Applying quantitative bias analysis to epidemiologic data. PDF
Preview Applying quantitative bias analysis to epidemiologic data.
Statistics for Biology and Health Matthew P. Fox Richard F. MacLehose Timothy L. Lash Applying Quantitative Bias Analysis to Epidemiologic Data Second Edition Statistics for Biology and Health SeriesEditors Mitchell Gail, Division of Cancer Epidemiology and Genetics, National Cancer Institute,Rockville,MD,USA Jonathan M. Samet, Department of Environmental & Occupational Health, UniversityofColoradoDenver-AnschutzMedicalCampus,Aurora,CO,USA Statistics for Biology and Health (SBH) includes monographs and advanced textbooks on statistical topics relating to biostatistics, epidemiology, biology, and ecology. Moreinformationaboutthisseriesathttp://link.springer.com/series/2848 (cid:129) (cid:129) Matthew P. Fox Richard F. MacLehose Timothy L. Lash Applying Quantitative Bias Analysis to Epidemiologic Data Second Edition MatthewP.Fox RichardF.MacLehose DepartmentofEpidemiology DivisionofEpidemiologyandCommunity BostonUniversitySchoolofPublic Health Health UniversityofMinnesotaSchoolofPublic Boston,MA,USA Health Minneapolis,MN,USA TimothyL.Lash DepartmentofEpidemiology RollinsSchoolofPublicHealth,Emory University Atlanta,GA,USA ISSN1431-8776 ISSN2197-5671 (electronic) StatisticsforBiologyandHealth ISBN978-3-030-82672-7 ISBN978-3-030-82673-4 (eBook) https://doi.org/10.1007/978-3-030-82673-4 ©SpringerNatureSwitzerlandAG2009,2021 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartofthe materialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped. 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ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface Biasanalysisquantifiestheinfluenceofsystematicerroronanepidemiologystudy’s estimateofoccurrenceorestimateofassociation.Thefundamentalmethodsofbias analysis in epidemiology have been well-described for decades, yet are seldom applied in published presentations of epidemiologic research. More complex approachestobias analysis—suchasprobabilisticbias analysis,directbias model- ing,andBayesianbiasanalysis—appearevenmorerarely.Wesuspectthatthereare bothsupply-sideanddemand-sideexplanationsforthescarcityofbiasanalysis.On thedemandside,journalreviewersandeditorsseldomrequestthatauthorsaddress systematicerrorasidefromlistingthemaslimitations oftheirstudy.Thislistingis often accompanied by explanations for why the limitations should not pose much concern.Onthesupplyside,methodsforbiasanalysisreceivelittleattentioninmost epidemiologycurriculaandareoftenscatteredthroughouttextbooksorabsentfrom them altogether, and they cannot be implemented easily using standard statistical computing software. Although there has been some increase in the use of these methodssincethepublicationofthefirsteditionofthistext,andtheaccompanying spreadsheetsandcodehaveimprovedtheaccessibilityofthemethods,theyarestill strongly underutilized. Our objective in this text is to reduce these supply-side barriers,withthehopethatdemandforquantitativebiasanalysiswillfollow. Thefirsteditionofthistextwaslaunchedafterwritingafewbiasanalysispapers in epidemiology journals. The American College of Epidemiology invited a day-long workshop on the methods of bias analysis, which became an outline, whichinturnbecamethefirsteditionofthistext.Thetextbeginswithadescription of the need for bias analysis and continues with two chapters on the legwork that mustbedoneattheoutsetofaresearchprojecttoplanandimplementabiasanalysis. This introductory section is followed by three chapters that explain simple bias analysis methods to address each of the fundamental threats to the validity of an epidemiology study’s estimate of association: selection bias, classification errors, anduncontrolledconfounding.Wethenextendthebiasanalysismethodsfromthese early chapters to probabilistic bias analysis, direct bias modeling, Bayesian bias v vi Preface analysis, and ultimately multiple bias analysis methods. The book concludes with twochaptersonthepresentationandinterpretationoftheresultsofabiasanalysis. This second edition contains substantial additions beyond what appeared in the first edition. We include a wider array of simple bias analysis methods to address each of the fundamental threats to validity. We have divided the chapters on probabilisticbiasanalysisintoapresentationofmethods appliedtosummary level dataandapresentationofmethodsappliedtorecordleveldata.Conceptualconnec- tions between