Hierarchical dynamic time-to-event models for post-treatment preventive care data on breast cancer survivors

This paper considers modelling data arising in post-treatment preventive care settings, where cancer patients who have undergone disease-directed treatment discontinue seeking preventive care services. Clinicians and public health researchers are interested in explaining such behavioural patterns by modelling the time-to-receiving care while accounting for several patient and treatment attributes. A key feature of such data is that a noticeable number of patients would never return for screening, a concept subtly different from censoring, where an individual does not return for screening in the given time frame of the study. Models distinguishing between these two concepts are known as cure rate models and are often preferred for data where a significant part of the population never experienced the endpoint. Building upon recent work on hierarchical cure model framework we propose modelling a sequence of latent events with a piecewise exponential distribution that remedies oversmoothing encountered in existing models with different latent distributions. We investigate simultaneous regression on the cure fraction and the latent event distribution and derive a flexible class of semiparametric cure rate models. © 2009 SAGE Publications

Use of Alternative Designs and Data Sources for Pediatric Trials

Children are considered a vulnerable group and as such are granted additional protection as research subjects. Research projects using children as research subjects are justifiable if the answer to the scientific question of the study cannot be obtained by enrolling adult subjects (cf. scientific necessity). Thus, there is an ethical obligation to explore innovative analytical strategies that seek balance between the feasibility of conducting a trial and maximizing the utilization of data on efficacy and safety. On this note, there is enthusiasm for implementing some less popular but efficient alternative designs for confirmatory pediatric trials. Within the pediatric extrapolation paradigm, examples of such designs, other than purely based on pharmacokinetic/pharmacodynamic data, are described in this article along with their advantages and disadvantages. This article will also discuss how to incorporate alternative data sources in the analysis of pediatric clinical trials. A discussion of existing approaches and a road-map to their utilization will be provided. Real case examples on the use of the approaches are provided