Download PDF
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
1   Hematology and Medical Oncology Department, Hospital General Universitario Morales Meseguer, University of Murcia, IMIB, Murcia, Spain
,
Paula Jimenez-Fonseca
2   Medical Oncology Department, Hospital Universitario Central de Asturias, Oviedo, Spain
,
Marcelo Garrido
3   Medical Oncology Department, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile
,
Ana Custodio
4   Medical Oncology Department, Hospital Universitario La Paz, Madrid, Spain
,
Raquel Hernandez
5   Medical Oncology Department, Hospital Universitario de Canarias, Tenerife, Spain
,
Alejandra Lacalle
6   Medical Oncology Department, Complejo Hospitalario de Navarra, Pamplona, Spain
,
Juana María Cano
7   Medical Oncology Department, Hospital General Universitario de Ciudad Real, Ciudad Real, Spain
,
Gema Aguado
8   Medical Oncology Department, Hospital Universitario Gregorio Marañon, Madrid, Spain
,
Eva Martínez de Castro
9   Medical Oncology Department, Hospital Universitario Marqués de Valdecilla, Santander, Spain
,
Felipe Alvarez Manceñido
10   Pharmacy Department, Hospital Universitario Central de Asturias, Oviedo, Spain
,
Ismael Macias
11   Medical Oncology Department, Hospital Universitario Parc Tauli, Sabadell, Spain
,
Laura Visa
12   Medical Oncology Department, Hospital Universitario El Mar, Barcelona, Spain
,
Marta Martín Richard
13   Medical Oncology Department, Hospital Universitario Santa Creu i Sant Pau, Barcelona, Spain
,
Monserrat Mangas
14   Medical Oncology Department, Hospital Galdakao-Usansolo, Galdakao-Usansolo, Spain
,
Manuel Sánchez Cánovas
1   Hematology and Medical Oncology Department, Hospital General Universitario Morales Meseguer, University of Murcia, IMIB, Murcia, Spain
,
Federico Longo
15   Medical Oncology Department, Hospital Universitario Ramón y Cajal, Madrid, Spain
,
Leticia Iglesias Rey
16   Medical Oncology Department, Complejo Hospitalario de Orense, Orense, Spain
,
Nieves Martínez Lago
17   Medical Oncology Department, Complejo Hospitalario Universitario de A Coruña, La Coruña, Spain
,
Alfonso Martín Carnicero
18   Medical Oncology Department, Hospital San Pedro, Logroño, Spain
,
Ana Sánchez
19   Medical Oncology Department, Hospital Universitario Doce de Octubre, Madrid, Spain
,
Aitor Azkárate
20   Medical Oncology Department, Hospital Universitario Son Espases, Mallorca, Spain
,
María Luisa Limón
21   Medical Oncology Department, Hospital Universitario Virgen del Rocío, Sevilla, Spain
,
Carolina Hernández Pérez
22   Medical Oncology Department, Hospital Universitario Nuestra Señora de la Candelaria, Tenerife, Spain
,
Avinash Ramchandani
23   Medical Oncology Department, Hospital Universitario Insular de Gran Canaria, Las Palmas de Gran Canaria, Spain
,
Paola Pimentel
24   Medical Oncology Department, Hospital Santa Lucia, Cartagena, Spain
,
Paula Cerdá
25   Medical Oncology Department, Centro Médico Teknon, Barcelona, Spain
,
Raquel Serrano
26   Medical Oncology Department, Hospital Universitario Reina Sofía, Córdoba, Spain
,
Aitziber Gil-Negrete
27   Medical Oncology Department, Hospital Universitario Donostia, San Sebastián, Spain
,
Miguel Marín
28   Medical Oncology Department, Hospital Universitario Virgen de la Arrixaca, Murcia, Spain
,
Alicia Hurtado
29   Medical Oncology Department, Hospital Universitario Fundación Alcorcón, Madrid, Spain
,
Rodrigo Sánchez Bayona
30   Medical Oncology Department, Clínica Universidad de Navarra, Pamplona, Spain
,
Javier Gallego
31   Medical Oncology Department, Hospital General Universitario de Elche, Elche, Spain
› Author Affiliations Funding None.
Further Information

Publication History

13 April 2019

21 June 2019

Publication Date:
28 August 2019 (online)

Also available at
    Permissions and Reprints

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

Ethical Approval

All procedures followed were in accordance with the ethical standards of the (institutional and national) committee responsible for human experimentation and with the Helsinki Declaration of 1964 and later versions. Informed consent was obtained from all patients before they were included in the study.


