Identifying Modifiable and Non-modifiable Risk Factors of Readmission and Short-Term Mortality in Chondrosarcoma: A National Cancer Database Study

Evans, Daniel R.; Lazarides, Alexander L.; Cullen, Mark M.; Somarelli, Jason A.; Blazer, Dan G.; Visguass, Julia D; Brigman, Brian E.; Eward, William C.

doi:10.1245/s10434-021-10802-8

Cited by 2 publications

(2 citation statements)

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“…Linear regression is used when the outcome of interest is continuous, whereas logistic regression is used for binary outcomes 2 . For example, in surgical oncology research, linear regression has been used to analyze patient‐reported outcomes scores (i.e, BREAST‐Q) 5 and healthcare costs, 6,7 while logistic regression has been used to analyze risk factors for complications, readmission, or reoperation 8,9 . Regression models that examine the relationship between an outcome and a single covariate are described as univariable.…”

Section: Introductionmentioning

confidence: 99%

“…2 For example, in surgical oncology research, linear regression has been used to analyze patient-reported outcomes scores (i.e, BREAST-Q) 5 and healthcare costs, 6,7 while logistic regression has been used to analyze risk factors for complications, readmission, or reoperation. 8,9 Regression models that examine the relationship between an outcome and a single covariate are described as univariable. A multivariable regression model is used to measure the relationship between multiple covariates and a single outcome and helps to control for confounding variables.…”

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confidence: 99%

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Methods in regression analysis in surgical oncology research‐best practice guidelines

Boe,

Vingan,

Kim

et al. 2023

Journal of Surgical Oncology

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BackgroundUsing real working examples, we provide strategies and address challenges in linear and logistic regression to demonstrate best practice guidelines and pitfalls of regression modeling in surgical oncology research.MethodsTo demonstrate our best practices, we reviewed patients who underwent tissue expander breast reconstruction between 2019 and 2021. We assessed predictive factors that affect BREAST‐Q Physical Well‐Being of the Chest (PWB‐C) scores at 2 weeks with linear regression modeling and overall complications and malrotation with logistic regression modeling. Model fit and performance were assessed.ResultsThe 1986 patients were included in the analysis. In linear regression, age [β = 0.18 (95% CI: 0.09, 0.28); p < 0.001], single marital status [β = 2.6 (0.31, 5.0); p = 0.026], and prepectoral pocket dissection [β = 4.6 (2.7, 6.5); p < 0.001] were significantly associated with PWB‐C at 2 weeks. For logistic regression, BMI [OR = 1.06 (95% CI: 1.04, 1.08); p < 0.001], age [OR = 1.02 (1.01, 1.03); p = 0.002], bilateral reconstruction [OR = 1.39 (1.09, 1.79); p = 0.009], and prepectoral dissection [OR = 1.53 (1.21, 1.94); p < 0.001] were associated with increased likelihood of a complication.ConclusionWe provide focused directives for successful application of regression techniques in surgical oncology research. We encourage researchers to select variables with clinical judgment, confirm appropriate model fitting, and consider clinical plausibility for interpretation when utilizing regression models in their research.

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