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Background: Recently, individualized or personalized medicine (PM) has become a buzz word in the academic as well as public debate surrounding health care. However, PM lacks a clear definition and is open to interpretation. This conceptual vagueness complicates public discourse on chances, risks and limits of PM. Furthermore, stakeholders might use it to further their respective interests and preferences. For these reasons it is important to have a shared understanding of PM. In this paper, we present a sufficiently precise as well as adequate definition of PM with the potential of wide acceptance. Methods: For this purpose, in a first step a systematic literature review was conducted to understand how PM is actually used in scientific practice. PubMed was searched using the keywords "individualized medicine"

Background: For several immune-mediated diseases, immunological analysis will become more complex in the future with datasets in which cytokine and gene expression data play a major role. These data have certain characteristics that require sophisticated statistical analysis such as strategies for non-normal distribution and censoring. Additionally, complex and multiple immunological relationships need to be adjusted for potential confounding and interaction effects. Objective: We aimed to introduce and apply different methods for statistical analysis of non-normal censored cytokine and gene expression data. Furthermore, we assessed the performance and accuracy of a novel regression approach in order to allow adjusting for covariates and potential confounding. Methods: For non-normally distributed censored data traditional means such as the Kaplan-Meier method or the generalized Wilcoxon test are described. In order to adjust for covariates the novel approach named Tobit regression on ranks was introduced. Its performance and accuracy for analysis of non-normal censored cytokine/gene expression data was evaluated by a simulation study and a statistical experiment applying permutation and bootstrapping. Results: If adjustment for covariates is not necessary traditional statistical methods are adequate for non-normal censored data. Comparable with these and appropriate if additional adjustment is required, Tobit regression on ranks is a valid method. Its power, type-I error rate and accuracy were comparable to the classical Tobit regression. Conclusion: Non-normally distributed censored immunological data require appropriate statistical methods. Tobit regression on ranks meets these requirements and can be used for adjustment for covariates and potential confounding in large and complex immunological datasets.

Introduction: A lack of studies on the effects of continuous blood pressure biofeedback led to the development of a screening and feedback software allowing for a `beat-to-beat' representation of arterial blood pressure. Through this method patients find that they can exert some control of their blood pressure. Patients and Methods: For evaluation purposes a pilot study was conducted with 84 hypertensive patients at the Klinik Hohenried, Germany. Two experimental groups which received the normal treatment program of the hospital and an additional 2 (20 patients) or 3-6 (22 patients) biofeedback sessions, respectively, were compared with one control group treated additionally with pseudo biofeedback (20 patients) and another control group (22 patients) which underwent the clinic program without biofeedback. Results: Both experimental groups yielded significantly more markedly blood pressure reductions (MAP) than the controls (p = 0.001), but patients who received 3-6 feedback sessions did not perform significantly better than patients who received only 2 sessions (p = 0.517). Conclusions: 2 sessions of feedback treatment can already have clinically relevant effects. This suggests a major influence of cognitive processes such as self-effectiveness and self-control.

Background: The incidence of tick-borne encephalitis showed a dramatic spike in several countries in Europe in 2006, a year that was unusually cold in winter but unusually warm and dry in summer and autumn. In this study we examine the possible causes of the sudden increase in disease: more abundant infected ticks and/or increased exposure due to human behaviour, both in response to the weather. Methods: For eight countries across Europe, field data on tick abundance for 2005-2007, collected monthly from a total of 41 sites, were analysed in relation to total annual and seasonal TBE incidence and temperature and rainfall conditions. Results: The weather in 2006-2007 was exceptional compared with the previous two decades, but neither the very cold start to 2006, nor the very hot period from summer 2006 to late spring 2007 had any consistent impact on tick abundance. Nor was the TBE spike in 2006 related to changes in tick abundance. Countries varied in the degree of TBE spike despite similar weather patterns, and also in the degree to which seasonal variation in TBE incidence matched seasonal tick activity. Conclusion: The data suggest that the TBE spike was not due to weather-induced variation in tick population dynamics. An alternative explanation, supported by qualitative reports and some data, involves human behavioural responses to weather favourable for outdoor recreational activities, including wild mushroom and berry harvest, differentially influenced by national cultural practices and economic constraints.