On methods to utilize HIV-RNA data measured by two different PCR assays.
The plasma HIV-RNA level has been used as the primary efficacy measurement in clinical trials to evaluate antiretroviral regimens in HIV-infected patients. It is measured by polymerase chain reaction (PCR) assays, which usually have limits of reliable quantification (LoQ). For example, the commercially available Amplicor Standard assay has a reliable range of 400-750,000 copies/mL while the Ultrasensitive assay has a range of 50-75,000 copies/mL. Values below the lower LoQ are usually reported as categorical variables such as " < 400 copies/mL" for the Standard assay and " < 50 copies/mL" for the Ultrasensitive assay. The Standard assay, which has a higher ceiling of 750,000 copies/mL, is typically used as the first tool to measure HIV-RNA levels; if a value of " < 400 copies/mL" is reported by the Standard assay, the plasma sample may be re-tested by the Ultrasensitive assay, which has a lower LoQ of 50 copies/mL, in an effort to quantify the HIV-RNA level. However, for the calculation of change from baseline in log10 HIV-RNA, which is an important efficacy endpoint, the additional data measured by the Ultrasensitive assay are usually ignored due to a lack of simple and appropriate statistical methods. The conventional approach, which only uses the Standard assay data, may result in loss of information; the naive approach, which simply replaces " < 400 copies/mL" reported by the Standard assay with corresponding Ultrasensitive assay results, may lead to a biased estimate because the two assays may have different assay variability; the likelihood-based approach, which can utilize all data from both assays, is computationally intensive and requires a large sample size, which may limit its use in practice. In this paper, we propose a simple imputation approach that, unlike the naive method, accounts for the different variability in the two assays. A simulation study is used to compare these approaches. An example from a clinical trial in HIV-infected patients is used to illustrate the proposed approach.
Joshua Chen
Y H
YH
Clinical Biostatistics, Merck Research Laboratories, North Wales, Pennsylvania 19454, USA. joshua_chen@merck.com
Fan
Chunpeng
C
Zhao
Jing
J
D015658:HIV Infections / Q000097:blood* / Q000175:diagnosis; D006679:HIV Seropositivity / Q000097:blood / Q000175:diagnosis; D015497:HIV-1 / Q000737:chemistry; D006801:Humans; D016133:Polymerase Chain Reaction / Q000379:methods* / Q000592:standards; D012367:RNA, Viral / Q000032:analysis / Q000097:blood*; D012107:Research Design / Q000592:standards
D012367:RNA, Viral