Table 5.
Multiple imputation implementation. Summaries are n (%) unless stated otherwise
| Characteristic | Summary |
|---|---|
| Reported number of imputations | |
| Yes | 92 (71%) |
| No | 38 (29%) |
| Number of imputations, median (25th - 75th percentiles) | 20 (3 – 100) |
| Multiple imputation method | |
| Multivariate imputation by chained equations | 87 (67%) |
| Multivariate normal imputation | 6 (5%) |
| Othera | 4 (3%) |
| Unclear | 33 (25%) |
| Software package used for conducting the multiple imputation analysis | |
| Stata | 40 (31%) |
| R | 33 (25%) |
| SAS | 26 (20%) |
| SPSS | 1 (1%) |
| Otherb | 14 (11%) |
| Unclear | 16 (12%) |
| All analysis variables included in imputation model | |
| Yes | 35 (27%) |
| No | 20 (15%) |
| Unclear | 75 (58%) |
| Auxiliary variables included in imputation model | |
| Yes | 42 (32%) |
| No | 16 (12%) |
| Unclear | 72 (55%) |
| Interactions included in imputation model | |
| Yes | 2 (2%) |
| No | 54 (42%) |
| Unclear | 74 (57%) |
| Reported type of models used for imputation (as % of papers that used multivariate imputation by chained equations, n=87) | |
| Yes | 18 (21%) |
| No | 69 (79%) |
| Stated how a final estimate and standard error were obtained | |
| Either stated, provided code or method could be deduced from software description | 45 (35%) |
| Not stated | 85 (65%) |
aImputation performed using a bootstrapping-based algorithm for panel data in R package Amelia II (n = 1), imputation performed in the pan package mitml for multilevel data (n = 1), referenced a paper where the MI methods are described rather than providing a description (n = 1), used a multiple imputation analysis for exposure and covariates without stating what the analysis was, and used Kaplan-Meier multiple imputation for the outcome as part of a sensitivity analysis (n = 1)
bStudy used two software packages for analysis but it was not clear which package was used for MI (n = 13), NORM software (n = 1)