Cox, D. R.; Oakes, D.: Analysis of Survival Data. Chapman and Hall, London – New York 1984, 201 S., £ 12,–

Enderlein, G.

doi:10.1002/bimj.4710290119

Cited by 17 publications

(12 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To identify factors associated with self-reported lymphedema, we fit Cox proportional hazards models and obtained estimates of the hazard ratio (HR) and its 95% confidence interval (CI) using time since diagnosis as the time scale [ 39 ]. Women were followed from date of breast cancer diagnosis to date of first lymphedema occurrence or until date of last follow-up.…”

Section: Methodsmentioning

confidence: 99%

Risk factors for self-reported arm lymphedema among female breast cancer survivors: a prospective cohort study

Togawa

Ma²,

Sullivan‐Halley³

et al. 2014

Breast Cancer Res

View full text Add to dashboard Cite

IntroductionLymphedema is a potentially debilitating condition that occurs among breast cancer survivors. This study examines the incidence of self-reported lymphedema, timing of lymphedema onset, and associations between sociodemographic, clinical and lifestyle factors and lymphedema risk across racial-ethnic groups using data from a multicenter, multiethnic prospective cohort study of breast cancer survivors, the Health, Eating, Activity and Lifestyle Study.MethodsA total of 666 women diagnosed with breast cancer staged as in situ, localized or regional disease at ages 35 to 64 years were recruited through the Surveillance, Epidemiology, and End Results registries in New Mexico (non-Hispanic white and Hispanic white), Los Angeles County (black), and Western Washington (non-Hispanic white) and followed for a median of 10.2 years. We evaluated sociodemographic factors, breast cancer- and treatment-related factors, comorbidities, body mass index (BMI), hormonal factors, and lifestyle factors in relation to self-reported lymphedema by fitting Cox proportional hazards models, estimating hazard ratios (HR) and 95% confidence intervals (CI).ResultsOver the follow-up period, 190 women (29%) reported lymphedema. The median time from breast cancer diagnosis to onset of lymphedema was 10.5 months (range: 0.5 to 134.9 months). Factors independently associated with lymphedema were total/modified radical mastectomy (versus partial/less than total mastectomy; HR = 1.37, 95% CI: 1.01 to 1.85), chemotherapy (versus no chemotherapy; HR = 1.48, 95% CI: 1.09 to 2.02), no lymph nodes removed (versus ≥10 lymph nodes removed; HR = 0.17, 95% CI: 0.08 to 0.33), pre-diagnostic BMI ≥30 kg/m2 (versus BMI <25 kg/m2; HR = 1.59, 95% CI: 1.09 to 2.31), and hypertension (versus no hypertension; HR = 1.49, 95% CI: 1.06 to 2.10). After adjusting for demographics and breast cancer- and treatment-related factors, no significant difference in lymphedema risk was observed across racial/ethnic groups. Analyses stratified by race/ethnicity showed that hypertension and chemotherapy were lymphedema risk factors only for black women.ConclusionsBreast cancer patients who have undergone extensive surgery or extensive lymph node dissection, or who have a higher BMI should be closely monitored for detection and treatment of lymphedema. Further studies are needed to understand the roles of chemotherapy and hypertension in the development of lymphedema.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-014-0414-x) contains supplementary material, which is available to authorized users.

show abstract

Section: Methodsmentioning

confidence: 99%

Risk factors for self-reported arm lymphedema among female breast cancer survivors: a prospective cohort study

Togawa

Ma²,

Sullivan‐Halley³

et al. 2014

Breast Cancer Res

View full text Add to dashboard Cite

show abstract

“…Differences in treatment dose (i.e., total sessions, treatment duration) between types of care were also examined with t-tests. Survival analyses were used to compare the two samples on time to achieve 95%mBMI (25,26). Individuals who were not weight restored by end-of-treatment (n = 73, 66.4%) were treated as "censored" observations, indicating that treatment response did not occur prior to termination of the measurement period.…”

Section: Statistical Analysesmentioning

confidence: 99%

Delivery of Family-Based Treatment for Adolescent Anorexia Nervosa in a Public Health Care Setting: Research Versus Non-Research Specialty Care

et al. 2020

View full text Add to dashboard Cite

Comparing evidence-based psychotherapy (EBP) to usual care typically demonstrates the superiority of EBPs, although this has not been studied for eating disorders EBPs such as family-based treatment (FBT). The current study set out to examine weight outcomes for adolescents with anorexia nervosa who received FBT through a randomized clinical research trial (RCT, n = 54) or non-research specialty care (n = 56) at the same specialist pediatric eating disorder service. Weight was recorded throughout outpatient treatment (up to 18 sessions over 6 months), as well as at 6-and 12-month follow-up. Survival curves were used to examine time to weight restoration [greater than 95% median body mass index (mBMI)] as predicted by type of care (RCT vs. non-research specialty care), baseline clinical and demographic characteristics, and their potential interaction. Results did not indicate a significant main effect for type of care, but there was a significant effect for baseline weight (p = .03), such that weight restoration was achieved faster across both treatment types for those with a higher initial %mBMI. These data suggest that weight restoration achieved in non-research specialty care FBT was largely similar to that achieved in a controlled research trial.

show abstract

“…While the K-M curves give us a visual representation of the survival of projects with varying attributes over time, the Cox Proportional-Hazards model allows us to fit a regression model to investigate the association between the health of projects and key project attributes. There are various parametric models available for modeling the relationship of duration with other attributes but the Cox Proportional-Hazards model allows estimation of effect parameters without consideration of the hazard function, which describes how the risk of event occurring changes over time [6]. We apply both the K-M estimator and the Cox Proportional-Hazards model on the data to estimate the effects of attributes on the overall health of an open source project.…”

Section: Methodsmentioning

confidence: 99%

Cheating Death

Ali

Parlett-Pelleriti

Linstead

2020

Proceedings of the 17th International Conference on Mining Software Repositories

View full text Add to dashboard Cite

We apply survival analysis methods to a dataset of publicly-available software projects in order to examine the attributes that might lead to their inactivity over time. We ran a Kaplan-Meier analysis and fit a Cox Proportional-Hazards model to a subset of Software Heritage Graph Dataset, consisting of 3052 popular Python projects hosted on GitLab/GitHub, Debian, and PyPI, over a period of 165 months. We show that projects with repositories on multiple hosting services, a timeline of publishing major releases, and a good network of developers, remain healthy over time and should be worthy of the effort put in by developers and contributors. CCS CONCEPTS • Software and its engineering → Collaboration in software development.

show abstract

Cox, D. R.; Oakes, D.: Analysis of Survival Data. Chapman and Hall, London – New York 1984, 201 S., £ 12,–

Cited by 17 publications

References 0 publications

Risk factors for self-reported arm lymphedema among female breast cancer survivors: a prospective cohort study

Risk factors for self-reported arm lymphedema among female breast cancer survivors: a prospective cohort study

Delivery of Family-Based Treatment for Adolescent Anorexia Nervosa in a Public Health Care Setting: Research Versus Non-Research Specialty Care

Cheating Death

Contact Info

Product

Resources

About