Abstract：
BACKGROUND: Using 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.
METHODS: To 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.
RESULTS: The 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.
CONCLUSIONS: We 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.
METHODS: To 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.
RESULTS: The 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.
CONCLUSIONS: We 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.
摘要：
背景：使用真实的工作示例，我们提供了线性和逻辑回归方面的策略和挑战,以展示肿瘤外科研究中回归建模的最佳实践指南和陷阱.
方法：为了展示我们的最佳实践，我们回顾了2019年至2021年间接受组织扩张器乳房再造的患者.我们通过线性回归模型评估了影响BREAST-Q胸部健康（PWB-C）评分的预测因素，并通过逻辑回归模型评估了总体并发症和旋转不良。评估模型拟合和性能。
结果：1986例患者被纳入分析。在线性回归中，年龄[β=0.18（95%CI：0.09，0.28）；p<0.001]，单身婚姻状况[β=2.6（0.31，5.0）；p=0.026]，胸前囊夹层[β=4.6（2.7，6.5）；p<0.001]在2周时与PWB-C显着相关。对于逻辑回归，BMI[OR=1.06（95%CI：1.04，1.08）；p<0.001]，年龄[OR=1.02（1.01，1.03）；p=0.002]，双侧重建[OR=1.39（1.09，1.79）；p=0.009]，和胸前夹层[OR=1.53(1.21,1.94);p<0.001]与并发症的可能性增加相关。
结论：我们为回归技术在肿瘤外科研究中的成功应用提供了重点指导。我们鼓励研究人员选择具有临床判断的变量，确认适当的模型拟合，并在研究中利用回归模型时考虑临床解释的合理性。
方法：为了展示我们的最佳实践，我们回顾了2019年至2021年间接受组织扩张器乳房再造的患者.我们通过线性回归模型评估了影响BREAST-Q胸部健康（PWB-C）评分的预测因素，并通过逻辑回归模型评估了总体并发症和旋转不良。评估模型拟合和性能。
结果：1986例患者被纳入分析。在线性回归中，年龄[β=0.18（95%CI：0.09，0.28）；p<0.001]，单身婚姻状况[β=2.6（0.31，5.0）；p=0.026]，胸前囊夹层[β=4.6（2.7，6.5）；p<0.001]在2周时与PWB-C显着相关。对于逻辑回归，BMI[OR=1.06（95%CI：1.04，1.08）；p<0.001]，年龄[OR=1.02（1.01，1.03）；p=0.002]，双侧重建[OR=1.39（1.09，1.79）；p=0.009]，和胸前夹层[OR=1.53(1.21,1.94);p<0.001]与并发症的可能性增加相关。
结论：我们为回归技术在肿瘤外科研究中的成功应用提供了重点指导。我们鼓励研究人员选择具有临床判断的变量，确认适当的模型拟合，并在研究中利用回归模型时考虑临床解释的合理性。
