Logistic Regression in Clinical Studies

Journal Article · Tue Feb 15 04:00:00 EST 2022 · International Journal of Radiation Oncology, Biology and Physics

DOI:https://doi.org/10.1016/J.IJROBP.2021.08.007· OSTI ID:23200907

Zabor, Emily C. ^[1]; Reddy, Chandana A.; Tendulkar, Rahul D. ^[2]; Patil, Sujata ^[1]

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1. Cleveland Clinic Taussig Cancer Institute, Cleveland (United States)
2. Department of Radiation Oncology, Cleveland Clinic Taussig Cancer Institute, Cleveland (United States)

• A logistic regression model is used when the outcome of interest is binary. The term “logistic” refers to the underlying “logit” (log odds) function that is used to model the binary outcome. • Odds ratios are produced from a logistic regression model and have a useful interpretation. • Tips, tricks and concepts used to fit logistic regression models are similar to those used in linear regression models. • Modeling building that is knowledge-based rather than automatic is preferred in most applications of logistic regression. • A logistic regression model that is overparameterized (ie, too many variables for too few events) can result in odds ratios that are implausibly large and confidence intervals that are wide and uninterpretable. These types of “overfitted” models should be avoided. • Logistic regression models can be fit using most standard statistical software.

• OSTI ID: 23200907
• Journal Information: International Journal of Radiation Oncology, Biology and Physics, Journal Name: International Journal of Radiation Oncology, Biology and Physics Journal Issue: 2 Vol. 112; ISSN IOBPD3; ISSN 0360-3016
• Country of Publication: United States
• Language: English

Related Subjects

62 RADIOLOGY AND NUCLEAR MEDICINE
COMPUTER CODES
NEOPLASMS
ORGANIZATIONAL MODELS
RADIOTHERAPY
SIMULATION