Supplementary Material

 

  • References

  • 1 Jimenez-Fonseca P, Carmona-Bayonas A, Calderon C. , et al. FOTROCAN Delphi consensus statement regarding the prevention and treatment of cancer-associated thrombosis in areas of uncertainty and low quality of evidence. Clin Transl Oncol 2017; 19 (08) 997-1009
    CrossrefPubMedSearch in Google Scholar
  • 2 Falanga A, Marchetti M, Vignoli A. Coagulation and cancer: biological and clinical aspects. J Thromb Haemost 2013; 11 (02) 223-233
    CrossrefPubMedSearch in Google Scholar
  • 3 Ay C, Dunkler D, Marosi C. , et al. Prediction of venous thromboembolism in cancer patients. Blood 2010; 116 (24) 5377-5382
    CrossrefPubMedSearch in Google Scholar
  • 4 Carmona-Bayonas A, Jimenez-Fonseca P, Fernández-Somoano A. , et al. Top ten errors of statistical analysis in observational studies for cancer research. Clin Transl Oncol 2018; 20 (08) 954-965
    CrossrefPubMedSearch in Google Scholar
  • 5 Chew HK, Wun T, Harvey D, Zhou H, White RH. Incidence of venous thromboembolism and its effect on survival among patients with common cancers. Arch Intern Med 2006; 166 (04) 458-464
    CrossrefPubMedSearch in Google Scholar
  • 6 Brand JS, Hedayati E, Bhoo-Pathy N. , et al. Time-dependent risk and predictors of venous thromboembolism in breast cancer patients: a population-based cohort study. Cancer 2017; 123 (03) 468-475
    CrossrefPubMedSearch in Google Scholar
  • 7 Khan UT, Walker AJ, Baig S, Card TR, Kirwan CC, Grainge MJ. Venous thromboembolism and mortality in breast cancer: cohort study with systematic review and meta-analysis. BMC Cancer 2017; 17 (01) 747
    CrossrefPubMedSearch in Google Scholar
  • 8 Sørensen HT, Mellemkjaer L, Olsen JH, Baron JA. Prognosis of cancers associated with venous thromboembolism. N Engl J Med 2000; 343 (25) 1846-1850
    CrossrefPubMedSearch in Google Scholar
  • 9 Lyman GH, Khorana AA, Kuderer NM. , et al. Venous thromboembolism prophylaxis and treatment in patients with cancer: American Society of Clinical Oncology clinical practice guideline update. J Clin Oncol [Internet] 2013; 31: 2189-204
    CrossrefPubMedSearch in Google Scholar
  • 10 Ay C, Posch F, Kaider A, Zielinski C, Pabinger I. Estimating risk of venous thromboembolism in patients with cancer in the presence of competing mortality. J Thromb Haemost 2015; 13 (03) 390-397
    CrossrefPubMedSearch in Google Scholar
  • 11 Austin PC, Lee DS, Fine JP. Introduction to the analysis of survival data in the presence of competing risks. Circulation 2016; 133 (06) 601-609
    CrossrefPubMedSearch in Google Scholar
  • 12 Scheike TH, Zhang M-J. Analyzing competing risk data using the R timereg package. J Stat Softw NIH Public Access; 2011: 38
    Search in Google Scholar
  • 13 Martinussen T, Scheike TH. Dynamic Regression Models for Survival Data. Berlin, Germany: Springer Science & Business Media; 2007
    Search in Google Scholar
  • 14 Blix K, Gran OV, Severinsen MT. , et al. Impact of time since diagnosis and mortality rate on cancer-associated venous thromboembolism: the Scandinavian Thrombosis and Cancer (STAC) cohort. J Thromb Haemost 2018; 16 (07) 1327-1335
    CrossrefPubMedSearch in Google Scholar
  • 15 Suissa S. Immortal time bias in pharmaco-epidemiology. Am J Epidemiol 2008; 167 (04) 492-499
    CrossrefPubMedSearch in Google Scholar
  • 16 Metcalf RL, Fry DJ, Swindell R. , et al. Thrombosis in ovarian cancer: a case control study. Br J Cancer 2014; 110 (05) 1118-1124
    CrossrefPubMedSearch in Google Scholar
  • 17 Geskus RB. Data Analysis with Competing Risks and Intermediate States. Boca Raton, FL: Chapman and Hall/CRC Press; 2015
    Search in Google Scholar
  • 18 Posch F, Riedl J, Reitter E-M. , et al. Hypercoagulabilty, venous thromboembolism, and death in patients with cancer. A multi-state model. Thromb Haemost 2016; 115 (04) 817-826
    Thieme ConnectPubMedSearch in Google Scholar
  • 19 Crowther MJ, Lambert PC. Parametric multistate survival models: flexible modelling allowing transition-specific distributions with application to estimating clinically useful measures of effect differences. Stat Med 2017; 36 (29) 4719-4742
