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Questions tagged [multiple-imputation]

Use this tag for questions involving multiple imputation, which refers to a set of stochastic imputation routines aimed at preserving the multivariate features of the data.

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Is there a way to remove multiple imputations that were created in Mplus from a dataset? [closed]

I have a .sav dataset which used multiple imputation data which was imputed using Mplus for some variables. I would like to remove these imputed values so that there's no data from multiple imputation,...
sask.r's user avatar
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Difference between copy increments and copy reference imputation

I'm reading up on reference-based imputation (Carpenter et al. 2013, but the explanation is a little bit too mathematical for me) and I'm not quite sure if I understand the difference between copy ...
Anonyme Ironikerin's user avatar
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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 ...
R Har's user avatar
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3 votes
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Bootstrap standard errors after multiple imputation

Following Rubin's rules for multiple imputation, I've calculated pooled estimates, group means in this case, with pooled standard errors. I checked this with a bootstrap and, assuming pooled standard ...
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1 answer
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Multiple Imputation method in RCT

We decided to use the multiple imputation method in a RCT to solve the problem of some follow-up missing data (for completely random reasons). I was planning on using the Multiple Imputation method ...
Mai's user avatar
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How to access weighted group means and standard errors after using mice and WeightIt? [closed]

Some background: I imputed and weighted data from two groups of people, one group in a certain organization and one outside of it. Ultimately, I want to compare how they develop psychological trait X ...
MHx01's user avatar
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2 answers
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Multiple Imputation for missing outcomes in Cox regression

Imagine an RCT with a time-to-event outcome which is analyzed using a Cox regression. There are four assessments (T1=before randomization, T2=3 weeks, T3=6 weeks, T4=12 weeks). Under the censoring at ...
Survival's user avatar
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Reasons for failure of convergence in multiple imputation in wide format of (longitudinally) repeated Likert items

Our group is working on a dataset of approximately 1000 patients with 10 complete variables at the time of an acute disease, who subsequently completed a questionnaire of 5 Likert items (questions ...
torwart's user avatar
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Multiple imputation process

Whether it is a method for dealing with monotonic or arbitrary missing data (FCS or MICE), there is a process I do not understand. Let's take the example of linear regression for continuous variables: ...
Guillaume's user avatar
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Multiple imputation of longitudinal data in SPSS

I'm attempting to analyse a longitudinal, retrospective dataset with measurements at various time-points. The data-set has a significant amount of missing data, up to 30% for the main outcome variable,...
R.A. Been's user avatar
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Does survey R package allow me to do beta regression?

I have a complex survey dataset with a response (dependent variable) bounded between 0 and 1, where I have applied multiple imputation to the dataset to account for missing data. The response formally ...
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Impact of correlation method using mice::quickpred()?

I use Multiple Imputation with MICE in R for dealing with missings in my survey data. I have two huge question marks in my head at the moment: As I have quite a lot of predictors (actually all ...
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7 votes
3 answers
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How to analyze a dichotomous outcome with 50% missing data?

I am researching predictors of dropout from a training program. I want so to see if personality traits add incremental variance above well-established predictors like age, fitness, and education. So, ...
E_H's user avatar
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2 votes
2 answers
43 views

How to compare 2 multiply imputed nested Cox proportional hazards models?

I've got 2 nested Cox models, which I fit to 10 imputed datasets. Pooling the regression coefficient estimates and associated p-values I've done already. I'm trying to work out if adding one extra ...
Isaac Allen's user avatar
2 votes
1 answer
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NA results after pooling estimates and coeff of mixed effects cox model from MICE imputation dataset

I need your help with my problem. So, after step of imputation missing data through MICE method, I got multiple imputed dataset. Then, I pooled the estimates and coefficients with mixed effect cox ...
Hoang-Giang Pham's user avatar
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Removing observations with missing target values in the test set

I'm building my first predictive model and seem to be having a fundamental confusion about missing target values. I'm predicting treatment outcome (with both regression and classification methods for ...
olke's user avatar
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4 answers
221 views

Why is multiple imputation not used more widely in Data Science? [closed]

I posted this question a few days ago on datascience.SE because I thought it was more relevant there: Why is multiple imputation not used more widely in Data Science? I have a background in ...
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Multiple imputation: deleting cases before imputation

