Modeling the Relationship of Survival to Longitudinal Data Measured with Error. Applications to Survival and CD4 Counts in Patients with AIDS

Tsiatis, Anastasios A.; DeGruttola, Victor; Wulfsohn, Michael

doi:10.1080/01621459.1995.10476485

Cited by 440 publications

(337 citation statements)

References 13 publications

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“…This approach could be extended to treat the SOFA scores as a longitudinal outcome in a joint model for the longitudinal and survival processes. Indeed, joint models for longitudinal and survival data have been the focus of intense research over the past two decades (Tsiatis et al, 1995; Wang and Taylor, 2001; Ibrahim et al, 2001; Brown and Ibrahim, 2003; Tsiatis and Davidian, 2004; Yu et al, 2004; Hanson et al, 2011; Ibrahim et al, 2010; Rizopoulos, 2012). An advantage of this modeling strategy is that it would allow for dynamic prediction of mortality, i.e., the ability to estimate whether or not a person will survive their ICU stay while they are still in the hospital (Yu et al, 2008; Garre et al, 2008; Proust-Lima and Taylor, 2009; Rizopoulos, 2011).…”

Section: Motivating Examplementioning

confidence: 99%

Variable-Domain Functional Regression for Modeling ICU Data

Gellar¹,

Colantuoni²,

Crainiceanu³

2014

Journal of the American Statistical Association

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We introduce a class of scalar-on-function regression models with subject-specific functional predictor domains. The fundamental idea is to consider a bivariate functional parameter that depends both on the functional argument and on the width of the functional predictor domain. Both parametric and nonparametric models are introduced to fit the functional coefficient. The nonparametric model is theoretically and practically invariant to functional support transformation, or support registration. Methods were motivated by and applied to a study of association between daily measures of the Intensive Care Unit (ICU) Sequential Organ Failure Assessment (SOFA) score and two outcomes: in-hospital mortality, and physical impairment at hospital discharge among survivors. Methods are generally applicable to a large number of new studies that record a continuous variables over unequal domains.

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Section: Motivating Examplementioning

confidence: 99%

Variable-Domain Functional Regression for Modeling ICU Data

Gellar¹,

Colantuoni²,

Crainiceanu³

2014

Journal of the American Statistical Association

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“…Nevertheless, these conditions are frequently violated in reality (e.g., Tsiatis, DeGruttola and Wulfsohn 1995; Wang et al 2008). Although longitudinal studies are designed to collect data on every individual in the studies at each assessment, missing observations arise ubiquitously.…”

Section: Introductionmentioning

confidence: 99%

Variable selection and inference procedures for marginal analysis of longitudinal data with missing observations and covariate measurement error

Tan

2015

Can J Statistics

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Summary In contrast to extensive attention on model selection for univariate data, research on model selection for longitudinal data remains largely unexplored. This is particularly the case when data are subject to missingness and measurement error. To address this important problem, we propose marginal methods that simultaneously carry out model selection and estimation for longitudinal data with missing responses and error-prone covariates. Our method have several appealing features: the applicability is broad because the methods are developed for a unified framework with marginal generalized linear models; model assumptions are minimal in that no full distribution is required for the response process and the distribution of the mismeasured covariates is left unspecified; and the implementation is straightforward. To justify the proposed methods, we provide both theoretical properties and numerical assessments.

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“…This assumption has been used before in methodological work 12,14,17 and implies increased measurement error for higher absolute (non-log) CD4 count and (non-log) viral load measurements which is in line with clinical knowledge 2,3,6,32 . The measurement error variance for the natural logarithm CD4 was obtained from an estimate of a study with a large sample 3 .…”

Section: Methodsmentioning

confidence: 64%

Simultaneous Treatment of Missing Data and Measurement Error in HIV Research Using Multiple Overimputation

et al. 2015

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Background Both CD4 count and viral load in HIV infected persons are measured with error. There is no clear guidance on how to deal with this measurement error in the presence of missing data. Methods We used multiple overimputation, a method recently developed in the political sciences, to account for both measurement error and missing data in CD4 count and viral load measurements from four South African cohorts of a Southern African HIV cohort collaboration. Our knowledge about the measurement error of lnCD4 and log10 viral load is part of an imputation model that imputes both missing and mismeasured data. In an illustrative example we estimate the association of CD4 count and viral load with the hazard of death among patients on highly active antiretroviral therapy by means of a Cox model. Simulation studies evaluate the extent to which multiple overimputation is able to reduce bias in survival analyses. Results Multiple overimputation emphasizes more strongly the influence of having a high baseline CD4 counts compared to a complete case analysis and multiple imputation (hazard ratio for >200 cells/mm3 vs. <25 cells/mm3: 0.21 [95%CI: 0.18;0.24] vs. 0.38 [0.29;0.48] and 0.29 [0.25;0.34] respectively). Similar results are obtained when varying assumptions about the measurement error, when using p-splines, and when evaluating time-updated CD4 count in a longitudinal analysis. The estimates of the association with viral load are slightly more attenuated when using multiple imputation instead of multiple overimputation. Our simulation studies suggest that multiple overimputation is able to reduce bias and mean squared error in survival analyses. Conclusions Multiple overimputation, which can be used with existing software, offers a convenient approach to account for both missing and mismeasured data in HIV research.

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Modeling the Relationship of Survival to Longitudinal Data Measured with Error. Applications to Survival and CD4 Counts in Patients with AIDS

Cited by 440 publications

References 13 publications

Variable-Domain Functional Regression for Modeling ICU Data

Variable-Domain Functional Regression for Modeling ICU Data

Variable selection and inference procedures for marginal analysis of longitudinal data with missing observations and covariate measurement error

Simultaneous Treatment of Missing Data and Measurement Error in HIV Research Using Multiple Overimputation

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