    CrossrefPubMedSearch in Google Scholar
  • 20 Marshall-Webb M, Bright T, Price T, Thompson SK, Watson DI. Venous thromboembolism in patients with esophageal or gastric cancer undergoing neoadjuvant chemotherapy. Dis Esophagus 2017; 30 (02) 1-7
    PubMedSearch in Google Scholar
  • 21 Larsen AC, Dabrowski T, Frøkjær JB. , et al. Prevalence of venous thromboembolism at diagnosis of upper gastrointestinal cancer. Br J Surg 2014; 101 (03) 246-253
    CrossrefPubMedSearch in Google Scholar
  • 22 Tetzlaff ED, Correa AM, Komaki R. , et al. Significance of thromboembolic phenomena occurring before and during chemoradiotherapy for localized carcinoma of the esophagus and gastroesophageal junction. Dis Esophagus 2008; 21 (07) 575-581
    CrossrefPubMedSearch in Google Scholar
  • 23 Carmona-Bayonas A, Jiménez-Fonseca P, Custodio A. , et al. Anthracycline-based triplets do not improve the efficacy of platinum-fluoropyrimidine doublets in first-line treatment of advanced gastric cancer: real-world data from the AGAMEMON National Cancer Registry. Gastric Cancer 2018; 21 (01) 96-105
    CrossrefPubMedSearch in Google Scholar
  • 24 Carmona-Bayonas A, Jiménez-Fonseca P, Lorenzo MLS. , et al. On the effect of triplet or doublet chemotherapy in advanced gastric cancer: results from a national cancer registry. J Natl Compr Canc Netw 2016; 14 (11) 1379-1388
    CrossrefPubMedSearch in Google Scholar
  • 25 Custodio A, Carmona-Bayonas A, Jiménez-Fonseca P. , et al. Nomogram-based prediction of survival in patients with advanced oesophagogastric adenocarcinoma receiving first-line chemotherapy: a multicenter prospective study in the era of trastuzumab. Br J Cancer 2017; 116 (12) 1526-1535
    CrossrefPubMedSearch in Google Scholar
  • 26 Carmona-Bayonas A, Jiménez-Fonseca P, Echavarria I. , et al; AGAMENON Study Group. Surgery for metastases for esophageal-gastric cancer in the real world: data from the AGAMENON national registry. Eur J Surg Oncol 2018; 44 (08) 1191-1198
    CrossrefPubMedSearch in Google Scholar
  • 27 Jiménez Fonseca P, Carmona-Bayonas A, Hernández R. , et al. Lauren subtypes of advanced gastric cancer influence survival and response to chemotherapy: real-world data from the AGAMENON National Cancer Registry. Br J Cancer [Internet] 2017; 117 (06) 775-782
    CrossrefPubMedSearch in Google Scholar
  • 28 Visa L, Jiménez-Fonseca P, Martínez EA. , et al; AGAMENON Study Group. Efficacy and safety of chemotherapy in older versus non-older patients with advanced gastric cancer: a real-world data, non-inferiority analysis. J Geriatr Oncol 2018; 9 (03) 254-264
    CrossrefPubMedSearch in Google Scholar
  • 29 Jiménez-Fonseca P, Carmona-Bayonas A, Sánchez Lorenzo ML. , et al. Prognostic significance of performing universal HER2 testing in cases of advanced gastric cancer. Gastric Cancer 2017; 20 (03) 465-474
    CrossrefPubMedSearch in Google Scholar
  • 30 Meira-Machado L, de Uña-Alvarez J, Cadarso-Suárez C, Andersen PK. Multi-state models for the analysis of time-to-event data. Stat Methods Med Res 2009; 18 (02) 195-222
    CrossrefPubMedSearch in Google Scholar
  • 31 Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: competing risks and multi-state models. Stat Med 2007; 26 (11) 2389-2430
    CrossrefPubMedSearch in Google Scholar
  • 32 Brand JS, Hedayati E, Humphreys K. , et al. Chemotherapy, genetic susceptibility, and risk of venous thromboembolism in breast cancer patients. Clin Cancer Res 2016; 22 (21) 5249-5255
    CrossrefPubMedSearch in Google Scholar
  • 33 Therneau T, Crowson C, Atkinson E. Using time dependent covariates and time dependent coefficients in the cox model. Survival Vignettes. Available at: https://cran.r-project.org/web/packages/survival/vignettes/timedep.pdf . Accessed July 27, 2019
    PubMed
  • 34 Harrell F. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. 2nd ed. New York: Springer; 2015
    Search in Google Scholar
  • 35 Dafni U. Landmark analysis at the 25-year landmark point. Circ Cardiovasc Qual Outcomes 2011; 4 (03) 363-371
