Showing papers by "Ross L. Prentice published in 2001"

Statistical modeling of large microarray data sets to identify stimulus-response profiles.

Abstract: A statistical modeling approach is proposed for use in searching large microarray data sets for genes that have a transcriptional response to a stimulus. The approach is unrestricted with respect to the timing, magnitude or duration of the response, or the overall abundance of the transcript. The statistical model makes an accommodation for systematic heterogeneity in expression levels. Corresponding data analyses provide gene-specific information, and the approach provides a means for evaluating the statistical significance of such information. To illustrate this strategy we have derived a model to depict the profile expected for a periodically transcribed gene and used it to look for budding yeast transcripts that adhere to this profile. Using objective criteria, this method identifies 81% of the known periodic transcripts and 1,088 genes, which show significant periodicity in at least one of the three data sets analyzed. However, only one-quarter of these genes show significant oscillations in at least two data sets and can be classified as periodic with high confidence. The method provides estimates of the mean activation and deactivation times, induced and basal expression levels, and statistical measures of the precision of these estimates for each periodic transcript.

Indirect Calorimetry Protocol Development for Measuring Resting Metabolic Rate as a Component of Total Energy Expenditure in Free-Living Postmenopausal Women

Abstract: An objective measure of energy intake is needed in epidemiologic studies to evaluate random and systematic error associated with dietary self-report tools. Total energy expenditure in weight-stable humans is accepted as a measure of energy intake, but doubly labeled water remains cost prohibitive for large studies. Our purpose was to develop a practical indirect calorimetry (IC) protocol for estimating resting metabolic rate (RMR) in free-living, postmenopausal women. We conducted duplicate IC measures 1 wk apart using a canopy system on 102 women ages 50-79 y from the Seattle area. We compared RMR for 0-5, 5-10, 5-15, 5-20, 5-25, 5-30, and 0- to 30-min IC segments and segments meeting stability criteria. The mean RMR for the first 5 min was significantly higher than other time segments (P = 0.001). Correlation coefficients between duplicate measures were high (r = 0.90). Use of defined stability criteria produced RMR measures that were 10-30 kcal (42-126 kJ) higher than the 5- to 10-min RMR measures and 40-60% of subjects did not achieve these stability criteria. For protocols including IC to assess RMR as a component of total energy expenditure in free-living, postmenopausal women, a single 10-min canopy study, excluding the first 5 min of data, produces reliable results with minimal subject burden.

A risk set calibration method for failure time regression by using a covariate reliability sample

Abstract: Regression parameter estimation in the Cox failure time model is considered when regression variables are subject to measurement error. Assuming that repeat regression vector measurements adhere to a classical measurement model, we can consider an ordinary regression calibration approach in which the unobserved covariates are replaced by an estimate of their conditional expectation given available covariate measurements. However, since the rate of withdrawal from the risk set across the time axis, due to failure or censoring, will typically depend on covariates, we may improve the regression parameter estimator by recalibrating within each risk set. The asymptotic and small sample properties of such a risk set regression calibration estimator are studied. A simple estimator based on a least squares calibration in each risk set appears able to eliminate much of the bias that attends the ordinary regression calibration estimator under extreme measurement error circumstances. Corresponding asymptotic distribution theory is developed, small sample properties are studied using computer simulations and an illustration is provided.

Statistical modeling to analyze large data arrays

Abstract: A method for analyzing large data arrays is provided. In one aspect, the invention provides a method for analysing data from two or more data arrays. Each array includes a plurality of members, each member provides a signal, and the data is indexed by one or more parameters. In one embodiment, the method includes fitting a model to the data; determining the goodness of the fit by evaluating the statistical significance of the fit; and determining the statistical significance of the signal. In another embodiment, the method further includes correcting the data for heterogeneity among members prior to fitting the model to the data.

Recalibration based on an approximate relative risk estimator in cox regression with missing covariates

Abstract: We consider estimation in Cox (1972) regression with missing covariates. We focus on the situation when observable covariates and surrogates are contin- uous. To estimate the induced relative risk, we approximate it by a function of the conditional mean and the conditional variance of the missing variable, given observable covariate and surrogate variables in the risk set at each failure time. This approach may be considered as a higher order extension of the usual regres- sion calibration method, and hence can be expected to reduce bias, especially when the relative risk or the estimating error from the surrogate variables is large. The proposed estimator arises from an approximate approach, so that the magnitude of any bias needs to be studied. Asymptotic distribution theory is developed and small sample performance is examined. We illustrate the method by an example from a medical study.

Methodologic challenges in chronic disease population research.

Abstract: Some recent important, and controversial, chronic disease population research settings are reviewed. These include studies of hormone replacement therapy and coronary heart disease; studies of dietary fat in relation to breast cancer; and studies of beta-carotene supplementation and lung cancer. In each case methodologic developments having a strong biostatistical component are identified as key to future progress. Some comments are also made on the need for an expanded disease prevention intervention development enterprise, and on the role that microarray genetic and genomic data may play in such development.

Incorporating age at onset information into the transmission/disequilibrium test.

Abstract: In this paper, we proposed two approaches for incorporating the age at onset into the transmission/disequilibrium test (TDT). Trios (affected offspring and their parents) were extracted from the first four replicate data sets of the general population type. Focusing on chromosome 6 where MG6 and MG7 reside, we compared the usual TDT with the newly proposed tests in terms of gene localization.