A hierarchical algorithm for multicentric matched cohort study designs.

OBJECTIVE: Lack of structural equality is a major issue to be addressed in observational studies. Their major disadvantage of these studies compared to randomized controlled trials is the vulnerability towards confounding, but they often better mirror real world patients and, therefore, entail an increased external validity. Numerous approaches have been developed to account for confounding in observational research, including multiple regression, subgroup analysis and matched cohort designs. The latter has been often described as a useful tool if large control data sets are available. METHODS: In this paper we present a hierarchical matching algorithm entailing two stages which enables a multicentric matched cohort study to be conducted. In particular, the algorithm defines the matching strategy as a combination of exact matching and a subsequent consideration of further matching variables to be controlled using a distance measure (e.g. the propensity score). RESULTS: The algorithm is applied to a study in interventional cardiology and demonstrates high flexibility and usefulness with regard to the aim of finding comparable cases of exposed and non-exposed patients from observational data. The algorithm increased structural equality by balancing the most important covariates which might be of different importance for the matching process. CONCLUSION: The implementation of the algorithm in the statistical software SAS offers high flexibility regarding an application to various data analysis projects. Specifically, it provides a broader range of features (e.g. diverse distance measures) when compared to other existing solutions for conducting matched cohort analyses.

How to work together between nuclear medicine and radiotherapy departments?

Among the available imaging techniques, functional imaging provided by nuclear medicine departments represents a tool of choice for the oncoradiotherapist for targeting tumour activity, with positron emission tomography as the main modality. Before, during or after radiotherapy, functional imaging helps guide the oncoradiotherapist in making decisions and in the strategic choice of pathology management. Setting up a working group to ensure perfect coordination at all levels is the first step. Key points for a common and coordinated management between the two departments are the definition of an organizational logistic, training of personnel at every levels, standardization of nomenclatures, the choice of adapted and common equipment, implementation of regulatory controls, and research/clinical routine continuum. The availability of functional examinations dedicated to radiotherapy in clinical routine is possible and requires a convergence of teams and a pooling of tools and techniques.

Augmented Weighted Estimators Dealing with Practical Positivity Violation to Causal inferences in a Random Coefficient Model.

The inverse probability of treatment weighted (IPTW) estimator can be used to make causal inferences under two assumptions: (1) no unobserved confounders (ignorability) and (2) positive probability of treatment and of control at every level of the confounders (positivity), but is vulnerable to bias if by chance, the proportion of the sample assigned to treatment, or proportion of control, is zero at certain levels of the confounders. We propose to deal with this sampling zero problem, also known as practical violation of the positivity assumption, in a setting where the observed confounder is cluster identity, i.e., treatment assignment is ignorable within clusters. Specifically, based on a random coefficient model assumed for the potential outcome, we augment the IPTW estimating function with the estimated potential outcomes of treatment (or of control) for clusters that have no observation of treatment (or control). If the cluster-specific potential outcomes are estimated correctly, the augmented estimating function can be shown to converge in expectation to zero and therefore yield consistent causal estimates. The proposed method can be implemented in the existing software, and it performs well in simulated data as well as with real-world data from a teacher preparation evaluation study.