EDITORIAL

Confounding by Indication: The Case of CalciumChannel Blockers

MARSHALL M. JOFFE*Division of Biostatistics, Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and

Biostatistics, University of Pennsylvania School of Medicine, 602 Blockley Hall, 423 Guardian Drive, Philadelphia,PA 19104-6021, USA

SUMMARY

Purpose Ð To review conceptual issues regarding confounding by indication in the context of studiesof calcium channel blockers (CCBs).Methods Ð Review of literature, with special attention to two articles in the current issue.Results Ð Con¯icting arguments about the presence of uncontrollable confounding by indication in

studies of CCBs are reviewed and criticized. Studies with potential confounding by indication canbene®t from appropriate analytic methods, including separating the e�ects of a drug taken at di�erenttimes, sensitivity analysis for unmeasured confounders, and instrumental variables and G-estimation.Conclusions Ð Whether confounding by indication accounts for observed associations is often

di�cult to determine; this is the case in studies of CCBs. When confounding by indication is suspected, avariety of methods to deal with it may be useful. Copyright # 2000 John Wiley & Sons, Ltd.

KEY WORDS Ð confounding; confounding by indication; calcium channel blockers; observational studies

Confounding by indication is a central issue inseveral current controversies in pharmacoepide-miology. Among these is the dispute about thepossible risks associated with the use of calciumchannel blockers (CCBs) for treatment of hyper-tension. Several recent studies have found associa-tions between CCBs and adverse outcomes,including myocardial infarction and death,1±5

Maxwell et al.,6 in an article appearing in thisissue, provide further evidence of association. Theauthors of these studies seek to attribute theassociations to the e�ects of CCBs; others, includ-ing Huse et al.,7 in a separate article also appearingin this issue, ®nd no association8±10 or attributethe associations found to confounding by

indication.7,11 The two articles are representativeof two approaches taken to investigate the role ofconfounding by indication in explaining treat-ment±outcome associations; their publication pro-vides a timely opportunity to review conceptualissues in confounding by indication in the contextof observational studies of CCBs.

Maxwell et al. performed a cohort study of CCBsand other antihypertensives and diuretics, andadjusted in the analysis for a number of covariates.After adjustment, the CCB nifedipine remainedassociated with all-cause and cardiac mortality;risks appeared higher among subjects who recentlyinitiated nifedipine. The authors note that adjust-ment for measured covariates did not substantiallychange associations between nifedipine use andoutcomes, while adjustment did change the associa-tions between other drugs and the same outcomes.Had there been confounding by indication, theseobservations would have been unexpected.

Huse et al. surveyed selected physicians in theUnited States about their use of di�erent classes ofantihypertensive medications, including CCBs,

Copyright # 2000 John Wiley & Sons, Ltd. Received 12 October 1999Revised 22 October 1999

Accepted 22 October 1999

PHARMACOEPIDEMIOLOGY AND DRUG SAFETY 9: 37±41 (2000)

*Correspondence to: Marshall M. Jo�e, Division of Biostatis-tics, Department of Biostatistics and Epidemiology, Center forClinical Epidemiology and Biostatistics, University of Pennsyl-vania School of Medicine, 602 Blockley Hall, 423 GuardianDrive, Philadelphia, PA 19104-6021, USA.

Contract/grant sponsor: National Heart, Lung, and BloodInstitute.Contract/grant number: 5-R29-HL59184-02.

ACE inhibitors, b-blockers, diuretics, and a-blockers. They found that physicians perceiveCCBs as being of particular use in treating high-risk patients with hypertension, and, consequently,are more likely to prescribe CCBs for each ofseveral classes of high-risk patients than foruncomplicated, mild hypertension. This latter®nding is not consistent for any of the other classesof antihypertensives examined. As the authorsnote, these ®ndings are consistent with the ®ndingsof other studies using claims data and surveys ofhypertensive patients. On the basis of these®ndings, Huse et al. argue that reported associa-tions between CCBs and cardiovascular events`may re¯ect selective use of these drugs to treathigh-risk hypertensive patients', i.e. confoundingby indication, not the adverse e�ect of CCBs,explains these associations.

These apparently contradictory argumentswould bene®t from further elaboration and deservescrutiny. There are two relevant questions here:(1) Is there confounding by indication? and (2) ifso, have studies of associations between CCBs andadverse events addressed it adequately?

Huse et al. present a strong prima facie case forconfounding by indication in studies of CCBs andcardiovascular events. To review, confoundersmust be associated with the treatment understudy and be independently associated with theoutcome of interest.12 Blood pressure, like otherfactors, ful®ls these criteria. Huse et al. provideevidence that higher blood pressure is associatedwith greater use of CCBs. There is abundantevidence that high blood pressure is independentlyassociated with cardiovascular events.

