Location: Leiden (Netherlands)
Statistical Aspects of Clinical Trials
Topics include Phase II / Phase III studies, randomization and blinding, power calculations (sample size), analysis of trial outcomes, superiority, inferiority and equivalence, flexible designs (interim analyses and stopping rules) and multiple imputation.
Basic methods and reasoning in Biostatistics
Subjects covered are among others: descriptive statistics, principles of statistical estimation and testing, cross tabulation and chi-square test, Student t-test and the correspondence with non-parametric counterparts, simple regression and correlation, introduction to analysis of variance, study design, interpretation of observational data, bias, multiple testing, good statistical practice, sample size calculation, pros and cons of p-values.
Using R for data analysis
R is an open-source, free environment for statistical computing and graphics. It provides a large repository of statistical analysis methods, both classic and new. However, R has a steep learning curve, due partly to its using a command-line type of user interface, rather than the usual pull-down menus.
Analysis of repeated measurements
The course covers statistical methods to be used in the situation where one or more outcome variables are repeatedly measured in time on the same experimental unit. For instance, in a clinical trial, the outcome variable can be measured at baseline and at different times during the treatment period. For this type of data, traditional regression models cannot be used since outcomes of the same subject may be correlated, and this should be taken into account in the statistical model.
This course will give a broad overview of topics in meta-analysis. Most standard topics in meta-analysis will be covered, such as risk of bias analysis, searching studies, fixed versus random effect models, heterogeneity, publication bias, differences between meta-analysis of randomized trials and meta-analysis observational studies.
This course considers both theoretical backgrounds and practical aspects of modeling data with regression models. The focus is on linear and logistic regression models, although other models, like Poisson models or non linear regression models for continuous data will also be discussed.
Location: Rotterdam (Netherlands)
Using R for Statistics in Medical Research
R has recently become one of the most popular languages for data analysis and statistics. This course teaches students the basics syntax and data types of this statistical programming language. The aim of this course is to equip students with the R knowledge needed to explore their own data, make data visualizations and perform basic statistical analysis.
This course covers statistical methods to be used when one or more variables are repeatedly measured in time on the same experimental unit. For instance, in a clinical trial, the outcome variable can be measured at baseline and at different times during the treatment period. In a meta-analysis, the study can be regarded as the experimental unit and the observations of patients within the same study as repeated measurements.
Missing Values in Clinical Research
Missing data frequently occur in clinical trials as well as observational studies. An important source for missing data are patients who leave the study prematurely, so-called dropouts. Alternatively, intermittent missing data might occur as well. Gain insight in various repeated measurements models and under which missing data mechanism they will provide valid estimates of the treatment effect. Learn how to perform (multiple) imputation for cross-sectional and longitudinal data in R.
Location: Amsterdam (Netherlands)
Missing data; consequences and solutions
Although researchers do their best to avoid missing data, it is a common problem in medical and epidemiological studies. How large your missing data problem is and how to deal with it depends on how much data is missing and why your data are missing. This three-day course provides you with tools how to evaluate and handle missing data in medical and epidemiological studies with different missing data rates.
This four-day course will explain the basic concepts of mixed models. It is an applied course, so the emphasis lies on the interpretation of the results from the mixed model analyses and not on the mathematical background. The course centres on the two most important applications of mixed models – multilevel analysis and longitudinal data analysis. Lectures are given in the morning and in the afternoon a computer practical is given using the statistical programs STATA, SPSS and MLwiN.