the two approaches are woven into all chapters. We have added a chapterondirectbiasmodeling,withparticularemphasisonregressionmethodsto address mismeasurement of continuous exposures, covariates, and outcomes. We havealsoaddedachapteronBayesianbiasanalysismethods,whichinmanyways providetheoreticalunderpinningsformanyoftheothermethodsinthisbook. Readersmightusethetextasanindependentresourcetoaddressbiasanalysisas theyconductepidemiologicresearch,asasecondarytextinaclassonepidemiologic methods,orasthecentraltextinanadvancedclassondataanalysisinepidemiologic researchthatfocusesonbiasanalysis.Wehopethatstudentswillfind,aswehave, that once they have completed one bias analysis, it becomes hard to imagine analyzing epidemiologic data without it. We have aimed the text at readers who have familiarity with epidemiologic research and intermediate data analysis skills. For those without those skills, we suggest a comprehensive methods text, such as ModernEpidemiology,whichcanbeusedinconjunctionwiththistexttoprovidea foundationinepidemiologicterminology,studydesign,anddataanalysis. An important adjunct resource for this textbook is the suite of freely available spreadsheets and software available for download at https://sites.google.com/site/ biasanalysis/.Wehaveendeavoredtoprovidespreadsheets,SAS,Rcode,andStan andJAGScodetoreplicatetheanalysespresentedherein.Weencouragereadersto downloadthesoftware,followtheexamplesinthetext,andthenmodifythecodeto implementtheirownbiasanalysis.Wewouldbedelightedtohearfromanyonewho improvesthetoolsordetectsanerror,andwewillpostrevisedtoolsastheybecome available.Likewise,wewelcomecomments,criticisms,anderrataregardingthetext fromreadersandwillmaintainalogofthisfeedbackatthesamewebsite. Funding for the first edition, some of which now carries over to this second edition, was made possible by grant G13LM008530 from the National Library of Medicine, NIH, DHHS. The views expressed in any written publication, or other media,donotnecessarilyreflecttheofficialpoliciesoftheDepartmentofHealthand Human Services,nordoesmention bytrade names,commercial practices,ororga- nizationsimplyendorsementbytheU.S.Government. Boston,MA,USA MatthewP.Fox Minneapolis,MN,USA RichardF.MacLehose Atlanta,GA,USA TimothyL.Lash Acknowledgments The authorsgratefullyacknowledge theimportantcontributionofAliza K.Finkto thefirsteditionofthistext.Althoughshedidnotparticipateinpreparingthissecond edition,herworkandideasremainfoundationaltoitscontent. Wealsothankourfriendsandcolleagueswhocontributedtothetextdirectlyor indirectly. We appreciate Charles Poole’s suggestion to the American College of Epidemiology that a short course on bias analysis would be of value, and we appreciate John Acquavella’s decision to accept that suggestion on behalf of the college.AsnotedinthePreface,thisday-longseminarprovidedtheoutlineforthe first edition. SanderGreenland participatedinthedevelopment andpresentation of the American College of Epidemiology workshops and has been instrumental in improvingthemethodsofbiasanalysis.Wearegratefulforhisinputanddedication to the topic. We also thank our colleagues who have a particular interest in bias analysismethods;theyhavechallengedustodevelopourideasandtocommunicate them clearly. We cannot list them all, so we acknowledge especially Carl Phillips, GeorgeMaldonado,AnneJurek,KenRothman,RebeccaSilliman,SoeSoeThwin, DanBrooks,SteveCole,TylerVanderWeele,ThomasAhern,LindsayCollin,Paul Gustafson, Lawrence McCandless, and Hailey Banack. We especially thank Paul GustafsonforreviewingpartsofChapters8and11,andGhassanHamraandHaitao ChufortheirreviewofChapter11. Thesecollaborationsandreviewshavebeenimmenselyhelpfulinpreparingthe text;theauthorsareresponsibleforanyerrororomission. We further acknowledge that parts of some chapters borrow, with permission, fromworkwehavepreviouslypublished.Inparticular: PartsofChapter1includecontentthatfirstappearedinLashTL.Heuristicthinking andinferencefromobservationalepidemiology.Epidemiology.2007;18:67–72. PartsofChapters13and14includecontentthatfirstappearedinLashTL,FoxMP, MacLehoseRF,MaldonadoG,McCandlessLC,GreenlandS.Goodpracticesfor quantitativebiasanalysis.IntJEpidemiol.2014;43:1969–85. vii Contents 1 Introduction,Objectives,andanAlternative. . . . . . . . . . . . . . . . . . 1 Introduction:BiasesinHealthResearch. . . . . . . . . . . . . . . . . . . . . . . 1 StatisticalInferenceinPublicHealthResearch. . . . . . . . . . . . . . . . . . 