## 23 SepIs pvalue.io reliable?

First of all, it is important to be aware that no statistical software guarantees the results it provides, even the most widely used software (including Excel). pvalue.io is still new. It is a graphical interface to R, which is a reference statistical analysis software, as well as SAS, Stata and SPSS. To...

## 18 OctShould medical students learn statistics?

Statistics are often considered complex and far from daily clinical practice. Legitimately, as a doctor or student, one may wonder whether it is useful to learn biostatistics and to perform statistical analyses by oneself. Learn statistics? It is quite possible to learn the basics of statistics without having a solid knowledge of...

## 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 In an attempt to make it simpler, we will call Y the outcome variable that we want to explain by X factors. (Use...

## 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 multivariable analyses

We can consider the following three types of analyses: descriptive analyses, univariate analyses and multivariable (often unproperly named multivariate) analyses Descriptive analyses are used to describe the data, and are useful for detecting problems Univariate and multivariable analyses allow statistical comparisons (obtaining a p-value), and only multivariable analyses allow confounding factors to...

## 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 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...