Survival Analysis Part III: Multivariate data analysis – choosing a model and assessing its adequacy and fit

Bradburn, Michael; Clark, Taane G.; Love, Sharon; Altman, Douglas G.

doi:10.1038/sj.bjc.6601120

Cited by 326 publications

(251 citation statements)

References 9 publications

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“…The alternative to the proportional hazards (PH) assumption is to allow the hazard ratio to vary over time. This can be checked by various methods, as described by [6,19,20].…”

Section: Goodness Of Fitmentioning

confidence: 99%

A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions

Latouche

Allignol

Beyersmann

et al. 2013

Journal of Clinical Epidemiology

378

353

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Competing risks endpoints are frequently encountered in hematopoietic stem cell transplantation where patients are exposed to relapse and treatment-related mortality. Both cause-specific hazards models and direct models for the cumulative incidence functions have been used for analyzing such competing risks endpoints. For both approaches, the popular models are of a proportional hazards type. Such models have been used for studying prognostic factors in acute and chronic leukemias. We argue that a complete understanding of the event dynamics requires that both hazards and cumulative incidence be analyzed side-by-side, and that this is generally the most rigorous scientific approach to analyzing competing risks data. That is, understanding the effects of covariates on cause-specific hazard and cumulative incidence functions go hand in hand. A case study illustrates our proposal.

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“…The alternative to the proportional hazards (PH) assumption is to allow the hazard ratio to vary over time. This can be checked by various methods, as described by [6,19,20].…”

Section: Goodness Of Fitmentioning

confidence: 99%

A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions

Latouche

Allignol

Beyersmann

et al. 2013

Journal of Clinical Epidemiology

378

353

View full text Add to dashboard Cite

show abstract

“…The proportional hazards assumption was assessed using trend tests of the Schoenfeld residuals with those that failed the assumption entered as continuous time dependent covariates. (Bradburn, Clark, Love, & Altman, 2003) Hazard ratios for variables treated as time dependent covariates vary over time and are reported at the median survival time. LMAX and DFBETA measures were used to test for influential observations and covariates, respectively.…”

Section: Analysis Proceduresmentioning

confidence: 99%

Risk of drug-related mortality during periods of transition in methadone maintenance treatment: A cohort study

Cousins

Teljeur

Mondaini

et al. 2011

Journal of Substance Abuse Treatment

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“…The coefficient b 1 represents the additional relative hazard for each unit increase in x 1 at any given time. This model is different from models with time-dependent coefficients (Bradburn et al, 2003b), in which the effect of a covariate changes rather than the value of the covariate itself, that is,…”

Section: In Our Clinical Trial We Collected Measurements At Prearranmentioning

confidence: 99%

Survival Analysis Part IV: Further concepts and methods in survival analysis

et al. 2003

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Survival Analysis Part III: Multivariate data analysis – choosing a model and assessing its adequacy and fit

Cited by 326 publications

References 9 publications

A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions

A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions

Risk of drug-related mortality during periods of transition in methadone maintenance treatment: A cohort study

Survival Analysis Part IV: Further concepts and methods in survival analysis

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