andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1330 knowledge-graph by maker-knowledge-mining
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Introduction: Aureliano Crameri writes: I have questions regarding one technique you and your colleagues described in your papers: the cross validation (Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box, with reference to Gelman, King, and Liu, 1998). I think this is the technique I need for my purpose, but I am not sure I understand it right. I want to use the multiple imputation to estimate the outcome of psychotherapies based on longitudinal data. First I have to demonstrate that I am able to get unbiased estimates with the multiple imputation. The expected bias is the overestimation of the outcome of dropouts. I will test my imputation strategies by means of a series of simulations (delete values, impute, compare with the original). Due to the complexity of the statistical analyses I think I need at least 200 cases. Now I don’t have so many cases without any missings. My data have missing values in different variables. The proportion of missing values is
sentIndex sentText sentNum sentScore
1 Aureliano Crameri writes: I have questions regarding one technique you and your colleagues described in your papers: the cross validation (Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box, with reference to Gelman, King, and Liu, 1998). [sent-1, score-0.496]
2 I think this is the technique I need for my purpose, but I am not sure I understand it right. [sent-2, score-0.154]
3 I want to use the multiple imputation to estimate the outcome of psychotherapies based on longitudinal data. [sent-3, score-0.685]
4 First I have to demonstrate that I am able to get unbiased estimates with the multiple imputation. [sent-4, score-0.213]
5 The expected bias is the overestimation of the outcome of dropouts. [sent-5, score-0.259]
6 I will test my imputation strategies by means of a series of simulations (delete values, impute, compare with the original). [sent-6, score-0.64]
7 Due to the complexity of the statistical analyses I think I need at least 200 cases. [sent-7, score-0.073]
8 The proportion of missing values is lower than 30%. [sent-10, score-0.361]
9 I set up the chained equations and generate 30-40 imputations. [sent-12, score-0.535]
10 Next I fill in the missings with the mean of the imputed values. [sent-13, score-0.411]
11 Among others I delete the outcome of 30 successful and 30 unsuccessful cases. [sent-15, score-0.703]
12 Then I impute again with the same regression equations used before. [sent-16, score-0.438]
13 Finally I pool the results according to the Rubin’s rules and I compare the pooled coefficients with the coefficients generated with the repaired dataset. [sent-17, score-0.836]
14 So I can test if the chained equations can discriminate between successful und unsuccessful courses of therapy. [sent-18, score-1.051]
15 Does such a proceeding correspond to a cross validation in your sense? [sent-19, score-0.531]
16 When you do your first imputation, don’t fill in missing entries with the mean of the imputed values. [sent-22, score-0.63]
17 Just use one of the completed datasets (with random imputations) that you created. [sent-23, score-0.239]
18 You can do the whole thing 10 times with different random imputations, but that probably won’t make a difference. [sent-24, score-0.09]
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