MEDICAL EVALUATIONS WITH IBM SPSS STATISTICS®
In-person events in Wiesbaden or online seminar on 2 days: €1,090 per person (net)
For all professionals in clinical and medical research who want to become competent in the use of statistics using IBM SPSS Statistics® or who would like to refresh their knowledge.
Dates for open training courses: on request!
LEARNING OBJECTIVES AND AGENDA
Goals:
IBM SPSS Statistics: Basics, Help, and Resources
Competent use of IBM SPSS Statistics in the medical field
Different techniques for data preparation depending on the research question
Learn the basics of medical statistics and implement them in IBM SPSS Statistics
Interpretation of results
IN-HOUSE SEMINAR
Seminars held at the customer's location
€1,390.00
per day up to 4 participants plus statutory VAT
All content of the in-house seminars is individually tailored and taught to specific target groups .
Intensive follow-up support enables participants to implement their knowledge in the shortest possible time.
Recommended seminar duration: 2 days
Rental fees for training notebook (on request): 60,- Euro (per day, per training computer)
WORKSHOP
You tell us your topics!
Price on request
plus statutory VAT and travel expenses if applicable
All workshop content is individually tailored and taught to specific target groups .
We are happy to conduct the workshop at your location, in Wiesbaden or online.
Rental fees for training notebook (on request): 60,- Euro (per day, per training computer)
Day 1
Basics of IBM SPSS Statistics, using the syntax editor
Data import: text files, databases, other sources
Visualizations and descriptive analysis of the data
Data preparation
Hypothesis testing: Parametric and non-parametric tests
Contingency analyses and risk parameters, especially risk ratio and odds ratio
Day 2
Power analysis in G*Power 3
Overview of Multivariate Statistics in SPSS
In-depth: Notes on variance analysis
Correlation analysis
Logistic regression
Medical Statistics/Survival Analysis in SPSS (the SPSS Survival module will be required later!)
Mortality tables
Kaplan-Meier estimators and charts
Cox regression
Course Content
The course is designed for anyone interested in medical statistics. It teaches techniques for analyzing medical data, estimation methods, and data visualization. All analyses are carried out together in IBM SPSS Statistics through numerous exercises. The seminar provides ample opportunity for hands-on practice in IBM SPSS Statistics alongside the instructor. Particular emphasis is placed on the use of SPSS syntax for documenting and replicating results.
The seminar focuses on the practical application of common statistical methods in medical statistics using the SPSS Statistics software.
We begin with the basics of IBM SPSS Statistics. The help system and ecosystem are introduced, along with special task views tailored for medical statistics. Relevant literature is also presented.
Since statistics almost always require data, the next section covers importing datasets into SPSS. In addition to text files, importing from database tables is explained. Exporting datasets and results in structured formats is also demonstrated. Other typical data sources (other statistical software, MS Excel, etc.) are addressed.
Once the data has been imported, visualizations and univariate analyses are needed to better understand it: Are the variable values plausible? Are there outliers? Are missing values present, and if so, how many? What does the data distribution look like? These are typical questions we will analyze in this section with IBM SPSS Statistics.
After diagnosing the data properties, the next step is data preparation: Skewed distributions can be transformed (depending on which statistical methods will be used). Missing values can be imputed, including through multiple imputation methods. Implausible values can be deleted or replaced with suitable alternatives. Categorical variables, if used in variance analysis (ANOVA or GLM), can be transformed into dummy variables, and so forth.
Once the data has been prepared, hypothesis testing can be performed (the hypotheses should, of course, already be recorded at the beginning of the analysis in a study protocol). These hypotheses are now tested using parametric methods such as the classic t-test, F-tests, etc., as well as nonparametric equivalents like the Mann-Whitney U test. We discuss assumptions, how to prepare the data in SPSS for testing, perform the tests (e.g., the t-test), and, of course, interpret the results.
Contingency analysis, especially the analysis of two-dimensional cross tables, plays a central role in medicine. For example, the 2x2 table (rows: drug vs. placebo or two drugs against each other; columns: outcome [treatment success vs. no success]) serves as the basis for calculating key medical risk parameters: risk ratio and odds ratio. In addition to calculating these parameters (including log-transformed values), confidence intervals and p-values are also computed.
Day 2 begins with power analyses, which are crucial in medical statistics for estimating sample sizes. Power analysis helps determine the necessary sample size for an experiment with respect to the alternative hypothesis, which is often (but not always) the main focus of interest. Here, we learn the underlying logic and make use of the excellent G*Power 3 software alongside IBM SPSS Statistics.
Multivariate statistics is now a vast field with many methods and specialized variants. An overview is therefore necessary to classify the methods and, if needed, select suitable approaches. What is available in the standard SPSS installation? Which add-on modules are useful? What role do measurement levels of dependent and independent variables, error distributions, and link functions play?
In medical statistics, certain methods account not only for the duration until an event occurs but also for cases where the event does not occur before the study ends. These are generally grouped under survival analysis. This includes life tables, Kaplan-Meier estimators and plots, as well as Cox regression, which allows for the control of time-independent covariates. The hazard ratio is used here. Implementation is carried out in SPSS with the survival module.
Additional course content and focus areas can be tailored upon request! You also have complete flexibility regarding the content if you book an in-house seminar—we will then fully customize the material to your needs.