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At least 1,135 records · Page 63Linked to original sources
PubMed1999

Group sequential procedures for repeated events data with frailty.

In this article we consider the design aspects of group sequential trials with recurrent study endpoints, where the subjects are from a heterogeneous population. The usual procedures of sequential analysis based on the "independent increments" property are no longer valid due to the heterogeneity of the study subjects, as pointed out by Cook and Lawless. We focus on the aspect of study planning and consider a special model (Poisson process with frailty) to investigate the effects of frailty (or subject heterogeneity). We prescribe the valid procedures and provide convenient tables for computing stopping boundaries and sample sizes. The methodology is illustrated through an example of an animal experiment with recurrent tumors, using procedures such as group sequential tests and repeated confidence intervals. Minimal cost analysis is considered for determining the optimal combination of study duration and sample size.

Animals↗
PubMed2004

How many samples are needed to build a classifier: a general sequential approach.

MOTIVATION: The standard paradigm for a classifier design is to obtain a sample of feature-label pairs and then to apply a classification rule to derive a classifier from the sample data. Typically in laboratory situations the sample size is limited by cost, time or availability of sample material. Thus, an investigator may wish to consider a sequential approach in which there is a sufficient number of patients to train a classifier in order to make a sound decision for diagnosis while at the same time keeping the number of patients as small as possible to make the studies affordable. RESULTS: A sequential classification procedure is studied via the martingale central limit theorem. It updates the classification rule at each step and provides stopping criteria to ensure with a certain confidence that at stopping a future subject will have misclassification probability smaller than a predetermined threshold. Simulation studies and applications to microarray data analysis are provided. The procedure possesses several attractive properties: (1) it updates the classification rule sequentially and thus does not rely on distributions of primary measurements from other studies; (2) it assesses the stopping criteria at each sequential step and thus can substantially reduce cost via early stopping; and (3) it is not restricted to any particular classification rule and therefore applies to any parametric or non-parametric method, including feature selection or extraction. AVAILABILITY: R-code for the sequential stopping rule is available at http://stat.tamu.edu/~wfu/microarray/sequential/R-code.html

Algorithms↗
PubMed2004

A simple procedure for estimating the false discovery rate.

MOTIVATION: The most used criterion in microarray data analysis is nowadays the false discovery rate (FDR). In the framework of estimating procedures based on the marginal distribution of the P-values without any assumption on gene expression changes, estimators of the FDR are necessarily conservatively biased. Indeed, only an upper bound estimate can be obtained for the key quantity pi0, which is the probability for a gene to be unmodified. In this paper, we propose a novel family of estimators for pi0 that allows the calculation of FDR. RESULTS: The very simple method for estimating pi0 called LBE (Location Based Estimator) is presented together with results on its variability. Simulation results indicate that the proposed estimator performs well in finite sample and has the best mean square error in most of the cases as compared with the procedures QVALUE, BUM and SPLOSH. The different procedures are then applied to real datasets. AVAILABILITY: The R function LBE is available at http://ifr69.vjf.inserm.fr/lbe CONTACT: broet@vjf.inserm.fr.

Algorithms↗
PubMed2006

Nonparametric pathway-based regression models for analysis of genomic data.

High-throughout genomic data provide an opportunity for identifying pathways and genes that are related to various clinical phenotypes. Besides these genomic data, another valuable source of data is the biological knowledge about genes and pathways that might be related to the phenotypes of many complex diseases. Databases of such knowledge are often called the metadata. In microarray data analysis, such metadata are currently explored in post hoc ways by gene set enrichment analysis but have hardly been utilized in the modeling step. We propose to develop and evaluate a pathway-based gradient descent boosting procedure for nonparametric pathways-based regression (NPR) analysis to efficiently integrate genomic data and metadata. Such NPR models consider multiple pathways simultaneously and allow complex interactions among genes within the pathways and can be applied to identify pathways and genes that are related to variations of the phenotypes. These methods also provide an alternative to mediating the problem of a large number of potential interactions by limiting analysis to biologically plausible interactions between genes in related pathways. Our simulation studies indicate that the proposed boosting procedure can indeed identify relevant pathways. Application to a gene expression data set on breast cancer distant metastasis identified that Wnt, apoptosis, and cell cycle-regulated pathways are more likely related to the risk of distant metastasis among lymph-node-negative breast cancer patients. Results from analysis of other two breast cancer gene expression data sets indicate that the pathways of Metalloendopeptidases (MMPs) and MMP inhibitors, as well as cell proliferation, cell growth, and maintenance are important to breast cancer relapse and survival. We also observed that by incorporating the pathway information, we achieved better prediction for cancer recurrence.

