A Flexible B‐Spline Model for Multiple Longitudinal Biomarkers and Survival

Brown, Elizabeth R.; Ibrahim, Joseph G.; DeGruttola, Victor

doi:10.1111/j.0006-341x.2005.030929.x

Cited by 158 publications

(142 citation statements)

References 22 publications

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“…Splines are piecewise polynomials that join in a smooth way at the so-called knots and are a flexible way to fit an unknown functional form (24). For most applications of this method, cubic splines are chosen (33). However, this method does not provide interpretable variables (34).…”

Section: Discussionmentioning

confidence: 99%

Dose-Response Modeling of Occupational Exposure to Polycyclic Aromatic Hydrocarbons with Biomarkers of Exposure and Effect

Pesch

Kappler

Straif³

et al. 2007

Cancer Epidemiology, Biomarkers &Amp; Prevention

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In regulatory toxicology, the dose-response relationship between occupational exposure and biomarkers is of importance in setting threshold values. We analyzed the relationships between occupational exposure to polycyclic aromatic hydrocarbons (PAH) and various biomarkers of internal exposure and DNA damage with data from 284 highly exposed male workers. Personal exposure to phenanthrene and other PAHs was measured during shift and correlated with the sum of 1À, 2+9À, 3À, and 4-hydroxyphenanthrenes in post-shift urine. PAHs and hydroxyphenanthrenes were associated with DNA damage assessed in WBC as 8-oxo-7,8-dihydro-2 ¶-deoxyguanosine/10 6 dGuo and strand breaks by Comet assay as Olive tail moment. Hydroxyphenanthrenes correlated with phenanthrene (Spearman r s = 0.70; P < 0.0001). No correlations could be found between strand breaks and exposure (r s = 0.01, P < 0.0001 for PAHs; r s = À0.03, P = 0.68 for hydroxyphenanthrenes). Correlations with 8-oxo-7,8-dihydro-2 ¶-deoxyguanosine/10 6 dGuo were weakly negative (r s = À0.22, P = 0.004 for PAHs) or flat (r s = À0.08, P = 0.31 for hydroxyphenanthrenes). Linear splines were applied to assess the relationships between the log-transformed variables. All regression models were adjusted for smoking and type of industry. For hydroxyphenanthrenes, 51.7% of the variance could be explained by phenanthrene and other predictors. Up to 0.77 Mg/m 3 phenanthrene, no association could be found with hydroxyphenanthrenes. Above that point, hydroxyphenanthrenes increased by a factor of 1.47 under a doubling of phenanthrene exposure (slope, 0.56; 95% confidence interval, 0.47-0.64). Hydroxyphenanthrenes may be recommended as biomarker of occupational PAH exposure, whereas biomarkers of DNA damage in blood did not show a dose-response relation to PAH exposure. (Cancer Epidemiol Biomarkers Prev 2007;16(9):1863 -73)

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Section: Discussionmentioning

confidence: 99%

Dose-Response Modeling of Occupational Exposure to Polycyclic Aromatic Hydrocarbons with Biomarkers of Exposure and Effect

Pesch

Kappler

Straif³

et al. 2007

Cancer Epidemiology, Biomarkers &Amp; Prevention

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“…The c i 's are random effects, z i and v i are design/baseline covariate vectors, and α is a vector of regression coefficients. The functions f (·) and/or g i (·) have been modeled in joint analysis applications with polynomials (Wang and Taylor 2001;Brown and Ibrahim 2003), Gaussian processes (Wang and Taylor 2001), and B-splines (Brown et al 2005).…”

Section: Longitudinal Componentmentioning

confidence: 99%

Predictive comparison of joint longitudinal-survival modeling: a case study illustrating competing approaches

2010

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The joint modeling of longitudinal and survival data has received extraordinary attention in the statistics literature recently, with models and methods becoming increasingly more complex. Most of these approaches pair a proportional hazards survival with longitudinal trajectory modeling through parametric or nonparametric specifications. In this paper we closely examine one data set previously analyzed using a two parameter parametric model for Mediterranean fruit fly (medfly) egg-laying trajectories paired with accelerated failure time and proportional hazards survival models. We consider parametric and nonparametric versions of these two models, as well as a proportional odds rate model paired with a wide variety of longitudinal trajectory assumptions reflecting the types of analyses seen in the literature. In addition to developing novel nonparametric Bayesian methods for joint models, we emphasize the importance of model selection from among joint and non joint models. The default in the literature is to omit at the outset non joint models from consideration. For the medfly data, a predictive diagnostic criterion suggests that both the choice of survival model and longitudinal assumptions can grossly affect model adequacy and prediction. Specifically for these data, the simple joint model used in by Tseng et al. (Biometrika 92:587-603, 2005) and models with much more flexibility in their longitudinal components are predictively outperformed by simpler analyses. This case study underscores the need for data analysts to compare on the basis of predictive performance different joint models and to include non joint models in the pool of candidates under consideration.

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“…In Henderson et al [19], a latent bivariate Gaussian process is introduced as a time-dependent variable in a proportional hazard model. Multivariate generalizations of such methods and the estimation procedures have been suggested recently [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Stochastic model for analysis of longitudinal data on aging and mortality

Yashin

Arbeev

Akushevich

et al. 2007

Mathematical Biosciences

133

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Aging-related changes in a human organism follow dynamic regularities, which contribute to the observed age patterns of incidence and mortality curves. An organism's "optimal" (normal) physiological state changes with age, affecting the values of risks of disease and death. The resistance to stresses, as well as adaptive capacity, declines with age. An exposure to improper environment results in persisting deviation of individuals' physiological (and biological) indices from their normal state (due to allostatic adaptation), which, in turn, increases chances of disease and death. Despite numerous studies investigating these effects, there is no conceptual framework, which would allow for putting all these findings together, and analyze longitudinal data taking all these dynamic connections into account. In this paper we suggest such a framework, using a new version of stochastic process model of aging and mortality. Using this model, we elaborated a statistical method for analyses of longitudinal data on aging, health and longevity and tested it using different simulated data sets. The results show that the model may characterize complicated interplay among different components of aging-related changes in humans and that the model parameters are identifiable from the data.

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A Flexible B‐Spline Model for Multiple Longitudinal Biomarkers and Survival

Cited by 158 publications

References 22 publications

Dose-Response Modeling of Occupational Exposure to Polycyclic Aromatic Hydrocarbons with Biomarkers of Exposure and Effect

Dose-Response Modeling of Occupational Exposure to Polycyclic Aromatic Hydrocarbons with Biomarkers of Exposure and Effect

Predictive comparison of joint longitudinal-survival modeling: a case study illustrating competing approaches

Stochastic model for analysis of longitudinal data on aging and mortality

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