BACKGROUND: Telemonitoring interventions featuring transmission of home glucose records to healthcare providers have resulted in improved glycemic control in patients with diabetes. No research has addressed the intensity or duration of telemonitoring required to sustain such improvements. PURPOSE: The DiaTel study (10 January 2005 to 1 November 2007) compared active care management (ACM) with home telemonitoring (n=73) to monthly care coordination (CC) telephone calls (n=77) among veterans with diabetes and suboptimal glycemic control. The purpose of the DiaTel Extension was to assess whether initial improvements could be sustained with interventions of the same or lower intensity among participants who re-enrolled in a 6-month extension of DiaTel. METHODS: DiaTel participants receiving ACM were re-assigned randomly to monthly CC calls with continued telemonitoring but no active medication management (ACM-to-CCHT, n=23) or monthly CC telephone calls (ACM-to-CC, n=21). DiaTel participants receiving CC were re-assigned randomly to continued CC (CC-to-CC, n=28) or usual care (UC, ie, CC-to-UC, n=29). Hemaglobin A1c (HbA1c) was assessed at 3 and 6 months following re-randomization. RESULTS: Marked HbA1c improvements observed in DiaTel ACM participants were sustained 6 months after re-randomization in both ACM-to-CCHT and ACM-to-CC groups. Lesser HbA1c improvements observed in DiaTel CC participants were sustained in both CC-to-CC and CC-to-UC groups. No benefit was apparent for continued transmission of glucose data among DiaTel ACM participants or continued monthly telephone calls among DiaTel CC participants 6 months after re-randomization. CONCLUSION: Significant improvements in HbA1c achieved using home telemonitoring and active medication management for 6 months were sustained 6 months later with interventions of decreased intensity in VA Health System-qualified veterans. CLINICAL TRIAL REG. NO: NCT00245882, http://www.clinicaltrials.gov.

MOTIVATION: Traditional genomic prediction models based on individual genes suffer from low reproducibility across microarray studies due to the lack of robustness to expression measurement noise and gene missingness when they are matched across platforms. It is common that some of the genes in the prediction model established in a training study cannot be matched to another test study because a different platform is applied. The failure of inter-study predictions has severely hindered the clinical applications of microarray. To overcome the drawbacks of traditional gene-based prediction (GBP) models, we propose a module-based prediction (MBP) strategy via unsupervised gene clustering. RESULTS: K-means clustering is used to group genes sharing similar expression profiles into gene modules, and small modules are merged into their nearest neighbors. Conventional univariate or multivariate feature selection procedure is applied and a representative gene from each selected module is identified to construct the final prediction model. As a result, the prediction model is portable to any test study as long as partial genes in each module exist in the test study. We demonstrate that K-means cluster sizes generally follow a multinomial distribution and the failure probability of inter-study prediction due to missing genes is diminished by merging small clusters into their nearest neighbors. By simulation and applications of real datasets in inter-study predictions, we show that the proposed MBP provides slightly improved accuracy while is considerably more robust than traditional GBP. AVAILABILITY: http://www.biostat.pitt.edu/bioinfo/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Reproducibility analyses of biologically relevant microarray studies have mostly focused on overlap of detected biomarkers or correlation of differential expression evidences across studies. For clinical utility, direct inter-study prediction (i.e. to establish a prediction model in one study and apply to another) for disease diagnosis or prognosis prediction is more important. Normalization plays a key role for such a task. Traditionally, sample-wise normalization has been a standard for inter-array and inter-study normalization. For gene-wise normalization, it has been implemented for intra-study or inter-study predictions in a few papers while its rationale, strategy and effect remain unexplored. RESULTS: In this article, we investigate the effect of gene-wise normalization in microarray inter-study prediction. Gene-specific intensity discrepancies across studies are commonly found even after proper sample-wise normalization. We explore the rationale and necessity of gene-wise normalization. We also show that the ratio of sample sizes in normal versus diseased groups can greatly affect the performance of gene-wise normalization and an analytical method is developed to adjust for the imbalanced ratio effect. Both simulation results and applications to three lung cancer and two prostate cancer data sets, considering both binary classification and survival risk predictions, showed significant and robust improvement of the new adjustment. A calibration scheme is developed to apply the ratio-adjusted gene-wise normalization for prospective clinical trials. The number of calibration samples needed is estimated from existing studies and suggested for future applications. The result has important implication to the translational research of microarray as a practical disease diagnosis and prognosis prediction tool.