Establishing anchor-based minimally important differences (MID) with the EORTC quality-of-life measures: a meta-analysis protocol.

INTRODUCTION: As patient assessment of health-related quality of life (HRQOL) in cancer clinical trials has increased over the years, so has the need to attach meaningful interpretations to differences in HRQOL scores between groups and changes within groups. Determining what represents a minimally important difference (MID) in HRQOL scores is useful to clinicians, patients and researchers, and can be used as a benchmark for assessing the success of a healthcare intervention. Our objective is to provide an evidence-based protocol to determine MIDs for the European Organisation for Research and Treatment for Cancer Quality of life Questionnaire core 30 (EORTC QLQ-C30). We will mainly focus on MID estimation for group-level comparisons. Responder thresholds for individual-level change will also be estimated. METHODS AND ANALYSIS: Data will be derived from published phase II and III EORTC trials that used the QLQ-C30 instrument, covering several cancer sites. We will use individual patient data to estimate MIDs for different cancer sites separately. Focus is on anchor-based methods. Anchors will be selected per disease site from available data. A disease-oriented and methodological panel will provide independent guidance on anchor selection. We aim to construct multiple clinical anchors per QLQ-C30 scale and also to compare with several anchor-based methods. The effects of covariates, for example, gender, age, disease stage and so on, will also be investigated. We will examine how our estimated MIDs compare with previously published guidelines, hence further contributing to robust MID guidelines for the EORTC QLQ-C30. ETHICS AND DISSEMINATION: All patient data originate from completed clinical trials with mandatory written informed consent, approved by local ethical committees. Our findings will be presented at scientific conferences, disseminated via peer-reviewed publications and also compiled in a MID 'blue book' which will be made available online on the EORTC Quality of Life Group website as a free guideline document

Statistical analysis of repeated outcomes of different types

This thesis focused on analyzing data with multiple outcome variables. The motivating data sets comprised longitudinal markers of patients’ disease state (e.g. B cells and CD4+ T cell) as well as information on the time to an event (e.g. death) or (multiple) recurrent event times (e.g. repeated bacterial and viral infections). It was interesting to study how these markers relate with the event times and how their updated values may change prognosis. This could help to guide decision making for patient care. In part I we applied joint modeling to study the association between longitudinal and survival data. We also performed dynamic predictions of survival probabilities for a new subject, using marker values that were accrued overtime. We present an extension of the application of joint modelling to a setting with multiple markers and multi-type recurring events. In part II we applied landmarking as an alternative to joint modelling for performing dynamic predictions of survival probabilities. Landmarking circumvents possible computational complications of fitting time-dependent covariates, making it easier to compute survival probabilities compared to using joint models. We present an extension of the application of landmarking to a setting with recurring events of the same type. In part III we focused on validating prediction models in the presence of multiply imputed data. It was unclear how resampling should be performed over the imputed data sets when internal model validation was performed via bootstrap resampling. We investigated four ways of handling the multiply imputed data sets in the validation procedure