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Questions tagged [multiple-imputation]

Multiple imputation refers to a set of stochastic imputation routines aimed at preserving the multivariate features of the data

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Is it ever recommended to use mean/multiple imputation when using tree-based predictive models?

Everytime that I am making some predictive model and I have missing data I impute categorical variables with something like "UNKNOWN" and numerical variables with some absurd number that will never be ...
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61 views

How does mice::mice work?

The idea of multiple imputation seems to be based on the decomposition $$ p(\theta \mid y_{\text{obs}}) = \int p(\theta \mid y_{\text{obs}}, y_{\text{mis}})p( y_{\text{mis}} \mid y_{\text{obs}}) \text{...
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Diagnosing why MICE fails to impute data with either pmm or passive imputation

I'm having the worst time getting mice (version 3.3.0 under R 3.4.4 in debian stretch) to impute missing values in a particular dataset. This dataset describe a scale development effort with planned ...
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16 views

Degrees of freedom after multiple imputation

Goodmorning everyone, In my research project, I made use of multiple imputation to replace missing values.SPSS lets me then run most of the tests on the imputed data set and provides output for 5 ...
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24 views

How to handle uncertain counts in poisson test

I am curious about performing poisson test where I have uncertainty about my count. For example, I expect to see 15 bunny rabbits per hike. On a given hike, I positively identify 19 bunny rabbits, and ...
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15 views

aregImpute or mice for imputation of survival data

I would like to use multiple imputation to analyse associations between an exposure variable (exp) and different disease risks in a dataset with some missing data (...
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19 views

Large discrepancy between complete-cases and imputed data

I would like to conduct a survival analysis using a dataset with approximately 12,000 participants (1100 events). However, complete data are available for only 9500 participants (820 events). I have ...
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25 views

Random effects vs Rubin's rule to obtain pooled parameter estimates from multiply imputed datasets

I would appreciate any help to understand the statistical difference between using random effects and Rubin's rule to obtain pooled parameter estimates from multiply imputed datasets. For example, if ...
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Obtaining measures of effect for contingency tables with multiply imputed data

The epi.2by2 function in the epiR package computes a chi-square test and provides measures of effect when count data are ...
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1answer
77 views

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

I have a dataset with 283 observation of 60 variables. My outcome variable is dichotomous (Diagnosis) and can be either of two diseases. I am comparing two types of diseases that often show much ...
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30 views

Is Structurally Missing Data a subset of Missing at Random Data?

I'm quite familiar with MCAR, MAR and MNAR (NMAR) data but I have just come across a new (for me) term: Structurally Missing Data (SMD). According to this page, Structurally missing data is data ...
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44 views

Understanding the algorithm behind aregImpute in R::Hmisc

I am dealing with time series data with gaps. I need to capture dominant frequencies in the data, and so have to perform an FFT on the data. However, since FFT requires evenly sampled data; I need to ...
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Descriptive statistics (frequencies, counts, proportions) after multiple imputation

I recently ran a multiple imputation using the mice package in R to generated imputed datasets. I have no problems with running inferential statistics on the pooled data (logistic and Cox regressions) ...
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17 views

All items missing for various questionnaires

this is actually the first time I'm working on a big dataset and I really hope someone can give me some advice on how to handle missing data. I tried to find information regarding my problem but can't ...
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16 views

How to deal with the problem of censoring in tree-based machine learning?

Censoring occurs when the outcome of a unit is not observed, because the unit is lost to follow up in a longitudinal study. Let $Y_t$ be the survival time at $t$. Then a unit is censored at $t'$ if $...
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31 views

Trend line for TS and using smoothing with mice

I have a time Series data like ...
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1answer
23 views

Multiple Imputation of Mutually Dependent Data

I'm trying to construct summary variables for proportion of time spent in different employment statuses over an individual's working life (e.g. % of time spent unemployed between ages 18-21). My ...
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How to test multiple regression assumptions when multiple imputation has been used?

I used multiple imputation on SPSS to deal with missing data in my study. I then carried out multiple regression from the imputed and original data-sets, using a split-file. I now have output for each ...
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75 views

Regression with missing Y’s

I use publicly available EU-Silc data to estimate the market price of social dwellings (subsidized dwellings). However my X variables are almost perfectly available,...
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1answer
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What should I do after multiple imputation in the data?

I have a data set with missing observations. I used VIM package in R for imputation. After imputation, I will try to run a ...
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73 views

How to use MICE in R to fill missing values in test set?

It seems that MICE does not have a "predict" function which allows to use a fitted mids object to predict the missing values in test data set. I can certainly ...
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1answer
40 views

Imputing nested time series data with R

Does anyone know what is the superior algorithm to impute data in time series? I had strong dropouts over time because it was free to participants how many times to participate in my study (otherwise ...
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1answer
44 views

Proper Imputation and bias-correction on degrading signal with Kalman Filtering?

A signal degrades in its quality. Some signals are far more robust to degradation while others are not. We will simulate degradation by randomly removing values from a function and then applying ...
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1answer
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Why don't people impute missing exposure data in database studies?

Investigators doing studies in large databases (e.g., EMR) in which there is often a lot of missing data usually (in my experience) want to exclude all subjects missing the exposure or outcome of ...
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Multiple Imputation on multi-site data

Suppose I would like to assess the relationship between Y and X (i.e. Y|X) on data collected from several different sites (i.e. 5) with one covariate Z and multiple auxiliary covariates which may ...
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How do I impute clustered data that is not time-series data?

