Meeting the needs of medical research with statistical methods

Meeting the needs of medical research with statistical methods Editorial (2840) Total Article Views Authors: Wu D Published Date April 2011 Volume 2011:1 Pages 1 - 2 DOI: http://dx.doi.org/10.2147/OAMS.S20259 Dongfeng Wu Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY, USA Open Access Medical Statistics is a brand new journal. The aim is to meet the needs of medical research with modern statistical methods. Medicine is a science dealing with health issues in a broad area, where chances or random events play a significant role. Statistics is a science that quantifies random events and helps researchers make valid conclusions and decisions; it involves data collection, data organization, and summarization, making statistical valid inferences and predictions. Modern medicine has been developed over hundreds of years, with new research areas coming up frequently; likewise for statistical science. Medical research needs statistical methods to test hypothesis, design clinical trials, make inferences and predictions, and develop future directions. Statistical research needs medical backgrounds and scenarios to motivate new methodologies and solutions. We hope our journal will be a good merge of these two broad areas, and bring mutual benefits to both. Post to: Cannotea Citeulike Del.icio.us Facebook LinkedIn Twitter Other articles by Dr Dongfeng Wu Projection of long-term outcomes using X-rays and pooled cytology in lung cancer screening Readers of this article also read: Alogliptin: a new addition to the class of DPP-4 inhibitors "Globalized public health.” A transdisciplinary comprehensive framework for analyzing contemporary globalization’s influences on the field of public health Food nanotechnology – an overview Subset-directed antiviral treatment of 142 herpesvirus patients with chronic fatigue syndrome Lyme disease: the next decade Treatment of attention deficit hyperactivity disorder with monoamine amino acid precursors and organic cation transporter assay interpretation Enhanced effect and mechanism of water-in-oil microemulsion as an oral delivery system of hydroxysafflor yellow A Comparison of various modeling approaches in the analysis of longitudinal data with a binary outcome: The Ontario Mother and Infant Study (TOMIS) III Analysis of tooth decay data in Japan using asymmetric statistical models Comparing the performance of different multiple imputation strategies for missing binary outcomes in cluster randomized trials: a simulation study