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Extending the Peters-Belson approach for assessing disparities to right censored time-to-event outcomes.

ABSTRACT: The Peters-Belson (PB) method was developed for quantifying and testing disparities between groups in an outcome by using linear regression to compute group-specific observed and expected outcomes. It has since been extended to generalized linear models for binary and other outcomes and to analyses with probability-based sample weighting. In this work, we extend the PB approach to right-censored survival analysis, including stratification if needed. The extension uses the theory and methods of expected survival on the basis of Cox regression in a reference population. Within the PB framework, among the groups to be compared, one group is chosen as the reference group, and outcomes in that group are modeled as a function of available predictors. By using this fitted model's estimated parameters, and the predictor values for a comparator group, the comparator group's expected outcomes are then calculated and compared, formally with testing and informally with graphics, with their observed outcomes. We derive the extension, show how we applied it in a study of incontinence in nursing home elderly, and discuss issues in implementing it. We used the 'survival' package in the R system to do computations.

SUBMITTER: Eberly LE

PROVIDER: S-EPMC3830614 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Extending the Peters-Belson approach for assessing disparities to right censored time-to-event outcomes.

Eberly Lynn E LE Hodges James S JS Savik Kay K Gurvich Olga O Bliss Donna Z DZ Mueller Christine C

Statistics in medicine 20130524 23

The Peters-Belson (PB) method was developed for quantifying and testing disparities between groups in an outcome by using linear regression to compute group-specific observed and expected outcomes. It has since been extended to generalized linear models for binary and other outcomes and to analyses with probability-based sample weighting. In this work, we extend the PB approach to right-censored survival analysis, including stratification if needed. The extension uses the theory and methods of e ...[more]

PMID: 23703882

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Project description:BackgroundIn recent years there is increasing interest in modeling the effect of early longitudinal biomarker data on future time-to-event or other outcomes. Sometimes investigators are also interested in knowing whether the variability of biomarkers is independently predictive of clinical outcomes. This question in most applications is addressed via a two-stage approach where summary statistics such as variance are calculated in the first stage and then used in models as covariates to predict clinical outcome in the second stage. The objective of this study is to compare the relative performance of various methods in estimating the effect of biomarker variability.MethodsA joint model and 4 different two-stage approaches (naïve, landmark analysis, time-dependent Cox model, and regression calibration) were illustrated using data from a large multi-center randomized phase III trial, the Ocular Hypertension Treatment Study (OHTS), regarding the association between the variability of intraocular pressure (IOP) and the development of primary open-angle glaucoma (POAG). The model performance was also evaluated in terms of bias using simulated data from the joint model of longitudinal IOP and time to POAG. The parameters for simulation were chosen after OHTS data, and the association between longitudinal and survival data was introduced via underlying, unobserved, and error-free parameters including subject-specific variance.ResultsIn the OHTS data, joint modeling and two-stage methods reached consistent conclusion that IOP variability showed no significant association with the risk of POAG. In the simulated data with no association between IOP variability and time-to-POAG, all the two-stage methods (except the naïve approach) provided a reliable estimation. When a moderate effect of IOP variability on POAG was imposed, all the two-stage methods underestimated the true association as compared with the joint modeling while the model-based two-stage method (regression calibration) resulted in the least bias.ConclusionRegression calibration and joint modelling are the preferred methods in assessing the effect of biomarker variability. Two-stage methods with sample-based measures should be used with caution unless there exists a relatively long series of longitudinal measurements and/or strong effect size (NCT00000125).

Dataset Information

Extending the Peters-Belson approach for assessing disparities to right censored time-to-event outcomes.

Publications

Extending the Peters-Belson approach for assessing disparities to right censored time-to-event outcomes.

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