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Missing values are a practical issue in the analysis of longitudinal data. Multiple imputation (MI) is a well-known likelihood-based method that has optimal properties in terms of efficiency and consistency if the imputation model is correctly specified. Doubly robust (DR) weighing-based methods protect against misspecification bias if one of the models, but not necessarily both, for the data or the mechanism leading to missing data is correct. We propose a new imputation method that captures the simplicity of MI and protection from the DR method. This method integrates MI and DR to protect against misspecification of the imputation model under a missing at random assumption. Our method avoids analytical complications of missing data particularly in multivariate settings, and is easy to implement in standard statistical packages. Moreover, the proposed method works very well with an intermittent pattern of missingness when other DR methods can not be used. Simulation experiments show that the proposed approach achieves improved performance when one of the models is correct. The method is applied to data from the fireworks disaster study, a randomized clinical trial comparing therapies in disaster-exposed children. We conclude that the new method increases the robustness of imputations. © 2013 The British Psychological Society.

Multiple imputation methods properly account for the uncertainty of missing data. One of those methods for creating multiple imputations is predictive mean matching (PMM), a general purpose method. Little is known about the performance of PMM in imputing non-normal semicontinuous data (skewed data with a point mass at a certain value and otherwise continuously distributed). We investigate the performance of PMM as well as dedicated methods for imputing semicontinuous data by performing simulation studies under univariate and multivariate missingness mechanisms. We also investigate the performance on real-life datasets. We conclude that PMM performance is at least as good as the investigated dedicated methods for imputing semicontinuous data and, in contrast to other methods, is the only method that yields plausible imputations and preserves the original data distributions. © 2014 The Authors.

In multiple imputation (MI), the resulting estimates are consistent if the imputation model is correct. To specify the imputation model, it is recommended to combine two sets of variables: those that are related to the incomplete variable and those that are related to the missingness mechanism. Several possibilities exist, but it is not clear how they perform in practice. The method that simply groups all variables together into the imputation model and four other methods that are based on the propensity scores are presented. Two of them are new and have not been used in the context of MI. The performance of the methods is investigated by a simulation study under different missing at random mechanisms for different types of variables. We conclude that all methods, except for one method based on the propensity scores, perform well. It turns out that as long as the relevant variables are taken into the imputation model, the form of the imputation model has only a minor effect in the quality of the imputations. © 2013 Copyright Taylor and Francis Group, LLC.

Studies on the use of performance enhancing drugs (PED) in fitness centres rely predominately on conventional survey methods using direct questioning. However, research indicates that direct questioning of sensitive information is characterized by under-reporting. The aim of the present study was to contrast direct questioning of different types of PED use by Dutch fitness centre members with results obtained with the Randomized Response Technique (RRT). Questionnaires were conducted among members of fitness centres. PED were classified into the following categories: anabolic steroids, prohormones, substances to counteract side-effects, growth hormone and/or insulin, stimulants (to reduce weight), and miscellaneous substances. A total of 718 athletes from 92 fitness centres completed the questionnaire. The conventional method resulted in prevalences varying between 0% and 0.4% for the different types of PED with an overall prevalence of 0.4%. RRT resulted in prevalences varying between 0.8% and 4.8% for the different types of PED with an overall prevalence of 8.2%. The overall prevalence of the two survey methods differed significantly. The current study showed that the conventional survey method using direct questioning led to an underestimation of the prevalence. Based on the RRT results, the percentage of users of PED among members of fitness centres is approximately 8.2%. Stimulants to lose weight had the highest prevalence, even higher than anabolic steroids. The key task for future preventive health work is to not only focus on anabolic steroid use, but also include interventions focusing on the use of stimulants to lose weight.