The DOI matches its OpenAlex record; 3 of the 3 references it cites were found in a registry or an index.
A prognostic model for ovarian cancer
Open accessAbstract
About 6000 women in the United Kingdom develop ovarian cancer each year and about two-thirds of the women will die from the disease. Establishing the prognosis of a woman with ovarian cancer is an important part of her evaluation and treatment. Prognostic models and indices in ovarian cancer should be developed using large databases and, ideally, with complete information on both prognostic indicators and long-term outcome. We developed a prognostic model using Cox regression and multiple imputation from 1189 primary cases of epithelial ovarian cancer (with median follow-up of 4.6 years). We found that the significant (P< or = 0.05) prognostic factors for overall survival were age at diagnosis, FIGO stage, grade of tumour, histology (mixed mesodermal, clear cell and endometrioid versus serous papillary), the presence or absence of ascites, albumin, alkaline phosphatase, performance status on the ZUBROD-ECOG-WHO scale, and debulking of the tumour. This model is consistent with other models in the ovarian cancer literature; it has better predictive ability and, after simplification and validation, could be used in clinical practice.
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Cites
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Proportional hazards tests and diagnostics based on weighted residuals
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“Information about recurrence was not felt to be robust so recurrence-free survival was not analysed.”
BackgroundMethodA critical look at methods for handling missing covariates in epidemiologic regression analyses.
Greenland, S., & Finkle, W. D. (1995) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a117592 · Cited by 891 · cited 1×
“This practice can lead to unrealistic hazard ratios and underestimation of standard errors (Greenland and Finkle 1995), but may be better than excluding whole cases.”
Background
Cited by
mice: Multivariate Imputation by Chained Equations in R
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870
“…et al. 2008), cardiovascular system (Ambler et al. 2005; van Buuren et al. 2006a; Chase et al. 2008; Byrne et al. 2009; Klein et al. 2009), cancer (Clark et al. 2001, 2003; Clark and Altman 2003; Royston et al. 2004; Barosi et al. 2007; Fernandes et al. 2008; Sharma et al. 2008; McCaul et al.…”
BackgroundGupta, D., & Lis, C. G. (2010) · Nutrition Journal · doi:10.1186/1475-2891-9-69 · Cited by 1,485
Multiple Imputation by Chained Equations (MICE): Implementation in Stata
Royston, P., & White, I. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i04 · Cited by 1,248
Marshall, A., Altman, D. G., Holder, R. L., & Royston, P. (2009) · BMC Medical Research Methodology · doi:10.1186/1471-2288-9-57 · Cited by 874
Self-citationSurvival analysis part II: multivariate data analysis – an introduction to concepts and methods
Bradburn, M. J., Clark, T. G., Love, S. B., & Altman, D. G. (2003) · British Journal of Cancer · doi:10.1038/sj.bjc.6601119 · Cited by 741
“In a previous analysis that involved some of the patients in the present data, Clark et al (2001) produced a nomogram to summarise the impact of these and other covariates, and thus allows the reader to predict the median survival and the 2- and 5year survival probabilities for patients with given prognostic informati…”
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Metadata
- Authors
- Clark, T. G., Stewart, M., Altman, D. G., Gabra, H., & Smyth, J. F.
- Venue
- British Journal of Cancer
- Publisher
- Springer Nature
- Published
- 2001-09-01
- Volume
- 85
- Issue
- 7
- Pages
- 944-952
- ISSN
- 0007-0920 (ISSN-L)
- OpenAlex
- W2007830228
- Cited by
- 192
- References
- 3
- FWCI
- 8.75
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Clark, T. G., Stewart, M., Altman, D. G., Gabra, H., & Smyth, J. F. (2001). A prognostic model for ovarian cancer. British Journal of Cancer. https://doi.org/10.1038/sj.bjc.6692030
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
