Antiplatelet therapy use and the risk of venous thromboembolic events in the Raloxifene Use for the Heart (RUTH) trial.

BACKGROUND: Raloxifene use in postmenopausal women with osteoporosis increases the risk of venous thromboembolic events (VTE) 2-fold compared with placebo. Platelet activation is involved in the pathophysiology of arterial thromboses more than venous thromboses, but aspirin may reduce VTE risk associated with estrogen use. This analysis examines the effects of concomitant antiplatelet therapy on VTE risk in raloxifene-treated women. METHODS: In the Raloxifene Use for the Heart (RUTH) trial, 10,101 postmenopausal women from 177 sites in 26 countries at increased risk of coronary heart disease (CHD) (primary prevention cohort) or with CHD (secondary prevention cohort) were randomized to placebo or raloxifene 60 mg/day and followed for a median 5.6 years. Reports of clinical symptoms of VTE were assessed. Concomitant use of antiplatelet agents (aspirin, clopidogrel, ticlopidine, dipyridamole) was allowed. Cox proportional hazard models, with use of warfarin, presence of fracture, and hospitalization as covariates, were used to estimate hazard ratios (HR) with 95% confidence intervals (CI). RESULTS: Overall, raloxifene use was associated with an increased VTE risk (HR 1.44, 95% CI 1.06-1.95) vs. placebo. Most women (72%) reported using aspirin, and 14.2% reported using nonaspirin antiplatelet agents during the study period. Users of antiplatelet agents were older, more likely to have CHD, and more likely to be hyperlipidemic. They had a higher VTE risk than nonusers. No difference in VTE risk was observed in women who used raloxifene alone vs. those who used raloxifene with antiplatelet agents during the study. The increase in VTE risk with raloxifene compared with placebo was not different between women who used antiplatelet agents at baseline (HR 1.44, 95% CI 0.98, 2.10) and those who did not use antiplatelet agents (HR 1.37, 95% CI 0.83, 2.27) (interaction p = 0.88). Similar conclusions were noted for aspirin and nonaspirin antiplatelet use. CONCLUSIONS: In RUTH, postmenopausal women treated with raloxifene had an increased risk of VTE compared with placebo. Concomitant use of aspirin or nonaspirin antiplatelet agents along with raloxifene did not change VTE risk.

Modification of Kolmogorov-Smirnov test for DNA content data analysis through distribution alignment.

The Kolmogorov-Smirnov (K-S) test is a statistical method often used for comparing two distributions. In high-throughput screening (HTS) studies, such distributions usually arise from the phenotype of independent cell populations. However, the K-S test has been criticized for being overly sensitive in applications, and it often detects a statistically significant difference that is not biologically meaningful. One major reason is that there is a common phenomenon in HTS studies that systematic drifting exists among the distributions due to reasons such as instrument variation, plate edge effect, accidental difference in sample handling, etc. In particular, in high-content cellular imaging experiments, the location shift could be dramatic since some compounds themselves are fluorescent. This oversensitivity of the K-S test is particularly overpowered in cellular assays where the sample sizes are very big (usually several thousands). In this paper, a modified K-S test is proposed to deal with the nonspecific location-shift problem in HTS studies. Specifically, we propose that the distributions are "normalized" by density curve alignment before the K-S test is conducted. In applications to simulation data and real experimental data, the results show that the proposed method has improved specificity.

At what scale should microarray data be analyzed?

INTRODUCTION: The hybridization intensities derived from microarray experiments, for example Affymetrix's MAS5 signals, are very often transformed in one way or another before statistical models are fitted. The motivation for performing transformation is usually to satisfy the model assumptions such as normality and homogeneity in variance. Generally speaking, two types of strategies are often applied to microarray data depending on the analysis need: correlation analysis where all the gene intensities on the array are considered simultaneously, and gene-by-gene ANOVA where each gene is analyzed individually. AIM: We investigate the distributional properties of the Affymetrix GeneChip signal data under the two scenarios, focusing on the impact of analyzing the data at an inappropriate scale. METHODS: The Box-Cox type of transformation is first investigated for the strategy of pooling genes. The commonly used log-transformation is particularly applied for comparison purposes. For the scenario where analysis is on a gene-by-gene basis, the model assumptions such as normality are explored. The impact of using a wrong scale is illustrated by log-transformation and quartic-root transformation. RESULTS: When all the genes on the array are considered together, the dependent relationship between the expression and its variation level can be satisfactorily removed by Box-Cox transformation. When genes are analyzed individually, the distributional properties of the intensities are shown to be gene dependent. Derivation and simulation show that some loss of power is incurred when a wrong scale is used, but due to the robustness of the t-test, the loss is acceptable when the fold-change is not very large.