Variable selection under multiple imputation using the bootstrap in a prognostic study

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 procedure of combining MI with bootstrapping for variable selection, results in multivariable prognostic models with good performance and is therefore attractive to apply on data sets with missing values

Esophageal Atresia and Tracheoesophageal Fistula

Esophageal atresia (EA) with or without tracheo- esophageal fistula (TEF) represents a congenital developmental anomaly. It is the most common congenital malformation of the esophagus char- acterized by an esophageal discontinuity (the upper esophagus terminates in a blind-ending pouch) and a possible tracheoesophageal connec- tio

Diseases of Renal Microcirculation: Diabetic Nephropathy

The prevalence of diabetes mellitus and its long-term vascular complications are increasing worldwide. Diabetic nephropathy is one of the main microvascular complications of diabetes and is characterized by the development of persistent macroalbuminuria (i.e., a urinary albumin excretion [UAE] >300 mg/24 h) or proteinuria (i.e., a urinary protein excretion >0.5 g/24 h). Characteristic glomerular changes of diabetic nephropathy include thickening of the glomerular basement membrane (GBM), mesangial expansion, and podocyte injury. Since type 1 and type 2 diabetic nephropathies share similar histologic characteristics as well as structural-functional relationships, one common classification is used to describe the pathologic classification of diabetic nephropathy for both type 1 and 2 diabetes. Although UAE should rather be considered as a continuous variable rather than using specific cutoff values, we describe the clinical course of diabetic nephropathy based on the classic approach using three stages based on urinary albumin excretion (i.e., normoalbuminuria, microalbuminuria, and macroalbuminuria). Diabetic nephropathy is a major independent risk factor for diabetes-related morbidity and mortality. However, a number of interventions are available that can reduce the risk of developing diabetic nephropathy and slow the progression hereof. Key treatment strategies that could reduce the incidence and progression of diabetic nephropathy include blood glucose control, blood pressure control, lipid-lowering therapy, and lifestyle interventions