I have a question related to an experiment with missing data using Monte Carlo Simulation in Mplus. The experiment is about longitudinal data having 4 time points Y1 – Y4 and 1 dichotomous variable X. I would like to generate MAR missing data of Y2, Y3 and Y4 when the cause of missing depends on Y1. More specifically, I would like to be able to control the propensity of missingness which is measured by the correlation between the indicator of missingness (R = 1 if missing, and R = 0 otherwise) and value of Y1 which is a cause of missingness. Is it possible to control the value of this correlation by adjusting values of alpha or beta in the MODEL MISSING command?
Thank you for your suggestion. I have looked at the Example 12.2. However, my experiment for MAR is different from that example. My Y2 depends on baseline value (Y1), which is not time-invariant covariate. Here is my code. MODEL MISSING: [Y2@ alpha ]; Y2 ON Y1 * beta ;
Question1: If Y1 has a normal distribution (mean = 50, variance = 100), can I estimate total % of missing data of Y2 in my data set by substituting mean of Y1 in this logistic model? Probability of missing data = ( 1/ (1 + exp (- (alpha + beta * Y1)) How accurate would my estimation be?
Question 2: How to adjust the values of Alpha and Beta to increase the propensity of missingness while keeping the probability of missing data the same?
Thank you for your suggestions. I would like to have your comments on this experiment. I am studying about the propensity of missingness, which will be measured by Pearson correlation coefficient between logit, log(P/(1-P), and cause of missingness variable. When the correlation coefficient between the logit function and cause of missing Z is specified, parameter â can be determined for a given value of missing percentage P. Beta = (Corr(logit(P),Z) / (Sqrt(P(1-P)) * Sigma(Z)) (approximately).
After getting the value of beta, I run experiment in Mplus to see the percentage of missing data actually obtained (P*) for different values of alpha. The value of alpha that gives missing percentage P* same as the specified value P will be the one I will use in further study. I would like to know if this is a plausible approach to find alpha and beta to use with Mplus MODEL MISSING command when I want to fix the propensity of missingness and percent of missing data. Thank you!
I haven't seen that approach and can't really comment on it. I tend to think of alpha as having to do with the degree of MCAR missingness - which you can pick any realistic level of - and beta the degree of MAR missingness.