Gerko Vink

How to handle missing data: A comparison of different approaches

Many researchers face the problem of missing data in longitudinal research. Especially, high risk samples are characterized by missing data which can complicate analyses and the interpretation of results. In the current study, our aim was to find the most optimal and best method to deal with the missing data in a specific study with many missing data on the outcome variable. Therefore, different techniques to handle missing data were evaluated, and a solution to efficiently handle substantial amounts of missing data was provided. A simulation study was conducted to determine the most optimal method to deal with the missing data. Results revealed that multiple imputation (MI) using predictive mean matching was the most optimal method with respect to lowest bias and the smallest confidence interval (CI) while maintaining power. Listwise deletion and last observation carried backward also scored acceptable with respect to bias; however, CIs were much larger and sample size almost halved using these methods. Longitudinal research in high risk samples could benefit from using MI in future research to handle missing data. The paper ends with a checklist for handling missing data.

Multiple Imputation of Squared Terms

We propose a new multiple imputation technique for imputing squares. Current methods yield either unbiased regression estimates or preserve data relations. No method, however, seems to deliver both, which limits researchers in the implementation of regression analysis in the presence of missing data. Besides, current methods only work under a missing completely at random (MCAR) mechanism. Our method for imputing squares uses a polynomial combination. The proposed method yields both unbiased regression estimates, while preserving the quadratic relations in the data for both missing at random and MCAR mechanisms.

Medium-term effectiveness of online behavioral training in migraine self-management: A randomized trial controlled over 10 months.

Aim This randomized, controlled trial examined the medium-term effectiveness of online behavioral training in migraine self-management (oBT; N = 195) versus waitlist control (WLC; N = 173) on attack frequency, indicators of self-management (primary outcomes), headache top intensity, use of rescue medications, quality of life and disability (secondary outcomes). Methods An online headache diary following the ICHD-II and questionnaires were completed at baseline (T0), post-training (T1) and six months later (T2). Missing data (T1: 24%; T2: 37%) were handled by multiple imputation. We established effect sizes (ES) and tested between-group differences over time with linear mixed modelling techniques based on the intention-to-treat principle. Results At T2, attack frequency had improved significantly in oBT (−23%, ES = 0.66) but also in WLC (−19%; ES = 0.52). Self-efficacy, internal and external control in migraine management – and triptan use – improved only in oBT, however. This indicates different processes in both groups and could signify (the start of) active self-management in oBT. Also, only oBT improved migraine-specific quality of life to a sizable extent. Conclusions oBT produced self-management gains but could not account for improved attack frequency, because WLC improved as well. The perspective that BT effects develop gradually, and that online delivery will boost BT outreach, justifies further research.

Generating missing values for simulation purposes: A multivariate amputation procedure

ABSTRACT Missing data form a ubiquitous problem in scientific research, especially since most statistical analyses require complete data. To evaluate the performance of methods dealing with missing data, researchers perform simulation studies. An important aspect of these studies is the generation of missing values in a simulated, complete data set: the amputation procedure. We investigated the methodological validity and statistical nature of both the current amputation practice and a newly developed and implemented multivariate amputation procedure. We found that the current way of practice may not be appropriate for the generation of intuitive and reliable missing data problems. The multivariate amputation procedure, on the other hand, generates reliable amputations and allows for a proper regulation of missing data problems. The procedure has additional features to generate any missing data scenario precisely as intended. Hence, the multivariate amputation procedure is an efficient method to accurately evaluate missing data methodology.

How to Obtain Valid Inference under Unit Nonresponse

Abstract Weighting methods are commonly used in situations of unit nonresponse with linked register data. However, several arguments in terms of valid inference and practical usability can be made against the use of weighting methods in these situations. Imputation methods such as sample and mass imputation may be suitable alternatives, as they lead to valid inference in situations of item nonresponse and have some practical advantages. In a simulation study, sample and mass imputation were compared to traditional weighting when dealing with unit nonresponse in linked register data. Methods were compared on their bias and coverage in different scenarios. Both, sample and mass imputation, had better coverage than traditional weighting in all scenarios. Imputation methods can therefore be recommended over weighting as they also have practical advantages, such as that estimates outside the observed data distribution can be created and that many auxiliary variables can be taken into account. The use of sample or mass imputation depends on the specific data structure.

The Dance of the Mechanisms: How Observed Information Influences the Validity of Missingness Assumptions

Missing data in scientific research go hand in hand with assumptions about the nature of the missingness. When dealing with missing values, a set of beliefs has to be formulated about the extent to...