141st APHA Annual Meeting

In This section

282542
Computing values for viral load below test detection limit and comparing its effect on measures of central tendency and outcome (medication error)

Wednesday, November 6, 2013 : 10:30 AM - 10:50 AM

Nisha Kini, MBBS, MPH , Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA
Bruce Barton, PhD , Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA
Adam Brady, MD , Department of Medicine, University of Massachusetts Medical School, Worcester, MA
Mireya Wessolossky, MD , Infectious Disease Division, University of Massachusetts Memorial Health Care, Worcester, MA
Introduction: Researchers are often required to analyze data where levels of a substance are below the detection limit (DL) of the test. We analyzed the distribution of viral load (VL) using six methods and compared the effect on measures of central tendency and outcome using the original dataset and a multiply imputed (MI) dataset from a sample of 149 HIV+ patients admitted to UMass Memorial Medical Center. Method: We used deletion, substitution with 0, substitution with DL/2, random number generation, substitution with DL, and binary variable (for outcome only) methods to test the effect on measures of central tendency and outcome using the original dataset and MI dataset. We measured central tendency by calculating the mean, median, standard deviation, standard error, and skewness, and the outcome by calculating the intercept and standard error. Results: Of VL values, 85% were below DL. Deletion method affected measures of central tendency and deletion and binary variable methods affected the outcome the most. The remaining methods were similar in all measured aspects of central tendency except median. The median took on the substituted value in all methods except deletion and random number generation. There was no difference in measures when comparing the original dataset and the MI dataset. Conclusion: Except deletion, all other methods provide a suitable estimate of the data. In our dataset, 85% of VL values were below DL so there was minimal variability in the results. Similar procedures need to be studied in datasets with fewer values below DL.

Learning Areas:
Biostatistics, economics
Epidemiology

Learning Objectives:
Discuss six different methods of handling data with test values below the detection limit of a test and compare their effect on measures of central tendency and outcome using the standard dataset and a multiply imputed dataset

Keywords: Statistics, HIV/AIDS

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I have over 2 years of experience working in the field of Public Health and I feel that this study will generate interest among my peers who work with data that have values below detection limit of a test
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.