Adult↗
PubMed1991

Analysis of the temporal patterns of benefits in the Health Insurance Plan of Greater New York trial by stage and age.

Reductions in breast cancer mortality in the Health Insurance Plan of Greater New York trial are examined by age at entry and stage at diagnosis using a stage shift cancer screening model. The results indicate that for women aged 40-49 years at entry, benefits are associated with an internal stage shift of stage 1 cancers that have a prognosis poorer than the usual stage 1 cancers. Further, the results indicate that after 18 years of follow-up, of the reduction of 16 fewer deaths in the intervention group, 12-15 of the deaths are related to this internal shift. Given that stage 1 cases inherently have relatively good survival, the time required to see the screening benefit is substantial. For women aged 50-64 years at entry, the results suggest that benefits are associated primarily with shifts to or within stage 1. Further, the results indicate that after 6 years, of the reduction of 31 fewer deaths in the intervention group, 22 of the deaths are related to external shifts to stage 1. Given that stage 2 cancers have poorer survival than stage 1 cancers, screening benefit is seen sooner for the older cohort than for the younger cohort.

Adult↗
PubMed2004

Interpretation by modelling of observations in radon radiation carcinogenesis.

Biophysical models for radon-related induction of lung cancer are developed with the aim of reducing the uncertainties in current risk estimates at low doses by a better understanding of the relevant mechanisms. These models can make use of the full dosimetric information when extracting information on, say, age-at-exposure, protraction or fractionation effects. It is found that irradiation by radon and its progeny does act on the initiating event of carcinogenesis (e.g. mutation), but its dominating effect is via promoting the division of already initiated cells. Data show that the concept of a unit of exposure giving, in an additive way, a unit of lung cancer risk is too limited, while relatively simple mechanistic assumptions described in this article do yield an adequate description of observations. Exposures in epidemiological data sets are measured with error. For various error models it has been shown that likelihood-based techniques of correction work reliably; likewise for biologically based cancer models. When several parameters are allowed to be exposure dependent, for example, initiation and promotion, then their relative importance is influenced.

Air Pollutants, Radioactive↗
PubMed2003

Validation of an integrated staging system toward improved prognostication of patients with localized renal cell carcinoma in an international population.

PURPOSE: Outcome prediction for patients with renal cell carcinoma is based on a combination of factors. In this study a previously published clinical outcome algorithm based on 1997 T stage, Fuhrman grade and performance score is validated using an international database. MATERIALS AND METHODS: A total of 1,060 patients from Nijmegen, the Netherlands (NN), MD Anderson (MDA) and University of California, Los Angeles (UCLA) who had localized renal cell carcinoma were evaluated for outcome prediction using a clinical outcome algorithm previously shown to stratify patients into low, intermediate and high risk groups. Validation was performed by comparing the 3 risk groups separately within the 3 centers as well as by comparing hazard ratios and concordance indices among the 3 centers. RESULTS: Estimated disease specific survival rates at 5 years for the low risk groups were 94% (NN), 92% (MDA) and 93% (UCLA). The 5-year disease specific survival rates for the intermediate risk groups were 65% (NN), 73% (MDA) and 78% (UCLA), while the rates for the high risk groups were 40% (NN), 30% (MDA) and 48% (UCLA). The concordance indices for each of the databases were 79% (NN), 86% (MDA) and 84% (UCLA). CONCLUSIONS: A clinical algorithm that uses only 3 prognostic variables (1997 T stage, Fuhrman grade and performance status) to stratify patients with localized renal cell carcinoma into 3 risk groups has been shown to be applicable to external databases. This algorithm may be useful for patient counseling, surveillance and identification of high risk patients for enrollment in clinical trials.

Algorithms↗