Statistical Interpretation of the RV144 HIV Vaccine Efficacy Trial in Thailand: A Case Study for Statistical Issues in Efficacy Trials

• Published in: The Journal of infectious diseases Vol. 203; no. 7; pp. 969 - 975
• Main Authors: Gilbert, Peter B., Berger, James O., Stablein, Donald, Becker, Stephen, Essex, Max, Hammer, Scott M., Kim, Jerome H., DeGruttola, Victor G.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.04.2011
• Subjects: AIDS vaccines; AIDS Vaccines - administration & dosage; AIDS Vaccines - immunology; Applied microbiology; Bayesian analysis; Biological and medical sciences; Experimentation; Female; Fundamental and applied biological sciences. Psychology; HIV; HIV Infections - immunology; HIV Infections - prevention & control; HIV/AIDS; Human immunodeficiency virus; Humans; Infections; Infectious diseases; Major and Brief Reports; Male; Medical sciences; Microbiology; Miscellaneous; P values; Placebos; Probabilities; Randomized Controlled Trials as Topic; Research Design - standards; Statistics as Topic; Thailand; Treatment Outcome; Vaccination; Vaccines, antisera, therapeutical immunoglobulins and monoclonal antibodies (general aspects); Virology; Thailand; Asia; South east Asia; Virus; Case study; Infection; Efficiency; Tropical zone; Retroviridae; Vaccine; Human immunodeficiency virus; Lentivirus; Statistical study
• ISSN: 0022-1899; 1537-6613
• DOI: 10.1093/infdis/jiq152

Recently, the RV144 randomized, double-blind, efficacy trial in Thailand reported that a prime-boost human immunodeficiency virus (HIV) vaccine regimen conferred ~30% protection against HIV acquisition. However, different analyses seemed to give conflicting results, and a heated debate ensued as scientists and the broader public struggled with their interpretation. The lack of accounting for statistical principles helped flame the debate, and we leverage these principles to provide a more scientific interpretation. We first address interpretation of frequentist results, including interpretation of P values, synthesis of results from multiple analyses (ie, intention-to-treat versus per-protocol/fully immunized), and accounting for external efficacy trials. Second, we address how Bayesian statistics, which provide clearly interpretable statements about probabilities that the vaccine efficacy takes certain values, provide more information for weighing the evidence about efficacy than do frequentist statistics alone. Third, we evaluate RV144 for completeness of end point ascertainment and integrity of blinding, necessary tasks for establishing robustly interpretable results.