Consultancy Methodology and Statistics
The department offers statistical advice and support on study design (e.g. sample size), data analysis and reporting to all researchers in the UMC+, all PhD students, and all FHML and FPN students during their master thesis (including WESP/WIP, Medicine).
Researchers who consider statistical help in their data analyses, are invited to consult us already in the phase of study design. In statistics, just like in health care, prevention is the better cure!
We welcome punctual and long-term collaboration, protocol and statistical analysis plan writing, participation in grant proposal writing and internships.
Statistical methods
Members of the department have expert knowledge on standard and advanced statistical methods, such as:
- Bayesian statistics
- Causal inference and causal mediation analysis
- Growth curve modeling
- Heath economic evaluations
- Intensive longitudinal data analysis
- Missing data
- Mixed/multilevel regression/longitudinal data analysis
- Mixture modeling
- Optimal designs, cost-effectiveness analysis and sample size
- Propensity scores
- Psychometrics including, reliability, validity and agreement
- Structural equations modeling (SEM)
- Survey sampling
- Survival analysis
- Test norming
Software expertise includes SPSS, R, Python, STATA, SAS, MATLAB, JAGS and BUGS (Bayesian stats), and GPower, PASS (power and sample size calculation).
For details, see the pages of our individual staff members.
