Candidate, M.S.

Etminan, M.

Mansournia, M.A.

Nazemipour, M.

eprints Iran University of Medical Sciences

COMMENTARIESPotential Biases inStudies of Acid-SuppressingDrugs and COVID-19 Infectionurrently, there is great interestCin identifying safe and effectiveexisting drug therapies for the preventionof coronavirus-2019 (COVID-19).Recently, there is a surge of observationalstudies, using big data, examining safetyand effectiveness of proton pump in-hibitors (PPIs) or histamine-2 receptorantagonists (H2RAs) on the risk of newonset COVID-19.1–6 The nature of thedisease, limitations of large databases, anduse of different methodologic approacheshave created unique challenges that mighthave contributed to contradictory studiesaddressing this question.Although some recent studies, espe-cially those that tightly controlled forconfounding bias, have not found an as-sociation,1,2,5 others have shown either aprotective3,4 or a harmful6 associationwith PPIs or H2RAs in COVID-19. Thesecontradictory results can be misleading toclinicians, policymakers, and patients,leading to unnecessary prescribing ofPPIs or H2RAs for COVID-19 prevention.Here we discuss potential biases thatmight have led to these contradictoryfindings including confounding bias,immortal time bias, selection bias, reversecausality bias, and sparse data bias usingexamples from published studies andcausal directed acyclic graphs (cDAGs).cDAGs are graphical tools that can enableclinician researchers to visualize andbetter understand the structure of epide-miological biases and allow them tocritique the methodologic rigor of obser-vational studies addressing COVID-19 inthe area of gastroenterology.7-9Specific Biases inEpidemiologic Studies ofAcid Suppressing Drugand COVID-19 StudiesThe structure of a number of biasesthat might have affected the studies ofgastric-suppressing drugs in COVID-19can be demonstrated using cDAGs. Amore comprehensive discussion oncDAGs is beyond the scope of this re-view, but can be found elsewhere.10,11In brief, cDAGs provide a blueprint ofall pertinent variables that play a rolein causal questions. Two variables, Aand B, are connected with an arrow tailthat starts from A and ends on variableB, meaning that A causes B. Variable Acannot cause itself; that is, cDAGs areacyclic. Adjacent arrows, regardless ofdirection, form paths in a cDAG.Finally, conditioning on a variablemeans restricting that to a subset of itsvalues. Conditioning can be done at thedata analysis stage (eg, regressionadjustment) or study design stage (eg,restriction).Confounding biasConfounding bias is one of the mostprevalent types of bias that can affectthe validity of observational studies. Ina recent retrospective cohort study,Freedberg et al3 demonstrated a pro-tective effect of famotidine use onCOVID-19 death or intubation (hazardratio, 0.42; 95% confidence interval[CI], 0.21–0.85). The study attemptedto control for confounding biasthrough propensity score (PS) match-ing, a statistical technique that at-tempts to balance measuredconfounders between famotidine usersand nonusers. However, PS matchingcan only adjust for measured con-founders and its usefulness dependson how well the PS model can balancemeasured confounders between the 2groups.The authors matched users andnonusers using a 5:1 nearest-neighbormatching strategy and a caliper of 0.2,which means that the PS scores for the2 members of a matched set couldhave varied by 20%. They thenassessed balance of confounding vari-ables between famotidine users andnonusers in the original and matchedsample using hypothesis testing wherefailure to reject the null hypothesis (P> .05) was used as a metric for goodbalance of confounders. This approachhas been shown to introduce bias9because hypothesis testing is a func-tion of sample size, and with only 84famotine users, a large P value canerroneously indicate optimal balanceof confounders. In fact, in the matchedsample, the percentage of the followingvariables—old age, diabetes, hyper-tension, and coronary artery dis-ease—among famotidine usersremained higher compared with non-users (at 48%, 29%, 35%, and 11% vs.43%, 25%, 30%, and 9%, respec-tively). Standardized mean differences(SMDs)12 are a useful and acceptedmetric for examining covariate balanceafter PS matching. As a general rule, aSMD of 10% is