Biomaterial Database

A semiparametric approach for the nonparametric transformation survival model with multiple covariates. 2007

Xiao Song, and Shuangge Ma, and Jian Huang, and Xiao-Hua Zhou

Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. songx@u.washington.edu

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.

UI	MeSH Term	Description	Entries
D008175	Lung Neoplasms	Tumors or cancer of the LUNG.	Cancer of Lung,Lung Cancer,Pulmonary Cancer,Pulmonary Neoplasms,Cancer of the Lung,Neoplasms, Lung,Neoplasms, Pulmonary,Cancer, Lung,Cancer, Pulmonary,Cancers, Lung,Cancers, Pulmonary,Lung Cancers,Lung Neoplasm,Neoplasm, Lung,Neoplasm, Pulmonary,Pulmonary Cancers,Pulmonary Neoplasm
D003198	Computer Simulation	Computer-based representation of physical systems and phenomena such as chemical processes.	Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000970	Antineoplastic Agents	Substances that inhibit or prevent the proliferation of NEOPLASMS.	Anticancer Agent,Antineoplastic,Antineoplastic Agent,Antineoplastic Drug,Antitumor Agent,Antitumor Drug,Cancer Chemotherapy Agent,Cancer Chemotherapy Drug,Anticancer Agents,Antineoplastic Drugs,Antineoplastics,Antitumor Agents,Antitumor Drugs,Cancer Chemotherapy Agents,Cancer Chemotherapy Drugs,Chemotherapeutic Anticancer Agents,Chemotherapeutic Anticancer Drug,Agent, Anticancer,Agent, Antineoplastic,Agent, Antitumor,Agent, Cancer Chemotherapy,Agents, Anticancer,Agents, Antineoplastic,Agents, Antitumor,Agents, Cancer Chemotherapy,Agents, Chemotherapeutic Anticancer,Chemotherapy Agent, Cancer,Chemotherapy Agents, Cancer,Chemotherapy Drug, Cancer,Chemotherapy Drugs, Cancer,Drug, Antineoplastic,Drug, Antitumor,Drug, Cancer Chemotherapy,Drug, Chemotherapeutic Anticancer,Drugs, Antineoplastic,Drugs, Antitumor,Drugs, Cancer Chemotherapy
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D016019	Survival Analysis	A class of statistical procedures for estimating the survival function (function of time, starting with a population 100% well at a given time and providing the percentage of the population still well at later times). The survival analysis is then used for making inferences about the effects of treatments, prognostic factors, exposures, and other covariates on the function.	Analysis, Survival,Analyses, Survival,Survival Analyses