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Edward L Korn

Publications and source records attributed to Edward L Korn.

14 recordsLinked to original sources
PubMed2005

Assessing surrogates as trial endpoints using mixed models.

Having a surrogate for a definitive endpoint in a clinical trial can sometimes be useful when it is impractical, invasive or very time consuming to obtain the definitive endpoint. This paper discusses methods for assessing whether the surrogate-endpoint results of a trial can be used in place of definitive-endpoint results. It is important when examining this trial-level surrogacy to include the possibility of trial-level effects and to distinguish whether the treatment arms are naturally ordered, e.g. A vs A+B rather than A vs B. Methods using mixed models of trial-level summaries are discussed and compared to fixed-effects models and to the possibility of using models of individual-level data. We give estimators for definitive-endpoint results of a trial that are predicted from the surrogate-endpoint results of the trial and a set of results from previous trials in which both the definitive and surrogate trial results were available. Graphical displays are also suggested. Two sets of trial results previously analysed for trial-level surrogacy are used as examples.

Antineoplastic Agents↗
PubMed2004

Chromosome transfer induced aneuploidy results in complex dysregulation of the cellular transcriptome in immortalized and cancer cells.

Chromosomal aneuploidies are observed in essentially all sporadic carcinomas. These aneuploidies result in tumor-specific patterns of genomic imbalances that are acquired early during tumorigenesis, continuously selected for and faithfully maintained in cancer cells. Although the paradigm of translocation induced oncogene activation in hematologic malignancies is firmly established, it is not known how genomic imbalances affect chromosome-specific gene expression patterns in particular and how chromosomal aneuploidy dysregulates the genetic equilibrium of cells in general. To model specific chromosomal aneuploidies in cancer cells and dissect the immediate consequences of genomic imbalances on the transcriptome, we generated artificial trisomies in a karyotypically stable diploid yet mismatch repair-deficient, colorectal cancer cell line and in telomerase immortalized, cytogenetically normal human breast epithelial cells using microcell-mediated chromosome transfer. The global consequences on gene expression levels were analyzed using cDNA arrays. Our results show that regardless of chromosome or cell type, chromosomal trisomies result in a significant increase in the average transcriptional activity of the trisomic chromosome. This increase affects the expression of numerous genes on other chromosomes as well. We therefore postulate that the genomic imbalances observed in cancer cells exert their effect through a complex pattern of transcriptional dysregulation.

Aneuploidy↗
PubMed2004

Strength of accumulating evidence and data monitoring committee decision making.

The data monitoring committee (DMC) is a vital component of a randomized clinical trial. Its responsibilities include stopping the trial early for extreme results. The decision to stop the trial must be based on a careful synthesis of statistical methodology and clinical judgment. It is critical to ensure the validity of this complex process. In this paper we present results of a survey of 21 DMC members conducted to investigate how they evaluate accumulating evidence. The results indicate that some DMC members may be over-interpreting developing trends in the data.

Antineoplastic Agents↗
PubMed2004

Correcting log ratios for signal saturation in cDNA microarrays.

MOTIVATION: Pixel saturation occurs when the pixel intensity exceeds a threshold and the recorded pixel intensity is truncated. Microarray experiments are commonly afflicted with saturated pixels. As a result, estimators of gene expression are biased, with the amount of bias increasing as a function of the proportion of pixels saturated. Saturation is directly related to the photomultiplier tube (PMT) voltage settings and RNA abundance and is not necessarily associated with poor array or poor spot quality. When choosing PMT settings, higher PMT settings are desired because of improved signal-to-noise ratios of low-intensity spots. This improved signal is somewhat offset by saturation of high-intensity spots. In practice, spots with saturated pixels are discarded or the biased value is used. Neither of these approaches is appealing, particularly the former approach when a highly expressed gene is discarded because of saturation. RESULTS: We present a method to correct for saturation using pixel-level data. The method is based on a censored regression model. Evaluations on several arrays indicate that the method performs well. Simulation studies suggest that the method is robust under certain model violations.

Algorithms↗
PubMed2004

Objective method of comparing DNA microarray image analysis systems.

Many image analysis systems are available for processing the images produced by laser scanning of DNA microarrays. The image processing system takes pixel-level intensity data and converts it to a set of gene-level expression or copy number summaries that will be used in further analyses. Image analysis systems currently in use differ with regard to the specific algorithms they implement, ease of use, and cost. Thus, it would be desirable to have an objective means of comparing systems. Here we describe a systematic method of comparing image processing results produced by different image analysis systems using a series of replicate microarray experiments. We demonstrate the method with a comparison of cDNA microarray data generated by the UCSF Spot and the GenePix image processing systems.

