Objective To revise the German guidelines and recommendations for ensuring Good Epidemiological Practice (GEP) that were developed in 1999 by the German Society for Epidemiology (DGEpi), evaluated and revised in 2004, supplemented in 2008, and updated in 2014. Methods The executive board of the DGEpi tasked the third revision of the GEP. The revision was arrived as a result of a consensus-building process by a working group of the DGEpi in collaboration with other working groups of the DGEpi and with the German Association for Medical Informatics, Biometry and Epidemiology, the German Society of Social Medicine and Prevention (DGSMP), the German Region of the International Biometric Society (IBS-DR), the German Technology, Methods and Infrastructure for Networked Medical Research (TMF), and the German Network for Health Services Research (DNVF). The GEP also refers to related German Good Practice documents (e.g. Health Reporting, Cartographical Practice in the Healthcare System, Secondary Data Analysis). Results The working group modified the 11 guidelines (after revision: 1 ethics, 2 research question, 3 study protocol and manual of operations, 4 data protection, 5 sample banks, 6 quality assurance, 7 data storage and documentation, 8 analysis of epidemiological data, 9 contractual framework, 10 interpretation and scientific publication, 11 communication and public health) and modified and supplemented the related recommendations. All participating scientific professional associations adopted the revised GEP. Conclusions The revised GEP are addressed to everyone involved in the planning, preparation, execution, analysis, and evaluation of epidemiological research, as well as research institutes and funding bodies.

Purpose: Identifying true therapeutic progress in patients with acute myeloid leukemia (AML) requires a comparison of treatment strategies and results on the basis of uniform patient selection. To foster comparability across five clinical studies, we introduced a common standard arm combined with a general upfront randomization and performed prospective analyses with adjustment for differences in prognostic baseline characteristics. Patients and Methods: Whereas the studies' own regimens differed in chemotherapies, risk adaption, and guidelines for allogeneic stem-cell transplantation, the standard arm contained uniform cytarabine- and anthracycline-based standard-dose remission induction and high-dose consolidation courses. Results: Of 2,995 evaluable patients aged 16 to 60 years, 290 patients were randomly assigned to the common standard arm. Seventy percent of the 290 achieved complete remissions (62% with complete recovery, 8% with incomplete recovery; 95% CI, 65% to 76%). Five-year survival probabilities were 44.3% (95% CI, 37.7% to 50.7%) for overall survival, 44.8% (95% CI, 37.0% to 52.2%) for relapse-free survival, and 31.5% (95% CI, 25.7% to 37.4%) for event-free survival. Neither the unadjusted survival probabilities of the Kaplan-Meier method nor their adjustment for prognostic variables in multiple Cox regression models led to statistically significant different results in the three survival end points when the outcomes of each study were compared with the standard arm. Conclusion: A strictly prospective comparison of different treatment strategies in patients with AML did not show clinically relevant outcome differences when compared through a common standard treatment arm. The results provide a representative basis for further therapeutic approaches.

Prognostic scores support clinicians in selecting risk-adjusted treatments and in comparatively assessing different results. For patients with chronic-phase chronic myeloid leukemia (CML), four baseline prognostic scores are commonly used. Our aim was to compare the prognostic performance of the scores and to arrive at an evidence-based score recommendation. In 2949 patients not involved in any score development, higher hazard ratios and concordance indices in any comparison demonstrated the best discrimination of long-term survival with the ELTS score. In a second step, of 5154 patients analyzed to investigate risk group classification differences, 23% (n = 1197) were allocated to high-risk by the Sokal score. Of the 1197 Sokal high-risk patients, 56% were non-high-risk according to the ELTS score and had a significantly more favorable long-term survival prognosis than the 526 high-risk patients according to both scores. The Sokal score identified too many patients as high-risk and relatively few (40%) as low-risk (versus 60% with the ELTS score). Inappropriate risk classification jeopardizes optimal treatment selection. The ELTS score outperformed the Sokal score, the Euro, and the EUTOS score regarding risk group discrimination. The recent recommendation of the European LeukemiaNet for preferred use of the ELTS score was supported with significant statistical evidence.