Note: The question has been edited to make it more focused, and the title has been changed to make it clearer. I have read questions/answers about how to select variables for imputation. This question ...
Verity's user avatar
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Cross-sectional variables in a time-series df with multiple imputation

I have a dataset with time-series and cross-sectional variables with a sample size of less than 100. I have three variables (var1, ...
jrcalabrese's user avatar
2 votes
1 answer
55 views

Visit and order sequence for multiple imputation in mice r

I want to use the R package MICE for Multiple Imputation and I have a question concerning the order of my dataset - regarding the order of my variables on the one hand and the order of my cases on the ...
rNewbie's user avatar
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1 vote
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After the mutiple imputation (MICE package in R), I still found that some variables are still with missing values. How to deal with it?

I have a relatively large data set with around 12000 samples with 550 variables. Originally, I have around 800 variables, I used a rule that if missing rate in each variable is larger than 80% I will ...
Steven Xu's user avatar
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Addressing High Missingness in Data Analysis for Feature Selection Methods

I'm working with a comprehensive dataset of 12,000 companies and 600 ESG metrics from Refinitiv, segmented into 10 sectors. In my feature selection process for determining influential ESG scores, I've ...
Question Anxiety's user avatar
1 vote
2 answers
311 views

Pooling p-values from hypothesis tests after multiple imputation

I'm working on a project that is using some more advanced statistical methods and coding than I'm normally used to and would appreciate some help. The project required me to do multiple imputation, ...
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Distribution of discrete, integer-valued Scores for Missing data imputation

In studies of aging, physical performance is assessed using instruments where the result is scored as a discrete integer variable that ranges from 0 to K. For example, a commonly used assessment tool ...
user67724's user avatar
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2 votes
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Variable selection in multiply imputed data

I have a dataset with approximately 1800 observations and I'm trying to fit a multivariable logistic regression model (250 cases, 1550 controls). There are 19 covariates (mix of continuous, ordinal ...
donm79's user avatar
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Multiple imputations generate values distributed differently from original dataset... does this mean my data is MNAR? Imputations still usable?

Quick question. I'm using the mice R package to impute missing data. I go by the presumption that the missing data are MAR, but I wouldn't be surprised if a few binary variables were MNAR. I followed ...
awastus's user avatar
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6 votes
2 answers
353 views

Multiple imputation of binary endpoint using underlying continuous variable

I have a response variable (Yes/No) by visit with some missing values. I am considering imputing the underlying continuous variable in SAS using proc MI. After this process, I will have, let's say, M ...
Kate's user avatar
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18 views

What counts as cross-sectional units (within which observationsare embedded)?

Observations can be nested/embedded within cross-sections or cross-sectional units that are measured. For instance, populations are distributed across regions. There are various methods for dealing ...
Johan's user avatar
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19 views

Using original or imputed datasets in analysis plan for survey data?

I'm working with survey data that includes about 100 variables and about 3,500 respondents. Out of the 100 variables in the dataset, a lot of them had more than 5-10% missing data, and many were ...
Ask128453's user avatar
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22 views

What is the best method for imputing binary (or integral/count) data?

I have a longitudinal dataset, and I want to create a composite score by including five healthy lifestyles to measure the overall lifestyle over time (use as predictor). Each lifestyle is a binary ...
zjppdozen's user avatar
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whether to set variable as ID or as nominal for multiple imputation

Using Amelia II for multiple imputation in R, I cannot find elaborate explanations of whether to set variables to being ID or nominal (perhaps a Stata forum post comes closest). How to choose? In my ...
Johan's user avatar
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2 votes
1 answer
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How to use priors to impute values at an individual level and replicate a distribution of the population?

I am trying to correct a variable from a survey that has measurement error. To do this, I have been taking this column as if it was missing and imputing new values based on the predictions of an ...
Santiago Valdivieso's user avatar
2 votes
3 answers
57 views

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

I'm working on a project where I need a variable for the total number of medications a patient is on. The PI is a clinician and I feel they would be able to use the resources at hand - case note and ...
Geoff's user avatar
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0 answers
182 views

how to run mediation analysis after multiple imputation in R?

