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The environment of children has drastically changed in Europe during the last decades as reflected in unhealthy dietary habits and a sedentary lifestyle. Nutrition obviously plays a part in the development of overweight and obesity in childhood. However, dietary factors and physical activity are also involved in the development of metabolic syndrome, diabetes, cardiovascular diseases, osteoporosis, and postural deformities like scoliosis. To stop the resulting epidemic of diet- and lifestyle-induced morbidity, efficient evidence-based approaches are needed. These issues are the focus of IDEFICS (Identification and prevention of dietary- and lifestyle-induced health effects in children and infants), an Integrated Project within the 6th framework program of the European Commission. The IDEFICS consortium comprises 24 research centers and small or medium enterprises across Europe. One part of IDEFICS will investigate the etiology of selected diet- and lifestyle-related diseases in 2- to 10-year-old children. The impact of sensory perception and other internal and external triggers of children's food choices and consumer behavior will be studied in this context. Another part of IDEFICS will develop and evaluate strategies for the primary prevention of diet- and lifestyle-related diseases. The results of the project shall contribute to the development of harmonized European guidelines on diet and lifestyle for health promotion and disease prevention in children.

The use of large databases has a high potential for future epidemiological research. Databases consisting of primary data as well as routine data that are mainly collected for administrative purposes will be of interest. This paper presents some large databases and some that are most relevant for epidemiological research, respectively, and shows how they might be exploited for research purposes. To make better use of large databases there are new requirements and challenges that we have to face regarding appropriate methods for their analysis. Some examples of such methods are given. An example of pharmaco-epidemiology illustrates linkage of several administrative databases and specific problems that have to be solved in this context. Finally, future challenges are discussed that have to be overcome on the way towards establishment, maintenance and analysis of large databases, especially regarding data protection, data quality and regular updates.

A novel approach for association testing in the presence of population stratification has been introduced by Pritchard et al. (2000a) and Pritchard et al. (2000b). The structured association approach is a two-tiered procedure that first estimates the population structure and then tests the null hypothesis H0: 'no association within subpopulations' in the second step. A power comparison of the stratified test for association (STRAT) (Pritchard et al., 2000b) and the Transmission-Disequilibrium-Test (TDT) (Spielman and Ewens, 1993a) in a simulation framework showed superiority of STRAT if allele frequencies or associations between allele and disease differ strongly in subpopulations. In more homogeneous situations, the TDT had greater power than STRAT. However, the TDT, based on family trios,that uses population controls, needs 50% more genotyping compared to STRAT. The Sib-Transmission-Disequilibrium-Test (S-TDT) needs the same amount of genotyping since it relays in its minimal configuration on pairs of siblings. This raises the question how the S-TDT (Spielman and Ewens, 1998a) performs compared to the population based methods STRAT and Genomic Controls (GC). In this paper, we present a simulation study accounting for two different models of population stratification in different settings of allele frequencies and under different risk models. The results showed that under a discrete as well as under an admixed population model, STRAT strongly outperformed the S-TDT and the GC when different alleles were associated in different subpopulations. In contrast, the S-TDT had greater power than STRAT when the same allele was associated in both subpopulations. Here, the GC was sometimes even more powerful than the S-TDT, depending on the population model and the allele frequency differences. A general recommendation for the use of one of the tests can therefore not be given.

Provided that there are no ethical concerns, the comparison of an active drug with placebo in a randomized two-arm clinical trial provides the most convincing way to demonstrate the efficacy of a new experimental treatment. However, in a placebo-controlled clinical trial it is not sufficient to demonstrate merely a statistically significant treatment difference. Regulatory authorities strongly recommend to assess additionally whether the observed treatment difference is also of clinical relevance. The inherent issue is the necessity of the a priori definition of what constitutes a clinically relevant difference in efficacy. This problem can be solved in a three-arm study by including an active control group. We address the necessary conditions in the gold standard design which allow the claim of efficacy for the new treatment with particular focus on assay sensitivity.

