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Predictors | Distribution, % (n ) | True regression coefficientsa |
Intercept | -- | -13.24 |
Difference in calf circumference of 3cm or more | 38 (306) | 0.60 |
Natural logarithm of the d-dimer levelb | 6.83 (1.49)b | 1.58 |
History of a leg trauma | 17 (136) | -0.50 |
(b) Mean (standard deviation). |
Table 1 - Distribution of the studied predictors: the (natural logarithm of) d-dimer level, history of a leg trauma (yes/no) and difference in calf circumference of 3cm or more (yes/no), and the true values of the logistic regression coefficients
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Introduction
What is new?
* Dropping a variable with missing data from the analyses or conducting a complete case analysis more often leads to biased effect estimates, decreased coverage of the confidence intervals, and a decreased discriminative ability of the multivariable model, compared with multiple imputation.
* To "provide" data according to the strict methodology of multiple imputation seems a better alternative than to give up or delete valuable observed data.
No matter how hard researchers try to prevent it, missing data occur frequently in medical research [1]. Commonly, researchers simply neglect all the data of patients with missing values because this is what standard software packages do when the data are analyzed (complete case analysis). Because this leads to a smaller dataset, it comes at least at the price of loss of power. Complete case analysis not necessarily leads to biased results. Under the condition that the missing values are missing completely at random (MCAR), meaning that the cause of missingness is pure coincidence, complete case analysis will not lead to biased results. As an alternative to complete case analysis, researchers tend to drop a variable from the analysis when it has missing values. However, both methods neglect valuable observed data.
Multiple imputation is a statistical technique that uses all observed data to fill in plausible values for the missing values [2-8]. This method receives increasing attention in the medical literature [9-16]. Nevertheless, many researchers seem unaware or uncertain about this approach to deal with missing values and still perform a complete case analysis or drop variables with missing values from the analysis [17]. The extent and sort of bias related to these approaches depend on the type of study. Diagnostic or prognostic studies often study the...