Questions tagged [mice]
MICE is an R package which implements Multivariate Imputation by Chained Equations using Fully Conditional Specification
216 questions
0 votes
0 answers
12 views
Imputation with mice for multilevel data that is only missing level 1 values
This is my first time attempting data imputation with the mice package. I've read some tutorials but am still confused about how to apply the different examples to ...
- 1
0 votes
0 answers
40 views
Multilevel analysis assumptions violation and singular warning
I am working on a project with 3 measures of cortisol and 3 measures of infant crying, thus I assess with multilevel if there is an association between the two. I have quite some missing data in my ...
- 1
4 votes
1 answer
118 views
Negative F-value from micombine.F in miceadds after pooling positive F-values
I'm using the mice and miceadds packages in R to perform multiple imputation and then analyze the results. Here's what I did: I performed multiple imputation on my dataset using the mice package. For ...
1 vote
0 answers
60 views
Pooled Point Prediction Intervals for MICE imputed data
I am performing binary prediction on a dataset which contains missingness, and so I am leveraging Multiple Imputation (MI). For example, creating a train-test split, I perform MI on the training data ...
2 votes
1 answer
156 views
Gam model inference following mice imputation
I’m working with a Generalized Additive Models in which I have the following model gam(outcome ~ s(Age) + exercise + diet) I’m missing values for exercise and diet ...
- 87
0 votes
0 answers
83 views
Multiple Imputation in RCTs
I want to impute data in an RCT using the mice package in R and have some questions regarding the imputation of missing outcomes. Outcomes were assessed at 5 assessment points, T1-T5. Scale-level or ...
- 133
1 vote
1 answer
256 views
How to properly adjust for confounders with IPW (WeightIt/weightthem) and plot adjusted survival curves with adjustedCurves?
I am estimating overall survival (OS) differences between two groups while adjusting for confounders. My workflow involves: Multiple imputation of missing data. Inverse probability weighting (IPW) ...
- 113
0 votes
0 answers
33 views
What is the right procedure for multivariate imputation of categorical data?
I wanted to know the multivariate approach to impute the categorical data. It is apparent that if I want to us sklearn.impute.IterativeImputer I need to encode the ...
2 votes
2 answers
273 views
Intuition behind generative methods for imputing missing data
I’m learning different approaches to impute a tabular dataset of mixed continuous and categorical variables, and with data assumed to be missing completely at random. I converted the categorical data ...
- 77
0 votes
0 answers
73 views
Rubin's rules Predicted values from mice imputed datasets and rms library
I am starting an analysis midway so my starting point is multiple imputed datasets created using the mice. ...
0 votes
1 answer
96 views
Nelson-Aalen cumulative hazard causing multicollinearity when performing mice in R
I am performing survival analysis and fairly new to multiple imputation and would be grateful for any insights/expertise with respect to this issue. I have large database and need to impute 2 ...
0 votes
1 answer
150 views
Strong potential of MNAR issue - can we still use MI i.e., mice() in R?
Study goal: estimate the proportion of patients who experience outcome Y (1=Yes, 0=No) within max 5 years of follow-up. Missing data issue: Outcome Y is missing for a large proportion of people (96% ...
1 vote
1 answer
124 views
Standard Error of repeated nested cross-validation
Is there a model-agnostic formula for the standard error of K-Fold cross validation, nested cross-validation, or repetead nested cross-validation prediction results? I just stumbled about this post ...
- 101
3 votes
1 answer
233 views
How to conduct statistical analysis on multiply imputed data?
I have a data.frame named mydata with 6 columns: status, times, t1, t2, t3, t4. However, t1, t2, t3, and t4 contain missing values in this dataset. I intend to impute these missing values using the ...
- 219
1 vote
0 answers
58 views
Multiple Imputation for Missing Outcome Data
I have spent an extensive amount of time trying to understand the possible role of MICE in helping to "fill in" missing outcome data. I am relatively new to both multiple imputation and ...
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