Censored data considerations and analytical approaches for salivary bioscience data

Hedyeh Ahmadi; Douglas A Granger; Katrina R Hamilton; Clancy Blair; Jenna L Riis

doi:10.1016/j.psyneuen.2021.105274

Censored data considerations and analytical approaches for salivary bioscience data

Psychoneuroendocrinology. 2021 Jul:129:105274. doi: 10.1016/j.psyneuen.2021.105274. Epub 2021 May 17.

Authors

Hedyeh Ahmadi¹, Douglas A Granger², Katrina R Hamilton³, Clancy Blair⁴, Jenna L Riis⁵

Affiliations

¹ Institute for Interdisciplinary Salivary Bioscience Research, University of California, Irvine, CA, USA. Electronic address: hedyeha@uci.edu.
² Institute for Interdisciplinary Salivary Bioscience Research, University of California, Irvine, CA, USA; Department of Psychological Science, University of California, Irvine, CA, USA; Department of Acute and Chronic Care, Johns Hopkins University School of Nursing, Baltimore, MD, USA; Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Salivary Bioscience Laboratory and Department of Psychology, University of Nebraska, Lincoln, NE, USA.
³ Institute for Interdisciplinary Salivary Bioscience Research, University of California, Irvine, CA, USA.
⁴ Department of Population Health and Department of Applied Psychology, New York University, New York, NY, USA.
⁵ Institute for Interdisciplinary Salivary Bioscience Research, University of California, Irvine, CA, USA; Department of Psychological Science, University of California, Irvine, CA, USA. Electronic address: jriis@uci.edu.

Abstract

Left censoring in salivary bioscience data occurs when salivary analyte determinations fall below the lower limit of an assay's measurement range. Conventional statistical approaches for addressing censored values (i.e., recoding as missing, substituting or extrapolating values) may introduce systematic bias. While specialized censored data statistical approaches (i.e., Maximum Likelihood Estimation, Regression on Ordered Statistics, Kaplan-Meier, and general Tobit regression) are available, these methods are rarely implemented in biobehavioral studies that examine salivary biomeasures, and their application to salivary data analysis may be hindered by their sensitivity to skewed data distributions, outliers, and sample size. This study compares descriptive statistics, correlation coefficients, and regression parameter estimates generated via conventional and specialized censored data approaches using salivary C-reactive protein data. We assess differences in statistical estimates across approach and across two levels of censoring (9% and 15%) and examine the sensitivity of our results to sample size. Overall, findings were similar across conventional and censored data approaches, but the implementation of specialized censored data approaches was more efficient (i.e., required little manipulations to the raw analyte data) and appropriate. Based on our review of the findings, we outline preliminary recommendations to enable investigators to more efficiently and effectively reduce statistical bias when working with left-censored salivary biomeasure data.

Keywords: C-reactive protein; Censored data; Saliva; Statistical analysis; Tobit regression.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Bias
C-Reactive Protein / analysis
Humans
Models, Statistical*
Saliva* / chemistry

Substances

C-Reactive Protein

Abstract

Publication types

MeSH terms

Substances

Grants and funding