The goal of my research is to understand whether MRI imaging characteristics can predict tumor pathology. The data consists of resected tumor samples, with multiple samples per patient. On the MRI, we ...
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Choosing Among Multiply Imputed Datasets

I am using multiple imputation to estimate treatment effects in a dataset that contains missing data. In some of my imputed datasets, the algorithm used in the analysis fails to converge; it's not ...
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When to use Multiple Imputation over Maximum Likelihood for Missing Data and vice-versa?

I've seen these being called the best techniques for dealing with missing data. But I'm wondering when to use one over the other and why? Edit: Why is this getting downvotes? I'm legitimately ...
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Imputing missing data with MICE where each observation has different levels

I have a set of observations that each consists of different levels. For example, I ask a $P$ individuals $N$ questions, each question with a possible $k_n$ discrete responses. This produces a table ...
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1answer
40 views

Per protocol or Imputation when missing is small (<5%)

if ~2% of my data is missing on the outcome (continuous scale), out of a total of 200, two in control and three in intervention group, do I need to impute? Or can I make a case that with such small ...
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1answer
225 views

Predictive Mean Matching as Single Imputation?

Multiple imputation is known to be advantageous compared to single imputation. However, in practice there are often non-statistical reasons why multiple imputation can not be used (e.g. the data ...
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32 views

Multiple imputation with composite variables

In my analyses, I often use urinary concentrations as measure of exposure to various compounds. As these are generally spot urines, they are 'adjusted' for dilution using urinary creatinine ...
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35 views

multilevel multiple imputation

I have been using the mitml package in R for multilevel multiple imputation of longitudinal data, there are 2 functions available for imputation, panImpute and jomoImpute , both use the joint modeling ...
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Procedure for identifying predictors via LASSO on imputed data

First time poster here, hello Everyone! I've tried to make my question as concise as possible. I am looking to identify the best set of predictors for rehospitalisation in my data set of 60 ...
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Multiple Imputation of Multilevel data

I am using Mice package in R for multiple imputation of a multilevel data where repeated measures are nested within individuals. But there is a bug in mice for which we need to convert the group ...
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Multiple Imputation in R; for one variable only certain date ranges

So I have a problem and I'm not sure there's a way to do this. I have 13 survey questions, and there are responses from 2008 to 2016. However, 3 of the items were only asked starting in 2009. Only ...
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138 views

Multiple imputation with a Cox model [closed]

I'm doing a study on 52 patients with breast cancers and looking for predictive factors of death. I have 17 variables to test for predictive value with some with 20% of missing data (some categorical ...
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99 views

Combining Gradient boosted trees after multiple imputation

Currently I am working with a gradient boosted tree model fit onto a multiple imputed dataset. For those who don't know multiple imputation: It predicts missing values and imputes that value with ...
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23 views

Combining multiple imputation with penalization

I have used Frank Harrell's excellent rms package both for penalization (pentrace and setting ...
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22 views

validity of tobit estimates after multiple imputation

I want to estimate tobit marginal effects using multiply imputed data, however I see that tobit is not among the estimation commands supported by Stata's MI prefix - I understand that the validity of ...
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36 views

Is there a way to estimate regression coefficients?

I'm currently working on a simulation study (based on empirical data) and for this simulation I created a model with multiple interaction terms. The interaction terms are between categorical variables,...
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161 views

Generating frequency table and survival curve after multiple imputation

I'm using the MICE package to generate 10 imputed datasets. After that, I know I should perform analysis on each dataset (propensity score matching, Chi-square, and Cox-regression in my case) and ...
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1answer
41 views

What is the risk of not including all of my model predictors in an imputation process?

I have a model that I want to run but a lot of my predictors have missing values. So I ran an imputation process using mice package in R - it took me 22.5 hours! ...
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35 views

How to deal with undetectable outcome values? (data missing not at random)

I conducted a sound propagation experiment in which recorded maned wolves calls were broadcasted at different sites(x3), hours(x6: 17h,18h,23h,05h,06h,11h), and with different speaker position (x2: ...
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1answer
103 views

Forecast (impute) missing discrete values in multiple time series

I'm looking to forecast (impute) missing discrete values in multiple time series in order to reach a target volume in a consolidated time serie. The context: I have salesmen that are selling ...
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Error Detection and missing data imputation in Wireless Sensor Networks

I'm working on wireless sensor networks and I wish to be capable of detecting if there are any outliers in the sensed data as well as imputing missing ones. I read a lot of articles which made me more ...
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25 views

Is it valid to use Random Forest imputation in blocks and combine results into a final dataset?

I have an extremely large dataset (26 variables and 105,556 observations) with missing values and I would like to use Random Forest imputation to impute some missing values. Since the dataset is so ...
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52 views

Larger standard error after multiple imputations

Missing data experts, I am working on a presentation demonstrating the benefits of using multiple imputations to handle data missingness. To demonstrate its benefits, I simulate two data sets; one ...
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481 views

MCAR test for large number variables and small sample size

I have a dataset with 101 observations and 402 columns (those columns comprise several multiple-item questionnaires). Among those 402 columns, 10 of them are categorical and the remaining are ...
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1answer
148 views

Likelihood ratio test for multiply imputed datasets?

I have a set of generalized linear models fit to 5 multiply imputed datasets. I am interested in testing the statistical significance of a set of predictors, coded from dummy variables. Rubin's ...