Logistic Regression in Clinical Studies

Zabor, Emily C.; Reddy, Chandana A.; Tendulkar, Rahul D.; Patil, Sujata

doi:10.1016/j.ijrobp.2021.08.007

Home/Archive/DOE OSTI

DOE OSTI · 23200907

Logistic Regression in Clinical Studies

Zabor, Emily C.·Reddy, Chandana A.·Tendulkar, Rahul D.·Patil, Sujata

View original source ↗Export metadata ↓

Abstract

• 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.

Explore related subjects

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

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Zabor, Emily C., Reddy, Chandana A., Tendulkar, Rahul D., Patil, Sujata. 2022-02-15. Logistic Regression in Clinical Studies. https://doi.org/10.1016/j.ijrobp.2021.08.007

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Generator, Chelator, and Kit Optimization (GeCKO): Bringing 44TI and 43/44gSc into Radiopharmacy Practice

To inform development of separation and generator technology relevant to the accelerator (and generator) production of 43Sc, 44Sc (from 44Ti), 45Ti, and 47Sc with fundamental, experimental exploration of the speciation and coordination chemistry of Ti and Sc.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2025

Cross sections of 147–149 Sm( 6 Li,x) reactions for the production of 149 Tb for targeted alpha therapy

Terbium-149g (t 1/2 = 4.12 h) is of particular interest for targeted alpha therapy cancer treatment due to its ability to decay via both alpha and positron emission, making it a potential theranostic nuclide. Due to many challenges facing its production, there are limited facilities worldwide that have demonstrated the ability to produce this nuclide in quantities sufficient for medical research. Since the Cyclotron Institute at Texas A&M University is a specialized accelerator facility capable of accelerating a wide variety of ions, we are investigating production pathway options. One of the major challenges facing its production is the known co-production of the excited isomeric state, 149m Tb (t 1/2 = 4.1 min). However, this state does not decay to the ground state of 149g Tb, negating any potential contribution to its yield. Due to its short-half life, the cross section for the population of this state has never been measured. After calculating several potential reaction yields using predictive models, the reactions of 147–149 Sm( 6 Li,xn) 149 Tb were identified as candidates. Lithium-6 beams of varied energies between 45-65 MeV were impinged on enriched 147 Sm, 148 Sm, and 149 Sm targets at the Cyclotron Institute at Texas A&M University, and the reaction products were measured immediately following irradiation using high-purity germanium detectors, enabling detection of both 149m Tb and 149g Tb. Cross sections for all nuclides produced in sufficient activity in these reactions were also measured and reported here. We conclude that the population of 149m Tb is much preferred over population of the ground state for these 6 Li-induced reactions, and it is necessary to explore other options for 149g Tb production.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2025

Source function from two-particle correlation function through entropy-regularized Richardson-Lucy deblurring

Source functions are obtained from p – p and d – α correlation functions by applying the Richardson-Lucy (RL) deblurring to the Koonin-Pratt (KP) equation. To prevent fitting of noise in the correlation function, total-variation (TV) regularization is employed that has been effective in ordinary image restoration. TV alone cannot ensure normalization of the source functions. To ensure the latter, we propose a maximum-entropy regularized RL algorithm (MEM-RL). We outline the MEM-RL formalism and optimization strategy for the KP equation, demonstrating its effectiveness on both simulated and experimental data, including the p – p and d – α correlation functions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