Biomedical subjects

Hemant Ishwaran

Publications and source records attributed to Hemant Ishwaran.

6 recordsLinked to original sources

PubMed2006

CART variance stabilization and regularization for high-throughput genomic data.

MOTIVATION: mRNA expression data obtained from high-throughput DNA microarrays exhibit strong departures from homogeneity of variances. Often a complex relationship between mean expression value and variance is seen. Variance stabilization of such data is crucial for many types of statistical analyses, while regularization of variances (pooling of information) can greatly improve overall accuracy of test statistics. RESULTS: A Classification and Regression Tree (CART) procedure is introduced for variance stabilization as well as regularization. The CART procedure adaptively clusters genes by variances. Using both local and cluster wide information leads to improved estimation of population variances which improves test statistics. Whereas making use of cluster wide information allows for variance stabilization of data. AVAILABILITY: Sufficient details for our CART procedure are given so that the interested reader can program the method for themselves. The algorithm is also accessible within the Java software package BAMarray(TM), which is freely available to non-commercial users at www.bamarray.com. CONTACT: hemant.ishwaran@gmail.com.

Artificial Intelligence↗

PubMed2006

BAMarraytrade mark: Java software for Bayesian analysis of variance for microarray data.

BACKGROUND: DNA microarrays open up a new horizon for studying the genetic determinants of disease. The high throughput nature of these arrays creates an enormous wealth of information, but also poses a challenge to data analysis. Inferential problems become even more pronounced as experimental designs used to collect data become more complex. An important example is multigroup data collected over different experimental groups, such as data collected from distinct stages of a disease process. We have developed a method specifically addressing these issues termed Bayesian ANOVA for microarrays (BAM). The BAM approach uses a special inferential regularization known as spike-and-slab shrinkage that provides an optimal balance between total false detections and total false non-detections. This translates into more reproducible differential calls. Spike and slab shrinkage is a form of regularization achieved by using information across all genes and groups simultaneously. RESULTS: BAMarray is a graphically oriented Java-based software package that implements the BAM method for detecting differentially expressing genes in multigroup microarray experiments (up to 256 experimental groups can be analyzed). Drop-down menus allow the user to easily select between different models and to choose various run options. BAMarraycan also be operated in a fully automated mode with preselected run options. Tuning parameters have been preset at theoretically optimal values freeing the user from such specifications. BAMarray provides estimates for gene differential effects and automatically estimates data adaptive, optimal cutoff values for classifying genes into biological patterns of differential activity across experimental groups. A graphical suite is a core feature of the product and includes diagnostic plots for assessing model assumptions and interactive plots that enable tracking of prespecified gene lists to study such things as biological pathway perturbations. The user can zoom in and lasso genes of interest that can then be saved for downstream analyses. CONCLUSION: BAMarray is user friendly platform independent software that effectively and efficiently implements the BAM methodology. Classifying patterns of differential activity is greatly facilitated by a data adaptive cutoff rule and a graphical suite. BAMarray is licensed software freely available to academic institutions. More information can be found at http://www.bamarray.com.

Algorithms↗

PubMed2004

Multireader, multicase receiver operating characteristic analysis: an empirical comparison of five methods.

RATIONALE AND OBJECTIVES: Several statistical methods have been developed for analyzing multireader, multicase (MRMC) receiver operating characteristic (ROC) studies. The objective of this article is to increase awareness of these methods and determine if their results are concordant for published datasets. MATERIALS AND METHODS: Data from three previously published studies were reanalyzed using five MRMC methods. For each method the 95% confidence intervals (CIs) for the mean of the readers' ROC areas for each diagnostic test, the P value for the comparison of the diagnostic tests' mean accuracies, and the 95% CIs for the mean difference in ROC areas of the diagnostic tests were reported. RESULTS: Important differences in P values and CIs were seen when using parametric versus nonparametric estimates of accuracy, and there were the expected differences for random-reader versus fixed-reader models. Controlling for these differences, the Dorfman-Berbaum-Metz (DBM), Obuchowski-Rockette, Beiden-Wagner-Campbell, and Song's multivariate Wilcoxon-Mann-Whitney (WMW) methods gave almost identical results for the fixed-reader model. For the random-reader model, the DBM, Obuchowski-Rockette, and Beiden-Wagner-Campbell methods yielded approximately the same inferences, but the CIs for the Beiden-Wagner-Campbell method tend to be broader. Ishwaran's hierarchical ROC sometimes yielded significance not found with other methods. Song's modification of DBM's jack-knifing algorithm sometimes led to different conclusions than the original DBM algorithm. CONCLUSION: In choosing and applying MRMC methods, it is important to recognize: (1) the distinction between random-reader and fixed-reader models, the uncertainties accounted for by each, and thus the level of generalizeability expected from each; (2) assumptions made by the various MRMC methods; and (3) limitations of a five- or six-reader study when the reader variability is great.

