Biomedical subjects

Xiao Song

Publications and source records attributed to Xiao Song.

10 recordsLinked to original sources

PubMed2006

Regularized binormal ROC method in disease classification using microarray data.

BACKGROUND: An important application of microarrays is to discover genomic biomarkers, among tens of thousands of genes assayed, for disease diagnosis and prognosis. Thus it is of interest to develop efficient statistical methods that can simultaneously identify important biomarkers from such high-throughput genomic data and construct appropriate classification rules. It is also of interest to develop methods for evaluation of classification performance and ranking of identified biomarkers. RESULTS: The ROC (receiver operating characteristic) technique has been widely used in disease classification with low dimensional biomarkers. Compared with the empirical ROC approach, the binormal ROC is computationally more affordable and robust in small sample size cases. We propose using the binormal AUC (area under the ROC curve) as the objective function for two-sample classification, and the scaled threshold gradient directed regularization method for regularized estimation and biomarker selection. Tuning parameter selection is based on V-fold cross validation. We develop Monte Carlo based methods for evaluating the stability of individual biomarkers and overall prediction performance. Extensive simulation studies show that the proposed approach can generate parsimonious models with excellent classification and prediction performance, under most simulated scenarios including model mis-specification. Application of the method to two cancer studies shows that the identified genes are reasonably stable with satisfactory prediction performance and biologically sound implications. The overall classification performance is satisfactory, with small classification errors and large AUCs. CONCLUSION: In comparison to existing methods, the proposed approach is computationally more affordable without losing the optimality possessed by the standard ROC method.

Biomarkers, Tumor↗

PubMed2006

A semiparametric approach for the nonparametric transformation survival model with multiple covariates.

The nonparametric transformation model makes no parametric assumptions on the forms of the transformation function and the error distribution. This model is appealing in its flexibility for modeling censored survival data. Current approaches for estimation of the regression parameters involve maximizing discontinuous objective functions, which are numerically infeasible to implement with multiple covariates. Based on the partial rank (PR) estimator (Khan and Tamer, 2004), we propose a smoothed PR estimator which maximizes a smooth approximation of the PR objective function. The estimator is shown to be asymptotically equivalent to the PR estimator but is much easier to compute when there are multiple covariates. We further propose using the weighted bootstrap, which is more stable than the usual sandwich technique with smoothing parameters, for estimating the standard error. The estimator is evaluated via simulation studies and illustrated with the Veterans Administration lung cancer data set.

Antineoplastic Agents↗

PubMed2006

A corrected pseudo-score approach for additive hazards model with longitudinal covariates measured with error.

In medical studies, it is often of interest to characterize the relationship between a time-to-event and covariates, not only time-independent but also time-dependent. Time-dependent covariates are generally measured intermittently and with error. Recent interests focus on the proportional hazards framework, with longitudinal data jointly modeled through a mixed effects model. However, approaches under this framework depend on the normality assumption of the error, and might encounter intractable numerical difficulties in practice. This motivates us to consider an alternative framework, that is, the additive hazards model, about which little research has been done when time-dependent covariates are measured with error. We propose a simple corrected pseudo-score approach for the regression parameters with no assumptions on the distribution of the random effects and the error beyond those for the variance structure of the latter. The estimator has an explicit form and is shown to be consistent and asymptotically normal. We illustrate the method via simulations and by application to data from an HIV clinical trial.

Acquired Immunodeficiency Syndrome↗

PubMed2006

[Chemical constituents of unsaponifiable matter from seed oil of Momordica cochinchinensis].

OBJECTIVE: To study the chemical constituents of unsaponifiable matter from the seed oil of Momordica cochinchinensis. METHOD: The fatty oil from the seeds of M. cochinchinensis was extracted with petroleum ether, and the saponification was carried out with potassium hydroxide. The unsaponifiable matter was isolated and purified by silica gel column chromatography, and the structures of their constituents were elucidated by means of IR, MS, 1H-NMR, and authentic chemicals. RESULT: Karounidiol (1), isokarounidiol (2), 5-dehydrokarounidiol (3), 7-oxodihydrokarounidiol (4), beta-sitosterol (5), stigmast-7-en-3beta-ol (6), and stigmast-7,22-dien-3beta-ol (7) were elucidated. CONCLUSION: These compounds were found in this plant for the first time.

