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https://hdl.handle.net/2440/44305
Type: | Journal article |
Title: | Statistics in review; Part 2: Generalised linear models time-to-event and time-series analysis evidence synthesis and clinical trials |
Author: | Moran, J. Solomon, P. |
Citation: | Critical Care and Resuscitation, 2007; 9(2):187-197 |
Publisher: | Australasian Academy of Critical Care Medicine |
Issue Date: | 2007 |
ISSN: | 1441-2772 2652-9335 |
Statement of Responsibility: | John L Moran and Patricia J Solomon |
Abstract: | In Part I, we reviewed graphical display and data summary, followed by a consideration of linear regression models. Generalised linear models, structured in terms of an exponential response distribution and link function, are now introduced, subsuming logistic and Poisson regression. Time-to-event ("survival") analysis is developed from basic principles of hazard rate, and survival, cumulative distribution and density functions. Semi-parametric (Cox) and parametric (accelerated failure time) regression models are contrasted. Time-series analysis is explicated in terms of trend, seasonal, and other cyclical and irregular components, and further illustrated by development of a classical Box-Jenkins ARMA (autoregressive moving average) model for monthly ICU-patient hospital mortality rates recorded over 11 years. Multilevel (random-effects) models and principles of meta-analysis are outlined, and the review concludes with a brief consideration of important statistical aspects of clinical trials: sample size determination, interim analysis and "early stopping". |
Keywords: | Humans Hospital Mortality Linear Models Survival Analysis Sample Size Clinical Trials as Topic |
Rights: | Copyright © 2007 Australian and New Zealand College of Anaesthetists |
Description (link): | http://www.cicm.org.au/journal_load.php?year=2007&month=June |
Published version: | http://www.cicm.org.au/journal/2007/june/ccr_09_2_0607_187.pdf |
Appears in Collections: | Aurora harvest 6 Mathematical Sciences publications |
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