The comparison of censored quantile regression methods in prognosis factors of breast cancer survival.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
14 09 2021
14 09 2021
Historique:
received:
15
01
2021
accepted:
25
08
2021
entrez:
15
9
2021
pubmed:
16
9
2021
medline:
17
12
2021
Statut:
epublish
Résumé
The Cox proportional hazards model is a widely used statistical method for the censored data that model the hazard rate rather than survival time. To overcome complexity of interpreting hazard ratio, quantile regression was introduced for censored data with more straightforward interpretation. Different methods for analyzing censored data using quantile regression model, have been introduced. The quantile regression approach models the quantile function of failure time and investigates the covariate effects in different quantiles. In this model, the covariate effects can be changed for patients with different risk and is a flexible model for controlling the heterogeneity of covariate effects. We illustrated and compared five methods in quantile regression for right censored data included Portnoy, Wang and Wang, Bottai and Zhang, Yang and De Backer methods. The comparison was made through the use of these methods in modeling the survival time of breast cancer. According to the results of quantile regression models, tumor grade and stage of the disease were identified as significant factors affecting 20th percentile of survival time. In Bottai and Zhang method, 20th percentile of survival time for a case with higher unit of stage decreased about 14 months and 20th percentile of survival time for a case with higher grade decreased about 13 months. The quantile regression models acted the same to determine prognostic factors of breast cancer survival in most of the time. The estimated coefficients of five methods were close to each other for quantiles lower than 0.1 and they were different from quantiles upper than 0.1.
Identifiants
pubmed: 34521936
doi: 10.1038/s41598-021-97665-x
pii: 10.1038/s41598-021-97665-x
pmc: PMC8440570
doi:
Types de publication
Comparative Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
18268Informations de copyright
© 2021. The Author(s).
Références
Klein, J. P. & Moeschberger, M. L. Survival Analysis: Techniques for Censored and Truncated Data Vol. 1230 (Springer, 2003).
doi: 10.1007/b97377
Kalbfleisch, J. D. & Prentice, R. L. The Statistical Analysis of Failure Time Data Vol. 360 (Wiley, 2011).
Xue, X., Xie, X. & Strickler, H. D. A censored quantile regression approach for the analysis of time to event data. Stat. Methods Med. Res. 27, 955–965 (2018).
pubmed: 27166408
doi: 10.1177/0962280216648724
Koenker, R. & Hallock, K. F. Quantile regression. J. Econ. Perspect. 15, 143–156 (2001).
doi: 10.1257/jep.15.4.143
Portnoy, S. Censored regression quantiles. J. Am. Stat. Assoc. 98, 1001–1012 (2003).
doi: 10.1198/016214503000000954
Peng, L. & Huang, Y. Survival analysis with quantile regression models. J. Am. Stat. Assoc. 103, 637–649 (2008).
doi: 10.1198/016214508000000355
Wang, H. J. & Wang, L. Locally weighted censored quantile regression. J. Am. Stat. Assoc. 104, 1117–1128 (2009).
doi: 10.1198/jasa.2009.tm08230
Bottai, M. & Zhang, J. Laplace regression with censored data. Biom. J. 52, 487–503 (2010).
pubmed: 20680972
doi: 10.1002/bimj.200900310
Yang, X., Narisetty, N. N. & He, X. A new approach to censored quantile regression estimation. J. Comput. Graph. Stat. 27, 417–425 (2018).
doi: 10.1080/10618600.2017.1385469
De Backer, M., El Ghouch, A. & Van Keilegom, I. Linear censored quantile regression: A novel minimum-distance approach. Scand. J. Stat. 47, 1275–1306 (2020).
doi: 10.1111/sjos.12475
Liu, Y. & Bottai, M. Mixed-effects models for conditional quantiles with longitudinal data. Int. J. Biostat. 5, 1 (2009).
doi: 10.2202/1557-4679.1186
Farcomeni, A. Quantile regression for longitudinal data based on latent Markov subject-specific parameters. Stat. Comput. 22, 141–152 (2012).
doi: 10.1007/s11222-010-9213-0
Lee, D. & Neocleous, T. Bayesian quantile regression for count data with application to environmental epidemiology. J. R. Stat. Soc. C 59, 905–920 (2010).
doi: 10.1111/j.1467-9876.2010.00725.x
Yuan, Y. & Yin, G. Bayesian quantile regression for longitudinal studies with nonignorable missing data. Biometrics 66, 105–114 (2010).
pubmed: 19459836
doi: 10.1111/j.1541-0420.2009.01269.x
Tanner, M. A. & Wong, W. H. The calculation of posterior distributions by data augmentation. J. Am. Stat. Assoc. 82, 528–540 (1987).
doi: 10.1080/01621459.1987.10478458
Koenker, R. & Bassett, G. Jr. Regression quantiles. Econometrica 46, 33–50 (1978).
doi: 10.2307/1913643
Li, R. & Peng, L. Quantile regression for left-truncated semicompeting risks data. Biometrics 67, 701–710 (2011).
pubmed: 21133883
doi: 10.1111/j.1541-0420.2010.01521.x
Peng, L. & Fine, J. P. Competing risks quantile regression. J. Am. Stat. Assoc. 104, 1440–1453 (2009).
doi: 10.1198/jasa.2009.tm08228
Fan, C., Ma, H. & Zhou, Y. Quantile regression for competing risks analysis under case-cohort design. J. Stat. Comput. Simul. 88, 1060–1080 (2018).
doi: 10.1080/00949655.2017.1419352
Luo, X., Huang, C. Y. & Wang, L. Quantile regression for recurrent gap time data. Biometrics 69, 375–385 (2013).
