| Type | Journal Article |
|---|---|
| Author | James R. Carpenter |
| Author | Melanie Smuk |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.202000196 |
| Volume | 63 |
| Issue | 5 |
| Pages | 915-947 |
| Publication | Biometrical Journal |
| ISSN | 1521-4036 |
| Date | 2021 |
| Extra | _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202000196 |
| DOI | 10.1002/bimj.202000196 |
| Accessed | 10/21/2021, 7:58:56 AM |
| Library Catalog | Wiley Online Library |
| Language | en |
| Abstract | Missing data are ubiquitous in medical research, yet there is still uncertainty over when restricting to the complete records is likely to be acceptable, when more complex methods (e.g. maximum likelihood, multiple imputation and Bayesian methods) should be used, how they relate to each other and the role of sensitivity analysis. This article seeks to address both applied practitioners and researchers interested in a more formal explanation of some of the results. For practitioners, the framework, illustrative examples and code should equip them with a practical approach to address the issues raised by missing data (particularly using multiple imputation), alongside an overview of how the various approaches in the literature relate. In particular, we describe how multiple imputation can be readily used for sensitivity analyses, which are still infrequently performed. For those interested in more formal derivations, we give outline arguments for key results, use simple examples to show how methods relate, and references for full details. The ideas are illustrated with a cohort study, a multi-centre case control study and a randomised clinical trial. |
| Short Title | Missing data |
| Date Added | 10/21/2021, 7:58:56 AM |
| Modified | 10/21/2021, 8:06:36 AM |
| Type | Journal Article |
|---|---|
| Author | Benjamin Gravesteijn |
| Author | Charlie Sewalt |
| Author | Esmee Venema |
| Author | Daan Nieboer |
| Author | Ewout W Steyerberg |
| URL | https://www.liebertpub.com/doi/abs/10.1089/neu.2020.7218 |
| Publication | Journal of Neurotrauma |
| ISSN | 0897-7151 |
| Date | January 20, 2021 |
| Extra | Publisher: Mary Ann Liebert, Inc., publishers |
| DOI | 10.1089/neu.2020.7218 |
| Accessed | 1/26/2021, 8:29:03 PM |
| Library Catalog | liebertpub.com (Atypon) |
| Abstract | In medical research, missing data is common. In acute diseases such as traumatic brain injury (TBI), even well conducted prospective studies may suffer from missing data in baseline characteristics and outcomes. Statistical models may simply drop patients with any missing values, potentially leaving a selected subset of the original cohort. Imputation is widely accepted by methodologists as an appropriate way to deal with missing data. We aim to provide practical guidance on handling missing data for prediction modelling. We hereto propose a five-step approach, centred around single and multiple imputation: 1) explore the missing data patterns; 2) choose a method of imputation; 3) perform imputation; 4) assess diagnostics of the imputation; and 5) analyse the imputed datasets. We illustrate these 5 steps with the estimation and validation of the IMPACT prognostic model in 1375 patients from the CENTER-TBI database, included in 53 centers across 17 countries, with moderate or severe TBI in the prospective European CENTER-TBI study. Future prediction modelling studies in acute diseases may benefit from following the suggested 5 steps for optimal statistical analysis and interpretation, after maximal effort have been made to minimize missing data. |
| Short Title | Missing data in prediction research |
| Date Added | 1/26/2021, 8:29:03 PM |
| Modified | 1/26/2021, 8:30:17 PM |
| Type | Journal Article |
|---|---|
| Author | Donders |
| Author | Geert J. M. G. van der Heijden |
| Author | Theo Stijnen |
| Author | Karel G. M. Moons |
| Volume | 59 |
| Pages | 1087-1091 |
| Publication | J Clin Epi |
| Date | 2006 |
| Extra | Citation Key: don06rev tex.citeulike-article-id= 13265491 tex.posted-at= 2014-07-14 14:09:58 tex.priority= 0 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
simple demonstration of failure of the add new category method (indicator variable)
| Type | Journal Article |
|---|---|
| Author | Karel G. M. Moons |
| Author | Rogier A. R. T. Donders |
| Author | Theo Stijnen |
| Author | Frank E. Harrell |
| URL | http://dx.doi.org/10.1016/j.jclinepi.2006.01.009 |
| Volume | 59 |
| Pages | 1092-1101 |
| Publication | J Clin Epi |
| Date | 2006 |
| Extra | Citation Key: moo06usi tex.citeulike-article-id= 13265492 tex.citeulike-linkout-0= http://dx.doi.org/10.1016/j.jclinepi.2006.01.009 tex.posted-at= 2014-07-14 14:09:58 tex.priority= 0 |
| DOI | 10.1016/j.jclinepi.2006.01.009 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
use of outcome variable; excellent graphical summaries of simulations
| Type | Journal Article |
|---|---|
| Author | Geert J. M. G. van der Heijden |
| Author | Donders |
| Author | Theo Stijnen |
| Author | Karel G. M. Moons |
| URL | http://dx.doi.org/10.1016/j.jclinepi.2006.01.015 |
| Volume | 59 |
| Pages | 1102-1109 |
| Publication | J Clin Epi |
| Date | 2006 |
| Extra | Citation Key: hei06imp tex.citeulike-article-id= 13265493 tex.citeulike-linkout-0= http://dx.doi.org/10.1016/j.jclinepi.2006.01.015 tex.posted-at= 2014-07-14 14:09:58 tex.priority= 0 |
| DOI | 10.1016/j.jclinepi.2006.01.015 |
| Date Added | 7/7/2018, 1:38:33 PM |
| Modified | 11/8/2019, 8:01:59 AM |
Invalidity of adding a new category or an indicator variable for missing values even with MCAR