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The previous article of big-data clinical trial series has introduced basic techniques in dealing with missing values. There are several R packages that allow advanced methods for managing missing data. Some useful methods include visual presentation of missing data pattern and correlation analysis 1. This article firstly creates a dataset containing five variables.
Three missing data classes are illustrated in creating the dataset by simulation. Then various tools for the exploration of missing data are introduced. Statisticians typically classify missing data into three categories. Missing completely at random MCAR refers to the presence of missing values on a variable that is unrelated to any other observed and unobserved variables 2 , 3. In other words, there is no systematic reason for the missing pattern.
Missing at random MAR is the presence of missing values on a variable that is related to other observed variables but not related to its own unobserved values. For example, a patient with lower lactate value is more likely to have a missing lactate value. A hemodynamically stable patient typically has a lower lactate value.
In the situation, a treating physician is less likely to order test for lactate. A dataset of observations is created by simulation. The dataset is used for illustration purpose and there is no clinical relevance. There are five variables including age, sex, lactate lac , white blood cell wbc and C-reactive protein crp.
In each simulation, I set a seed to allow readers to replicate the results. The variable age has complete values for all observations. It is assumed that our population has mean age of 67 with standard deviation of The abs function is employed to avoid negative values.