Confounding is thought particularly likely toarise and hard to control in studies of outcomes thetreatment is intended to a�ect.13±15 It arisesbecause perceived risk of adverse events is oftenclosely related to indications for treatment; forexample, high blood pressure is both an indicationfor antihypertensive use and a predictor of adversecardiovascular events. To control confounding inobservational case±control and cohort designs, onemust (1) identify, measure and record enoughconfounding variables, and (2) use appropriateanalytic methods. Some argue that it is di�cult tocontrol because indicators for use are often subtleand remain imperfectly recorded;13±15 the unfami-liarity of appropriate analytical methods presentsanother barrier to control.

Have enough confounders been accounted for instudies of CCBs? Both papers argue, to di�erent

ends, that one can learn about unmeasured con-founders and confounding from measured factors.The argument is sometimes advanced that ifadjustment for known covariates fails to changethe measure of e�ect, there must be little residualconfounding; Maxwell et al. appear to use a variantof this argument. This argument presumably restson the following logic: epidemiologists are aware ofthe problem of confounding, and seek to recordinformation on all variables they believe to beconfounders; if there are any confounders, theirthoroughness would allow them to measure at leastsome: thus, if there is no change in e�ect estimatesafter control for several measured factors, theremust be little confounding overall and little residualconfounding. When control for measured factorsreveals confounding, it is then more likely that thereis residual confounding. A similar argument ispossible for the role of mismeasured or misclassi-®ed confounders, adjustment for which may lead topartial control of confounding.16 Similarly, implicitin Huse et al.'s conclusion that confounding byindication may be a problem is that the strongassociations of measured risk predictors with CCBuse makes it more plausible that unmeasured riskpredictors are also so associated. Such argumentsare often plausible but are scarcely watertight, andso should be assessed on a case-by-case basis.

Even identifying and accurately measuringimportant covariates and controlling for themwill often be insu�cient. Such di�cult-to-controlconfounding by indication can arise when the e�ectof the medication masks the reasons for its use.14 Inthe case of CCBs, higher or hard-to-control bloodpressure can prompt the use of CCBs, which maythen lower blood pressure. Thus, people usingCCBs will be at higher risk than non-users, evenafter controlling for this blood pressure measure.This type of unmeasured confounding is particu-larly likely to arise when treatment levels arerecorded or decisions made at times when covariate(e.g. blood pressure) measurements are not avail-able to or used by study investigators.17,18 This willbe common when study investigators are notresponsible for patient care, and would appear tobe the case in the study of Maxwell et al.; suchmasking might account for the failure of control ofmeasured factors to alter substantially the associa-tion of CCBs and adverse outcomes.

Similar associations among unobserved variable,treatment, and observed outcome can arise whenalready present but occult disease makes thetreatment more likely; this `reverse causation' is

38 EDITORIAL

Copyright # 2000 John Wiley & Sons, Ltd. Pharmacoepidemiology and Drug Safety, 9: 37±41 (2000)

sometimes considered a kind of confounding byindication.19 Despite similar associations amongvariables, appropriate responses to the problemsdi�er. In the `reverse causation' case, there aresome subjects (those with unobserved disease) forwhom treatment can have no e�ect on theunderlying true outcome, which has already beendetermined before the last doses of treatment wereprovided or taken; the analyst would like to excludesuch subjects from the study as ineligible. In somecases, the appropriate approach will be moreambiguous. When the outcome variable (e.g.observed disease) may be viewed as the observablemanifestation of a continuous characteristic (e.g.disease severity), a subject may already havedisease, and so be misclassi®ed as eligible todevelop disease; however, subsequent treatmentcould slow, prevent, or even reverse diseaseprogression, and so a real e�ect is possible.

Many drugs, including CCBs, may have bothacute and long-lasting e�ects on users. When this isthe case, investigators may properly want to learnabout the joint e�ects of drug doses taken atdi�erent times over an extended period. Whenthere is further confounding by an intermediaterisk predictor, such as blood pressure, standardmethods of estimating the associations of treatmentreceived and the risk of the ultimate event (e.g.relative risk or Cox regressions) will provide biasedmeasures of these joint e�ects, no matter how oneseeks to account or control for the intermedi-ate.20,21 The recently proposed case±time±controldesign is designed to estimate a unitary treatmente�ect and so does not deal with these problemsadequately.22±24 Robins has developed severalapproaches for dealing with these problems;20,21

they require repeated measures of treatment andconfounding variables in each subject. To the bestof my knowledge, these methods have only beenapplied once to deal with confounding by indica-tion, for estimating the e�ects of zidovudine (AZT)from observational data.17,18

When one is not con®dent that one hasadequately measured all the time-varying con-founding variables, as will be common, what canbe done? There are several options. First one canperform a sensitivity analysis, in which one allowsdepartures from assumptions that all confoundershave been measured adequately. For example, onemight postulate the existence of an unmeasuredconfounder and hypothesize about or place limitson how strongly it may be associated with thetreatment, outcome, or both.25±27 Although this is

clearly a step in the right direction, such analysesare generally speculative, because it is di�cult toassess accurately the dependence of treatmentdecisions or outcome on unmeasured and some-times unnamed factors;28 sometimes externalinformation may be used to do this.18,29 Methodsfor sensitivity analysis have been developed forestimating the joint e�ects of doses received over anextended period.28

