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Information sur le cours
Rencontrez l'équipe enseignante -
Jeu de données du cours 1
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Jeu de données du cours 2
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MODULE A1: INTRODUCTION AUX STATISTIQUES AVEC R ET STATAA1.1 Qu'est-ce que les Statistiques?
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A1.2.1a Introduction à Stata
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A1.2.2b: Introduction à R
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A1.2.2c: Introduction to SPSS
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A1.3: Statistiques Descriptives
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A1.4: Estimations et Intervalles de Confiance
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A1.5: Tests d'Hypothèses
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A1.6: Transformation de Variables
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Fin du Module A11 Quiz
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MODULE A2: CALCULS DE PUISSANCE STATISTIQUE & DE TAILLE D’ÉCHANTILLONA2.1 Concepts Clés
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A2.2 Calculs de puissance pour une différence de moyennes
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A2.3 Calculs de puissance pour une différence de proportions
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A2.4 Calcul de taille d’échantillon pour les essais randomisés (RCTs)
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A2.5 Calculs de taille d’échantillon pour les études transversales (ou sondages)
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A2.6 Calcul de taille d'échantillon pour un devis cas-contrôle
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Fin du Module A21 Quiz
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MODULE B1: RÉGRESSION LINÉAIREB1.1 Corrélation et Nuages de Points (scatterplots)
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B1.2 Différences Entre Moyennes (ANOVA à un facteur)
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B1.3 Régression Linéaire Univariée
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B1.4 Régression Linéaire Multivariée
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B1.5 Sélection de Modèles et Tests F
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B1.6 Diagnostics de Régression
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Fin du Module B11 Quiz
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MODULE B2: COMPARAISONS MULTIPLES & MESURES RÉPÉTÉESB2.1 ANOVA Approfondie— Tests Post-Hoc
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B2.2 Correction pour Comparaisons Multiples
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B2.3 ANOVA à deux facteurs (Two-way ANOVA)
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B2.4 Mesures Répétées et Test T Apparié
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B2.5 ANOVA pour Mesures Répétées
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Fin du Module B21 Quiz
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MODULE B3: MÉTHODES NON-PARAMETRICB3.1 Hypothèses des Tests Paramétriques
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B3.2 Test U de Mann-Whitney
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B3.3 Test de Kruskal-Wallis
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B3.4 Test des rangs signés de Wilcoxon
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B3.5 Test de Friedman
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B3.6 Corrélation des Rangs de Spearman
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Fin du Module B31 Quiz
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MODULE C1: DONNÉES BINAIRES & RÉGRESSION LOGISTIQUEC1.1 Introduction à la prévalence, au Risque, aux Cotes (Odds) et aux Taux
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C1.2 Le Test du Chi Carré & le Test de Tendance
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C1.3 Régression Logistique Univariée
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C1.4 Régression Logistique Multivariée
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Fin du Module C11 Quiz
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MODULE C2: DONNÉES DE SURVIEC2.1 Introduction aux Données de Survie
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C2.2 Fonction de Survie de Kaplan-Meier & Test du Log-Rank
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C2.3 Régression de Cox à Risque Proportionnel
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C2.4 Régression de Poisson
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Fin du Module C21 Quiz
Learning Outcomes
By the end of this session, students will be able to:
- Continue practicing basic software commands
- Learn how to explore the dataset, identifying the different types of variable stored
- Calculate the different measures of location and spread
- Plot frequency distributions and histograms
You can download a copy of the slides here: A2.1 Key Concepts
Video A2.1 Introduction to Power and Sample Size Calculation (11 minutes)
A2.1 PRACTICAL: R
The power package
We can estimate sample size and power using the R package ‘pwr’. First you need to install the package and load the library:
install.packages(“pwr”)
library(“pwr”)
Once this package is installed, we can start calculating our needed sample size to test hypotheses or we can estimate the amount of power a study had to detect a difference is one existed. The next sections will show you how to do this.
Look at the help file for the function pwr.t.test. What is the default value for significance level?
What information do you need to conduct a sample size estimate for difference between means?
Answer
The default value for significance level (denoted as ‘sig.level‘) in the function is 0.05. This is indicated by sig.level=0.05, and can be changed by specifying a different number in the function. So you can work out the sample size you would need for different significance levels.
To conduct a sample size estimate between means you need the power and alpha values you have decided on, the estimated mean of each group in the population, or the estimated difference between groups in the population, and the estimated population standard deviation.
A2.1 PRACTICAL: Stata
The power command
Calculations for power and sample size in Stata can be performed using the power command. If you look at the help file, you will see that you can use this command to compute a sample size, the power or an effect size. You do not need to have a data set loaded.
Look at the setup of the power command:
power method …, n(numlist) [power_options …]
You need to choose the method you are calculating a power estimate for. To do this, ask yourself the following questions: do you have 1 sample, 2 independent samples or 2 paired samples? Additionally, do you want to compare means from continuous outcomes or proportions from binary/categorical outcomes? The practical today will explore the power command and the one on Session 28 will familiarise you with some of the other options of this command.
Question A2.1: What are Stata’s default values for power and significance level in the power command? Can you see how to change them?
Answer
The default is for 80% power and a 5% significance level (denoted as ‘alpha’). You can change these using the options power() and alpha().
A2.1 PRACTICAL: SPSS
Calculations for power and sample size in SPSS are performed using the ‘Power Analysis’ option under the ‘Analyze’ menu.
Take some time to have a look through the different test types which you can estimate power and sample size for.
You need to choose the method you are calculating a power estimate for. To do this, ask yourself the following questions: do you have 1 sample, 2 independent samples or 2 paired samples? Additionally, do you want to compare means from continuous outcomes or proportions from binary/categorical outcomes?
Take some time to open some of the power analysis tests in SPSS and have a look through them.
What is the default value for significance level?
What information do you need to conduct a sample size estimate for difference between means?
Answer
The default value for significance level (denoted as ‘alpha‘) in SPSS is 0.05. This is easily changed in the Power Analysis window once you have selected your test type. So you can work out the sample size you would need for different alpha values.
To conduct a sample size estimate between means you need the power and alpha values you have decided on, the estimated mean of each group in the population, or the estimated difference between groups in the population, and the estimated population standard deviation.