indicative of a goodbalance between variables.13 The per-centage difference for variables ageand hypertension in the matchedsample in the Freedberg study was 5%,which translates into a SMD of 10.03%and 10.66%, respectively, which areboth slightly >10%. The cohort studyby Lee et al,5 which did not find anassociation between PPI use andCOVID-19 also used a PS matchinganalysis and demonstrated a SMD of<10% for all the 14 covariatesincluded in the PS analysis.Moreover, a caliper of 0.2 for PSsbetween matched users and nonuserscan lead to residual confounding owingto the substantial variation betweenthe PSs allowed by this approach. Acaliper of 0.01–0.05 on the originalscale14 or 0.2 of the standard deviationon the logit scale has been recom-mended. Residual confounding mayexplain the protective effect seen withfamotidine use in this study.Residual confounding in this studymight have been further accentuatedowing to a small ratio of the number ofmatched exposed to the number of PSmodel parameters, also referred to asthe number of events per variable(EPV). It is recommended EPV to be10 in logistic regression modelling,15which for this specific case looks at thenumber of the exposed as the responsevariable in the PS model is the expo-sure. In this study of 84 famotidineGastroenterology 2021;160:1443–1446Figure 1. (A) Confounding bias: chronic obstructive pulmonary disease (COPD)severity is a confounder as it is a common cause of coronavirus disease-2019(COVID-19) and proton pump inhibitor (PPI) use through gastrointestinal (GI)symptom severity. (B) Immortal time bias (selection bias): Conditioning on variableS, which represents selection upon having survived 7 days before COVID-19diagnosis opens a previously blocked path that spuriously associates famotidineuse with COVID-19 mortality. (C) Reverse causality: COVID-19 can affect PPI usethrough early COVID-19 symptoms, and creates a confounded association be-tween PPI use and COVID-19 diagnosis.COMMENTARIESusers, the investigators included 21variables as indicators of potentialconfounders in the estimation of PSmodel: 84/22 (21 covariatesþ1, themodel intercept) which yields an EPVof 3.8. This number is much less than10, suggesting the possibility of sparsedata bias16,17 in the PS model co-efficients, which can affect the validityof the PS estimation and yield sub-optimal balance of confounders (be-tween famotidine users and nonusers),potentially leading to residualconfounding.Perhaps amore consequential effectof sparse data bias is on the magnitudeof the effect size of epidemiologicstudies. For example, in the study byAlmario et al,6 those taking PPIs twicedaily experienced an approximately 3.5higher risk of developing COVID-19compared with nonusers (odds ratio[OR], 3.67; 95% CI, 2.93–4.60). In thisstudy, the EPV is computed as 198/32 ¼ 6.2 (31 covariatesþ1) indicativeof a low EPV resulting in possiblyinflated ORs. Statistical techniques canbe used to correct for sparse data biasand shrink the effect size.18In another study, Elmunzer et al19examined the preadmission exposureto acid-suppressing drugs and the oddsof death or need for mechanical venti-lation in patients who contract COVID-19. No benefit was observed in theadjusted analysis for H2RA or PPI usewith respect to death. Similar toFreedberg et al,3 Elmunzer et al alsoused a PS-matched analysis using a 0.2caliper which resulted in a risk differ-ence of 9.72% (95% CI, 1.26%–18.2%).Again, a caliper of 0.2 might mean thatthe PS matched analysis might not havefully corrected for confounding bias.Confounding bias might have alsoexplained harmful association shownbetween PPI use and COVID-19.Almario et al6 undertook a survey inpatients with gastrointestinal symp-toms who had also contracted COVID-19. The study found that subjectsusing a PPI once a day had double theodds of contracting COVID-19compared with nonusers (OR, 2.15;95% CI, 1.90–2.44). The increase inrisk observed might be due to unmea-sured confounding such as chronicobstructive pulmonary disease (COPD)severity. This is reflected in Figure 1A,1444where subjects with more severe COPDmight experience more severe GIsymptoms.20 Consequently, those withmore severe GI symptoms are morelikely to take PPIs. COPD severity isalso a recognized risk factor for COVID-19 making it a classic confounder.Immortal