Imputing missing laboratory results may return erroneous values because they are not missing at random

Background and Objectives: Regression models incorporating laboratory tests treat unordered tests as missing and are often imputed. Imputation typically assumes that data are missing at random(MAR, test's order status is unrelated to its result after accounting for other variables). This study examined the validity of this assumption. Methods: We included 14 biochemistry tests. All tests were measured regardless of test order status. Test-stratified multiple linear regression determined the independent association between test result and order status after adjusting for patient age, sex, comorbidities, and patient location. Testing likelihood models were created for all tests using hospital-wide data. Results: Four hundred thirty-four patients were included (mean age [standard deviation] 60.7 [19.1], 50.5% female). In 9 of 14 tests (64.2%), test results were significantly associated with order status after adjustment. Results were significantly more abnormal when tests were ordered for 6 tests and significantly more normal for 3 tests. Test abnormality increased as testing likelihood decreased. Conclusions: These data suggest that laboratory data are often not MAR. The direction and extent of differences in missing laboratory test values varies between tests. Overall the abnormality of ordered tests increased as testing likelihood decreased. These results suggest that imputating missing laboratory data may return biased values. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

In this study, the association between laboratory test results and test order status was examined. The results showed that the missing data in laboratory tests are not missing at random. The likelihood of testing also affects the test results. Therefore, imputing missing laboratory data may lead to biased results.