrido

³Medical Oncology Department, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile

,

Ana Custodio

⁴Medical Oncology Department, Hospital Universitario La Paz, Madrid, Spain

,

Raquel Hernandez

⁵Medical Oncology Department, Hospital Universitario de Canarias, Tenerife, Spain

,

Alejandra Lacalle

⁶Medical Oncology Department, Complejo Hospitalario de Navarra, Pamplona, Spain

,

Juana María Cano

⁷Medical Oncology Department, Hospital General Universitario de Ciudad Real, Ciudad Real, Spain

,

Gema Aguado

⁸Medical Oncology Department, Hospital Universitario Gregorio Marañon, Madrid, Spain

,

Eva Martínez de Castro

⁹Medical Oncology Department, Hospital Universitario Marqués de Valdecilla, Santander, Spain

,

Felipe Alvarez Manceñido

¹⁰Pharmacy Department, Hospital Universitario Central de Asturias, Oviedo, Spain

,

Ismael Macias

¹¹Medical Oncology Department, Hospital Universitario Parc Tauli, Sabadell, Spain

,

Laura Visa

¹²Medical Oncology Department, Hospital Universitario El Mar, Barcelona, Spain

,

Marta Martín Richard

¹³Medical Oncology Department, Hospital Universitario Santa Creu i Sant Pau, Barcelona, Spain

,

Monserrat Mangas

¹⁴Medical Oncology Department, Hospital Galdakao-Usansolo, Galdakao-Usansolo, Spain

,

Manuel Sánchez Cánovas

¹Hematology and Medical Oncology Department, Hospital General Universitario Morales Meseguer, University of Murcia, IMIB, Murcia, Spain

,

Federico Longo

¹⁵Medical Oncology Department, Hospital Universitario Ramón y Cajal, Madrid, Spain

,

Leticia Iglesias Rey

¹⁶Medical Oncology Department, Complejo Hospitalario de Orense, Orense, Spain

,

Nieves Martínez Lago

¹⁷Medical Oncology Department, Complejo Hospitalario Universitario de A Coruña, La Coruña, Spain

,

Alfonso Martín Carnicero

¹⁸Medical Oncology Department, Hospital San Pedro, Logroño, Spain

,

Ana Sánchez

¹⁹Medical Oncology Department, Hospital Universitario Doce de Octubre, Madrid, Spain

,

Aitor Azkárate

²⁰Medical Oncology Department, Hospital Universitario Son Espases, Mallorca, Spain

,

María Luisa Limón

²¹Medical Oncology Department, Hospital Universitario Virgen del Rocío, Sevilla, Spain

,

Carolina Hernández Pérez

²²Medical Oncology Department, Hospital Universitario Nuestra Señora de la Candelaria, Tenerife, Spain

,

Avinash Ramchandani

²³Medical Oncology Department, Hospital Universitario Insular de Gran Canaria, Las Palmas de Gran Canaria, Spain

,

Paola Pimentel

²⁴Medical Oncology Department, Hospital Santa Lucia, Cartagena, Spain

,

Paula Cerdá

²⁵Medical Oncology Department, Centro Médico Teknon, Barcelona, Spain

,

Raquel Serrano

²⁶Medical Oncology Department, Hospital Universitario Reina Sofía, Córdoba, Spain

,

Aitziber Gil-Negrete

²⁷Medical Oncology Department, Hospital Universitario Donostia, San Sebastián, Spain

,

Miguel Marín

²⁸Medical Oncology Department, Hospital Universitario Virgen de la Arrixaca, Murcia, Spain

,

Alicia Hurtado

²⁹Medical Oncology Department, Hospital Universitario Fundación Alcorcón, Madrid, Spain

,

Rodrigo Sánchez Bayona

³⁰Medical Oncology Department, Clínica Universidad de Navarra, Pamplona, Spain

,

Javier Gallego

³¹Medical Oncology Department, Hospital General Universitario de Elche, Elche, Spain

› Author Affiliations

Abstract

Research into cancer-associated thrombosis (CAT) entails managing dynamic data that pose an analytical challenge. Thus, methods that assume proportional hazards to investigate prognosis entail a risk of misinterpreting or overlooking key traits or time-varying effects. We examined the AGAMENON registry, which collects data from 2,129 patients with advanced gastric cancer. An accelerated failure time (AFT) multistate model and flexible competing risks regression were used to scrutinize the time-varying effect of CAT, as well as to estimate how covariates dynamically predict cumulative incidence. The AFT model revealed that thrombosis shortened progression-free survival and overall survival with adjusted time ratios of 0.72 and 0.56, respectively. Nevertheless, its prognostic effect was nonproportional and disappeared over time if the subject managed to survive long enough. CAT that occurred later had a more pronounced prognostic effect. In the flexible competing risks model, multiple covariates were seen to have significant time-varying effects on the cumulative incidence of CAT (Khorana score, secondary thromboprophylaxis, high tumor burden, and cisplatin-containing regimen), whereas other predictors exerted a constant effect (signet ring cells and primary thromboprophylaxis). The model that assumes proportional hazards was incapable of capturing the effect of these covariates and predicted the cumulative incidence in a biased way. This study evinces that flexible and multistate models are a useful and innovative method to describe the dynamic effect of variables associated with CAT and should be more widely used.

Keywords

cancer - thrombosis - time-varying effects - multistate models - flexible models

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References

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