Angela M. Wood

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Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical(More)
1Department of Social Medicine, University of Bristol, Bristol BS8 2PR 2MRC Biostatistics Unit, Institute of Public Health, Cambridge CB2 0SR 3Clinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute, and University of Melbourne, Parkville, Victoria 3052, Australia 4Cancer and Statistical Methodology Groups, MRC Clinical Trials(More)
CONTEXT Associations of major lipids and apolipoproteins with the risk of vascular disease have not been reliably quantified. OBJECTIVE To assess major lipids and apolipoproteins in vascular risk. DESIGN, SETTING, AND PARTICIPANTS Individual records were supplied on 302,430 people without initial vascular disease from 68 long-term prospective studies,(More)
BACKGROUND The relevance to coronary heart disease (CHD) of cytokines that govern inflammatory cascades, such as interleukin-6 (IL-6), may be underestimated because such mediators are short acting and prone to fluctuations. We evaluated associations of long-term circulating IL-6 levels with CHD risk (defined as nonfatal myocardial infarction [MI] or fatal(More)
Multiple imputation is a popular technique for analysing incomplete data. Given the imputed data and a particular model, Rubin's rules (RR) for estimating parameters and standard errors are well established. However, there are currently no guidelines for variable selection in multiply imputed data sets. The usual practice is to perform variable selection(More)
BACKGROUND Randomized controlled trials almost always have some individuals with missing outcomes. Inadequate handling of these missing data in the analysis can cause substantial bias in the treatment effect estimates. We examine how missing outcome data are handled in randomized controlled trials in order to assess whether adequate steps have been taken to(More)
BACKGROUND Missing outcome data from randomized trials lead to greater uncertainty and possible bias in estimating the effect of an experimental treatment. An intention-to-treat analysis should take account of all randomized participants even if they have missing observations. PURPOSE To review and develop imputation methods for missing outcome data in(More)
OBJECTIVE To determine whether cesarean delivery is independently associated with later subfertility. DESIGN Retrospective cohort study. SETTING Maternity records kept for Scotland, 1980-1999. PATIENT(S) The study included 109,991 women who had first births between 1980 and 1984, excluding multiple or preterm births and perinatal deaths. (More)
CONTEXT The value of assessing various emerging lipid-related markers for prediction of first cardiovascular events is debated. OBJECTIVE To determine whether adding information on apolipoprotein B and apolipoprotein A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 to total cholesterol and high-density lipoprotein cholesterol (HDL-C)(More)
BACKGROUND Within-person variability in measured values of a risk factor can bias its association with disease. The extent of this regression dilution bias for plasma fibrinogen was investigated using repeat measurement data collected at varying time intervals on 27 247 adults in 15 prospective studies. METHODS Regression dilution ratios (RDRs) were(More)