Molecular Structure↗

PubMed2005

A marginal model approach for analysis of multi-reader multi-test receiver operating characteristic (ROC) data.

The receiver operating characteristic curve is a popular tool to characterize the capabilities of diagnostic tests with continuous or ordinal responses. One common design for assessing the accuracy of diagnostic tests involves multiple readers and multiple tests, in which all readers read all test results from the same patients. This design is most commonly used in a radiology setting, where the results of diagnostic tests depend on a radiologist's subjective interpretation. The most widely used approach for analyzing data from such a study is the Dorfman-Berbaum-Metz (DBM) method (Dorfman et al., 1992) which utilizes a standard analysis of variance (ANOVA) model for the jackknife pseudovalues of the area under the ROC curves (AUCs). Although the DBM method has performed well in published simulation studies, there is no clear theoretical basis for this approach. In this paper, focusing on continuous outcomes, we investigate its theoretical basis. Our result indicates that the DBM method does not satisfy the regular assumptions for standard ANOVA models, and thus might lead to erroneous inference. We then propose a marginal model approach based on the AUCs which can adjust for covariates as well. Consistent and asymptotically normal estimators are derived for regression coefficients. We compare our approach with the DBM method via simulation and by an application to data from a breast cancer study. The simulation results show that both our method and the DBM method perform well when the accuracy of tests under the study is the same and that our method outperforms the DBM method for inference on individual AUCs when the accuracy of tests is not the same. The marginal model approach can be easily extended to ordinal outcomes.

Breast Neoplasms↗

PubMed2005

On corrected score approach for proportional hazards model with covariate measurement error.

In the presence of covariate measurement error with the proportional hazards model, several functional modeling methods have been proposed. These include the conditional score estimator (Tsiatis and Davidian, 2001, Biometrika 88, 447-458), the parametric correction estimator (Nakamura, 1992, Biometrics 48, 829-838), and the nonparametric correction estimator (Huang and Wang, 2000, Journal of the American Statistical Association 95, 1209-1219) in the order of weaker assumptions on the error. Although they are all consistent, each suffers from potential difficulties with small samples and substantial measurement error. In this article, upon noting that the conditional score and parametric correction estimators are asymptotically equivalent in the case of normal error, we investigate their relative finite sample performance and discover that the former is superior. This finding motivates a general refinement approach to parametric and nonparametric correction methods. The refined correction estimators are asymptotically equivalent to their standard counterparts, but have improved numerical properties and perform better when the standard estimates do not exist or are outliers. Simulation results and application to an HIV clinical trial are presented.

Acquired Immunodeficiency Syndrome↗

PubMed2004

The presence of frailty in elderly persons with chronic renal insufficiency.

BACKGROUND: Frailty has been defined as a tool to define individuals who lack functional reserve and are at risk for functional decline. We hypothesized that chronic renal insufficiency (CRI) would be associated with a greater prevalence of frailty and disability in the elderly. METHODS: This cross-sectional analysis used baseline data collected from the Cardiovascular Health Study, which enrolled 5,888 community-dwelling adults aged 65 years or older from 4 clinical centers in the United States. Renal insufficiency is defined as a serum creatinine level of 1.3 mg/dL or greater (> or =115 micromol/L) in women and 1.5 mg/dL or greater (> or =133 micromol/L) in men. Frailty is defined by the presence of 3 of the following abnormalities: unintentional weight loss, self-reported exhaustion, measured weakness, slow walking speed, and low physical activity. Disability is defined as any self-reported difficulty with activities of daily living. RESULTS: Among 5,808 participants with creatinine levels measured at entry, 15.9% of men (n = 394) and 7.6% of women (n = 254) had CRI. Prevalences of frailty (15% versus 6%; P < 0.001) and disability (12% versus 7%; P = 0.001) were greater in participants with CRI compared with those with normal renal function. After multivariate adjustment for comorbidity, CRI remained significantly associated with frailty (odds ratio, 1.76; 95% confidence interval, 1.28 to 2.41), but not disability (odds ratio, 1.26; 95% confidence interval, 0.94 to 1.69). CONCLUSION: Elderly persons with CRI have a high prevalence of frailty, which may signal their risk for progression to adverse health outcomes. If confirmed in other studies, identification of frailty in patients with CRI may warrant special interventions to preserve their independence, quality of life, and survival.