Time BiasIn a recent cohort, Mather et al22followed 878 famotidine users andassessed the risk of COVID-19mortalitybetween this group and nonusers. Bydesign, famotidine users could havereceived the drug within 7 days ofCOVID-19 screening or hospital admis-sion. This means users had to be alivefor 7 days (immortal period) beforebeing eligible to receive the drug whilenonusers could have incurred theoutcome (death) at any time during thestudy period. This type of conditioningcan introduce immortal time bias,21which can lead to a spurious protec-tive effect of a drug as shown by thestudy which reported an OR of 0.37(95% CI, 0.16–0.86) consistent with aprofound protective effect. The struc-ture of immortal time bias in thisexample resembles selection bias. Se-lection bias generally occurs in cohortstudies where 1 group of subjects (eg,famotidine users) are treated differ-ently than the other group (nonusers).In this example, users were allowed tobe alive 7 days before COVID-19diagnosis, whereas nonusers couldhave died at any time. Thus, famotidineusers with less severe comorbid con-ditions were more likely to be includedin the study, leading to an observedCOMMENTARIESprotective effect secondary to selectionbias as depicted in Figure 1B.In Figure 1B, variable S representshaving been selected into the studybased on the 7-day conditioning stepdefined by the investigators. The ar-rows from famotidine use and variableU (which represents the unmeasuredrisk factor COPD severity) to variable Sindicate famotidine users with less co-morbid conditions (eg, less severeCOPD) were more likely to be includedin the study. The square around S re-flects that the analysis is limited tofamotidine users and nonusersincluded in the sample. Thus, S is acommon effect of famotidine and U, alsoreferred to a collider. Unlike in ourconfounding example where condi-tioning (through adjustment) on aconfounder blocks its effect, condition-ing on a collider actually introducesbias by opening a path that was previ-ously closed by variable S. For example,the conditioning step in the studydesign (variable S) will open the pathfamotidine / S ) U / mortality.Through this path, famotidine use willbe associated with COVID-19 mortalityas a result of selection bias, whereaswithout this conditioning, according tothe cDAG, no association exists betweenfamotidine use and COVID-19 mortal-ity. A time-dependent Cox regressionanalysis should have been used to avoidimmortal time bias.Selection bias might have alsoaffected the study by Almario et al.6 Inthis study, 264,058 potential partici-pants were identified, but only 48.8%patients agreed to participate. It ispossible that patients who agreed toparticipate in the survey did so owing tomonetary incentives, a proxy for lowsocioeconomic status, which can alsoaffect their risk of contracting COVID-19.Reverse causality biasReverse causality bias mightexplain the protective effect observedwith PPI use in the study by Blancet al.4 This was a case-control studyamong elderly hospitalized patientswith an average age of 84 years. PPIuse was assessed 15 days beforeCOVID-19 diagnosis. The study found aprotective effect with PPI use (OR,0.43; 95% CI, 0.23-0.81) comparedwith non-PPI users. This profoundprotective effect might be explained byreverse causality bias. This bias refersto a situation where symptoms of thedisease (which in the case of COVID-19can start days or weeks before diag-nosis) can affect drug use. For example,elderly patients in this study mighthave decided to discontinue using aPPI, possibly to decrease poly-pharmacy and maximize adherence,upon experiencing early COVID-19symptoms and resorted to takingmore essential medications such asantidiabetics or antihypertensives.This is reflected in Figure 1C, wherethe arrow from COVID-19 to PPI usesuggests that early COVID-19 symp-toms can affect PPI use, representingreverse causality. Figure 1C illustratesthat a consequence of reverse causalityis a confounded association betweenPPI use and COVID-19 diagnosis.Summary andConclusionsWe have identified a number ofbiases including confounding bias,immortal time bias, selection bias andreverse causality bias that might haveaffected the results of PPI/H2Ras andCOVID-19 diagnosis. These biases wereidentified based on the informationavailable in the