In drug development, a common choice for the primary analysis is to assess mean changes via analysis of (co)variance with missing data imputed by carrying the last or baseline observations forward (LOCF, BOCF). These approaches assume that data are missing completely at random (MCAR). Multiple imputation (MI) and likelihood-based repeated measures (MMRM) are less restrictive as they assume data...

Effort prediction is a very important issue for software project management. Historical project data sets are frequently used to support such prediction. But missing data are often contained in these data sets and this makes prediction more difficult. One common practice is to ignore the cases with missing data, but this makes the originally small software project database even smaller and can ...

• A range of different approaches have been suggested for the multivariate modelling of the geographical distribution of different but potentially related diseases. We suggest an addition to these methods which incorporates a discrete mixture of latent factors, as opposed to using CAR or MCAR random effect formulations. Our proposal provides for a potentially richer range of dependency structur...

3 Results 6 3.1 Fully observed variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.2 Partially observed variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.3 Pairwise comparisons between methods . . . . . . . . . . . . . . . . . . . . 7 3.3.1 Comparison of bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.3.2 Comparison of precision . . . . . ...

Abstract. Value-added models have been widely used to assess the contributions of individual teachers and schools to students’ academic growth based on longitudinal student achievement outcomes. There is concern, however, that ignoring the presence of missing values, which are common in longitudinal studies, can bias teachers’ value-added scores. In this article, a flexible correlated random ef...

Missing covariate data often arise in biomedical studies, and analysis of such data that ignores subjects with incomplete information may lead to inefficient and possibly biased estimates. A great deal of attention has been paid to handling a single missing covariate or a monotone pattern of missing data when the missingness mechanism is missing at random. In this article, we propose a semipara...

BACKGROUND Missing data is a challenge for all studies; however, this is especially true for electronic health record (EHR)-based analyses. Failure to appropriately consider missing data can lead to biased results. While there has been extensive theoretical work on imputation, and many sophisticated methods are now available, it remains quite challenging for researchers to implement these metho...

BACKGROUND Multiple imputation is frequently used to deal with missing data in healthcare research. Although it is known that the outcome should be included in the imputation model when imputing missing covariate values, it is not known whether it should be imputed. Similarly no clear recommendations exist on: the utility of incorporating a secondary outcome, if available, in the imputation mod...

The importance of learning distance functions is gradually being acknowledged by the machine learning community, and different techniques are suggested that can successfully learn a strong distance function in many various contexts. Nevertheless the studies in the area are still rather fragmentary; they lack systematic analysis and focus on a limited circle of application domains. In this paper...

BACKGROUND Missing data are a common problem in prospective studies with a long follow-up, and the volume, pattern and reasons for missing data may be relevant when estimating the cost of illness. We aimed to evaluate the effects of different methods for dealing with missing longitudinal cost data and for costing caregiver time on total societal costs in Alzheimer's disease (AD). METHODS GERA...