Abrahamowicz, Michal

Bucher, Heiner C.

Klein, Marina B.

Moodie, Erica E M.

Weber, Rainer

Young, Jim

English

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Incomplete Modeling of theEffect of AntiretroviralTherapy on the Risk ofCardiovascular EventsTO THE EDITOR—The paper by Desai andcolleagues [1] presents some difﬁcultiesfor the reader.The authors represent exposure intheir marginal structural models as thecurrent use of a single speciﬁc drug ordrug combination. The idea that thecurrent risk of a cardiovascular disease(CVD) event depends on a single currentantiretroviral drug or combination is bio-logically implausible [2–4]. The authorsimplicitly acknowledge this when they re-port that some exposures appear to havenonlinear cumulative effects. Althoughthis is far more plausible, they do not pre-sent these results. The authors note thatassuming a linear cumulative effect couldlead to misleading results, but it is hard tosee why assuming an even simpler doseresponse relationship will give resultsthat are any less misleading. The solutionto this problem is to carry out ﬂexible cu-mulative exposure modelling [5].The authors use the approach of Coleand Hernan to select variables for model-ing treatment initiation and censoring.Cole and Hernan conclude that selectingthese variables “requires a thoughtfulprocess,” and they encourage authors topresent the results of sensitivity analysisusing different sets of variables [6]. Thereader has no idea of the variables thatthese authors considered when modelingtreatment initiation and censoring andthe sensitivity of results to the choicesmade. Their SAS code suggests that theauthors used only the most recent CD4cell count and viral load for every drugand combination. In our work we foundthat exposure to abacavir depended onvariables such as dyslipidaemia, lipodys-trophy, and a previous CVD event andthat prescribing behavior changed afterthe D:A:D published their ﬁndings onabacavir in 2008 [7]. Residual confound-ing seems likely if the authors used thesame simple model for every drug andcombination.The authors include a large number ofvariables in their Cox models. Full resultsare not given, but it seems as if thesemodels contained 30 to 40 covariates.The resulting estimates are probably tooprecise, because seldom used drugs anddrug combinations are omitted, and theireffects are then ignored [8] and probablysomewhat inﬂated because of small sam-ple bias (especially with myocardial in-farction as the outcome) [9]. A bettersolution to the problem of multiple expo-sures is hierarchical modeling, with addi-tional modeling of likely associationsbetween the effects of drugs in the samedrug class or between the effects of com-binations that share components, andwith an explicit acknowledgement of re-sidual effects due to exposures omittedfrom the model [8]. In this way, the au-thors might have been able to identifycombinations whose effect differed fromthe sum of its components.Some of the variables used in these Coxmodels had many missing values. Miss-ing values were replaced using multipleimputation, but the reader does notknow what imputation model was used,or how results changed when missingvalues were replaced, or the sensitivityof results to other plausible imputationmodels [10].So what should a prudent reader con-clude? That some common antiretroviralcombinations contain drugs that elevatethe risk of CVD? – Yes, but we knew that.That some combinations are more or lessrisky than the sum of their components?– In our opinion there is little evidencehere to support such conclusions. Margin-al structural modeling, multiple regression,and multiple imputation are delicate toolsthat can account for time-dependent con-founding, multiple exposures, and missingdata. But modeling these complexities re-quires careful thought—one cannot simplyrerun a SAS macro.NotePotential conﬂicts of interest. All authors:No reported conﬂicts.All authors have submitted the ICMJE Formfor Disclosure of Potential Conﬂicts of Interest.Conﬂicts that the editors consider relevant to thecontent of the manuscript have been disclosed.Jim Young,1 Erica E.M. Moodie,2Michal Abrahamowicz,2 Marina B. Klein,3Rainer Weber,4 and Heiner C. Bucher1,51Basel Institute for Clinical Epidemiology andBiostatistics, University Hospital Basel, Switzerland;2Department of Epidemiology, Biostatistics andOccupational Health, McGill University, and3Department of Medicine, Royal Victoria Hospital,McGill University Health Centre, Montreal, Canada;4Division of Infectious Diseases and HospitalEpidemiology, University Hospital and University ofZürich, and 5Division of Infectious Diseases andHospital Epidemiology, University Hospital Basel,SwitzerlandReferences1. Desai M, Joyce V, Bendavid E, et al. Risk ofcardiovascular events associated with currentexposure to HIV antiretroviral therapies in aUS veteran population. Clin Infect Dis 2015;61:445–522. Law MG, Friis-Moller N, El Sadr WM, et al.The use of the Framingham equation to pre-dict myocardial infarctions in HIV-infectedpatients: comparison with observed events1206 • CID 2015:61 (1 October) • CORRESPONDENCEin the D:A:D Study. HIV Med 2006; 7:218–30.3. Boccara F. Cardiovascular complications andatherosclerotic manifestations in the HIV-infected population: type, incidence andassociated risk factors. AIDS 2008; 22(suppl 3):S19–26.4. Gibellini D, Borderi M, Clo A, et al. Antire-troviral molecules and cardiovascular diseas-es. New Microbiol 2012; 35:359–75.5. Xiao Y, Abrahamowicz M, Moodie EEM,Weber R, Young J. Flexible marginal struc-tural models for estimating the cumulativeeffect of a time-dependent treatment on thehazard: reassessing the cardiovascular riskof didanosine treatment in the Swiss HIVCohort. J Am Stat Assoc 2014; 109:455–64.6. Cole SR, Hernan MA. Constructing inverseprobability weights for marginal structuralmodels. Am J Epidemiol 2008; 168:656–64.7. Young J, Xiao Y,Moodie EEM, et al. The effectof cumulating exposure to abacavir on the riskof cardiovascular disease events in patientsfrom the Swiss HIV Cohort Study. J AcquirImmune Deﬁc Syndr 2015; 69:413–21.8. Greenland S. When should epidemiologic re-gressions use random coefﬁcients? Biomet-rics 2000; 56:915–21.9. Greenland S, Schwartzbaum JA, Finkle WD.Problems due to small samples and sparsedata in conditional logistic regression analy-sis. Am J Epidemiol 2000; 151:531–9.10. Kenward MG, Carpenter J. Multiple imputa-tion: current perspectives. Stat Methods MedRes 2007; 16:199–218.Correspondence: Jim Young, PhD, Basel Institute for ClinicalEpidemiology and Biostatistics, Hebelstrasse 10, University Hos-pital Basel, CH-4031 Basel, Switzerland (james.young@usb.ch).Clinical Infectious Diseases® 2015;61(7):1206–7© The Author 2015. Published by Oxford University Presson behalf of the Infectious Diseases Society of America. Allrights reserved. For Permissions, please e-mail: journals.permissions@oup.com.DOI: 10.1093/cid/civ515CORRESPONDENCE • CID 2015:61 (1 October) • 1207

Incomplete modeling of the effect of antiretroviral therapy on the risk of cardiovascular events

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Incomplete modeling of the effect of antiretroviral therapy on the risk of cardiovascular events

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