studies we havereviewed without having access to thestudy protocols or data. Thus, wecannot state with certainty if thesebiases actually affected the results.In studies of acid-suppressingdrugs in COVID-19 where confound-ing bias was appropriately controlledfor, no benefit with these drugs wasfound. Clinicians should be cognizantof these biases when reviewing futurestudies on this topic.MAHYAR ETMINANDepartment of Ophthalmology and VisualSciences and MedicineUniversity of British ColumbiaVancouver, British Columbia, CanadaMARYAM NAZEMIPOUROsteoporosis Research CenterEndocrinology and Metabolism ClinicalSciences InstituteTehran University of Medical Sciences, andPsychosocial Health Research InstituteIran University of Medical SciencesTehran, IranMOHIT SODHI CANDIDATEDepartment of Ophthalmology and VisualSciences and MedicineUniversity of British ColumbiaVancouver, British Columbia, CanadaMOHAMMAD ALI MANSOURNIADepartment of Epidemiology andBiostatisticsSchool of Public HealthTehran University of Medical SciencesTehran, IranReferences1. Fan X, Liu Z, Miyata T, et al. Effectof acid suppressants on the risk ofCOVID-19: a propensity scorematched study using UK BiobankAcid Suppressants and the risk ofCOVID-19. Gastroenterology 2021;160:155–158.e5.2. 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CHecklist forstatistical Assessment of MedicalPapers: the CHAMP statement. BrJ Sports Med 2021.8. Mansournia MA, Collins GS,Nielsen RO, et al. A CHecklist for1445COMMENTARIESstatistical Assessment of MedicalPapers (the CHAMP statement):explanation and elaboration. Br JSports Med 2021.9. Etminan M, Brophy JM, Collins GS,et al. To adjust or not to adjust: therole of different covariates in car-diovascular observational studies.Am Heart J 2021 Mar 12;S0002-8703(21)00073-9. https://doi.org/10.1016/j.ahj.2021.03.008.10. Etminan M, Collins GS,Mansournia MA. Using causal dia-grams to improve the design andinterpretation of medical research.Chest 2020;158:S21–S28.11. Williamson EJ, Aitken Z, Lawrie J,et al. Introduction to causal dia-grams for confounder selection.Respirology 2014;19:303–311.12. Austin PC. Optimal caliper widthsfor propensity-score matchingwhen estimating differences inmeans and differences in pro-portions in observational studies.Pharm Stat 2011;10:150–161.13. Austin PC. An introduction topropensity score methods for1446reducing the effects of confound-ing in observational studies.Multivariate Behav Res 2011;46:399–424.14. Hernan MA, Robins JM. Causalinference: what if. Boca Raton, FL:Chapman & Hall/CRC, 2020.15. Peduzzi P, Concato J, Kemper E,et al. A simulation study of thenumber of events per variable inlogistic regression analysis. J ClinEpidemiol 1996;49:1373–1379.16. Greenland S, Mansournia MA.Penalization, bias reduction, anddefault priors in logistic and relatedcategorical and survival regressions.Stat Med 2015;34:3133–3143.17. Mansournia MA, Geroldinger A,Greenland S, et al. Separation inlogistic regression: causes, conse-quences, and control. Am J Epi-demiol 2018;187:864–870.18. Greenland S, Mansournia MA,Altman DG. Sparse data bias: aproblem hiding in plain sight. BMJ2016;352:i1981.19. Elmunzer BJ, Wolf BJ,Scheiman JM, et al. Associationbetween pre-admission acid sup-pressive medication exposure andseverity of illness in patientshospitalized with COVID-19.Gastroenterology 2020 Nov 20[Epub ahead of print].20. Lee AL, Goldstein RS. Gastro-esophageal reflux disease in COPD:links and risks. Int J Chron ObstructPulmon Dis 2015;10:1935.21. Mather JF, Seip RL, McKay RG.Impact of famotidine use on clinicaloutcomes of hospitalized patientswith COVID-19. Am J Gastro-enterol 2020;115:1617–1623.22. Suissa S. Immortal time bias inpharmacoepidemiology. Am J Epi-demiol 2008;167:492–499.Conflicts of interestThe authors have made the following disclosures:Mr Sodhi and Drs Mansournia and Nazemipourhave no conflicts to report. Dr Etminan hasconsulted in the ranitidine and cancer litigation.Most current article© 2021 by the AGA Institute0016-5085/$36.00https://doi.org/10.1053/j.gastro.2020.11.053

Potential Biases in Studies of Acid-Suppressing Drugs and COVID-19 Infection

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Potential Biases in Studies of Acid-Suppressing Drugs and COVID-19 Infection

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