PubMed · 15988872
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Shawn McKenzie. Make your RIS/PACS work ... for you.. https://pubmed.ncbi.nlm.nih.gov/15988872/
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In typical small-area studies of health and environment we wish to make inference on the relationship between individual-level quantities using aggregate, or ecological, data. Such ecological inference is often subject to bias and imprecision, due to the lack of individual-level information in the data. Conversely, individual-level survey data often have insufficient power to study small-area variations in health. Such problems can be reduced by supplementing the aggregate-level data with small samples of data from individuals within the areas, which directly link exposures and outcomes. We outline a hierarchical model framework for estimating individual-level associations using a combination of aggregate and individual data. We perform a comprehensive simulation study, under a variety of realistic conditions, to determine when aggregate data are sufficient for accurate inference, and when we also require individual-level information. Finally, we illustrate the methods in a case study investigating the relationship between limiting long-term illness, ethnicity and income in London.
During the outbreak of an epidemic disease, for example, the severe acute respiratory syndrome (SARS), the number of daily infected cases often exhibit multiple trends: monotone increasing during the growing stage, stationary during the stabilized stage and then decreasing during the declining stage. Lam first proposed modelling a monotone trend by a geometric process (GP) [X(i), i=1,2,...] directly such that [a(i-1)X(i), i=1,2,...] forms a renewal process for some ratio a>0 which measures the direction and strength of the trend. Parameters can be conveniently estimated using the LSE methods. Previous GP models limit to data with only a single trend. For data with multiple trends, we propose a moving window technique to locate the turning point(s). The threshold GP model is fitted to the SARS data from four regions in 2003.
The standard approach for analysing a randomized clinical trial is based on intent-to-treat (ITT) where subjects are analysed according to their assigned treatment group regardless of actual adherence to the treatment protocol. For therapeutic equivalence trials, it is a common concern that an ITT analysis increases the chance of erroneously concluding equivalence. In this paper, we formally investigate the impact of non-compliance on an ITT analysis of equivalence trials with a binary outcome. We assume 'all-or-none' compliance and independence between compliance and the outcome. Our results indicate that non-compliance does not always make it easier to demonstrate equivalence. The direction and magnitude of changes in the type I error rate and power of the study depend on the patterns of non-compliance, event probabilities, the margin of equivalence and other factors.