[The ABC's of medical statistics. Reading and understanding clinical trials]. / Das ABC der medizinischen Statistik. Klinische Studien lesen und verstehen.

Clinical trials test hypotheses that are accepted or rejected according to a predetermined probability of error (level of significance). Significance does not however mean relevance. Good parameters of relevance are absolute risk reduction and based on this the calculation of the number of patients who need to be treated for one additional patient to benefit. The randomized controlled trial is the gold standard for comparative evaluation of effects. In the ideal scenario it is designed so that a difference established by statistical methods becomes probable. In non-inferiority studies care should be taken that no equivalence is shown but rather that the difference is not greater than a predefined margin of error for differences. Meta-analyses of studies with similar endpoints have the potential to improve the level of evidence. Since the findings of meta-analyses depend on the studies included, critical assessment of the results is essential.

[The ABC's of medical statistics. Reading and understanding clinical trials]. / Das ABC der medizinischen Statistik. Klinische Studien lesen und verstehen.

Clinical trials test hypotheses that are accepted or rejected according to a predetermined probability of error (level of significance). Significance does not however mean relevance. Good parameters of relevance are absolute risk reduction and based on this the calculation of the number of patients who need to be treated for one additional patient to benefit. The randomized controlled trial is the gold standard for comparative evaluation of effects. In the ideal scenario it is designed so that a difference established by statistical methods becomes probable. In non-inferiority studies care should be taken that no equivalence is shown but rather that the difference is not greater than a predefined margin of error for differences. Meta-analyses of studies with similar endpoints have the potential to improve the level of evidence. Since the findings of meta-analyses depend on the studies included, critical assessment of the results is essential.

Determinants of heart failure self-care behaviour in community-based patients: a cross-sectional study.

BACKGROUND: Self-care behaviour in patients with heart failure (HF) represents a series of specific actions that patients should take, as an important treatment component. AIMS: The aim of this study was to identify potential determinants of HF self-care in ambulatory patients with stable systolic HF. METHODS: In a cross-sectional study of 318 patients with chronic systolic HF recruited in 48 German primary care practices, we evaluated the patient-reported European HF Self-care Behaviour scale (EHFScBs) assessments (range 12-60, where lower scores indicate better self-care). Potential determinants included socio-demographic (e.g. age, living status), clinical (e.g. NYHA class, LVEF, NT-proBNP levels, co-morbidities), behavioural (e.g. smoking and alcohol intake), psychosocial (SF-36 scales and KCCQ domains, e.g. quality of life and self-efficacy) and depression status (PHQ-D), plus previous health care utilisation. Mixed regression modelling was applied. RESULTS: Patients had a mean (SD) age of 69.0 (10.4) years and were 71% male. They had a good overall EHFScBs score of 24.7 (7.8) (n=274). In the final regression model (n=271), six determinants were retained (ß; descriptive p-value): self-efficacy (-0.24; <.001), age (-0.22; <.001), prosthetic heart valve (-0.14; .01), referrals to cardiologists (-0.14; .02), peripheral arterial disease (0.13; .03) and quality of life (0.16; .02). CONCLUSION: In this exploratory cross-sectional study, the potential non-modifiable and modifiable risk factors and resources involved in patients' HF self-care were at the individual and organisational level. Self-efficacy and quality of life are potentially modifiable, so these could be targeted for improvement by enhancing patient motivation, HF education and further supporting a collaborative care approach.

[The ABC's of medical statistics. Reading and understanding clinical trials]. / Das ABC der medizinischen Statistik.

Clinical trials test hypotheses that are accepted or rejected according to a predetermined probability of error (level of significance). Significance does not however mean relevance. Good parameters of relevance are absolute risk reduction and based on this the calculation of the number of patients who need to be treated for one additional patient to benefit. The randomized controlled trial is the gold standard for comparative evaluation of effects. In the ideal scenario it is designed so that a difference established by statistical methods becomes probable. In non-inferiority studies care should be taken that no equivalence is shown but rather that the difference is not greater than a predefined margin of error for differences. Meta-analyses of studies with similar endpoints have the potential to improve the level of evidence. Since the findings of meta-analyses depend on the studies included, critical assessment of the results is essential.

Optimal two-stage designs for single-arm phase II oncology trials with two binary endpoints.

OBJECTIVES: In phase II clinical trials in oncology, the potential efficacy of a new treatment regimen is assessed in terms of anticancer activity. The standard approach consists of a single-arm two-stage design where a single binary endpoint is compared to a specified target value. However, a new drug would still be considered promising if it showed a lower tumor response rate than the target level but would lead, for example, to disease stabilization. METHODS: We present an analytical solution for the calculation of the type I and type II error rate for a two-stage design where the hypothesis test considers two endpoints and provide optimal and minimax solutions. Furthermore, the problem of inference about the two single endpoints following rejection of the global null hypothesis is addressed by deriving a multiple test procedure that controls the experimentwise type I error rate in the strong sense. RESULTS: The proposed methods are illustrated with a real data example, and the new design is tabulated for a wide range of parameter values. Similar to two-stage designs with a single endpoint, the characteristics of optimal and minimax designs with two endpoints with respect to expected and maximum sample size can be quite different. Therefore, the choice of an admissible design may be a valuable compromise. CONCLUSIONS: The new procedure extends Simon's two-stage design to two endpoints. This approach allows a more comprehensive assessment of the overall picture of anti-tumor efficacy of a new treatment than restriction to a single outcome.