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82 votes
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 ...
Isabella Ghement's user avatar
35 votes
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 ...
Robert Long's user avatar
  • 63.1k
11 votes
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 (...
Frank Harrell's user avatar
10 votes

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),...
Noah's user avatar
  • 34.7k
9 votes

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 ...
AdamO's user avatar
  • 63.5k
9 votes
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) ...
Noah's user avatar
  • 34.7k
9 votes
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, ...
EdM's user avatar
  • 94.6k
9 votes
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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 ...
PBulls's user avatar
  • 5,153
8 votes
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 ...
Björn's user avatar
  • 33.3k
8 votes
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 ...
Christian Geiser's user avatar
8 votes

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 ...
Christian Hennig's user avatar
7 votes

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)...
Aanish's user avatar
  • 71
7 votes
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 ...
Frank Harrell's user avatar
7 votes

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

While they don't provide a strict criterion in their study, Graham et al., 2007 did a Monte Carlo simulation of different $m$ values and came up with a table of estimates based off that data. Here $\...
Shawn Hemelstrand's user avatar
7 votes

Would it be preferable to use statistical imputation instead of a subject matter expert's subjective estimate for missing data?

I would avoid the term "missing data" here. You are using number of medications as a proxy for disease complexity. For some patients, you can't directly know the number of medications, but ...
Harvey Motulsky's user avatar
7 votes

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

First, I don't see how variables at time 2 can be sensibly used to predict dropout at time 2. So, I don't think you need to even worry about the missing data. I'm not sure why you collected it. You ...
Peter Flom's user avatar
  • 124k
6 votes
Accepted

How to combine/pool binomial confidence intervals after multiple imputation?

This is indeed an interesting problem. The issue is that the standard errors that are based on the central limit theorem for proportions are often undesirable because proportions are a computed ...
Robert Kubinec's user avatar
6 votes
Accepted

Why do I need to run a model on multiple imputed datasets?

The imputed values on your datasets obtained through multiple imputation are predictions from statistical models themselves, and vary according to probabilistic distributions as any predictions from ...
Kenji's user avatar
  • 858
6 votes
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Multiple imputation for missing data in longitudinal study

Multiple imputation is an appropriate approach for your situation but you need to account for the multilevel nature of your data. The observations are nested within participants and this fact needs to ...
Jeffrey Girard's user avatar
6 votes
Accepted

Calculating pooled p-values manually

This is for anyone who is interested, after reading pp. 37-43 in Flexible Imputation of Missing Data by Stef van Buuren. If we call the adjusted degrees of freedom ...
llewmills's user avatar
  • 2,161
6 votes
Accepted

Including dependent variables in multiple imputation model when they have missing values

See Kontopantelis et al. (2017), who describe the proper way to handle this situation. You should definitely retain the DV in the imputation model and use it to impute the predictors. You should use ...
Noah's user avatar
  • 34.7k
6 votes

Margins after mice?

The general approach to analysis of missing data using multiple imputation is create several complete datasets, let's say $m$, using whatever multiple imputation alogorithm you choose perform the ...
Robert Long's user avatar
  • 63.1k
6 votes
Accepted

Is there a way to impute chi-square data?

mice does not implicitly coerce character variables in your data.frame into categorical ...
R Carnell's user avatar
  • 5,413
5 votes

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 ...
Ben Ogorek's user avatar
  • 5,407
5 votes

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 ...
horaceT's user avatar
  • 3,362
5 votes

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 ...
RNB's user avatar
  • 606
5 votes

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: <...
Giuseppe Biondi-Zoccai's user avatar
5 votes

perform Random Forest AFTER multiple imputation with MICE

The combine function in randomForest makes it possible to combine multiple randomForest ...
Timothy's user avatar
  • 81
5 votes

How to pool c-statistic/AUROC (or any bounded variable) after using multiple imputation techniques?

The c-index is a useful measure of predictive discrimination because it is easy to interpret and at least moderately sensitive. It is not a full-information proper accuracy scoring rule. It is not ...
Frank Harrell's user avatar
5 votes

Is there an R function that performs LASSO regression on multiple imputed datasets and pools results together?

There is both a technical and a conceptual problem here. Technically, glm.mids() is designed as part of the mice package to ...
EdM's user avatar
  • 94.6k

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