I have run a multiple imputation model and after it I used the with() and pool() functions to pool all my results using linear regression to get one estimate. I want to run mediation analysis on this ...
yusefsoliman's user avatar
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1 answer
175 views

How to combine results from Wilcoxon Rank Sum Test for multiple imputed data sets from proc MI in SAS

Endpoint information: We have seizure count collected for every day and therefore there will be some missing for some days. We got average seizure frequency per 28-day, for an interval. That is, (...
Janet Xu's user avatar
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47 views

Missing data and missing not at random

My outcome is a child cognition scores (y). I will skip the exposure for now. My covariates are regular features like , ...
Science11's user avatar
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0 votes
2 answers
138 views

how to determine which imputed data to use in R

I have a dataset of almost 100 variables. These variables are Likert scale questions from 1 to 5 or 1 to 3. I converted the variables that I wanted to impute to categorical variables. Then I used this ...
yusefsoliman's user avatar
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1 answer
31 views

How can I perform an analysis of the NHIS imputed income variables?

I have downloaded five family income variables from https://nhis.ipums.org/nhis-action/variables/group?id=economic_income (INCPPOINT1, INCPPOINT2, INCPPOINT3, INCPPOINT4, INCPPOINT5) for they years ...
tryingtogetsmth's user avatar
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33 views

Multiple imputation - complicated variable selection in linked datasets

I attended the course on multiple imputations, where it was stated (to my understanding) that when imputing the missing data on some predictors we should use all variables that will be fitted in the ...
Milo's user avatar
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1 vote
0 answers
150 views

When should one set variables to 0 in the predictor matrix for multiple imputation?

Background. I am using multiple imputation using the "mice" package in R (https://cran.r-project.org/web/packages/mice/mice.pdf) to handle missing data in a large public dataset I am ...
jumbo-owl's user avatar
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59 views

Congeniality between imputation model and analysis model

I have a question about congeniality between imputation model and analysis model. Suppose: Research goal: To estimate the prevalence of disease A among a population as well as among different ...
Willi Zhang's user avatar
1 vote
0 answers
72 views

Multilevel multiple imputation in practice using R

I'm currently involved in a project where I want to address missing data using multiple imputation. I'm using healthcare data in a longitudinal setting with 16 time points, where observations are ...
actual-garlic's user avatar
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0 answers
18 views

Include fully-observed variables as a response or a covariate in multivariate normal imputation?

I have a few questions regarding the specification of multivariate normal imputation model (MVNI): Suppose I have the following substantive analysis model, and there is missing values in X1. I want to ...
Willi Zhang's user avatar
1 vote
0 answers
46 views

MICE for longitudinal data - shall I include both id and time variable for imputing outcome

The missing variable in my longitudinal data set is the outcome variable. I try to use mice in R to do multiple imputation. The final model is mixed effect model fitted by lmer. The data set contains ...
Charlotte's user avatar
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66 views

Multivariate normal imputation model Jomo R package

I have three questions about multivariate normal imputation using R package jomo. I modified the example in jomo to illustrate ...
Willi Zhang's user avatar
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0 answers
47 views

Pooling methods after multiple imputation

I am a biginner for multiple imputation. Now trying pool all the results, but wondering how to do so. I need to make a table for number of patietns in each categories, percentage, and OR and 95%CI for ...
Haruka Hayashi's user avatar
1 vote
1 answer
240 views

Multiple imputation for subgroup analysis

Suppose I am interested in fitting a linear regression model as follows: Y = a + b1 * age(continuous) + b2 * sex + b3 * income This model will be run in both the whole sample and subgroups (defined by ...
Willi Zhang's user avatar
1 vote
0 answers
215 views

Data contains missing values after multiple imputation using mice without logged events (i.e., no evidence for constant values or multicollinearity) [closed]

After the multiple imputation (pmm method) using the mice package, there are still missing values in my dataset (although the number of missing values was reduced). I have checked that there was no ...
Dale's user avatar
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2 votes
2 answers
62 views

Using Multiple Imputation Techniques in Data Analysis

I recently worked with two different statisticians who both suggested different strategies for dealing with imputation of missing data. For the sake of this discussion, I'll call them Statistician A ...
alliecat966's user avatar
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20 views

Multiple imputation in longitudinal studies

I am conducting a population-based study on the changes and stability of sexual identity (e.g., heterosexual, homosexual) over time. The study is based on a cohort of participants who were followed up ...
Willi Zhang's user avatar

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