## 22 JulSurvival analysis

When the outcome variable is binary and it is possible to switch permanently from one state to another, we can carry out survival analyses This type of analysis can take into account the lost to follow-up The most commonly used statistical model for survival analysis in medical studies is...

## 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 Dans un effort de simplification, nous appellerons Y la variable que l'on souhaite expliquer par des facteurs X. (Faites appel à vos lointains...

## 08 JulLogistic regression

When the outcome variable is binary and not censored, the appropriate statistical model is logistic regression; When there is only one explanatory variable which is qualitative, 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 quantitative and continuous, the appropriate statistical model is the linear regression When there is only one explanatory variable which is qualitative, 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 JulUnivariate and multivariate analyses

There are three types of analyses: descriptive analyses, univariate analyses and multivariate (or multivariable) analyses Descriptive analyses are used to describe the data, and are useful for detecting problems Univariate and multivariate analyses allow statistical comparisons (obtaining a p-value), and only multivariate analyses allow confounding factors to be taken into account Descriptive analyses Before...

## 03 JulTransformation of numerical variables

In statistical modeling, it is often necessary to group 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 link between the...

## 03 JulHow to perform a multivariate analysis when you have too few number of subjects

It is sometimes surprising not to be able to carry out a multivariate 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. multivariate analyses for which the outcome variable is numerical, it is necessary to have...