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Table 1.  TNMB Staging Systema
TNMB Staging Systema
Table 2.  Summary of Patient and Tumor Characteristics
Summary of Patient and Tumor Characteristics
Table 3.  Staging Reclassification by the Seventh and Eighth Editions of the TNM Staging System and the TNMB Staging System for Non–Small Cell Lung Cancera
Staging Reclassification by the Seventh and Eighth Editions of the TNM Staging System and the TNMB Staging System for Non–Small Cell Lung Cancera
Table 4.  Performance and Improvement Measures From Adoption of the Seventh and Eighth Editions of the TNM Staging System and the TNMB Staging System for Non–Small Cell Lung Cancera
Performance and Improvement Measures From Adoption of the Seventh and Eighth Editions of the TNM Staging System and the TNMB Staging System for Non–Small Cell Lung Cancera
1.
Detterbeck  FC, Boffa  DJ, Kim  AW, Tanoue  LT.  The eighth edition lung cancer stage classification.  Chest. 2017;151(1):193-203. doi:10.1016/j.chest.2016.10.010PubMedGoogle ScholarCrossref
2.
Chansky  K, Detterbeck  FC, Nicholson  AG,  et al.  The IASLC Lung Cancer Staging Project: external validation of the revision of the TNM stage groupings in the eighth edition of the TNM Classification of Lung Cancer.  J Thorac Oncol. 2017;12(7):1109-1121. doi:10.1016/j.jtho.2017.04.011PubMedGoogle ScholarCrossref
3.
Yang  L, Wang  S, Zhou  Y,  et al.  Evaluation of the 7th and 8th editions of the AJCC/UICC TNM staging systems for lung cancer in a large North American cohort.  Oncotarget. 2017;8(40):66784-66795. doi:10.18632/oncotarget.18158PubMedGoogle Scholar
4.
D’Amico  TA.  Molecular biologic staging of lung cancer.  Ann Thorac Surg. 2008;85(2):S737-S742. doi:10.1016/j.athoracsur.2007.11.047PubMedGoogle ScholarCrossref
5.
Kratz  JR, He  J, Van Den Eeden  SK,  et al.  A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies.  Lancet. 2012;379(9818):823-832. doi:10.1016/S0140-6736(11)61941-7PubMedGoogle ScholarCrossref
6.
Kratz  JR, Van den Eeden  SK, He  J, Jablons  DM, Mann  MJ.  A prognostic assay to identify patients at high risk of mortality despite small, node-negative lung tumors.  JAMA. 2012;308(16):1629-1631. doi:10.1001/jama.2012.13551PubMedGoogle ScholarCrossref
7.
Kratz  JR, Tham  PT, Mulvihill  MS,  et al.  Analytical validation of a practical molecular assay prognostic of survival in nonsquamous non-small cell lung cancer.  Diagn Mol Pathol. 2013;22(2):65-69. doi:10.1097/PDM.0b013e318273fb61PubMedGoogle ScholarCrossref
8.
Kratz  JR, Haro  GJ, Cook  NR,  et al.  Incorporation of a molecular prognostic classifier improves conventional non-small cell lung cancer staging.  J Thorac Oncol. 2019;14(7):1223-1232. doi:10.1016/j.jtho.2019.03.015PubMedGoogle ScholarCrossref
9.
Ettinger  DS, Wood  DE, Aisner  DL,  et al.  Non-small cell lung cancer, version 5.2017, NCCN clinical practice guidelines in oncology.  J Natl Compr Canc Netw. 2017;15(4):504-535. doi:10.6004/jnccn.2017.0050PubMedGoogle ScholarCrossref
10.
Arriagada  R, Dunant  A, Pignon  JP,  et al.  Long-term results of the international adjuvant lung cancer trial evaluating adjuvant cisplatin-based chemotherapy in resected lung cancer.  J Clin Oncol. 2010;28(1):35-42. doi:10.1200/JCO.2009.23.2272PubMedGoogle ScholarCrossref
11.
Pignon  JP, Tribodet  H, Scagliotti  GV,  et al; LACE Collaborative Group.  Lung adjuvant cisplatin evaluation: a pooled analysis by the LACE Collaborative Group.  J Clin Oncol. 2008;26(21):3552-3559. doi:10.1200/JCO.2007.13.9030PubMedGoogle ScholarCrossref
12.
Royston  P.  Explained variation for survival models.  Stata J. 2006;6:83-96. doi:10.1177/1536867X0600600105Google ScholarCrossref
13.
Pencina  MJ, D’Agostino  RB  Sr, Steyerberg  EW.  Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.  Stat Med. 2011;30(1):11-21. doi:10.1002/sim.4085PubMedGoogle ScholarCrossref
14.
Uno  H, Cai  T. survIDINRI package version 1.1-1. R Core Team. http://www.R-project.org/. Published 2019. Accessed May 1, 2019.
Original Investigation
Oncology
December 6, 2019