Algorithms↗
PubMed2003

Breast cancer classification and prognosis based on gene expression profiles from a population-based study.

Comprehensive gene expression patterns generated from cDNA microarrays were correlated with detailed clinico-pathological characteristics and clinical outcome in an unselected group of 99 node-negative and node-positive breast cancer patients. Gene expression patterns were found to be strongly associated with estrogen receptor (ER) status and moderately associated with grade, but not associated with menopausal status, nodal status, or tumor size. Hierarchical cluster analysis segregated the tumors into two main groups based on their ER status, which correlated well with basal and luminal characteristics. Cox proportional hazards regression analysis identified 16 genes that were significantly associated with relapse-free survival at a stringent significance level of 0.001 to account for multiple comparisons. Of 231 genes previously reported by others [van't Veer, L. J., et al. (2002) Nature 415, 530-536] as being associated with survival, 93 probe elements overlapped with the set of 7,650 probe elements represented on the arrays used in this study. Hierarchical cluster analysis based on the set of 93 probe elements segregated our population into two distinct subgroups with different relapse-free survival (P < 0.03). The number of these 93 probe elements showing significant univariate association with relapse-free survival (P < 0.05) in the present study was 14, representing 11 unique genes. Genes involved in cell cycle, DNA replication, and chromosomal stability were consistently elevated in the various poor prognostic groups. In addition, glutathione S-transferase M3 emerged as an important survival marker in both studies. When taken together with other array studies, our results highlight the consistent biological and clinical associations with gene expression profiles.

Breast Neoplasms↗
PubMed2002

A testing procedure for survival data with few responders.

In the course of designing a clinical trial, investigators are often faced with the possibility that only a fraction of the patients will benefit from the experimental treatment. A proper clinical trial design requires prospective specification of the testing procedures to be used in the analysis. In the absence of reliable prognostic factors capable of identifying the appropriate subset of patients, there is a need for a test procedure that will be sensitive to a range of possible fractions of responders. Focusing on survival data, we propose guidelines for selecting a proper test procedure based on the anticipated proportion of responding patients. These guidelines suggest that the logrank test should be used when the fraction of responders is expected to be greater than 0.5, otherwise procedures based on weighted linear rank tests are preferable. Overall this approach provides good power properties when the treatment affects only a small proportion of patients while protecting against substantial loss of power when all patients are affected. Use of the procedure is illustrated with data from two published randomized studies.

Animals↗
PubMed2002

A comment on futility monitoring.

Futility monitoring of randomized clinical trials is becoming widely used. In this paper we discuss several concerns associated with aggressive monitoring for lack of activity in studies comparing experimental and control treatment arms: (1) stopping for futility when the experimental arm is doing better than the control arm, (2) conditional power not being low at the time of stopping, (3) potential loss of power to detect clinically interesting differences that are smaller than the design alternative, and (4) sensitivity of the power to the departure from the proportional hazards assumption. Our investigation suggests that aggressive futility rules do not generally reduce power (relative to less aggressive rules) under incorrect design assumptions such as overstatement of the target treatment effect or mild violations of the proportional hazards assumption. On the other hand, aggressive monitoring rules may result in an early termination for futility when the experimental arm is doing better than the control arm (in some cases nontrivially better, especially when trials are designed for unrealistically large effects). Thus, aggressive monitoring rules may fail to provide sufficiently convincing evidence to influence clinical practice or to establish a standard of treatment.

Guidelines as Topic↗
PubMed2002

Design of a binary biomarker study from the results of a pilot study.

Biomarkers are increasingly used in clinical and epidemiologic studies. Prior to these studies, small pilot studies are often conducted to assess the reproducibility of the biomarker. This article discusses how the results of a pilot study can be used to design subsequent studies when the biomarker is a binary assessment. We consider situations in which the pilot study has two factors (e.g., laboratory and individual) that are either crossed or nested. We discuss how binary random-effects models can be used for estimating the sources of variation and how parameter estimates from these models can be used to appropriately design future studies. We also show that fitting a linear variance components model that ignores the binary nature of the data is a simple alternative method that results in nearly unbiased and moderately efficient estimators of important design parameters. We illustrate the methodology with data from a study assessing the reproducibility of p53 immunohistochemistry in bladder tumors.

Biomarkers↗