G-estimation,21 one of the methods referencedabove for estimating the joint e�ects of time-varying treatments, provides another option byallowing use of ideas from instrumental variables(IV) methods,30,31 long popular in econometrics. InIV methods, the analyst attempts to ®nd a variable(the instrument) whose association with the out-come can arise only through its e�ect on thetreatment received and through associations withmeasured confounding variables. One mightbelieve that there is adequate measurement ofconfounding variables for some subset of theperson-time in the study; one might view thetreatment assignment in that subset as an instru-ment. The prototypical case where this is plausibleis a randomized trial with noncompliance; random-ization ensures that the initial random assignment,but not subsequent compliance changes, is uncon-founded.30,32 Similar situations may arise inobservational studies of time-varying treatmentswhen predictors of initiation of drug therapy aremore thoroughly assessed than predictors ofdiscontinuation.18 One can test whether variousmodels for treatment e�ects are consistent withthese assumptions and exploit such tests forestimating those e�ects.18,21 In the case of CCBs,one might consider identifying subjects by pro-vider; Huse et al. report that some `providers'patterns of prescribing CCBs are more sensitive toblood pressure than others'. There may be lessconfounding by indication among subjects whosephysicians do not consider higher blood pressurean indication. While, as in Huse et al.'s study, itmay be very di�cult to obtain information directlyfrom physicians, it may be possible to infer fromdata recorded in electronic form routinely in thecourse of practice.

There is a formal connection between IVmethods and ecologic analysis, and, in fact,some33 have proposed using ecologic analysis instudies of intended e�ects, with geographic area asa putative instrumental variable, related to practicebut without independent association with theoutcome. The absence of individual-level data

EDITORIAL 39

Copyright # 2000 John Wiley & Sons, Ltd. Pharmacoepidemiology and Drug Safety, 9: 37±41 (2000)

imposes severe restrictions on the analyses that canbe performed;34 such restrictions are not necessaryin G-estimation/IV analyses using individual-leveldata.

The extensions of IV methods can also be used toconvert a problem where the direction of bias isunknown to one where it is known. For somedrugs, people who initiate drug use may have aworse prognosis than those who do not initiate it;this could make the drug appear unduly harmfuland could explain the elevated risk among subjectswho recently initiated treatment. Huse et al.provide evidence for this. However, people mayalso be removed from the drug because of indica-tions of poor health when using it; this could makethe drug appear unduly bene®cial. When there is acombination of these competing biases, it may bedi�cult to assess the magnitude and direction ofthe confounding. One could base a G-estimation/IV analysis on the working assumption of noconfounding for medication initiation. In suchanalyses, initiators would appear more sick thannon-initiators (except perhaps for the e�ect oftreatment), and estimates obtained would provide alower bound for how e�cacious CCBs are. Thiswould be of particular interest if even thisparticular bound indicated that CCBs were e�ca-cious.15

As is widely recognized, including by Maxwellet al., the use of observational studies to investigateintended drug e�ects is fraught with di�culties.Better understanding of the determinants of drugusage and of the underlying causal structure canimprove observational research into such e�ects.Research into the determinants of drug usage canhelp identify potential sources of confounding inobservational studies of intended drug e�ects. Suchresearch can sound an alert about possibleconfounding by indication, as done by Huse et al.,and can be used to guide the data collection andanalysis. This can be particularly valuable ifpreviously unrecognized determinants of drugusage are identi®ed.

Observational research on intended drug e�ectswould bene®t from more explicitness about andbetter understanding of underlying causal structure.Discussions of possible confounding by indicationshould thus distinguish routinely between con-founding by measured and by unmeasured factorsand make more explicit their arguments aboutresidual confounding. Further, methods such asthose mentioned previously, which seeks to dis-entangle the e�ects of treatments received at

di�erent times from each other and from con-founding by measured and unmeasured factors,can improve the design and analysis of thesestudies. Even with such improvements, observa-tional methods for investigating intended druge�ects still rely crucially on unveri®able assump-tions about the magnitude of unaccounted-forconfounding by indication.

ACKNOWLEDGEMENTS

This work was sponsored by a grant from theNational Heart, Lung, and Blood Institute (5-R29-HL59184-02).

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KEY POINTS

. Whether CCBs are harmful has not beenadequately resolved, in part due to possibleconfounding by indication.

. Arguments about residual confoundingshould be made explicit.

. Some as yet not widely used methods have animportant role to play in observational studiesin the presence of confounding by indication.

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EDITORIAL 41

Copyright # 2000 John Wiley & Sons, Ltd. Pharmacoepidemiology and Drug Safety, 9: 37±41 (2000)