Variable selection under multiple imputation using the bootstrap in a prognostic study
Abstract
BACKGROUND: Missing data is a challenging problem in many prognostic studies. Multiple imputation (MI) accounts for imputation uncertainty that allows for adequate statistical testing. We developed and tested a methodology combining MI with bootstrapping techniques for studying prognostic variable selection.
METHOD: In our prospective cohort study we merged data from three different randomized controlled trials (RCTs) to assess prognostic variables for chronicity of low back pain. Among the outcome and prognostic variables data were missing in the range of 0 and 48.1%. We used four methods to investigate the influence of respectively sampling and imputation variation: MI only, bootstrap only, and two methods that combine MI and bootstrapping. Variables were selected based on the inclusion frequency of each prognostic variable, i.e. the proportion of times that the variable appeared in the model. The discriminative and calibrative abilities of prognostic models developed by the four methods were assessed at different inclusion levels.
RESULTS: We found that the effect of imputation variation on the inclusion frequency was larger than the effect of sampling variation. When MI and bootstrapping were combined at the range of 0% (full model) to 90% of variable selection, bootstrap corrected c-index values of 0.70 to 0.71 and slope values of 0.64 to 0.86 were found.
CONCLUSION: We recommend to account for both imputation and sampling variation in sets of missing data. The new…
How strong is the evidence?
Evidence Hub reads this as a randomized trial, done in people. Its evidence level is 2 of 5 (High · randomized trial). Randomized trials of a reasonable size are the strongest single-study design.
Read the paper
Related studies
- Combining in silico and in vitro approaches to identification of potent inhibitor against phospholipase A2 (PLA2)
- In silico and in vitro studies: investigating the chemical composition, DFT, molecular docking, and dynamic simulation of Satureja candidissima (Munby) Briq essential oil as a potential antibacterial agent
- Quercetin combined with ciprofloxacin and gentamicin inhibits biofilm formation and virulence in Staphylococcus aureus
- In silico study of the mechanism of action, pharmacokinetic and toxicological properties of some N-methylanthranilates and their analogs
- Anticonvulsant Effects of Citronellal, Daidzin, and Phytol, Possibly Through GABAA Receptor and Voltage-Gated Sodium Channel Interaction Pathways: In Vivo and In Silico Studies
- 2-Phenylcyclopropylmethylamine (PCPMA) Derivatives as D3R-Selective Ligands for 3D-QSAR, Docking and Molecular Dynamics Simulation Studies
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.