Activities of Daily Living↗

PubMed2004

Evaluating markers for selecting a patient's treatment.

Selecting the best treatment for a patient's disease may be facilitated by evaluating clinical characteristics or biomarker measurements at diagnosis. We consider how to evaluate the potential impact of such measurements on treatment selection algorithms. For example, magnetic resonance neurographic imaging is potentially useful for deciding whether a patient should be treated surgically for Carpal Tunnel Syndrome or should receive less-invasive conservative therapy. We propose a graphical display, the selection impact (SI) curve that shows the population response rate as a function of treatment selection criteria based on the marker. The curve can be useful for choosing a treatment policy that incorporates information on the patient's marker value exceeding a threshold. The SI curve can be estimated using data from a comparative randomized trial conducted in the population as long as treatment assignment in the trial is independent of the predictive marker. Estimating the SI curve is therefore part of a post hoc analysis to determine whether the marker identifies patients that are more likely to benefit from one treatment over another. Nonparametric and parametric estimates of the SI curve are proposed in this article. Asymptotic distribution theory is used to evaluate the relative efficiencies of the estimators. Simulation studies show that inference is straightforward with realistic sample sizes. We illustrate the SI curve and statistical inference for it with data motivated by an ongoing trial of surgery versus conservative therapy for Carpal Tunnel Syndrome.

Algorithms↗

PubMed2002

An estimator for the proportional hazards model with multiple longitudinal covariates measured with error.

In many longitudinal studies, it is of interest to characterize the relationship between a time-to-event (e.g. survival) and several time-dependent and time-independent covariates. Time-dependent covariates are generally observed intermittently and with error. For a single time-dependent covariate, a popular approach is to assume a joint longitudinal data-survival model, where the time-dependent covariate follows a linear mixed effects model and the hazard of failure depends on random effects and time-independent covariates via a proportional hazards relationship. Regression calibration and likelihood or Bayesian methods have been advocated for implementation; however, generalization to more than one time-dependent covariate may become prohibitive. For a single time-dependent covariate, Tsiatis and Davidian (2001) have proposed an approach that is easily implemented and does not require an assumption on the distribution of the random effects. This technique may be generalized to multiple, possibly correlated, time-dependent covariates, as we demonstrate. We illustrate the approach via simulation and by application to data from an HIV clinical trial.

Journal Article↗

PubMed2002

A semiparametric likelihood approach to joint modeling of longitudinal and time-to-event data.

Joint models for a time-to-event (e.g., survival) and a longitudinal response have generated considerable recent interest. The longitudinal data are assumed to follow a mixed effects model, and a proportional hazards model depending on the longitudinal random effects and other covariates is assumed for the survival endpoint. Interest may focus on inference on the longitudinal data process, which is informatively censored, or on the hazard relationship. Several methods for fitting such models have been proposed, most requiring a parametric distributional assumption (normality) on the random effects. A natural concern is sensitivity to violation of this assumption; moreover, a restrictive distributional assumption may obscure key features in the data. We investigate these issues through our proposal of a likelihood-based approach that requires only the assumption that the random effects have a smooth density. Implementation via the EM algorithm is described, and performance and the benefits for uncovering noteworthy features are illustrated by application to data from an HIV clinical trial and by simulation.

Anti-HIV Agents↗