Regression toward the mean – a detection method for unknown population mean based on Mee and Chua's algorithm

<p>Abstract</p> <p>Background</p> <p>Regression to the mean (RTM) occurs in situations of repeated measurements when extreme values are followed by measurements in the same subjects that are closer to the mean of the basic population. In uncontrolled studies such changes are likely to be interpreted as a real treatment effect.</p> <p>Methods</p> <p>Several statistical approaches have been developed to analyse such situations, including the algorithm of Mee and Chua which assumes a known population mean <it>μ</it>. We extend this approach to a situation where <it>μ </it>is unknown and suggest to vary it systematically over a range of reasonable values. Using differential calculus we provide formulas to estimate the range of <it>μ </it>where treatment effects are likely to occur when RTM is present.</p> <p>Results</p> <p>We successfully applied our method to three real world examples denoting situations when (a) no treatment effect can be confirmed regardless which <it>μ </it>is true, (b) when a treatment effect must be assumed independent from the true <it>μ </it>and (c) in the appraisal of results of uncontrolled studies.</p> <p>Conclusion</p> <p>Our method can be used to separate the wheat from the chaff in situations, when one has to interpret the results of uncontrolled studies. In meta-analysis, health-technology reports or systematic reviews this approach may be helpful to clarify the evidence given from uncontrolled observational studies.</p

Systemic chemotherapy with doxorubicin, cisplatin and capecitabine for metastatic hepatocellular carcinoma

BACKGROUND: Although numerous chemotherapeutic agents have been tested, the role of systemic chemotherapy for hepatocellular carcinoma (HCC) has not been clarified. New therapeutic strategies are thus needed to improve outcomes, and we designed this study with new effective drug combination. METHODS: Twenty-nine patients with histologically-confirmed, metastatic HCC received a combination chemotherapy with doxorubicin 60 mg/m(2 )and cisplatin 60 mg/m(2 )on day 1, plus capecitabine 2000 mg/m(2)/day as an intermittent regimen of 2 weeks of treatment followed by a 1-week rest. RESULTS: The median age was 49 years (range, 32–64) and 19 patients were hepatitis B virus seropositive. Child-Pugh class was A in all patients and 4 had Zubrod performance status of 2. The objective response rate was 24% (95% CI 9–40) with 6 stable diseases. The chemotherapy was generally well tolerated despite one treatment-related death. CONCLUSION: Combination chemotherapy with doxorubicin, cisplatin and capecitabine produced modest antitumor activity with tolerable adverse effects in patients with metastatic HCC

Development of Transgenic Cloned Pig Models of Skin Inflammation by DNA Transposon-Directed Ectopic Expression of Human β1 and α2 Integrin

Integrins constitute a superfamily of transmembrane signaling receptors that play pivotal roles in cutaneous homeostasis by modulating cell growth and differentiation as well as inflammatory responses in the skin. Subrabasal expression of integrins α2 and/or β1 entails hyperproliferation and aberrant differentiation of keratinocytes and leads to dermal and epidermal influx of activated T-cells. The anatomical and physiological similarities between porcine and human skin make the pig a suitable model for human skin diseases. In efforts to generate a porcine model of cutaneous inflammation, we employed the Sleeping Beauty DNA transposon system for production of transgenic cloned Göttingen minipigs expressing human β1 or α2 integrin under the control of a promoter specific for subrabasal keratinocytes. Using pools of transgenic donor fibroblasts, cloning by somatic cell nuclear transfer was utilized to produce reconstructed embryos that were subsequently transferred to surrogate sows. The resulting pigs were all transgenic and harbored from one to six transgene integrants. Molecular analyses on skin biopsies and cultured keratinocytes showed ectopic expression of the human integrins and localization within the keratinocyte plasma membrane. Markers of perturbed skin homeostasis, including activation of the MAPK pathway, increased expression of the pro-inflammatory cytokine IL-1α, and enhanced expression of the transcription factor c-Fos, were identified in keratinocytes from β1 and α2 integrin-transgenic minipigs, suggesting the induction of a chronic inflammatory phenotype in the skin. Notably, cellular dysregulation obtained by overexpression of either β1 or α2 integrin occurred through different cellular signaling pathways. Our findings mark the creation of the first cloned pig models with molecular markers of skin inflammation. Despite the absence of an overt psoriatic phenotype, these animals may possess increased susceptibility to severe skin damage-induced inflammation and should be of great potential in studies aiming at the development and refinement of topical therapies for cutaneous inflammation including psoriasis

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

<p>Abstract</p> <p>Background</p> <p>The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.</p> <p>Methods</p> <p>We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.</p> <p>Results</p> <p>Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).</p> <p>Conclusions</p> <p>We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.</p

Defining the PACS Profession: An Initial Survey of Skills, Training, and Capabilities for PACS Administrators

The need for specialized individuals to manage picture archiving and communications systems (PACS) has been recognized with the creation of a new professional title: PACS administrator. This position requires skill sets that bridge the current domains of radiology technologists (RTs), information systems analysts, and radiology administrators. Health care organizations, however, have reported difficfiulty in defining the functions that a PACS administrator should perform—a challenge compounded when the tries to combine this complex set of capabilities into one individual. As part of a larger effort to define the PACS professional, we developed an extensive but not exclusive consensus list of business, technical, and behavioral competencies desirable in the dedicated PACS professional. Through an on-line survey, radiologists, RTs, information technology specialists, corporate information officers, and radiology administrators rated the importance of these competencies. The results of this survey are presented, and the implications for implementation in training and certification efforts are discussed