## 11 JulMissing data handling

When a parameter has not been measured for all patients in the study, we are talking about missing data There are few studies without missing data If missing data are present, they should be described and a strategy chosen to address them Missing data is a common problem in the...

## 10 JulConditions of Regression Models

Tests and statistical models all have conditions to be used.In this article, we describe the conditions of regression models, as well as how they are checked by pvalue.io In an attempt to make it simpler, we will call Y the response variable that we want to explain by X factors. (Use...

## 08 JulLogistic regression

When the response variable is binary and not censored, the appropriate statistical model is logistic regression; When there is only one explanatory variable which is categorical, the logistic regression yields a result similar to a Chi2 test; In an attempt to simplify this, we will name Y the variable that...

## 04 JulLinear regressions

When the outcome variable is numerical and continuous, the appropriate statistical model is the linear regression When there is only one explanatory variable which is categorical, linear regression yields a result close to a Welch or Student T test In an attempt to simplify this, we will name Y the variable that...

## 03 JulUnivariable and multivariable analyses

We can consider the following three types of analyses: single variable descriptive statistics, univariable analyses (often named univariable) and multivariable (often unproperly named multivariate) analyses Single variable descriptive statistics are used to describe the data, and are useful for detecting problems Univariable and multivariable analyses allow statistical comparisons (obtaining a p-value), and...

## 03 JulTransformation of numerical variables

In statistical modeling, it is often necessary to group the values the numerical variables to create classes in order to meet the conditions of the model. If we have no a priori idea about the appropriate grouping, it is preferable to base ourselves on the splines representing the relationship...

## 03 JulHow to perform a multivariable analysis when you have too few observations

It is sometimes surprising not to be able to carry out a multivariable analysis because the number of subjects is too small while the file contains several hundred observations (patients, subjects). Linear regressions For linear regressions, i. e. multivariable analyses for which the outcome variable is numerical, it is necessary to have...