CiteULike: jyuh Abbas

Clinical trial optimization: Monte Carlo simulation Markov model for planning clinical trials recruitment.

I Abbas — 2008-06-14T08:49:02-00:00

Contemporary clinical trials, Vol. 28, No. 3. (May 2007), pp. 220-231.

INTRODUCTION: The patient recruitment process of clinical trials is an essential element which needs to be designed properly. METHODS: In this paper we describe different simulation models under continuous and discrete time assumptions for the design of recruitment in clinical trials. RESULTS: The results of hypothetical examples of clinical trial recruitments are presented. The recruitment time is calculated and the number of recruited patients is quantified for a given time and probability of recruitment. The expected delay and the effective recruitment durations are estimated using both continuous and discrete time modeling. CONCLUSION: The proposed type of Monte Carlo simulation Markov models will enable optimization of the recruitment process and the estimation and the calibration of its parameters to aid the proposed clinical trials. A continuous time simulation may minimize the duration of the recruitment and, consequently, the total duration of the trial.

Cardiac troponin I concentration is commonly increased in nondialysis patients with CKD: experience with a sensitive assay.

EJ Lamb — 2008-04-18T02:43:13-00:00

American journal of kidney diseases : the official journal of the National Kidney Foundation, Vol. 49, No. 4. (April 2007), pp. 507-516.

BACKGROUND: Cardiac troponin (cTn) concentrations commonly are increased in patients with chronic kidney disease (CKD) in the absence of an acute coronary syndrome. cTn T (cTnT) concentration reportedly is increased more commonly than cTn I (cTnI). Using a sensitive cTnI assay, we studied cTnI concentrations in predialysis patients with CKD who did not have an acute coronary event. STUDY DESIGN: Observational cohort study. SETTING AND PARTICIPANTS: Nondialysis patients with CKD attending an outpatient clinic. PREDICTOR: Plasma cTnI was measured using the cTnI-Ultra assay (Bayer HealthCare LLC, Diagnostics Division, Tarrytown, NY), the same manufacturer's standard cTnI assay, and a cTnT assay (Roche Diagnostics PLC, East Sussex, UK). OUTCOMES AND MEASUREMENTS: Prevalence of increased cTn concentration, effect of clinical variables on cTnI-Ultra concentration, and independent associations between cTn assays and all-cause mortality by using multiple regression modeling. RESULTS: Plasma cTnI-Ultra concentration exceeded the upper limit of normal in 33% of patients compared with 18% with the cTnI-standard assay and 43% with the cTnT assay. Age, vascular disease, parathyroid hormone concentration, and left ventricular mass, but not kidney function, had independent effects on plasma cTnI-Ultra concentrations. There were 39 deaths during follow-up. Survival was decreased in patients with baseline cTnI-Ultra concentrations of 0.040 ng/mL or greater (54% versus 83%; P < 0.001), cTnI-standard concentrations of 0.07 ng/mL or greater (55% versus 78%; P = 0.02), and cTnT concentrations of 0.01 ng/mL or greater (59% versus 89%; P < 0.001). Only cTnT concentration was an independent predictor of death. LIMITATION: Only all-cause mortality was recorded. CONCLUSION: Using a sensitive assay, we found that the prevalence of increased cTnI concentrations in patients with CKD is similar to that observed for cTnT. cTnT concentration, but not cTnI, was independently associated with death.

SNPExpress: integrated visualization of genome-wide genotypes, copy numbers and gene expression levels.

Mathijs Sanders — 2008-02-01T12:58:06-00:00

BMC Genomics, Vol. 9, No. 1. (25 January 2008)

ABSTRACT: BACKGROUND: Accurate analyses of comprehensive genome-wide SNP genotyping and gene expression data sets is challenging for many researchers. In fact, obtaining an integrated view of both large scale SNP genotyping and gene expression is currently complicated since only a limited number of appropriate software tools are available. RESULTS: We present SNPExpress, a software tool to accurately analyze Affymetrix and Illumina SNP genotype calls, copy numbers, polymorphic copy number variations (CNVs) and Affymetrix gene expression in a combinatorial and efficient way. In addition, SNPExpress allows concurrent interpretation of these items with Hidden-Markov Model (HMM) inferred Loss-of-Heterozygosity (LOH)- and copy number regions. CONCLUSION: The combined analyses with the easily accessible software tool SNPExpress will not only facilitate the recognition of recurrent genetic lesions, but also the identification of critical pathogenic genes.