    CrossrefPubMedSearch in Google Scholar
  • 36 R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2019. Available at: https://www.R-project.org/ . Accessed August 12, 2019
    PubMed
  • 37 de Wreede LC, Fiocco M, Putter H. The mstate package for estimation and prediction in non- and semi-parametric multi-state and competing risks models. Comput Methods Programs Biomed 2010; 99 (03) 261-274
    CrossrefPubMedSearch in Google Scholar
  • 38 Jackson CH. flexsurv: a platform for parametric survival modeling in R. J Stat Softw; 2016 ;70:pii: i08
    PubMed
  • 39 Khorana AA, Kuderer NM, Culakova E, Lyman GH, Francis CW. Development and validation of a predictive model for chemotherapy-associated thrombosis. Blood 2008; 111 (10) 4902-4907
    CrossrefPubMedSearch in Google Scholar
  • 40 Parpia S, Julian JA, Thabane L, Lee AY, Rickles FR, Levine MN. Competing events in patients with malignant disease who are at risk for recurrent venous thromboembolism. Contemp Clin Trials 2011; 32 (06) 829-833
    CrossrefPubMedSearch in Google Scholar
  • 41 Prandoni P, Lensing AW, Piccioli A. , et al. Recurrent venous thromboembolism and bleeding complications during anticoagulant treatment in patients with cancer and venous thrombosis. Blood 2002; 100 (10) 3484-3488
    CrossrefPubMedSearch in Google Scholar
  • 42 Muriel A, Jiménez D, Aujesky D. , et al; RIETE Investigators. Survival effects of inferior vena cava filter in patients with acute symptomatic venous thromboembolism and a significant bleeding risk. J Am Coll Cardiol 2014; 63 (16) 1675-1683
    CrossrefPubMedSearch in Google Scholar
  • 43 Font C, Carmona-Bayonas A, Beato C. , et al. Clinical features and short-term outcomes of cancer patients with suspected and unsuspected pulmonary embolism: the EPIPHANY study. Eur Respir J 2017; 49 (01) pii:1600282
    PubMedSearch in Google Scholar
  • 44 Prentice RL. A log gamma model and its maximum likelihood estimation. Biometrika Oxford Univ Press 1974; 61: 539-544
    PubMedSearch in Google Scholar
  • 45 van Es N, Di Nisio M, Cesarman G. , et al. Comparison of risk prediction scores for venous thromboembolism in cancer patients: a prospective cohort study. Haematologica 2017; 102 (09) 1494-1501
    CrossrefPubMedSearch in Google Scholar
  • 46 Campigotto F, Neuberg D, Zwicker JI. Accounting for death as a competing risk in cancer-associated thrombosis studies. Thromb Res 2012; 129 (Suppl. 01) S85-S87
    CrossrefPubMedSearch in Google Scholar
  • 47 Lee KW, Bang SM, Kim S. , et al. The incidence, risk factors and prognostic implications of venous thromboembolism in patients with gastric cancer. J Thromb Haemost 2010; 8 (03) 540-547
    CrossrefPubMedSearch in Google Scholar
  • 48 Siewert JR, Böttcher K, Roder JD. , et al. Venous thromboembolism in patients receiving perioperative chemotherapy for esophagogastric cancer. Br J Surg Wiley Online Library 1993; 80: 1015-1018
    PubMedSearch in Google Scholar
  • 49 Starling N, Rao S, Cunningham D. , et al. Thromboembolism in patients with advanced gastroesophageal cancer treated with anthracycline, platinum, and fluoropyrimidine combination chemotherapy: a report from the UK National Cancer Research Institute Upper Gastrointestinal Clinical Studies Group. J Clin Oncol 2009; 27 (23) 3786-3793
    CrossrefPubMedSearch in Google Scholar
  • 50 Bosch DJ, Van Dalfsen QA, Mul VEM, Hospers GA, Plukker JT. Increased risk of thromboembolism in esophageal cancer patients treated with neoadjuvant chemoradiotherapy. Am J Surg 2014; 208 (02) 215-221
    CrossrefPubMedSearch in Google Scholar
  • 51 Kang MJ, Ryoo B-Y, Ryu M-H. , et al. Venous thromboembolism (VTE) in patients with advanced gastric cancer: an Asian experience. Eur J Cancer 2012; 48 (04) 492-500
    CrossrefPubMedSearch in Google Scholar
  • 52 Di Nisio M, Porreca E, Otten H-M, Rutjes AW. Primary prophylaxis for venous thromboembolism in ambulatory cancer patients receiving chemotherapy. Cochrane Database Syst Rev 2014; (08) CD008500
    PubMedSearch in Google Scholar