# Tag Info

Accepted

### Multiple Imputation by Chained Equations (MICE) Explained

MICE is a multiple imputation method used to replace missing data values in a data set under certain assumptions about the data missingness mechanism (e.g., the data are missing at random, the data ...
• 20.5k
Accepted

### How do the number of imputations & the maximum iterations affect accuracy in multiple imputation?

Let's just go through the parameters one by one: data doesn't require explanation m is the number of imputations, generally ...
• 63.1k
Accepted

### How to analyze a dichotomous outcome with 50% missing data?

Your description implies that you committed the mortal sin of not pre-specifying the final model in the sense that you tried different models in a way not unlike stepwise variable selection does (...
• 95.1k

### Choosing $m$ value when using multiple imputation (MI)

I believe our current best practice is to use the two-step procedure described in von Hippel (2020) and his Statistical Horizons article, which is to estimate the fraction of missing information (FMI),...
• 34.7k

### Applying Rubin's rule for combining multiply imputed datasets

You correctly wrote down the pooled estimator: $$\bar{U} = \frac{1}{m} \sum_{i=1}^m U_i$$ Where $U_i$ represents the analytic results from the $i$-th imputed dataset. Normally, analytic results ...
• 63.5k
Accepted

### multiple imputation and propensity scores

My understanding is that you should generate individual propensity score models for each data set, then match, then estimate outcomes, then combine the estimates into one. 1) ...
• 34.7k
Accepted

### Rubin's rule from scratch for multiple imputations

After multiple imputation of data sets (MI) and analyzing each of the imputed sets separately, Rubin's rules do have you take the mean over those imputations as the point estimate. For inference, ...
• 94.6k
Accepted

### Multiple imputation of binary endpoint using underlying continuous variable

Rubin's rules work on means and their standard errors, so they are only really valid if a normal approximation is appropriate for your statistic. PROC FREQ provides ...
• 5,153
Accepted

### Best way to combine MCMC inference with multiple imputation?

One well-known approach is exactly what you describe (if I understood correctly): i.e. combine the inferences from the analyses of a large number of imputed datasets (each analyzed separately) by just ...
• 33.3k
Accepted

### Why is multiple imputation not used more widely in Data Science?

A lot of people in applied fields do not realize that "traditional" methods for handling missing data (e.g., listwise or pairwise deletion) are actually more problematic and rest on stronger ...
• 3,676

### How to analyze a dichotomous outcome with 50% missing data?

If you want to predict what happens at T2 from data at T1, you could run a three classes model with "dropout", "no dropout", and "T2 missing" as the classes. Note that ...
• 25.6k

### How to improve running time for R MICE data imputation

You can use quickpred() from mice package using which you can limit the predictors by specifying the mincor (Minimum correlation)...
• 71
Accepted

### How to use restricted cubic splines with the R mice imputation package

You are right that the imputation model needs to be as rich or richer than the outcome model. The fact that imputation based on full maximum likelihood estimation and imputation done by ...
• 95.1k

• 63.1k
Accepted

### Is there a way to impute chi-square data?

mice does not implicitly coerce character variables in your data.frame into categorical ...
• 5,413

### How to improve running time for R MICE data imputation

I made a wrapper for the mice function that includes one extra argument, droplist, where you can pass a character vector of ...
• 5,407

### how to check missing data is missing at random or not?

Here is one way to test the missingness-at-random assumption. Suppose the question on participant's income has some missing entries. Run a logistic regression with income as your response and ...
• 3,362

### What imputation methods can be used for missing not at random covariate values in a survival analysis?

As far as I am aware, MI methods such as Multiple Imputation by Chained Equations (MICE) or Random Forest imputation, are both methods that will allow you to impute missing values for many variables ...
• 606

### multiple imputation and propensity scores

As I previously stated, instead of doing propensity matching it can be reasonable to use inverse probability of treatment weighting after missing data imputation. Suitable Stata examples follow: <...

### perform Random Forest AFTER multiple imputation with MICE

The combine function in randomForest makes it possible to combine multiple randomForest ...
• 81