Statistical analysis of single-copy assays when some observations are zero

Observational and interventional studies for HIV cure research often use single-copy assays to quantify rare entities in blood or tissue samples. Statistical analysis of such measurements presents challenges due to tissue sampling variability and frequent findings of 0 copies in the sample analysed. We examined four approaches to analysing such studies, reflecting different ways of handling observations of 0 copies: (A) replace observations of 0 copies with 1 copy; (B) add 1 to all observed numbers of copies; (C) treat observations of 0 copies as left-censored at 1 copy; and (D) leave the data unaltered and apply a method for count data, negative binomial regression. Because research seeks to estimate general patterns rather than individuals' values, we argue that unaltered use of 0 copies is suitable for research purposes and that altering those observations can introduce bias. When applied to a simulated study comparing preintervention to postintervention measurements within 12 participants, methods A-C showed more attenuation than method D in the estimated intervention effect, less chance of finding