Time-Dependent Covariates in the Cox Proportional-Hazards Regression Model

Fisher, Lloyd D.; Lin, Danyu

doi:10.1146/annurev.publhealth.20.1.145

Cited by 845 publications

(639 citation statements)

References 13 publications

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“…The extended Cox model introduces time dependency by interacting the covariates with a function of the time waited, ( ), (Pettitt andDaud, 1990, Fisher andLin, 1999):…”

Section: Econometric Specificationmentioning

confidence: 99%

Waiting times and socioeconomic status: Evidence from England

Laudicella

Siciliani

Cookson

2012

Social Science & Medicine

113

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link ABSTRACTWaiting times for elective surgery are often referred to as an equitable rationing mechanism in publicly-funded healthcare systems providing access to care not on the basis on willingness to pay or socioeconomic status. This study uses patient level administrative data from the Hospital Episode Statistics database in England to investigate whether patients with higher socioeconomic status (as measured by small area level income and education deprivation) wait less than other patients. The analysis focuses on the time waited for an elective hip replacement in 2001. Overall, it provides evidence of inequity in waiting times favouring more educated individuals and, to a lesser extent, richer individuals. The results from log-linear regression models and duration analysis bring evidence that inequalities occur within hospital providers and over large part of the waiting time distribution. Controlling for hospital heterogeneity reduces bias in the measurement of inequality experienced by the lowest income groups.

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“…The extended Cox model introduces time dependency by interacting the covariates with a function of the time waited, ( ), (Pettitt andDaud, 1990, Fisher andLin, 1999):…”

Section: Econometric Specificationmentioning

confidence: 99%

Waiting times and socioeconomic status: Evidence from England

Laudicella

Siciliani

Cookson

2012

Social Science & Medicine

113

View full text Add to dashboard Cite

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“…Cox proportional hazards regressions are widely used in the analysis of time-to-event data with right censoring (Box-Steffensmeier & Jones, 2004;Fisher & Lin, 1999), meaning that for some observations the period of monitoring ends before the event occurs. Rather than resorting to the inherently biased step of omitting or truncating such observations, Cox proportional hazard regressions make maximum unbiased use of information available in the data in carrying out the analysis.…”

Section: Analytic Techniquementioning

confidence: 99%

Are Washington Circle performance measures associated with decreased criminal activity following treatment?

Garnick

Horgan

Lee

et al. 2007

Journal of Substance Abuse Treatment

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This study examines the association between adherence to during-treatment process measures of quality (defined as initiation and engagement in treatment as developed by the Washington Circle) and outcome measures (defined as arrests and incarcerations) in the following year. The data come from the Oklahoma Department of Mental Health and Substance Abuse Services (ODMHSAS) administrative data system linked to data from state agencies involved in criminal justice. Clients who initiated a new episode of outpatient treatment and who engaged in treatment were significantly less likely to be arrested or incarcerated during the following year. Initiation of substance abuse treatment alone, without engagement in treatment, was not significantly associated with arrests or incarcerations. These findings validate the clinical importance of the Washington Circle performance measures of initiation and engagement. These "process of care" measures can make a difference when used as a target for quality improvement at treatment facilities.

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“…To model the relationship between the various measures of obesity and risk of CVD in a dynamic survival model way 11 an extended Cox regression model involving timedependent (time-varying) variables [12][13][14][15] was used. The demographic and health behaviour variables included in the analysis were age, sex and country of birth at baseline, and the time-varying values of education level, marital status, smoking status, alcohol consumption (ounces/month) and physical activity.…”

Section: Discussionmentioning

confidence: 99%

Estimating the risk of cardiovascular disease using an obese-years metric

Abdullah

Amin

Stoelwinder

et al. 2013

Obesity Research & Clinical Practice

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Objective: To examine the association between obese-years and the risk of cardiovascular disease (CVD).Study design: Prospective cohort study. Setting: Boston, USA. Participants: 5036 participants of the Framingham Heart Study were examined.Methods: Obese-years was calculated by multiplying for each participant the number of body mass index (BMI) units above 29 kg/m 2 by the number of years lived at that BMI during approximately 50 years of follow-up. The association between obese-years and CVD was analysed using time-dependent Cox regression adjusted for potential confounders and compared with other models using the Akaike information criterion (AIC). The lowest AIC indicated better fit.Primary outcome: CVD. Results: The median cumulative obese-years was 24 (range 2-556 obese-years). During 138 918 personyears of follow-up, 2753 (55%) participants were diagnosed with CVD. The incidence rates and adjusted HR (AHR) for CVD increased with an increase in the number of obese-years. AHR for the categories 1-24.9, 25-49.9, 50-74.9 and ≥75 obese-years were, respectively, 1.31 (95% CI 1.15 to 1.48), 1.37 (95% CI 1.14 to 1.65), 1.62 (95% CI 1.32 to 1.99) and 1.80 (95% CI 1.54 to 2.10) compared with those who were never obese (ie, had zero obese-years). The effect of obese-years was stronger in males than females. For every 10 unit increase in obese-years, the AHR of CVD increased by 6% (95% CI 4% to 8%) for males and 3% (95% CI 2% to 4%) for females. The AIC was lowest for the model containing obese-years compared with models containing either the level of BMI or the duration of obesity alone.Conclusions: This study demonstrates that obeseyears metric conceptually captures the cumulative damage of obesity on body systems, and is found to provide slightly more precise estimation of the risk of CVD than the level or duration of obesity alone.

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Time-Dependent Covariates in the Cox Proportional-Hazards Regression Model

Cited by 845 publications

References 13 publications

Waiting times and socioeconomic status: Evidence from England

Waiting times and socioeconomic status: Evidence from England

Are Washington Circle performance measures associated with decreased criminal activity following treatment?

Estimating the risk of cardiovascular disease using an obese-years metric

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