Robust analysis of biomarker data with informative missingness using a two-stage hypothesis test in an HIV treatment interruption trial: AIEDRP AIN503/ACTG A5217.

TitleRobust analysis of biomarker data with informative missingness using a two-stage hypothesis test in an HIV treatment interruption trial: AIEDRP AIN503/ACTG A5217.
Publication TypeJournal Article
Year of Publication2006
AuthorsMesser K, Vaida F, Hogan C
JournalContemp Clin Trials
Volume27
Issue6
Pagination506-17
Date Published2006 Dec
ISSN1551-7144
KeywordsAcquired Immunodeficiency Syndrome, Anti-HIV Agents, Bias (Epidemiology), Biomarkers, CD4 Lymphocyte Count, Data Collection, Disease Progression, HIV Infections, HIV-1, Humans, Outcome Assessment (Health Care), Patient Dropouts, Randomized Controlled Trials as Topic, Reproducibility of Results, Time Factors, Viral Load
Abstract

Clinical trial AIN503/A5217 investigates whether a period of early treatment with antiretroviral therapy might lower the viral setpoint in subjects recently infected with HIV-1. We consider two statistical issues. First, even under the null hypothesis control arm subjects are more likely than treatment arm subjects to be missing final outcome data because of disease progression. The analysis must adjust for this missing data, or it may be unacceptably biased. Second, comparing outcomes between treatment and control arms at identical times post-randomization gives different information than comparing outcomes at the same amount of time off-therapy, as measured post-randomization. This may make interpretation of results problematic. We formulate the null hypothesis of the study as exchangeability under a time-shift between arms, which we call "time delay" between the study arms. This captures clinically relevant information, and allows us to formalize a two-stage hypothesis test in which stage one is a comparison between arms at identical times post-randomization, and stage two is a comparison between arms at identical times off-therapy, as measured post-randomization. Importantly, within this framework we can show that the two-stage test can be adjusted for the missing data using a simple worst-rank substitution.

DOI10.1016/j.cct.2006.07.003
Alternate JournalContemp Clin Trials
PubMed ID16962381
Grant List5 U01 AI043638 / AI / NIAID NIH HHS / United States