Comparison of Conventional TNM and Novel TNMB Staging Systems for Non–Small Cell Lung Cancer

Author Affiliations
  • 1Division of Adult Cardiothoracic Surgery, University of California, San Francisco
  • 2Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
JAMA Netw Open. 2019;2(12):e1917062. doi:10.1001/jamanetworkopen.2019.17062
Key Points

Question  Compared with conventional TNM staging for non–small cell lung cancer, does incorporation of a molecular prognostic classifier by the TNMB staging system improve estimation of disease-free survival?

Findings  In this cohort study of 238 patients who underwent surgical resection of non–small cell lung cancer, 66.8% of patients were reclassified using the molecular prognostic classifier within the TNMB staging system. The eighth edition of the TNM system, compared with the seventh edition of the TNM system, was not associated with improvement of any reclassification measures; however, compared with the TNM eighth edition, use of the TNMB system was associated with improved overall model fit, enhanced identification of high-risk patients, and better differentiation of patients without vs patients with recurrence.

Meaning  The TNMB staging system may be associated with improved estimation of disease-free survival compared with conventional TNM staging.

Abstract

Importance  Improved staging for non–small cell lung cancer (NSCLC) represents a critical unmet need. External validations of the eighth edition of the TNM staging system have yielded disappointing results, with persistently high mortality observed in early-stage disease.

Objective  To determine whether incorporation of a molecular prognostic classifier into conventional TNM staging for NSCLC improves estimation of disease-free survival.

Design, Setting, and Participants  This cohort study was conducted at an academic, quaternary care medical center from 2012 to 2018. A consecutive series of 238 patients underwent surgical resection of stage I to IIIC nonsquamous NSCLC and had molecular prognostic classifier testing performed. Data analysis was conducted in May 2019.

Exposures  Patients were restaged according to the seventh and eighth editions of the TNM staging system and the novel TMMB staging system, which maintains the order and structure of the eighth edition of the TNM but downstages or upstages according to low or high molecular risk, respectively.

Main Outcomes and Measures  The primary outcome was disease-free survival 3 years from the time of surgical resection. Reclassification statistics were then used to evaluate performance and improvement measures of the TNM seventh and eighth editions and the TNMB staging system.

Results  Two hundred thirty-eight patients (144 [60.5%] female; median [interquartile range] age, 70 [63-75] years) were analyzed. The median (interquartile range) follow-up was 25 (14-40) months, and the disease-free survival rate was estimated to be 58.3% (95% CI, 45.7% to 69.0%). One hundred fifty-nine patients (66.8%) were reclassified by the TNMB staging system. Overall model fit remained the same for the seventh and eighth editions of the TNM staging system, whereas the R2 statistic (change from 0.22 to 0.31), concordance index (change from 0.68 to 0.73), and log-rank χ2 (change from 38 to 108) were all associated with improvements after TNMB adoption. The TNMB system, compared with the TNM eighth edition, was associated with enhanced identification of high-risk patients and better differentiation of those without recurrence from those who had recurrence (net reclassification improvement, 0.28; 95% CI, 0.08 to 0.46; P < .001), whereas the eighth edition compared with the seventh edition was not associated with improvement of this measure (net reclassification improvement, 0.02; 95% CI, –0.18 to 0.21; P = .87).

Conclusions and Relevance  The TNMB staging system was associated with improved estimation of disease-free survival compared with conventional TNM staging. Incorporation of a molecular prognostic classifier into staging for NSCLC may lead to better identification of high-risk patients.