pubmed: 23489055
pmcid: 4123128
doi: 10.1111/biom.12010
Sun, X., Peng, L., Huang, Y. & Lai, H. J. Generalizing quantile regression for counting processes with applications to recurrent events. J. Am. Stat. Assoc. 111, 145–156 (2016).
pubmed: 27212738
pmcid: 4872875
doi: 10.1080/01621459.2014.995795
Frumento, P. & Bottai, M. An estimating equation for censored and truncated quantile regression. Comput. Stat. Data Anal. 113, 53–63 (2017).
doi: 10.1016/j.csda.2016.08.015
Narisetty, N. & Koenker, R. Censored quantile regression survival models with a cure proportion. J. Econ. https://doi.org/10.1016/j.jeconom.2020.12.005 (2021).
doi: 10.1016/j.jeconom.2020.12.005
Chen, S. Quantile regression for duration models with time-varying regressors. J. Econ. 209, 1–17 (2019).
doi: 10.1016/j.jeconom.2018.11.015
Moghadami, F. Z., Abolghasemi, J., Asgari, D. A. & Gohari, M. Survival analysis of patients with breast cancer using the Aalen's additive hazard model. (2011).
Saki, A., Hajizadeh, E. & Tehranian, N. Evaluating the risk factors of breast cancer using the analysis of tree models. Horizon Med. Sci. 17, 60–68 (2011).
Akarolo-Anthony, S. N., Ogundiran, T. O. & Adebamowo, C. A. Emerging breast cancer epidemic: Evidence from Africa. Breast Cancer Res. 12, 1–4 (2010).
doi: 10.1186/bcr2737
Khodabakhshi, R., RezaGohari, M., Moghadamifard, Z., Foadzi, H. & Vahabi, N. Disease-free survival of breast cancer patients and identification of related factors. Razi J. Med. Sci. 18, 27–33 (2011).
Roué, T. et al. Predictive factors of the survival of women with invasive breast cancer in French Guiana: The burden of health inequalities. Clin. Breast Cancer 16, e113–e118 (2016).
pubmed: 27036361
doi: 10.1016/j.clbc.2016.02.017
Davino, C., Furno, M. & Vistocco, D. Quantile Regression: Theory and Applications Vol. 988 (Wiley, 2013).
Efron, B. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability 831–853 (1967).
Neocleous, T., Branden, K. V. & Portnoy, S. Correction to censored regression quantiles by S. Portnoy, 98 (2003), 1001–1012. J. Am. Stat. Assoc. 101, 860–861 (2006).
doi: 10.1198/016214506000000087
Nelder, J. A. & Mead, R. A simplex method for function minimization. Comput. J. 7, 308–313 (1965).
doi: 10.1093/comjnl/7.4.308
Beran, R. Nonparametric Regression with Randomly Censored Survival Data (Springer, 1981).
Hartmann-Johnsen, O. J., Kåresen, R., Schlichting, E. & Nygård, J. F. Better survival after breast-conserving therapy compared to mastectomy when axillary node status is positive in early-stage breast cancer: A registry-based follow-up study of 6387 Norwegian women participating in screening, primarily operated between 1998 and 2009. World J. Surg. Oncol. 15, 1–10 (2017).
doi: 10.1186/s12957-017-1184-6
Litière, S. et al. Breast conserving therapy versus mastectomy for stage I-II breast cancer: 20 year follow-up of the EORTC 10801 phase 3 randomised trial. Lancet Oncol. 13, 412–419 (2012).
pubmed: 22373563
doi: 10.1016/S1470-2045(12)70042-6
Hartmann-Johnsen, O. J., Kåresen, R., Schlichting, E. & Nygård, J. F. Survival is better after breast conserving therapy than mastectomy for early stage breast cancer: A registry-based follow-up study of Norwegian women primary operated between 1998 and 2008. Ann. Surg. Oncol. 22, 3836–3845 (2015).
pubmed: 25743325
pmcid: 4595537
doi: 10.1245/s10434-015-4441-3
Hofvind, S. et al. Women treated with breast conserving surgery do better than those with mastectomy independent of detection mode, prognostic and predictive tumor characteristics. Eur. J. Surg. Oncol. 41, 1417–1422 (2015).
pubmed: 26253193
doi: 10.1016/j.ejso.2015.07.002
Quan, M. L. et al. The effect of surgery type on survival and recurrence in very young women with breast cancer. J. Surg. Oncol. 115, 122–130 (2017).
pubmed: 28054348
doi: 10.1002/jso.24489
Saadatmand, S., Bretveld, R., Siesling, S. & Tilanus-Linthorst, M. M. Influence of tumour stage at breast cancer detection on survival in modern times: Population based study in 173 797 patients. BMJ 351, 4901 (2015).
doi: 10.1136/bmj.h4901
Rottenberg, Y., Naeim, A., Uziely, B., Peretz, T. & Jacobs, J. M. Breast cancer among older women: The influence of age and cancer stage on survival. Arch. Gerontol. Geriatr. 76, 60–64 (2018).
pubmed: 29459246
doi: 10.1016/j.archger.2018.02.004
Yu, K. & Moyeed, R. A. Bayesian quantile regression. Stat. Probab. Lett. 54, 437–447 (2001).
doi: 10.1016/S0167-7152(01)00124-9
Alhamzawi, R. & Yu, K. Bayesian Tobit quantile regression using g-prior distribution with ridge parameter. J. Stat. Comput. Simul. 85, 2903–2918 (2015).
doi: 10.1080/00949655.2014.945449
Alhamzawi, R. & Ali, H. T. M. Bayesian tobit quantile regression with penalty. Commun. Stat. Simul. Comput. 47, 1739–1750 (2018).
doi: 10.1080/03610918.2017.1323224