3 TheTreatmentofUncertaintyinNonrandomizedResearch. . . . . . . 8 WhenBiasAnalysisWillBeMostUseful. . . . . . . . . . . . . . . . . . . 10 JudgmentsUnderUncertainty. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 TheDual-ProcessModelofCognition. . . . . . . . . . . . . . . . . . . . . . 12 AnchoringandAdjustment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Overconfidence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 FailuretoAccountfortheBase-Rate. . . . . . . . . . . . . . . . . . . . . . . 15 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2 AGuidetoImplementingQuantitativeBiasAnalysis. . . . . . . . . . . 25 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 ReducingError. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 ReducingErrorbyDesign. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 ReducingErrorintheAnalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . 29 QuantifyingError. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 EvaluatingthePotentialValueofQuantitativeBiasAnalysis?. . . . . 32 PlanningforBiasAnalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 CreatingaDataCollectionPlanforBiasAnalysis. . . . . . . . . . . . . . 36 CreatinganAnalyticPlanforaBiasAnalysis. . . . . . . . . . . . . . . . . . . 38 TypeofData:Record-LevelVersusSummary. . . . . . . . . . . . . . . . 38 TypeofBiasAnalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 OrderofBiasAnalysisAdjustments. . . .. . . . . .. . . . . .. . . . . . .. 41 BiasAnalysisTechniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 SimpleBiasAnalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 MultidimensionalBiasAnalysis. . . . .. . . . . . .. . . . . . .. . . . . . .. 43 ProbabilisticBiasAnalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 ix x Contents MultipleBiasModeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 DirectBiasModelingandMissingDataMethods. . . . . . . . . . . . . . 47 BayesianBiasAnalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 AssigningValuesandDistributionstoBiasParameters. . . . . . . . . . . . 49 DirectedAcyclicGraphs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3 DataSourcesforBiasAnalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 BiasParameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 InternalDataSources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 SelectionBias. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 UncontrolledConfounding. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 InformationBias. . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .. . . . . 60 DesignofInternalValidationStudies. . . . . . . . . . . . . . . . . . . . . . . 61 LimitationsofInternalValidationStudies. . . . . . . . . . . . . . . . . . . . 65 ExternalDataSources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 SelectionBias. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 UnmeasuredConfounder. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 InformationBias. . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .. . . . . 69 ExpertOpinion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4 SelectionBias. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 DefinitionsandTerms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 Conceptual. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 DepictingSelectionBiasUsingCausalGraphs. . . . . . . . . . . . . . . . 80 DesignConsiderations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 BiasAnalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 MotivationforBiasAnalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 SourcesofData. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 SimpleBias-AdjustmentforDifferentialInitialParticipation. . . . . . . . 85 Example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 IntroductiontoBiasAnalysis. . . . .. . . . .. . . . . .. . . . . .. . . . . .. 86 BiasAnalysisbyProjectingtheExposedProportionAmong Nonparticipants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 BiasAnalysisUsingSelectionProportions. . . . . . . . . . . . . . . . . . . 90 BiasAnalysisUsingInverseProbabilityofParticipation Weighting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 SimpleBias-AdjustmentforDifferentialLoss-to-Follow-up. . . . . . . . . 93 Example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 BiasAnalysisbyModelingOutcomes. . . . . . . . . . . . . . . . . . . . . . 94
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