Analysis of Variance↗

PubMed2004

Health-related quality of life after coronary artery bypass grafting: a gender analysis using the Duke Activity Status Index.

OBJECTIVE: Our objectives were to document the preoperative and postoperative functional status of patients undergoing coronary artery bypass grafting, to examine factors that influence functional recovery, and to determine whether gender differences exist in the preoperative and postoperative functional status with the Duke Activity Status Index. METHODS: One thousand eight hundred twenty-five patients undergoing isolated coronary artery bypass grafting had baseline and follow-up quality-of-life surveys. Mean follow-up from baseline to postoperative Duke Activity Status Index was 8.0 months for women and men. The influence of 47 variables, in addition to baseline scores on postoperative functional status, was examined with logistic ordinal modeling. An ordinal model for the follow-up score was determined by means of backward selection, with variables retained if they satisfied the criterion of a P value of less than.05. RESULTS: Median baseline Duke Activity Status Index scores (women, 21.5; men, 32.2; P <.001) and first follow-up scores (women, 42.7; men, 58.2; P <.001) were lower in women than in men. Patients who were older and those who had chronic obstructive pulmonary disease, myocardial infarction, stroke, diabetes, vascular disease, postoperative serious infection, and return to the operating room had lower postoperative scores. After adjusting for these factors, women still had lower follow-up scores (odds ratio for men, 2.1 [95% confidence interval, 1.7-2.6]; P <.001). CONCLUSIONS: A number of preoperative factors, operative variables, and postoperative events are associated with functional recovery after coronary revascularization. In addition, female gender is associated with more postoperative functional impairment after adjusting for these perioperative variables.

Adult↗

PubMed2002

Use of the logical analysis of data method for assessing long-term mortality risk after exercise electrocardiography.

BACKGROUND: Logical Analysis of Data is a methodology of mathematical optimization on the basis of the systematic identification of patterns or "syndromes." In this study, we used Logical Analysis of Data for risk stratification and compared it to regression techniques. METHODS AND RESULTS: Using a cohort of 9454 patients referred for exercise testing, Logical Analysis of Data was applied to identify syndromes based on 20 variables. High-risk syndromes were patterns of up to 3 findings associated with >5-fold increase in risk of death, whereas low-risk syndromes were associated with >5-fold decrease. Syndromes were derived on a randomly derived training set of 4722 patients and validated in 4732 others. There were 15 high-risk and 26 low-risk syndromes. A risk score was derived based on the proportion of possible high risk and low risk syndromes present. A value > or =0, meaning the same or a greater proportion of high-risk syndromes, was noted in 979 patients (21%) in the validation set and was predictive of 5-year death (11% versus 1%, hazard ratio 8.3, 95% CI 5.9 to 11.6, P<0.0001), accounting for 67% of events. Calibration of expected versus observed death rates based on Logical Analysis of Data and Cox regression showed that both methods performed very well. CONCLUSION: Using the Logical Analysis of Data method, we identified subsets of patients who had an increased risk and who also accounted for the majority of deaths. Future research is needed to determine how best to use this technique for risk stratification.

Cardiovascular Diseases↗

PubMed2002

Staging of neuroblastoma at imaging: report of the radiology diagnostic oncology group.

PURPOSE: To compare the accuracies of computed tomography (CT), magnetic resonance (MR) imaging, and bone scintigraphy in staging disease in patients with neuroblastoma. MATERIALS AND METHODS: Ninety-six children with newly diagnosed neuroblastoma were enrolled in a multicenter prospective cohort study. CT, MR, and bone scintigraphy were used to evaluate tumor stage. Sensitivity and specificity values and receiver operating characteristic (ROC) curve analyses were used to compare the accuracy of CT, MR, and scintigraphy for tumor staging. RESULTS: Eighty-eight patients were eligible for staging analysis, and 45 patients who underwent surgery at initial diagnosis were eligible for analysis of local tumor extent. CT and MR had sensitivities of 43% and 83%, respectively (P <.01), and specificities of 97% and 88%, respectively (P >.05), for detection of stage 4 disease. Areas under the ROC curves for CT and MR were 0.81 and 0.85, respectively (P =.06); that for scintigraphy was 0.83. Addition of scintigraphy to both CT and MR increased the areas under the ROC curves to 0.90 and 0.88, respectively. Accuracy of CT and MR for staging disease confined to the chest or abdomen (stages 1, 2, and 3) was poor. CONCLUSION: MR alone and CT and MR combined with bone scintigraphy enable the accurate detection of stage 4 disease. Both CT and MR perform poorly for local tumor staging.

Bone Neoplasms↗