Non-randomized

• Principal investigator

Dr. Claudia Schmoor
Center for Clinical Trials (Zentrum Klinische Studien, ZKS)
University Hospital Freiburg
Elsässer Str. 2, 79110 Freiburg, Germany
Phone: ++49 (0)761 270 7371
Fax: ++49 (0)761 270 7373

• Researchers

Dr. Claudia Schmoor	cs@imbi.uni-freiburg.de	++49 (0)761 270 7371
Dipl. Math. Christine Gall	chg@imbi.uni-freiburg.de	++49 (0)761 203 7703
Dr. Erika Graf	egr@imbi.uni-freiburg.de	++49 (0)761 203 5000
Dipl. Stat. Susanne Stampf	susta@imbi.uni-freiburg.de	++49 (0)761 203 7705

• Summary

It is generally accepted that randomized controlled trials (RCTs) are the gold standard for the comparison of treatments in clinical trials. Randomized treatment assignment shall provide reliable unbiased estimates of causal treatment effects. Nevertheless, there are reasons to perform non-randomized clinical studies in situations where randomization is not possible, feasible, or not enforceable. But observational intervention studies might show association structures instead of causal relationships due to confounding. The crude association of treatment with outcome then fails to admit a causal conclusion.
To cope with this problem in settings with time--constant associations, proposals of two kinds have been introduced: Those that use the relationship between prognostic factors and the outcome variable, such as multiple regression, and those that are based on the relationship between prognostic factors and treatment assignment, such as propensity score or instrumental variable methods. The statistical properties of the propensity score applied to different situations and used in certain forms e.g. for stratification and matching are still not fully explored and further research is needed. In this project, we explore the behaviour of propensity score methods and compare it to conventional regression approaches. Additional emphasis lies on the assessment of their practical applicability.
Another focus of the project is the investigation of causal effects in data sets with a more complex association structure, especially with interdependancies changing over time. Thus, the influence of time-dependent confounders must be correctly modeled which is split in two steps. A structural model is defined to characterize the causal relation between treatment regime and outcome and the confounding mechanism is formulated separately. The common ground of the two focal points is causal modeling by means of counterfactuals.

• Publications

1. Hochholzer W, Trenk D, Bestehorn HP, Fischer B, Valina CM, Ferenc M, Gick M, Caputo A, Büttner HJ, Neumann FJ: Impact of the degree of peri-interventional platelet inhibition after loading with clopidogrel on early clinical outcome of elective coronary stent placement. Journal of the American College of Cardiology, 2006; 48: 1742-1750.
2. Müller C, Caputo A, Schumacher M, von Minckwitz G, Schütte M, Hilfrich J, Kaufmann M: Clinical response by palpation during primary systemic therapy with 4 dose-dense cycles adriamycin and docetaxel in patients with operable breast cancer: further results from a randomized controlled trial, 2006. Manuscript, submitted.
3. Senn S, Graf E, Caputo A: Comparison of propensity score based methods and linear regression techniques, 2006. Manuscript, submitted.
4. von Minckwitz G, Raab G, Caputo A, Schütte M, Hilfrich J, Blohmer J, Gerber B, Costa S, Merkle E, Eidtmann H, Lampe D, Jackisch C, du Bois A, Kaufmann M: Doxorubicin with cyclophosphamide followed by docetaxel every 21 days compared with doxorubicin and docetaxel 14 days as preoperative treatment in operable breast cancer: The GEPARDUO Study of the German Breast Group. Journal of Clinical Oncology, 2005; 23: 2676-2685.
5. Caputo A, Graf E, Schmoor C: Evidence from a partially randomized trial – a comparison of different approaches. Control Clin Trials, 2003, 97S.
6. Caputo A, Foraita R, Klasen S, Pigeot I: Undernutrition in Benin - An analysis based on graphical models. Soc Sci Med, 2003; 56: 1677-1691.
7. Vist GE, Hagen KB, Devereaux PJ, Jackowski D, Oxman AD: Do the outcomes of patients who participate in radnomised controlled trials differ from those of similar patients who do not participate? (Cochrane Methodology Protocol). In: The Cochrane Database of Methodology Revies in The Cochrane Library, Issue 3, 2003. Oxford: Update Software.
8. Sauerbrei W, Bastert G, Bojar H, Beyerle C, Neumann RLA, Schmoor C, Schumacher M for the German Breast Cancer Study Group: Randomized 2x2 trial evaluating hormonal treatment and the duration of chemotherapy in node positive breast cancer patients: an update based on 10 years of follow up. J Clin Oncol, 2000; 18, 94-101.
9. Schmoor C, Bastert G, Dunst J, Bojar H, Christmann D, Unbehaun V, Tümmers G, Bauer W, Sauerbrei W, Schumacher M for the German Breast Cancer Study Group (GBSG): Randomized trial on the effect of radiotherapy in addition to 6 cycles of CMF in node positive breast cancer patients. Int J Cancer, 2000; 86, 408-415.
10. Schmoor C, Olschewski M, Schumacher M: Adding evidence from non-randomized patients to a randomized trial. 8th International Cochrane Colloquium, 2000, Abstract Book, p. 33.
11. Schmoor C, Schumacher M: Methodological arguments for the necessity of randomized trials in high-dose chemotherapy for breast cancer. Breast Cancer Res Treat, 1999; 54: 31-38.
12. Pigeot I, Caputo A, Heinicke A: A graphical chain model derived from a model selection strategy for the sociologists graduates study. Biometrical J, 1999; 41: 217-234.
13. Rauschecker HF, Sauerbrei W, Gatzemeier W, Sauer R, Schauer A, Schmoor C, Schumacher M for the German Breast Cancer Study Group (GBSG): Eight-year results of a prospective non-randomized study on therapy of small breast cancer. Eur J Cancer, 1998; 34, 315-328.
14. Graf E: The propensity score in the analysis of therapeutic studies. Biometrical J, 1997; 39: 297-307.
15. Schmoor C, Olschewski M, Schumacher M: Randomized and non-randomized patients in clinical trials: experiences with comprehensive cohort studies. Stat Med, 1996; 15: 263-271.