Titlebook: Biostatistics With ‘R‘: A Guide for Medical Doctors; Marco Moscarelli Book 2023 The Editor(s) (if applicable) and The Author(s), under exc

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,Data Types in “R”,: numeric and non-numeric..However, they can also be described more specifically. Numeric variables with no decimals (e.g. number/s of bypasses performed) are defined as “integers”. Non-numeric variables with two levels (e.g. male or female) are called “factors”, and with more than two levels “chara

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Data Distribution,f a standard normal distribution are and how to graphically and numerically ascertain if a variable is normally distributed. Histograms, quantile-quantile, boxplot and density plot are all important graphical tools to inspect for normality. This chapter will also explain the basics of ., the most us

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Linear Regression,st in medicine are numeric, such as length of hospital stay, amount of bleeding, etc. The outcome of interest is also named the dependent or response variable. Intuitively, the independent variables or explanatory variables are the covariates that may influence the dependent variables. Independent v

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Logistic Regression,is .. Logistic regression is frequently encountered in scientific medical papers, but is also pivotal for machine learning. We use logistic regression when we are interested in classification, to compute the probability of the outcome of interest occurring with certain predictors. Notably, the outco

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Time-to-Event Analysis,e outcome is mortality. For the latter, the outcome can be any binary event (i.e. cancer relapse, re-hospitalisation, etc.). Time-to-event/survival analysis is somewhat similar to logistic regression, since the outcome of interest is binary. Yet the underlying math is different, and for survival ana