Abstract
Background There have been many attempts to identify variables associated with ventral hernia recurrence; however, it is unclear which statistical modeling approach results in models with greatest internal and external validity. We aim to assess the predictive accuracy of models developed using five common variable selection strategies to determine variables associated with hernia recurrence. Methods Two multicenter ventral hernia databases were used. Database 1 was randomly split into “development” and “internal validation” cohorts. Database 2 was designated “external validation”. The dependent variable for model development was hernia recurrence. Five variable selection strategies were used: (1) “clinical”—variables considered clinically relevant, (2) “selective stepwise”—all variables with a P value <0.20 were assessed in a step-backward model, (3) “liberal stepwise”—all variables were included and step-backward regression was performed, (4) “restrictive internal resampling,” and (5) “liberal internal resampling.” Variables were included with P < 0.05 for the Restrictive model and P < 0.10 for the Liberal model. A time-to-event analysis using Cox regression was performed using these strategies. The predictive accuracy of the developed models was tested on the internal and external validation cohorts using Harrell's C-statistic where C > 0.70 was considered “reasonable”. Results The recurrence rate was 32.9% (n = 173/526; median/range follow-up, 20/1-58 mo) for the development cohort, 36.0% (n = 95/264, median/range follow-up 20/1-61 mo) for the internal validation cohort, and 12.7% (n = 155/1224, median/range follow-up 9/1-50 mo) for the external validation cohort. Internal validation demonstrated reasonable predictive accuracy (C-statistics = 0.772, 0.760, 0.767, 0.757, 0.763), while on external validation, predictive accuracy dipped precipitously (C-statistic = 0.561, 0.557, 0.562, 0.553, 0.560). Conclusions Predictive accuracy was equally adequate on internal validation among models; however, on external validation, all five models failed to demonstrate utility. Future studies should report multiple variable selection techniques and demonstrate predictive accuracy on external data sets for model validation.
Original language | English |
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Pages (from-to) | 159-167 |
Number of pages | 9 |
Journal | Journal of Surgical Research |
Volume | 206 |
Issue number | 1 |
DOIs | |
State | Published - Nov 1 2016 |
Bibliographical note
Publisher Copyright:© 2016 Elsevier Inc.
Funding
M.K.L.: This work was supported by the Center for Clinical and Translational Sciences, which is funded by National Institutes of Health Clinical and Translational Award UL1 TR000371 and KL2 TR000370 from the National Center for Advancing Translational Sciences. The National Center for Research Resources or the National Institutes of Health was not responsible for the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.
Funders | Funder number |
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National Institutes of Health (NIH) | UL1 TR000371 |
National Center for Advancing Translational Sciences (NCATS) | KL2TR000370 |
Center for Clinical and Translational Sciences, University of Texas Health Science Center at Houston |
Keywords
- Bootstrapping
- Hernia recurrence
- Multivariate
- Predictive model
- Regression
ASJC Scopus subject areas
- Surgery