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    Change of statistical significance when excluding heterogenic studies?

    I get it now, thank you! For each assessment, we kept leaving out studies one by one until heterogeneity dropped below 35% as we found this to be acceptable. The example I described in my first post started with I^2 = 93%, but was reduced to 0% while the pooled estimate became statistically...
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    Change of statistical significance when excluding heterogenic studies?

    So, you mean the absolute size of the SE of each individual study and its relative size compared to the other studies (which would be the heterogeneity)? Is there a way to find out which factor contributes more to my result becoming statistically significant?
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    Change of statistical significance when excluding heterogenic studies?

    Hi all, I have performed a meta-analysis and found a certain pooled effect with 95% CI that was not statistically significant (p>0.05). Consequently, I have done a sensitivity analysis for which I have excluded studies that contributed most to heterogeneity. In order to do so I used the...
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    Imputation of data

    Hi everyone, I need some help with my imputed data: 1) I had a dataset with missing data for baseline variables and outcome variables. Through multiple imputation in SPSS (10 imputations, 50 iterations, PMM for scale variables) I imputed the missing data for the baseline variables. When I...
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    Handling missing data

    Alright, I will do that. Thanks for all your advice!!!!
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    Handling missing data

    Thank you for your instructions, you are amazing!! This has been incredibly helpful! I used the multiple imputation feature that's built-in into SPSS. However, I have one variable that is gives me some concerns. We noted whether patients used pain medication, but for some patients it was only...
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    Handling missing data

    I applied multiple imputation using the imputation feature in SPSS. I am not completely sure how to analyze the new data set though. When performing imputation one needs to choose the number of imputations for a missing value. It is set at 5 by default. This means my data set (i.e. population...
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    Handling missing data

    Yes, I fully agree that pain score is important. The treatment was not randomized. It's a cohort study, so every patient received the treatment. We have formed two groups that we compare based on the cause of their symptoms. Our hypothesis was that the treatment efficacy is not different between...
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    Handling missing data

    Big thank you for your help! So I have a total of 486 patients for which I have (complete or incomplete) data. For most baseline characteristics I have 461 cases for which I have complete data. If I include baseline pain score I only have complete data for 223 cases. There are a few secondary...
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    Handling missing data

    My primary question was what the efficacy was of a certain injection treatment in two groups with different causes for the same disease. That way I wanted to compare whether the efficacy was different between the groups. In order to answer this question we used two variables: a pain score...
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    Handling missing data

    Hello all! I have collected patient data retrospectively but for some variables (both numerical and categorical) data is missing. I have 13 variables for which data is missing ranging between 2 and 369 missing cases on a total of 486 cases (so complete data is acquired if for some variable 486...