A Robust Method for Proportional Hazards Regression

Minder, Ch. E.; Bednarski, Tadeusz

doi:10.1002/(sici)1097-0258(19960530)15:10<1033::aid-sim215>3.0.co;2-y

Cited by 24 publications

(30 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Functional relations of bone marrow blasts and cytopenias with prognostic risk were analyzed to define appropriate categories for score calculation. 22 Robust Cox models 23 for survival, time to transformation, and combination of both were built to derive the relative weights within the score. To compensate for possible heterogeneities, analyses were stratified by data source, year of diagnosis, and age.…”

Section: Methodsmentioning

confidence: 99%

Revised International Prognostic Scoring System for Myelodysplastic Syndromes

Greenberg

Tuechler

Schanz

et al. 2012

Blood

2,792

2,476

View full text Add to dashboard Cite

AbstractThe International Prognostic Scoring Sytem (IPSS) is an important standard for ssessing prognosis of primary untreated adult patients with myelodysplastic syndromes (MDS). To refine the IPSS, MDS patient databases from international institutions were coalesced to assemble a much larger combined database (Revised-IPSS [IPSS-R], n = 7012, IPSS, n = 816) for analysis. Multiple statistically weighted clinical features were used to generate a prognostic categorization model. Bone marrow cytogenetics, marrow blast percentage, and cytopenias remained the basis of the new system. Novel components of the current analysis included: 5 rather than 3 cytogenetic prognostic subgroups with specific and new classifications of a number of less common cytogenetic subsets, splitting the low marrow blast percentage value, and depth of cytopenias. This model defined 5 rather than the 4 major prognostic categories that are present in the IPSS. Patient age, performance status, serum ferritin, and lactate dehydrogenase were significant additive features for survival but not for acute myeloid leukemia transformation. This system comprehensively integrated the numerous known clinical features into a method analyzing MDS patient prognosis more precisely than the initial IPSS. As such, this IPSS-R should prove beneficial for predicting the clinical outcomes of untreated MDS patients and aiding design and analysis of clinical trials in this disease.

show abstract

Section: Methodsmentioning

confidence: 99%

Revised International Prognostic Scoring System for Myelodysplastic Syndromes

Greenberg

Tuechler

Schanz

et al. 2012

Blood

2,792

2,476

View full text Add to dashboard Cite

show abstract

“…It has been well documented in the literature that the Cox model is sensitive even to slight departures from the assumptions (Samuels (1978), Bednarski (1989), Minder and Bednarski (1996)), and that its IF is not bounded (Reid and Crépeau, 1985). Valsecchi et al (1996) provide a detailed illustration of how long survivors, for instance, may affect the estimates.…”

Section: Robust Survival Analysismentioning

confidence: 99%

An overview of robust methods in medical research

Farcomeni

Ventura

2010

Stat Methods Med Res

View full text Add to dashboard Cite

Robust statistics is an extension of classical parametric statistics that specifically takes into account the fact that the assumed parametric models used by the researchers are only approximate. In this paper we review and outline how robust inferential procedures may routinely be applied in practice in the biomediacal research. Numerical illustrations are given for the t-test, regression models, logistic regression, survival analysis and ROC curves, showing that robust methods are often more appropriate than standard procedures.

show abstract

“…However the use of such weighting in combination with robust modeling methods is not consistently implemented. For example, sampling weights are implemented in one robust method that focuses on robustness to variation in proportional hazards (PH) over time [5,7], but not in another that is more robust to influential outliers [3,8,9]. These two methods incorporate a similar approach to robustness, i.e.…”

Section: Methodsmentioning

confidence: 99%

Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis

Sitlani

Lumley

McKnight

et al. 2020

BMC Med Res Methodol

View full text Add to dashboard Cite

Background: Cox proportional hazards regression models are used to evaluate associations between exposures of interest and time-to-event outcomes in observational data. When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be incorporated into the regression model to obtain unbiased estimating equations. Methods: Robust Cox methods have been developed to better estimate associations when there are influential outliers in the exposure of interest, but these robust methods do not incorporate sampling weights. In this paper, we extend these robust methods, which already incorporate influence weights, so that they also accommodate sampling weights. Results: Simulations illustrate that in the presence of influential outliers, the association estimate from the weighted robust method is closer to the true value than the estimate from traditional weighted Cox regression. As expected, in the absence of outliers, the use of robust methods yields a small loss of efficiency. Using data from a case-cohort study that is nested within the Multi-Ethnic Study of Atherosclerosis (MESA) longitudinal cohort study, we illustrate differences between traditional and robust weighted Cox association estimates for the relationships between immune cell traits and risk of stroke. Conclusions: Robust weighted Cox regression methods are a new tool to analyze time-to-event data with sampling, e.g. case-cohort data, when exposures of interest contain outliers.

show abstract

A Robust Method for Proportional Hazards Regression

Cited by 24 publications

References 1 publication

Revised International Prognostic Scoring System for Myelodysplastic Syndromes

Revised International Prognostic Scoring System for Myelodysplastic Syndromes

An overview of robust methods in medical research

Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis

Contact Info

Product

Resources

About