David N Borg1,2, Robert Nguyen3, Nicholas J Tierney4,5. 1. Menzies Health Institute Queensland, The Hopkins Centre, Griffith University, Brisbane, Australia. 2. School of Allied Health Sciences, Griffith University, Brisbane, Australia. 3. Department of Statistics, School of Mathematics and Statistics, University of New South Wales, Sydney, Australia. 4. Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia. 5. Australian Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Melbourne, Australia.
Abstract
METHODS: A survey of 136 articles published in 2019 (sampled at random) was conducted to determine whether a statement about missing data was included. RESULTS: The proportion of studies reporting on missing data was low, at 11.0% (95% confidence interval = 6.3% to 17.5%). RECOMMENDATIONS: We recommend that researchers describe the number and percentage of missing values, including when there are no missing values. Exploratory analysis should be conducted to explore missing values, and visualisations describing missingness overall should be provided in the paper, or at least in supplementary materials. Missing values should almost always be imputed, and imputation methods should be explored to ensure they are appropriately representative. Researchers should consider these recommendations and pay greater attention to missing data and its influence on research results.
METHODS: A survey of 136 articles published in 2019 (sampled at random) was conducted to determine whether a statement about missing data was included. RESULTS: The proportion of studies reporting on missing data was low, at 11.0% (95% confidence interval = 6.3% to 17.5%). RECOMMENDATIONS: We recommend that researchers describe the number and percentage of missing values, including when there are no missing values. Exploratory analysis should be conducted to explore missing values, and visualisations describing missingness overall should be provided in the paper, or at least in supplementary materials. Missing values should almost always be imputed, and imputation methods should be explored to ensure they are appropriately representative. Researchers should consider these recommendations and pay greater attention to missing data and its influence on research results.