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Objectives: Analysis of covariance (ANCOVA) is widely applied in practice and its use is recommended by regulatory guidelines. However, the required sample size for ANCOVA depends on parameters that are usually uncertain in the planning phase of a study. Sample size recalculation within the internal pilot study design allows to cope with this problem. From a regulatory viewpoint it is preferable that the treatment group allocation remains masked and that the type I error is controlled at the specified significance level. The characteristics of blinded sample size reassessment for ANCOVA in non-inferiority studies have not been investigated yet. We propose an appropriate method and evaluate its performance.Methods: In a simulation study, the characteristics of the proposed method with respect to type I error rate, power and sample size are investigated. It is illustrated by a clinical trial example how strict control of the significance level can be achieved.Results: A slight excess of the type I error rate beyond the nominal significance level was observed. The extent of exceedance increases with increasing non-inferiority margin and increasing correlation between outcome and covariate. The procedure assures the desired power over a wide range of scenarios even if nuisance parameters affecting the sample size are initially mis-specified.Conclusions: The proposed blinded sample size recalculation procedure protects from insufficient sample sizes due to incorrect assumptions about nuisance parameters in the planning phase. The original procedure may lead to an elevated type I error rate, but methods are available to control the nominal significance level. [ABSTRACT FROM AUTHOR]

Background: In the planning of clinical trials with count outcomes such as the number of exacerbations in chronic obstructive pulmonary disease (COPD) often considerable uncertainty exists with regard to the overall event rate and the level of overdispersion which are both crucial for sample size calculations.Objectives: To develop a sample size reestimation strategy that maintains the blinding of the trial, controls the type I error rate and is robust against misspecification of the nuisance parameters in the planning phase in that the actual power is close to the target.Methods: The operation characteristics of the developed sample size reestimation procedure are investigated in a Monte Carlo simulation study.Results: Estimators of the overall event rate and the overdispersion parameter that do not require unblinding can be used to effectively adjust the sample size without inflating the type I error rate while providing power values close to the target.Conclusions: If only little information is available regarding the size of the overall event rate and the overdispersion parameter in the design phase of a trial, we recommend the use of a design with sample size reestimation as the one suggested here. Trials in COPD are expected to benefit from the proposed sample size reestimation strategy. [ABSTRACT FROM AUTHOR]

Objectives: In some circumstances controlled trials are not feasible and treatments can only be evaluated using clinical databases. Here we consider the situation where treatment is introduced at a particular calendar time and can only be evaluated by comparison with historical controls. In these circumstances Heuer and Abel recommended using change-point methods to search for change in characteristics over the whole study period rather than simply comparing treated and untreated patients. Their recommendation is to only conclude that the intervention had an effect if a change-point could be demonstrated close in time to the introduction of the new treatment. This reduces the risk of false positives caused by confounding changes in population characteristics or changes in patient management. For binary data we develop a method that follows their philosophy and apply it to an observational study in the treatment of pin sites after orthopaedic surgery.Methods: Tests for change in binomial probabilities based on Brownian bridge and Hansen's approximation for maximally selected chi(2) statistics are compared to an exact test by Worsley. The approximate method is generalized to logistic regression models allowing for covariates.Results: The agreement of the exact and approximate method is good for sample sizes of 100 or more. The actual test size of the Hansen approximate test allowing for covariates is close to the nominal level, whereas the Brownian bridge approximation is slightly conservative. The change in pin site treatment significantly reduces the risk of infection for both adults and children.Conclusions: We consider the Hansen approximation to provide a very good and very simple method for obtaining the p-value when testing for a change in binary data event probabilities, with or without covariates. [ABSTRACT FROM AUTHOR]

Objectives: Methods for change point (also sometimes referred to as threshold or breakpoint) detection in binary sequences are not new and were introduced as early as 1955. Much of the research in this area has focussed on asymptotic and exact conditional methods. Here we develop an exact unconditional test., Methods: An unconditional exact test is developed which assumes the total number of events as random instead of conditioning on the number of observed events. The new test is shown to be uniformly more powerful than Worsley's exact conditional test and means for its efficient numerical calculations are given. Adaptions of methods by Berger and Boos are made to deal with the issue that the unknown event probability imposes a nuisance parameter. The methods are compared in a Monte Carlo simulation study and applied to a cohort of patients undergoing traumatic orthopaedic surgery involving external fixators where a change in pin site infections is investigated., Results: The unconditional test controls the type I error rate at the nominal level and is uniformly more powerful than (or to be more precise uniformly at least as powerful as) Worsley's exact conditional test which is very conservative for small sample sizes. In the application a beneficial effect associated with the introduction of a new treatment procedure for pin site care could be revealed., Conclusions: We consider the new test an effective and easy to use exact test which is recommended in small sample size change point problems in binary sequences.

At present, most documentation forms and item catalogs in healthcare are not accessible to the public. This applies to assessment forms of routine patient care as well as case report forms (CRFs) of clinical and epidemiological studies. On behalf of the German chairs for Medical Informatics, Biometry and Epidemiology six recommendations to developers and users of documentation forms in healthcare were developed. Open access to medical documentation forms could substantially improve information systems in healthcare and medical research networks. Therefore these forms should be made available to the scientific community, their use should not be unduly restricted, they should be published in a sustainable way using international standards and sources of documentation forms should be referenced in scientific publications.