Flexible geoadditive survival analysis of non-Hodgkin lymphoma in Peru

  1. Flores, Claudio
  2. Rodríguez Girondo, Mar
  3. Cadarso Suárez, Carmen María
  4. Gómez Melis, Guadalupe
  5. Casanova, Luis
  6. Kneib, Thomas
Zeitschrift:
Sort: Statistics and Operations Research Transactions

ISSN: 1696-2281

Datum der Publikation: 2012

Ausgabe: 36

Nummer: 2

Seiten: 221-230

Art: Artikel

Andere Publikationen in: Sort: Statistics and Operations Research Transactions

Zusammenfassung

Knowledge of prognostic factors is an important task for the clinical management of Non Hodgkin Lymphoma (NHL). In this work, we study the variables affecting survival of NHL in Peru by means of geoadditive Cox-type structured hazard regression models while accounting for potential spatial correlations in the survival times. We identified eight covariates with significant effect for overall survival. Some of them are widely known such as age, performance status, clinical stage and lactic dehydrogenase, but we also identified hemoglobin, leukocytes and lymphocytes as covariates with a significant effect on the overall survival of patients with NHL. Besides, the effect of continuous covariates is clearly nonlinear and hence impossible to detect with the classical Cox method. Although the spatial component does not show a significant effect, the results show a trend of low risk in certain areas.

Bibliographische Referenzen

  • Brezger, A., Kneib, T. and Lang, S. (2005). BayesX: Analyzing Bayesian structural additive regression models. Journal of Statistical Software, 14, i11.
  • Buchholz, A. and Sauerbrei, W. (2011). Comparison of procedures to assee non-linear and time-varying effects in multivariable models for survival data. Biometrical Journal, 53(2), 308–331.
  • Cox, D. R. (1972). Regression models and life tables (with discussion). Journal of the Royal Statistical Society Series B, 34, 187–220.
  • Eilers, P. H. and Marx, B. D. (1996). Flexible smoothing using B-splines and penalties. Statistical Science, 11, 89–121.
  • Friedberg, J. W., Mauch, P. M., Rimsza, L. M. and Fisher, R. I. (2008). Non-Hodgkin’s lymphomas. In: DeVita, V. T., Lawrence, T. S., Rosenberg, S. A., eds. DeVita, Hellman, and Rosenberg’s Cancer: Principles and Practice of Oncology. 8th ed. Philadelphia, Pa: Lippincott Williams &Wilkins; 2278–2292.
  • Hennerfeind, A., Brezger, A. and Fahrmeir, L. (2006). Geoadditive survival models. Journal Of the American Statistical Association, 101, 1065–1075.
  • Kneib, T. and Fahrmeir, L. (2007). A mixed model approach for geoadditive hazard regression. Scandinavian Journal of Statistics, 34 207–228.
  • Shipp, M. A., Harrington, D. P. and Aderson, J. R. et al. (1993). A predictive model for aggressive nonHodgkin’s lymphoma. The International non-Hodgkin’s lymphoma prognostic factors project. The New England Journal of Medicine, 329, 987–994.
  • Therneau, T. M. and Grambsch, P. M. (2000). Modelling Survival Data: Extending the Cox Model. New York: Springer.