Introduction

Improved staging for non–small cell lung cancer (NSCLC) represents a critical unmet need. The eighth edition of the TNM staging system (hereafter referred to as “eighth edition”) was recently adopted in 2018.1 Despite the advances of the molecular genetic era, the TNM system remains dependent on tumor size (T), nodal status (N), and the presence of metastasis (M). Importantly, external validations of the eighth edition have failed to show improvement in estimation of survival. In particular, risk stratification of early-stage disease continues to underperform, with 5-year overall mortality rates as high as 40% to 45% in patients with pathologic stage I disease.2,3

Because of the heterogeneity in outcomes for early-stage disease, our group and others have previously suggested that further refinement to staging may not be possible without adoption of biological predictors.4 We previously reported the largest and most comprehensive independent validation of a molecular prognostic classifier of tumor biology in NSCLC.5-7 The molecular prognostic classifier reliably identifies patients with early-stage, nonsquamous NSCLC at high risk for mortality after surgical resection.

On the basis of an international, multicenter cohort of 1373 patients from 1997 to 2007, we then developed and validated a novel staging system, TNMB (with “B” denoting “biology”), that incorporated the molecular prognostic classifier into the eighth edition.8 The TNMB staging system maintains the order and structure of the eighth edition, but downstages or upstages patients if they are found to be at low or high molecular risk, respectively (Table 1). In that retrospective analysis,8 survival estimations remained unchanged after adoption of each successive conventional staging system from the sixth to eighth editions of the TNM. In contrast, the addition of the molecular prognostic classifier within the TNMB staging system was associated with improved overall estimation by all reclassification metrics.

In this study, we present a prospective validation of the TNMB staging system in an independent cohort of 238 patients who underwent surgical resection of stage I to IIIC, nonsquamous NSCLC with molecular prognostic classifier testing at the University of California, San Francisco from 2012 to 2018. Patients were restaged according to the seventh edition of the TNM (hereafter referred to as “seventh edition”), the eighth edition, and the TMMB staging system, and reclassification statistics were then used to evaluate performance and improvement measures of each of the staging systems.

Methods
Prospective Patient Cohort

This cohort study prospectively followed 238 patients who underwent surgical resection of stage I to IIIC nonsquamous NSCLC with molecular prognostic classifier testing during the time of cancer treatment at University of California, San Francisco from 2012 to 2018. All patients were treated with curative intent per National Comprehensive Cancer Network guidelines.9 Patients were restaged using pathologic tumor size and nodal status according to the seventh edition, the eighth edition, and the TMMB staging system.8

The primary outcome was disease-free survival 3 years from the time of surgical resection. Recurrence and vital status were prospectively obtained from patient contact, medical records, and the cancer center’s database. Reclassification statistics were used to evaluate performance and improvement measures of each of the staging systems.

Patients found to have adenocarcinoma in situ or who developed second primary NSCLC were excluded from analysis. Patients who received neoadjuvant or adjuvant therapy according to National Comprehensive Cancer Network guidelines were included in this study, but those who received adjuvant therapy solely on the basis of the molecular prognostic classifier were excluded because adjuvant therapy has been shown to improve the prognosis of high-risk patients.10,11

The study was approved by the University of California, San Francisco institutional review board, and written informed consent was obtained from all study participants. Race was self-classified by study participants and is presented in this study because there are well-known differences in disease heterogeneity among races. This prospective cohort study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

TNMB Staging System

The TNMB system incorporates a molecular prognostic classifier into the eighth edition.5-8 The molecular prognostic classifier was developed in our laboratory and integrates expression levels of 11 cancer-related target genes (BAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR, and WNT3A) against a background of 3 reference genes (ESD, TBP, and YAP1). The molecular prognostic classifier provides an estimate of low, intermediate, and high risk of mortality.

