Literature DB >> 2288553

Improved methodology for analyzing local and distant recurrence.

R Gelman1, R Gelber, I C Henderson, C N Coleman, J R Harris.   

Abstract

Studies of radiation therapy and/or surgery in the treatment of cancer frequently use actuarial methods to estimate curves of time to local failure and compare two such curves with statistical methods originally developed for survival data. In such analyses, patients who fail first in distant sites or die before local failure are considered censored for time to local failure. While the arithmetic of these calculations is usually correct, the interpretation of the results is almost universally incorrect. For example, an actuarial Kaplan-Meier curve of time to breast recurrence after breast conserving treatment consistently overestimates the percentage of patients who would benefit from a subsequent mastectomy. Actuarial methods require the assumption that time to local failure and time to distant failure are statistically independent. For most human malignancies this is not a reasonable assumption, since there are always some patient subgroups at high risk of both local and distant failure and some patient subgroups at low risk for either type of failure. Without the assumption of independence, the time to local failure distribution is not well defined. The basic problem is that estimating time to local failure falls into the category of analyzing "competing risks," since the various causes of failure are competing for the same patient. For this reason, the effects of a particular treatment on local failure cannot be assessed separately from its effects on distant failure. This report explains the concepts of statistical independence, nonidentifiability, and competing risks and illustrates the pitfalls of using actuarial methods to assess local tumor control.(ABSTRACT TRUNCATED AT 250 WORDS)

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Year:  1990        PMID: 2288553     DOI: 10.1200/JCO.1990.8.3.548

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  18 in total

1.  Relationship between regulatory status, quality of care, and three-year mortality in Canadian residential care facilities: a longitudinal study.

Authors:  Gina Bravo; Marie-France Dubois; Philippe De Wals; Réjean Hébert; Lise Messier
Journal:  Health Serv Res       Date:  2002-10       Impact factor: 3.402

2.  The use and interpretation of competing risks regression models.

Authors:  James J Dignam; Qiang Zhang; Masha Kocherginsky
Journal:  Clin Cancer Res       Date:  2012-01-26       Impact factor: 12.531

3.  Prognostic factors and natural history in lymph node-negative breast cancer patients.

Authors:  R Arriagada; L E Rutqvist; L Skoog; H Johansson; A Kramar
Journal:  Breast Cancer Res Treat       Date:  1992       Impact factor: 4.872

4.  Estimating subject-specific dependent competing risk profile with censored event time observations.

Authors:  Yi Li; Lu Tian; Lee-Jen Wei
Journal:  Biometrics       Date:  2010-07-09       Impact factor: 2.571

5.  Ovarian irradiation and prednisone following surgery and radiotherapy for carcinoma of the breast.

Authors:  J W Meakin; J L Hayward; T Panzarella; W E Allt; F A Beale; R D Bulbrook; R S Bush; R M Clark; P J Fitzpatrick; N V Hawkins; R D Jenkin; J F Pringle; W D Rider
Journal:  Breast Cancer Res Treat       Date:  1996       Impact factor: 4.872

6.  Aggressive therapy for locoregional recurrence after mastectomy in stage II and III breast cancer patients.

Authors:  E M Mora; S E Singletary; A U Buzdar; D A Johnston
Journal:  Ann Surg Oncol       Date:  1996-03       Impact factor: 5.344

7.  Competing causes of death from a randomized trial of extended adjuvant endocrine therapy for breast cancer.

Authors:  Judith-Anne W Chapman; Daniel Meng; Lois Shepherd; Wendy Parulekar; James N Ingle; Hyman B Muss; Michael Palmer; Changhong Yu; Paul E Goss
Journal:  J Natl Cancer Inst       Date:  2008-02-12       Impact factor: 13.506

8.  Evaluation of preoperative radiation therapy in operable colorectal cancer.

Authors:  W T Sause; T F Pajak; R D Noyes; R Dobelbower; J Fischbach; S Doggett; M Mohiuddin
Journal:  Ann Surg       Date:  1994-11       Impact factor: 12.969

9.  Benefits and limitations of Kaplan-Meier calculations of survival chance in cancer surgery.

Authors:  Elfriede Bollschweiler
Journal:  Langenbecks Arch Surg       Date:  2003-08-14       Impact factor: 3.445

10.  Choice and interpretation of statistical tests used when competing risks are present.

Authors:  James J Dignam; Maria N Kocherginsky
Journal:  J Clin Oncol       Date:  2008-08-20       Impact factor: 44.544

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