Approval of a drug on the market is typically considered as the successful endpoint of a long development phase. This often leads to reduced interest in further research for the indication approved, while in contrast the patient's as well as physician's interest in further research increases. This paper discusses the main areas of effectiveness, safety and post-marketing practice regarding sensible study types for the specific area and the potential gain of knowledge.

The increasing number of approved efficacious therapies for various indications raises the question of whether the inclusion of a placebo group is still justified. In addition, pharmaceutical companies and regulatory agencies acknowledge that it may be sufficient to prove that a new therapy is comparable to an approved reference therapy regarding efficacy and safety in some situations. This becomes especially striking for the approval to market a generic drug. Another, perhaps even more important example is the increasing resistance of bacteria which calls for new antibiotics based on new therapeutic principles without having the claim for better efficacy. In these situations, a comparable efficacy would constitute progress. In the present paper, we discuss the numerous methodological challenges and approaches to overcome these problems that occur when it is not possible or even not wanted to use the classic approach of a randomized placebo-controlled superiority trial. Here, the field of medical biometry, which has proved in the last 25 years to be an integral part of the development of new drugs, demonstrates its suitability as a flexible and scientifically based means to fulfil the requirements resulting from clinical practice. Starting from the fact that statistical methods are not able to prove "equality" of two treatments, "shifted" hypotheses are considered and their importance for the different study designs is discussed. We show how the classic hypotheses known from placebo-controlled clinical trials can be embedded in this concept. The implications of this approach for the analysis and interpretation of study results is further discussed. The relevant guidelines of the European and US regulatory agencies are taken into account.

Clinical trials only allow for a limited assessment of adverse drug reactions (ADRs) due to their restricted number of patients, their short duration, and their narrow inclusion criteria. To assess potential ADRs in the population actually treated after drug approval, different systems for signal generation such as spontaneous reporting systems have been established and are required by drug laws in many countries. As of today epidemiological methods for signal confirmation for rare adverse events or events with long induction periods use record linkage databases such as the United Health Care (Ingenix) in the USA or the practice databases such as the General Practice Research Database in Great Britain. However, this approach also has its limitations. This type of database will be presented in this paper. In addition, databases operated by German public health insurance will be described. The topic of linking different data sources in Germany to support pharmaco-vigilance activities will be addressed.

The prevalence of obesity and overweight in childhood and among adolescents is dramatically increasing worldwide with high impact on their self-esteem and on their health as adults. Therefore, effective programs for primary prevention are urgently required. The focus of these programs should not only be on nutrition but also account for the complete sociocultural environment of the child. This paper will describe some of the more important programs on the one hand and present an exemplary concept for a scientifically observed program for prevention of obesity in preschool children on the other hand. The discussion will be completed by a short overview of programs for secondary prevention. In summary, it can be concluded that no sufficiently evaluated programs for primary prevention of obesity in childhood currently exist in Germany.

In non-inferiority trials, where non-inferiority of a new experimental drug compared to an active control has to be shown, it may be advisable to use an additional placebo group for internal validation if ethically justifiable. The focus of this paper is on such designs. Assuming normality and homogeneity of variances we will derive a statistical test procedure which turns out to be equivalent to the assessment based on Fieller's confidence interval. Based on the power function of this test, sample size calculations are carried out to achieve a given power. Additionally, the optimal allocation of the total sample size is derived. As an alternative to this parametric procedure, the bootstrap percentile interval is discussed and finally compared with Fieller's confidence interval in a study on mildly asthmatic patients., (Copyright 2003 John Wiley & Sons, Ltd.)

Carrying out a statistical analysis, the researcher is concerned with the problem of choosing an appropriate statistical technique from a large number of competing methods. Most common statistical software offer different methods for analysing the data without giving any support regarding the adequacy of a method for a particular data set. This paper outlines the main features of the computer system CORA which provides a statistical analysis of stratified contingency tables and additionally supports the researcher at the different steps of this analysis. Here, the support given by the system consists of two different aspects. On the one hand, the help system of CORA contains general information on the implemented statistical methods which can be obtained on request. On the other hand, an advice tool recommends an adequate statistical method which depends on the actual empirical case-control data to be analysed. To build up the advice tool, a set of rules being discovered by machine learning from simulation studies is integrated into the system CORA.