			Home About us Contact

Covariate Data (covariate + data)

Distribution by Scientific Domains

Mathematics and Statistics	50%

Selected Abstracts

Multipoint affected sibpair linkage methods for localizing susceptibility genes of complex diseases

GENETIC EPIDEMIOLOGY, Issue 2 2003
David V. Glidden
Abstract Recently, Liang et al. ([2001] Hum. Hered. 51:64,78) proposed a general multipoint linkage method for estimating the chromosomal position of a putative susceptibility locus. Their technique is computationally simple and does not require specification of penetrance or a mode of inheritance. In complex genetic diseases, covariate data may be available which reflect etiologic or locus heterogeneity. We developed approaches to incorporating covariates into the method of Liang et al. ([2001] Hum. Hered. 51:64,78) with particular attention to exploiting age-at-onset information. The results of simulation studies, and a worked data example using a family data set ascertained through probands with schizophrenia, suggest that utilizing covariate information can yield substantial efficiency gains in localizing susceptibility genes. Genet Epidemiol 24: 107,117, 2003. © 2003 Wiley-Liss, Inc. [source]

Individual patient data meta-analysis of randomized anti-epileptic drug monotherapy trials

JOURNAL OF EVALUATION IN CLINICAL PRACTICE, Issue 2 2000
Paula R. Williamson PhD
Abstract Meta-analysis may be based on either aggregate data or individual patient data (IPD). Three reasons why IPD are desirable for the meta-analysis of anti-epileptic drug (AED) monotherapy trials are: (1) to undertake a more complete analysis of time-to-event outcomes; (2) to investigate the interaction between AED and type of epilepsy; and (3) to undertake re-analysis of the trial to obtain results for all relevant outcomes. We demonstrate that IPD meta-analysis is possible in AED research. Problems arose from missing data at four levels: (1) unknown trials; (2) known trials but no IPD supplied; (3) known trials but missing outcome data for some individuals within trials; and (4) known trials but missing covariate data for some individuals within trials. Empirical evidence of the reliability of meta-analyses based on aggregate rather than individual patient data is still lacking. Examples of other benefits such projects may bring include improvements to the design of a new trial in the area, in terms of the sample size considerations, the definition of outcomes and data collection. [source]

A Bayesian hierarchical mixture model for platelet-derived growth factor receptor phosphorylation to improve estimation of progression-free survival in prostate cancer

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 1 2010
Satoshi Morita
Summary., Advances in understanding the biological underpinnings of many cancers have led increasingly to the use of molecularly targeted anticancer therapies. Because the platelet-derived growth factor receptor (PDGFR) has been implicated in the progression of prostate cancer bone metastases, it is of great interest to examine possible relationships between PDGFR inhibition and therapeutic outcomes. We analyse the association between change in activated PDGFR (phosphorylated PDGFR) and progression-free survival time based on large within-patient samples of cell-specific phosphorylated PDGFR values taken before and after treatment from each of 88 prostate cancer patients. To utilize these paired samples as covariate data in a regression model for progression-free survival time, and be cause the phosphorylated PDGFR distributions are bimodal, we first employ a Bayesian hierarchical mixture model to obtain a deconvolution of the pretreatment and post-treatment within-patient phosphorylated PDGFR distributions. We evaluate fits of the mixture model and a non-mixture model that ignores the bimodality by using a supnorm metric to compare the empirical distribution of each phosphorylated PDGFR data set with the corresponding fitted distribution under each model. Our results show that first using the mixture model to account for the bimodality of the within-patient phosphorylated PDGFR distributions, and then using the posterior within-patient component mean changes in phosphorylated PDGFR so obtained as covariates in the regression model for progression-free survival time, provides an improved estimation. [source]

Bayesian Analysis for Generalized Linear Models with Nonignorably Missing Covariates

BIOMETRICS, Issue 3 2005
Lan Huang
Summary We propose Bayesian methods for estimating parameters in generalized linear models (GLMs) with nonignorably missing covariate data. We show that when improper uniform priors are used for the regression coefficients, ,, of the multinomial selection model for the missing data mechanism, the resulting joint posterior will always be improper if (i) all missing covariates are discrete and an intercept is included in the selection model for the missing data mechanism, or (ii) at least one of the covariates is continuous and unbounded. This impropriety will result regardless of whether proper or improper priors are specified for the regression parameters, ,, of the GLM or the parameters, ,, of the covariate distribution. To overcome this problem, we propose a novel class of proper priors for the regression coefficients, ,, in the selection model for the missing data mechanism. These priors are robust and computationally attractive in the sense that inferences about , are not sensitive to the choice of the hyperparameters of the prior for , and they facilitate a Gibbs sampling scheme that leads to accelerated convergence. In addition, we extend the model assessment criterion of Chen, Dey, and Ibrahim (2004a, Biometrika91, 45,63), called the weighted L measure, to GLMs and missing data problems as well as extend the deviance information criterion (DIC) of Spiegelhalter et al. (2002, Journal of the Royal Statistical Society B64, 583,639) for assessing whether the missing data mechanism is ignorable or nonignorable. A novel Markov chain Monte Carlo sampling algorithm is also developed for carrying out posterior computation. Several simulations are given to investigate the performance of the proposed Bayesian criteria as well as the sensitivity of the prior specification. Real datasets from a melanoma cancer clinical trial and a liver cancer study are presented to further illustrate the proposed methods. [source]