Statistical Analysis

Disease-free survival was defined as the time from surgical resection to recurrence, death, or censoring. Patients were administratively censored 3 years after surgical resection, and it was assumed that censoring was independent of survival time. The Kaplan-Meier estimate and log-rank test were used to evaluate disease-free survival in Stata statistical software version 15.1 (StataCorp). Overall model performance was assessed by the concordance index (C-index) and the Royston modification of the Nagelkerke R2 statistic using stcoxgrp and str2ph, respectively, in Stata version 15.1.12

The categorical net reclassification improvement (NRI) was calculated from the survival probabilities of the ordered staging systems among those moving up and down the stage classifications.13 The NRI was calculated using survIDINRI in R statistical software version 3.4.4 (R Project for Statistical Computing).14

For all statistical tests, a prespecified 2-sided α = .05 was considered significant. All analyses were performed in Stata statistical software version 15.1 unless otherwise noted. Of note, adoption of the eighth edition refers to a comparison of the eighth vs seventh editions, and adoption of TNMB refers to a comparison of TNMB vs the eighth edition. Data analysis was conducted in May 2019.

Results

Two hundred thirty-eight patients underwent surgical resection of stage I to IIIC nonsquamous NSCLC with curative intent and underwent molecular prognostic classifier testing (Table 2). The median (interquartile range) age was 70 (63-75) years, and patients were predominantly female (144 [60.5%]), white (159 [66.8%]), and had a history of smoking (159 [66.8%]). The main histologic subtype was adenocarcinoma (231 [97.1%]), and more than 75% had stage I disease. Adjunctive treatment was given to all patients per National Comprehensive Cancer Network recommendations. Twenty-two patients (9.5%) received neoadjuvant therapy, and 50 patients (21.0%) received adjuvant therapy. The cohort had a median (interquartile range) follow-up of 25 (14-40) months, and 3-year disease-free survival rate was estimated to be 58.3% (95% CI, 45.7%-69.0%).

Because of the time frame of the study, patients from 2012 to 2017 were formally staged according to the seventh edition, and patients from 2018 were formally staged according to the eighth edition after its adoption. All patients were then restaged according to the seventh edition, eighth edition, and TNMB staging systems to allow for comparison of survival estimation (Table 3). On the basis of the molecular prognostic classifier, 124 (52.1%), 67 (28.1%), and 47 (20.0%) patients were found to have low, intermediate, and high risk of mortality, respectively. One hundred fifty-nine patients (66.8%) were reclassified by the TNMB staging system. Incorporation of the molecular prognostic classifier within the TNMB staging system led to both upward and downward classification of patients.

The TNMB staging system was associated with improved overall model fit compared with conventional staging (Table 4). As opposed to a minimal change in the modified Nagelkerke R2 statistic with adoption of the eighth edition (seventh edition R2 = 0.21; eighth edition R2 = 0.22), TNMB staging was associated with superior model fit demonstrated by an increase of the R2 to 0.31. Similarly, the log-rank χ2 statistic for trend by stage decreased after adoption of the eighth edition (seventh edition χ2 = 85; eighth edition χ2 = 38), but increased after adoption of the TNMB staging system (χ2 = 108). Moreover, the C-index, or area under the receiver operating characteristic curve, remained static after adoption of the eighth edition (seventh edition C-index = 0.68; eighth edition C-index = 0.68), but increased to 0.73 in TNMB staging. These findings illustrate that TNMB is associated with an improved overall model fit as well as improved discrimination compared with conventional staging.

The NRI was also assessed from adoption of each of the staging systems. Compared with the seventh edition, staging with the eighth edition was not associated with a change in the NRI (NRI, 0.02; 95% CI –0.18 to 0.21; P = .87). In contrast, adoption of TNMB staging compared with the eighth edition showed a significant increase in the NRI to 0.28 (95% CI, 0.08 to 0.46; P < .001). This finding demonstrates that TNMB staging was associated with improved identification of high-risk patients and differentiation of those with recurrence from those without recurrence.

Discussion

Heterogeneity in outcomes for early-stage disease in NSCLC persists despite ongoing revisions to traditional TNM staging. Many patients who were thought to have early-stage disease never received adjuvant therapy but later proved to have metastasis. Although the recently adopted eighth edition staging system was developed on the largest and most comprehensive data set to date, external validations have failed to show improvement in survival estimation.2,3 This suggests that further refinement to staging may not be possible with continued use of traditional clinicopathologic characteristics.

