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.
Highlights
Researchers across a wide range of disciplines measure analytes in saliva to index the activity, reactivity, and regulation of physiologic systems (see Granger and Taylor (2020) for review)
Sensitivity analyses—To assess whether the size of our sample considerably affected the patterns of results from our models, we conducted a series of sensitivity analyses for all descriptive statistics, correlations, and regression analyses using a random subsample of 100 participants. To ensure these analyses provided an appropriate evaluation of the effect of sample size on our results, we randomly selected a subsample with the same levels of censoring under both LLOS conditions as seen in the full sample
Our results demonstrate that the deletion approach can result in considerable differences in descriptive statistics and correlation and parameter estimates when compared to censored data and conventional substitution approaches
Summary
Researchers across a wide range of disciplines measure analytes in saliva to index the activity, reactivity, and regulation of physiologic systems (see Granger and Taylor (2020) for review). Dispatches from the cutting edge of this effort reveal unique statistical challenges (e.g., Riis et al, 2020) It is common for measurements from oral fluids to show skewed or atypical distributions. In salivary bioscience, censoring is the result of sample determinations falling outside the lower or upper limits of an assay’s measurement range (see Fig. 1; Helsel, 2005, 2012). Data are left-censored when values fall below the assay’s lowest measurable concentration (i.e., the lower limit of quantification, sensitivity, or detection). Left censoring is common when levels of a salivary analyte are naturally very low or when an assay’s lower limit of measurement is inappropriately matched to the expected range of values. For analytes present at high levels in saliva, or for assays with inappropriately low upper limits of measurement, right censoring can present a problem. Right-censored data occur when analyte determinations are high relative to an assay’s scale of measurement, and this issue is most often resolved by retesting samples on dilution (Chard, 1990)
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