LFU:online

At the end of this course, the participants are able to plan, analyze and publish interventional studies in psychoherapy research and education. Successful participants will know how to adjust for baseline performance, to analyze subgroups, to do interim analyses, to test for equivalence instead of difference, to deal with missing data, to deal with multiple outcome variables, to analyze binary data, count data, and event times, to deal with clustered data (e.g. group therapy), and to compare natural (i.e., not randomized groups). The participants will acquire some very basic R skills, but most of the analyses can be done with SPSS or R. In the end, students will be able to plan and analyze interventional studies in real world-settings, including basic preprocessing of data, import and export of different files, and standardized reporting.

Participants are expected to have basic knowledge in statistical testing and some English skills for the written material. The course is suited for students who wish to analyze their own data.

Using examples from interventional studies in clinical and educational psychology and medicine, we review the basic study design in clinical trials, including statistical tests and sample size planning. Step by step we learn how to extend the methodology to adjust for baseline performance, to analyze subgroups, to do interim analyses, to test for equivalence instead of difference, to deal with missing data, to deal with multiple outcome variables, to analyze binary data, count data, and event times, to deal with clustered data (e.g. group therapy), and to compare natural (i.e., not randomized groups).

Lecture and exercise sheets

The course is planned as in-classroom teaching, but individual lectures can be taught online. In exceptional cases, I participate in the lecture online.

exercise sheets + written exam

Bradburn MJ (2003) Survival analysis Part II. Br J Cancer 89, 431–436

Chan AW (2013) SPIRIT 2013 statement. Ann Int Med 158, 200–207

Chan AW (2013) SPIRIT 2013 explanation and elaboration. BMJ 346, e7586

Clark TG (2003) Survival analysis Part I. Br J Canc 89, 232–238

CHMP (2005) Guideline on the Choice of the Noninferiority Margin

CHMP (2007) Reflection Paper on Methodological Issues … Adaptive Design

CHMP (2010) Guideline on Missing Data in Confirmatory Clinical Trials

CHMP (2019) Guideline on the Investigation of Subgroups in Confirmatory Clinical Trials

CHMP (2015) Guideline on Adjustment for Baseline Covariates in Clinical Trials

CHMP (2016) Guideline on Multiplicity Issues in Clinical Trials

Coxe S (2009) The analysis of count Data. J Personality Assess 91, 121–136

von Elm E (2007) Strengthening the … (STROBE) statement. Lancet 370, 1453–1457

Greene CJ et al (2008) Noninferiority and equivalence designs. J Traum Stress 21, 433–439

Hammer GP (2009). Avoiding bias in observational studies. Dtsch Ärztebl Int 106, 664–668

Hemming K (2017) How to design efficient cluster randomised trials. BMJ 358, j3064

Hooper R (2018) Analysis of cluster … at baseline. BMJ 360, k1121

ICH (1998) Statistical Principles for Clinical Trials. Step E9

Kabisch M (2011) Randomized controlled trials. Dtsch Ärztebl Int 108, 663–668

Kuss O (2016) Propensity score. Dtsch Ärztebl Int 113, 597–603

Michaels D, Monforton C (2005) Manufacturing uncertainty. Am J Pub Health 95, S39–S48

Park HA (2013) An introduction to logistic regression. J Kor Acad Nurs 43, 154–164

Röhrig B (2010) Sample size calculation …. Dtsch Ärztebl Int 107, 552–556

Simmons JP (2011) False-positive psychology. Psych Sci 22, 1359–1366

Simon R (1989) Optimal two-stage designs for phase II clinical trials. Contr Clin Trials 10, 1–10

Victor A et al (2009) Judging a plethora of p-values. Dtsch Ärztebl Int 107, 50–56

Wellek S, Blettner M (2012) Establishing equivalence … clinical trials. Dtsch Ärztebl Int 109, 674–679