We previously developed and validated a novel staging system, TNMB, from a retrospective cohort that incorporated a robust molecular prognostic classifier of tumor biology into the eighth edition.8 The TNMB staging system maintains the order and priority of conventional staging, but downstages or upstages patients found to be at low or high molecular risk, respectively. The TNMB staging system was used because it is simple, transparent, and clinically intuitive. The incorporation of a fourth category representing biology is easy for clinicians to apply, and it allows for flexibility to test and incorporate future refinements from novel characterizations of tumor biology.

In this study, the TNMB staging system was prospectively validated on an independent cohort of 238 patients who underwent surgical resection of NSCLC with curative intent at University of California, San Francisco from 2012 to 2018. Incorporation of a molecular prognostic classifier by the TNMB staging system was associated with improved survival estimation compared with conventional staging. Overall model fit remained the same after adoption of recent revisions to conventional staging, whereas the R2 statistic, C-index, and log-rank χ2 all improved after TNMB adoption. Moreover, the TNMB system was associated with enhanced NRI, whereas there was no change in this measure following adoption of the eighth edition. Importantly, the TNMB system was associated with improved identification of high-risk patients and was better able to differentiate between patients with or without recurrence compared with conventional staging.

Limitations

This study has several limitations. First, because of the sample size of patients in this study, we were not able to provide data regarding the agreement in observed and estimated risks of recurrence or death between staging systems, nor were we able to assess progression-free survival by Kaplan-Meier analysis because of the large number of risk categories in the eighth edition and TNMB staging systems. Second, because ours is a quaternary care center, follow-up data from patients subsequently receiving care locally could have been inaccurate despite our use of multiple redundant methods to obtain follow-up data. Third, some of our patients who were staged using the seventh edition at the time of surgical resection did not have information regarding the size of the lepidic component or invasion of surrounding structures. Because this information is used in the eighth edition, a small number of patients may, therefore, have been staged inaccurately.

Conclusions

TNMB staging was associated with improved risk discrimination in a prospective cohort of patients with lung cancer. The addition of information from a molecular prognostic classifier resulted in improved model fit and better estimations of disease-free survival compared with conventional TNM staging. The molecular classifier and TNMB staging system used in this study may serve as the basis for the modification of subsequent staging systems for NSCLC and may enable more effective application of adjuvant therapy in patients with early-stage disease.

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Article Information

Accepted for Publication: October 10, 2019.

Published: December 6, 2019. doi:10.1001/jamanetworkopen.2019.17062

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2019 Haro GJ et al. JAMA Network Open.

Corresponding Author: Johannes R. Kratz, MD, Division of Adult Cardiothoracic Surgery, University of California, San Francisco, 500 Parnassus Ave, Ste MUW-405, San Francisco, CA 94143 (johannes.kratz@ucsf.edu).

Author Contributions: Dr Haro had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Haro, Mann, Kratz.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Haro, Sheu, Mann.

Critical revision of the manuscript for important intellectual content: Haro, Cook, Woodard, Mann, Kratz.

Statistical analysis: Haro, Cook, Mann.

Obtained funding: Mann, Kratz.

Administrative, technical, or material support: Sheu, Mann, Kratz.

Supervision: Woodard, Mann, Kratz.

Conflict of Interest Disclosures: Drs Kratz and Mann reported having a consulting relationship with and receiving personal fees and nonfinancial support from Razor Genomics Inc, the company that established a Clinical Laboratory Improvement Amendments–certified laboratory and developed the molecular assay based on University of California, San Francisco technology, and being inventors of related technology owned by the University of California, for which the University submitted patent applications that have been licensed to Razor Genomics Inc. Dr Mann reported having patent 9,476,098 issued and licensed and with royalties paid. Dr Kratz also had a patent to the Prognostic Assay For Early-Stage Lung Cancer issued and with royalties paid. No other disclosures were reported.

Additional Contributions: Shayne Cordoza, RN, and Ann Griffin, PhD (both from University of California, San Francisco), provided assistance in data abstraction and showed dedication to our patients. David Jablons, MD (University of California, San Francisco), provided input on the design and implementation of this study. Ms Cordoza and Drs Griffin and Jablons were not compensated for their contributions.

