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Standard implementations of multiple imputation (MI) approaches provide unbiased inferences based on an assumption of underlying missing at random (MAR) mechanisms. However, in the presence of missing data generated by missing not at random (MNAR) mechanisms, MI is not satisfactory. Originating in an econometric statistical context, Heckman's model, also called the sample selection method, deals with selected samples using two joined linear equations, termed the selection equation and the outcome equation. It has been successfully applied to MNAR outcomes. Nevertheless, such a method only addresses missing outcomes, and this is a strong limitation in clinical epidemiology settings, where covariates are also often missing. We propose to extend the validity of MI to some MNAR mechanisms through the use of the Heckman's model as imputation model and a two-step estimation process. This approach will provide a solution that can be used in an MI by chained equation framework to impute missing (either outcomes or covariates) data resulting either from a MAR or an MNAR mechanism when the MNAR mechanism is compatible with a Heckman's model. The approach is illustrated on a real dataset from a randomised trial in patients with seasonal influenza. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

Agreement between two assays is usually based on the concordance correlation coefficient (CCC), estimated from the means, standard deviations, and correlation coefficient of these assays. However, such data will often suffer from left-censoring because of lower limits of detection of these assays. To handle such data, we propose to extend a multiple imputation approach by chained equations (MICE) developed in a close setting of one left-censored assay. The performance of this two-step approach is compared with that of a previously published maximum likelihood estimation through a simulation study. Results show close estimates of the CCC by both methods, although the coverage is improved by our MICE proposal. An application to cytomegalovirus quantification data is provided., (Copyright © 2014 John Wiley & Sons, Ltd.)

A variable is 'systematically missing' if it is missing for all individuals within particular studies in an individual participant data meta-analysis. When a systematically missing variable is a potential confounder in observational epidemiology, standard methods either fail to adjust the exposure-disease association for the potential confounder or exclude studies where it is missing. We propose a new approach to adjust for systematically missing confounders based on multiple imputation by chained equations. Systematically missing data are imputed via multilevel regression models that allow for heterogeneity between studies. A simulation study compares various choices of imputation model. An illustration is given using data from eight studies estimating the association between carotid intima media thickness and subsequent risk of cardiovascular events. Results are compared with standard methods and also with an extension of a published method that exploits the relationship between fully adjusted and partially adjusted estimated effects through a multivariate random effects meta-analysis model. We conclude that multiple imputation provides a practicable approach that can handle arbitrary patterns of systematic missingness. Bias is reduced by including sufficient between-study random effects in the imputation model., (Copyright © 2013 John Wiley & Sons, Ltd.)

Multiple imputation is a strategy for the analysis of incomplete data such that the impact of the missingness on the power and bias of estimates is mitigated. When data from multiple studies are collated, we can propose both within-study and multilevel imputation models to impute missing data on covariates. It is not clear how to choose between imputation models or how to combine imputation and inverse-variance weighted meta-analysis methods. This is especially important as often different studies measure data on different variables, meaning that we may need to impute data on a variable which is systematically missing in a particular study. In this paper, we consider a simulation analysis of sporadically missing data in a single covariate with a linear analysis model and discuss how the results would be applicable to the case of systematically missing data. We find in this context that ensuring the congeniality of the imputation and analysis models is important to give correct standard errors and confidence intervals. For example, if the analysis model allows between-study heterogeneity of a parameter, then we should incorporate this heterogeneity into the imputation model to maintain the congeniality of the two models. In an inverse-variance weighted meta-analysis, we should impute missing data and apply Rubin's rules at the study level prior to meta-analysis, rather than meta-analyzing each of the multiple imputations and then combining the meta-analysis estimates using Rubin's rules. We illustrate the results using data from the Emerging Risk Factors Collaboration., (© 2013 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

In the competing-risks setting, to test the effect of a covariate on the probability of one particular cause of failure, the Fine and Gray model for the subdistribution hazard can be used. However, sometimes, competing risks data cannot be considered as independent because of a clustered design, for instance in registry cohorts or multicentre clinical trials. Frailty models have been shown useful to analyse such clustered data in a classical survival setting, where only one risk acts on the population. Inclusion of random effects in the subdistribution hazard has not been assessed yet. In this work, we propose a frailty model for the subdistribution hazard. This allows first to assess the heterogeneity across clusters, then to incorporate such an effect when testing the effect of a covariate of interest. Based on simulation study, the effect of the presence of heterogeneity on testing for covariate effects was studied. Finally, the model was illustrated on a data set from a registry cohort of patients with acute myeloid leukaemia who underwent bone marrow transplantation., (Copyright 2006 John Wiley & Sons, Ltd.)

Local influence measures have been shown to be a useful tool in identifying influential individuals, and assessing model behaviour towards small perturbations. In the setting of competing risks, we developed a measure of local influence for the Fine and Gray model for the subdistribution hazard. The plot of local influence showed some relationship with time failure rank. This was illustrated on a real data set from a randomized clinical trial in acute promyelocytic leukaemia and on a simulation study., (Copyright 2005 John Wiley & Sons, Ltd.)