Statistical methods in HIV/AIDS

STATISTICS IN MEDICINE Statist. Med. 2008; 27:4635 Published online 12 March 2008 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/sim.3258 Foreword Statistical methods in HIV/AIDS The worldwide HIV/AIDS epidemic of the past several decades has stimulated a very impressive amount of methodological innovation in biostatistics, epidemiology, mathematical biology, and related disciplines. These advances have in turn contributed to the considerable progress made in surveillance, prevention strategies, and therapeutics. From time to time, there have been attempts to inventory new methods and supporting theoretical developments related to HIV/AIDS, as in Fusaro et al. [1] and Foulkes [2]. Do we need an update again? Perhaps, but the breadth of research summarized in 1998 was enormous (369 references listed); tackling another decade worth of research is a daunting prospect. As an alternative, it seems appropriate to call special attention to HIV-related research supported recently by the National Institute of Allergy and Infectious Diseases. The 10 papers that follow, all of which meet this criterion, cover a range of topics. Some address new versions of familiar problems: how to analyze time-to-event data when the events are observed only within intervals and with error (Zhang and Lagakos), how to employ novel modeling approaches to carry out and interpret covariate adjustments in the evaluation of treatment effects (Tsiatis, Davidian, Zhang, and Lu; Huang, Liang, and Wu; Robins, Orellana, Hernan, and Rotnitzky), and how to establish immune response-based surrogate markers for vaccine efficacy (Gilbert, Qin, and Self). Others, however, deal with problems involving new kinds of observations, including indices of viral dynamics (Perelson and Ribeiro) and HIV viral genotype (Schumi and DeGruttola; Pond, Poon, Zarate, Smith, Little, Pillai, Ellis, Wong, Brown, Richman, and Frost; Ahn, Seillier-Moiseiwitsch, and Koch). Yet another explores the use of modeling to gain understanding of the interplay of multiple outcome measures in studying the effects of certain HIV vaccine candidates (Wick). The research presented here nicely illustrates the difficulty in labeling science as either basic or applied. While the motivating problems relate directly to challenges in discovery of preventive or therapeutic interventions for HIV, solutions involve conceptual and theoretical advances with much wider applicability. It is this synergy that has attracted gifted and productive methodology researchers to the field for more than two decades. REFERENCES 1. Fusaro RE, Jewell NP, Hauck WW, Heilbron DC, Kalbfleisch JD, Neuhaus JM, Ashby MA. An annotated bibliography of quantitative methodology relating to the AIDS epidemic. Statistical Science 1989; 4(3):264–281. 2. Foulkes MA. Advances in HIV/AIDS statistical methodology over the past decade. Statistics in Medicine 1998; 17:1–25. DENNIS DIXON National Institutes of Health Bethesda, MD, U.S.A. Copyright 2008 John Wiley & Sons, Ltd.

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Transcript of Statistical methods in HIV/AIDS

STATISTICS IN MEDICINEStatist. Med. 2008; 27:4635Published online 12 March 2008 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/sim.3258

Foreword

Statistical methods in HIV/AIDS

The worldwide HIV/AIDS epidemic of the past several decades has stimulated a very impressiveamount of methodological innovation in biostatistics, epidemiology, mathematical biology, andrelated disciplines. These advances have in turn contributed to the considerable progress made insurveillance, prevention strategies, and therapeutics. From time to time, there have been attemptsto inventory new methods and supporting theoretical developments related to HIV/AIDS, as inFusaro et al. [1] and Foulkes [2]. Do we need an update again? Perhaps, but the breadth ofresearch summarized in 1998 was enormous (369 references listed); tackling another decade worthof research is a daunting prospect.

As an alternative, it seems appropriate to call special attention to HIV-related research supportedrecently by the National Institute of Allergy and Infectious Diseases. The 10 papers that follow,all of which meet this criterion, cover a range of topics. Some address new versions of familiarproblems: how to analyze time-to-event data when the events are observed only within intervalsand with error (Zhang and Lagakos), how to employ novel modeling approaches to carry out andinterpret covariate adjustments in the evaluation of treatment effects (Tsiatis, Davidian, Zhang,and Lu; Huang, Liang, and Wu; Robins, Orellana, Hernan, and Rotnitzky), and how to establishimmune response-based surrogate markers for vaccine efficacy (Gilbert, Qin, and Self). Others,however, deal with problems involving new kinds of observations, including indices of viraldynamics (Perelson and Ribeiro) and HIV viral genotype (Schumi and DeGruttola; Pond, Poon,Zarate, Smith, Little, Pillai, Ellis, Wong, Brown, Richman, and Frost; Ahn, Seillier-Moiseiwitsch,and Koch). Yet another explores the use of modeling to gain understanding of the interplay ofmultiple outcome measures in studying the effects of certain HIV vaccine candidates (Wick).

The research presented here nicely illustrates the difficulty in labeling science as either basicor applied. While the motivating problems relate directly to challenges in discovery of preventiveor therapeutic interventions for HIV, solutions involve conceptual and theoretical advances withmuch wider applicability. It is this synergy that has attracted gifted and productive methodologyresearchers to the field for more than two decades.

REFERENCES

1. Fusaro RE, Jewell NP, Hauck WW, Heilbron DC, Kalbfleisch JD, Neuhaus JM, Ashby MA. An annotatedbibliography of quantitative methodology relating to the AIDS epidemic. Statistical Science 1989; 4(3):264–281.

2. Foulkes MA. Advances in HIV/AIDS statistical methodology over the past decade. Statistics in Medicine 1998;17:1–25.

DENNIS DIXON

National Institutes of HealthBethesda, MD, U.S.A.