References
1.
Detterbeck  FC, Boffa  DJ, Kim  AW, Tanoue  LT.  The eighth edition lung cancer stage classification.  Chest. 2017;151(1):193-203. doi:10.1016/j.chest.2016.10.010PubMedGoogle ScholarCrossref
2.
Chansky  K, Detterbeck  FC, Nicholson  AG,  et al.  The IASLC Lung Cancer Staging Project: external validation of the revision of the TNM stage groupings in the eighth edition of the TNM Classification of Lung Cancer.  J Thorac Oncol. 2017;12(7):1109-1121. doi:10.1016/j.jtho.2017.04.011PubMedGoogle ScholarCrossref
3.
Yang  L, Wang  S, Zhou  Y,  et al.  Evaluation of the 7th and 8th editions of the AJCC/UICC TNM staging systems for lung cancer in a large North American cohort.  Oncotarget. 2017;8(40):66784-66795. doi:10.18632/oncotarget.18158PubMedGoogle Scholar
4.
D’Amico  TA.  Molecular biologic staging of lung cancer.  Ann Thorac Surg. 2008;85(2):S737-S742. doi:10.1016/j.athoracsur.2007.11.047PubMedGoogle ScholarCrossref
5.
Kratz  JR, He  J, Van Den Eeden  SK,  et al.  A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies.  Lancet. 2012;379(9818):823-832. doi:10.1016/S0140-6736(11)61941-7PubMedGoogle ScholarCrossref
6.
Kratz  JR, Van den Eeden  SK, He  J, Jablons  DM, Mann  MJ.  A prognostic assay to identify patients at high risk of mortality despite small, node-negative lung tumors.  JAMA. 2012;308(16):1629-1631. doi:10.1001/jama.2012.13551PubMedGoogle ScholarCrossref
7.
Kratz  JR, Tham  PT, Mulvihill  MS,  et al.  Analytical validation of a practical molecular assay prognostic of survival in nonsquamous non-small cell lung cancer.  Diagn Mol Pathol. 2013;22(2):65-69. doi:10.1097/PDM.0b013e318273fb61PubMedGoogle ScholarCrossref
8.
Kratz  JR, Haro  GJ, Cook  NR,  et al.  Incorporation of a molecular prognostic classifier improves conventional non-small cell lung cancer staging.  J Thorac Oncol. 2019;14(7):1223-1232. doi:10.1016/j.jtho.2019.03.015PubMedGoogle ScholarCrossref
9.
Ettinger  DS, Wood  DE, Aisner  DL,  et al.  Non-small cell lung cancer, version 5.2017, NCCN clinical practice guidelines in oncology.  J Natl Compr Canc Netw. 2017;15(4):504-535. doi:10.6004/jnccn.2017.0050PubMedGoogle ScholarCrossref
10.
Arriagada  R, Dunant  A, Pignon  JP,  et al.  Long-term results of the international adjuvant lung cancer trial evaluating adjuvant cisplatin-based chemotherapy in resected lung cancer.  J Clin Oncol. 2010;28(1):35-42. doi:10.1200/JCO.2009.23.2272PubMedGoogle ScholarCrossref
11.
Pignon  JP, Tribodet  H, Scagliotti  GV,  et al; LACE Collaborative Group.  Lung adjuvant cisplatin evaluation: a pooled analysis by the LACE Collaborative Group.  J Clin Oncol. 2008;26(21):3552-3559. doi:10.1200/JCO.2007.13.9030PubMedGoogle ScholarCrossref
12.
Royston  P.  Explained variation for survival models.  Stata J. 2006;6:83-96. doi:10.1177/1536867X0600600105Google ScholarCrossref
13.
Pencina  MJ, D’Agostino  RB  Sr, Steyerberg  EW.  Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.  Stat Med. 2011;30(1):11-21. doi:10.1002/sim.4085PubMedGoogle ScholarCrossref
14.
Uno  H, Cai  T. survIDINRI package version 1.1-1. R Core Team. http://www.R-project.org/. Published 2019. Accessed May 1, 2019.
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