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This e-book aims to present the most critical aspects of knowledge about using experiments in economics and practical tools for using them. The topic is extended to the more advanced and increasing in popularity area of biometric research. The book is divided into three parts mirroring experimentation. The first part provides theoretical background and tips about organising own research. The chapter is concluded with a guide focused on writing a research report in APA style. This part includes an example of the actual research report. The next part has two chapters, and both are guided tours allowing to plan and conduct eye-tracking research and electrodermal activity research (EDA). The chapters contain details about preparing experiments, conducting them, using the dedicated software to analyse collected data and interpreting the default charts. The last part is devoted to the data analysis and is universal, goes beyond the biometric experiments. There are three chapters in this part covering the standard procedures used in the analysis of experiments. The first part includes tests for one hypothesis: parametric t-test and One-Way ANOVA and non-parametric siblings: Mann Whitney U test and Kruskal-Wallis test. The next part describes tests allowing testing more hypotheses: ANOVA without repetition and ANOVA with repetitions. Furthermore, the last chapter deals with dependent samples, which are a popular approach in experiments. This part describes the dependent sample t-test and Wilcoxon test. The effect sizes calculations are included; each test is shown with screenshots from SPSS and some additional screenshots from Excel. This approach allows following the procedure step by step. The examples help easily understand procedures and interpretations; they were chosen from areas of sustainability and innovations to match the general idea of the e-books series prepared within the CENETSIE program. The book contains texts that can be useful in the teaching process. It can be helpful in graduate programs in economics and business schools. Programs of doctoral schools cab benefit from this book as well.

Biometry --- Demography. --- Research.

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Medical statistics --- Biometry

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In survival analysis, time-varying covariates are endogenous when their measurements are directly related to the event status and incomplete information occur at random points during the follow-up. Consequently, the time-dependent Cox model leads to biased estimates. Joint models (JM) allow to correctly estimate these associations combining a survival and longitudinal sub-models by means of a shared parameter (i.e., random effects of the longitudinal sub-model are inserted in the survival one). This study aims at showing the use of JM to evaluate the association between a set of inflammatory biomarkers and Covid-19 mortality. During Covid-19 pandemic, physicians at Istituto Clinico di Città Studi in Milan collected biomarkers (endogenous time-varying covariates) to understand what might be used as prognostic factors for mortality. Furthermore, in the first epidemic outbreak, physicians did not have standard clinical protocols for management of Covid-19 disease and measurements of biomarkers were highly incomplete especially at the baseline. Between February and March 2020, a total of 403 COVID-19 patients were admitted. Baseline characteristics included sex and age, whereas biomarkers measurements, during hospital stay, included log-ferritin, log-lymphocytes, log-neutrophil granulocytes, log-C-reactive protein, glucose and LDH. A Bayesian approach using Markov chain Monte Carlo algorithm were used for fitting JM. Independent and non-informative priors for the fixed effects (age and sex) and for shared parameters were used. Hazard ratios (HR) from a (biased) time-dependent Cox and joint models for log-ferritin levels were 2.10 (1.67-2.64) and 1.73 (1.38-2.20), respectively. In multivariable JM, doubling of biomarker levels resulted in a significantly increase of mortality risk for log-neutrophil granulocytes, HR=1.78 (1.16-2.69); for log-C-reactive protein, HR=1.44 (1.13-1.83); and for LDH, HR=1.28 (1.09-1.49). Increasing of 100 mg/dl of glucose resulted in a HR=2.44 (1.28-4.26). Age, however, showed the strongest effect with mortality risk starting to rise from 60 years.

Survival analysis (Biometry)

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Biomathematics. Biometry. Biostatistics --- Genetics --- bio-informatica --- genetica

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Chemistry --- Biomathematics. Biometry. Biostatistics --- bio-informatica --- proteïnen