Lower urinary tract symptoms (LUTS) in women are a source of illness, pain, and embarrassment, which can affect work, social interactions, and athletic and recreational activities in women from adolescence to old age. Almost 50% of women will experience LUTS at some point in their lifetime. Symptoms are harmful enough in themselves, but untreated, they can lead to more serious problems such as incontinence, infections, pelvic inflammation and psychological distress. The NIDDK program on Prevention of Lower Urinary tract Symptoms in women Research Consortium (PLUS-RC) is intended to broadly identify and evaluate the relative importance of the risk and protective factors for LUTS by conducting the necessary research studies to establish the scientific basis for future prevention intervention studies for lower urinary tract symptoms and conditions in women. Knowledge of such risk factors can lead to interventional strategies, which may be testable in future clinical trials.
Specific aims of the PLUS-RC Scientific and Data Coordinating Center (SDCC) are to (1) conduct reviews of literature and meta-analyses of previous publications; (2) with the other PLUS-RC investigators, plan, design, and prioritize new epidemiologic and clinical studies of LUTS and implement an ancillary studies program; (3) provide data coordination and monitoring capabilities in support of PLUS-RC research studies; (4) provide comprehensive and innovative data analyses in support of presentation and publication of results, and construct high-quality datasets, which will be available to other researchers; and (5) provide logistical support for meetings and conference calls of the Steering and Planning Committee, all subcommittees, and an External Expert Panel for the efficient and ethical execution of consortium objectives. We bring together a uniquely strong team with a long history of accomplishment in biostatistics, epidemiology, and the conduct of large multi-center studies, extensive expertise in meta-analysis, observational data analysis, epidemiology specifically related to women's health, skills in behavioral modification and prevention science, and clinical care experience in the diagnosis and treatment of LUTS in women.
Lower urinary tract symptoms in women are a source of illness, pain, and embarrassment, which can affect work, social interactions, and athletic and recreational activities in women from adolescence through old age. An estimated 50% of women will experience lower urinary tract symptoms some point during their lifetime. Such symptoms are harmful enough in themselves, but they can lead to incontinence, infections, pelvic inflammatory disease, and psychological distress. The objective of this program is to carry out epidemiologic studies to determine biological and behavioral factors that can lead to lower urinary tract symptoms and related illnesses, and to find ways to prevent progression to clinical symptoms and more serious complications. This proposal for the PLUS-RC Scientific and Data Coordinating Center describes our research team, resources, and approaches we will take to support the consortium accomplishing its goals.
|Ma, Xiaoyue; Lin, Lifeng; Qu, Zhiyong et al. (2018) Performance of Between-study Heterogeneity Measures in the Cochrane Library. Epidemiology 29:821-824|
|Harlow, Bernard L; Bavendam, Tamara G; Palmer, Mary H et al. (2018) The Prevention of Lower Urinary Tract Symptoms (PLUS) Research Consortium: A Transdisciplinary Approach Toward Promoting Bladder Health and Preventing Lower Urinary Tract Symptoms in Women Across the Life Course. J Womens Health (Larchmt) 27:283-289|
|Ma, Xiaoye; Lian, Qinshu; Chu, Haitao et al. (2018) A Bayesian hierarchical model for network meta-analysis of multiple diagnostic tests. Biostatistics 19:87-102|
|Lin, Lifeng; Chu, Haitao; Murad, Mohammad Hassan et al. (2018) Empirical Comparison of Publication Bias Tests in Meta-Analysis. J Gen Intern Med 33:1260-1267|
|Lin, Lifeng; Chu, Haitao (2018) Quantifying publication bias in meta-analysis. Biometrics 74:785-794|
|Lin, Lifeng; Chu, Haitao (2018) Bayesian multivariate meta-analysis of multiple factors. Res Synth Methods 9:261-272|
|Lin, Lifeng; Zhang, Jing; Hodges, James S et al. (2017) Performing Arm-Based Network Meta-Analysis in R with the pcnetmeta Package. J Stat Softw 80:|
|Lin, Lifeng; Chu, Haitao; Hodges, James S (2017) Alternative measures of between-study heterogeneity in meta-analysis: Reducing the impact of outlying studies. Biometrics 73:156-166|
|Lin, Lifeng; Chu, Haitao; Hodges, James S (2016) Sensitivity to Excluding Treatments in Network Meta-analysis. Epidemiology 27:562-9|
|Zhang, Jing; Yuan, Yiping; Chu, Haitao (2016) The Impact of Excluding Trials from Network Meta-Analyses - An Empirical Study. PLoS One 11:e0165889|