Abstract
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.
Keywords
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References
- Ref Type: Online Source. 2022 (Available at)www.mdcalc.comDate accessed: January 3, 2022
- Risk-adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases.Med Care. 2008; 46: 232-239
- Leave 'em alone - why continuous variables should be analyzed as such.Neuroepidemiology. 2008; 30: 138-139
- Reasons for ordering laboratory tests and relationship with frequency of abnormal results.Scand J Prim Health Care. 2010; 28: 18-23
- Clinical prdiction models: a practical approach to development, validation, and updating. 2nd ed. Springer, Cham, Switzerland2019: 127-156
- Evaluating the state of the art in missing data imputation for clinical data.Brief Bioinform. 2022; 23: bbab489
- Missing data in clinical research: a tutorial on multiple imputation.Can J Cardiol. 2021; 37: 1322-1331
- Statistical analysis with missing data. 2nd ed. Wiley, New York2002
- Developing a prognostic model in the presence of missing data: an ovarian cancer case study.J Clin Epidemiol. 2003; 56: 28-37
- Comparison of imputation methods for missing laboratory data in medicine.BMJ Open. 2013; 3: e002847
- A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.J Chronic Dis. 1987; 40: 373-383
- Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.Med Care. 2005; 43: 1130-1139
- Improved comorbidity adjustment for predicting mortality in Medicare populations.Health Serv Res. 2003; 38: 1103-1120
- Multivariable regression model building by using fractional polynomials: description of SAS, STATA and R programs.Comput Stat Data Anal. 2006; 50: 3464-3485
Article info
Publication history
Published online: December 14, 2022
Accepted:
December 12,
2022
Footnotes
Conflict of interest: None declared.
Funding: None.
Declarations of interest: None declared.
Identification
Copyright
© 2022 Elsevier Inc. All rights reserved.
