Further controlled randomized studies with a sufficient number of patients are required. It was conducted in a small sample size which reduces the level of evidence. We cannot confirm a relationship between bevacizumab C trough, SS and clinical benefit but this is the first real-world study trying to show a relationship between bevacizumab C trough, SS and disease control in mCRC. The Cox regression showed association between higher median C trough, SS with better OS (HR 0.86, 95% CI: 0.73–1.01, p = 0.060), but not with PFS. The GEE model did not suggest any positive association between bevacizumab C trough, SS and clinical benefit (OR 0.99, 95% CI: 0.98–1.02, p = 0.863). groups (fe model) is different from the effect of v & k between groups. Data included 50 bevacizumab C trough, SS from 27 patients. Basic regression in Stata (see do file ols Both test the null hypothesis that. To test the association between C trough, SS in each patient with overall survival (OS) or progression-free survival (PFS), Cox proportional hazard models were developed. Generalized estimating equations (GEE) analysis was performed. C trough, SS were drawn, coinciding with the radiological evaluation of the response (progression or clinical benefit). This prospective observational study in patients with metastatic colorectal cancer (mCRC) aims to evaluate, in a real-life setting, the relationship between bevacizumab through concentrations at steady state (C trough, SS) and disease control. 3) for an introduction to linear regression using Stata.Dohoo, Martin, and Stryhn(2012,2010) discuss linear regression using examples from epidemiology, and Stata datasets and do-les used in the text are available. The bysort command is very useful for deriving by-group summary. regress Linear regression 5 SeeHamilton(2013, chap. Limited literature is available for bevacizumab exposure-response relationship and there is not a concentration threshold associated with an optimal disease control. Regression analysis would likely produce coefficient estimates for a regression models.
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