Statistical analysis with measurement error or misclassification strategy, method and application

497 Pages·2017·4.668 MB·English

by  Yi, Grace Y

Springer Series in Statistics
Grace Y. Yi
Statistical Analysis
with Measurement
Error or
Misclassifi cation
Strategy, Method and Application
Springer Series in Statistics
Advisors:
P.Bickel,P.Diggle,S.E.Feinberg,U.Gather,
S.Zeger
Moreinformationaboutthisseriesathttp://www.springer.com/series/692
Grace Y. Yi
Statistical Analysis with
Measurement Error or
Misclassification
Strategy, Method and Application
123
GraceY.Yi
DepartmentofStatisticsandActuarialScience
UniversityofWaterloo
Waterloo,Canada
ISSN0172-7397 ISSN2197-568X (electronic)
SpringerSeriesinStatistics
ISBN978-1-4939-6638-7 ISBN978-1-4939-6640-0 (eBook)
DOI10.1007/978-1-4939-6640-0
LibraryofCongressControlNumber:2016951935
©SpringerScience+BusinessMedia,LLC2017
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Tomyhusband,Wenqing,mychildren,MorganandJoy,
andmyparents,LiangyuandZhizhen
Foreword
This book is an authoritative addition to the literature on measurement error and
misclassification.Iliketothinkofthefieldmorebroadlyasstatisticalanalysiswhen
variablesaresubjecttouncertaintyofmeasurement,althoughthecontextofmeasure-
menterrorandmisclassificationisdifferentfromthecontextofuncertaintyquantifi-
cationinappliedmathematicsandcomputermodeling.
This book differs considerably from previous books by Fuller (1987), Carroll
et al. (1995, 2006), Gustafson (2004), and Buonaccorsi (2010) because of its com-
prehensiveoverviewoftopicsinlifetimedataanalysis,oftencalledsurvivalanalysis.
If they touch at all on this important topic, which has quite a large literature, they
touch it only very lightly. Grace Yi’s book covers proportional hazard/Cox regres-
sion,additivehazardsurvivalmodels,andrecurrenteventdataandisthefirsttextto
covertheseimportanttopicsindetail.Ofcourse,thefactthattheauthorisanexpert
onthesetopicsisveryimportant,andanyonewantingtoknowaboutuncertaintyof
measurementinlifetimedataanalysiswillwantthistextastheirguide.
Threeotherchaptersarealsounique:(a)longitudinaldataanalysis,(b)multistate
and Markov models, and (c) case–control studies. Again, these topics are touched
upononlylightlybytheotherbooks,butGraceYihasgivenusaterrificoverviewof
theliterature,onenotavailableelsewhere.Ihappentoknowquitealotaboutcase–
controlandotherretrospective studies,andIamimpressedbythebook’s coverage
ofthearea,andtheimportantwarningsthatgowiththisformofsampling.
Not only are new topics covered in this book, but in addition they are covered
extremelywell.Notjustauthoritatively,butalsoGraceYihasmadegreateffortsto
communicatetheimportantideaswell.Thebookcanbeusedinteachingcourses,at
VII
VIII Foreword
all levels ranging all the way up to advanced seminars. I though treasure the book
becauseIknowthatIhavearesourceforunderstandingissuesinlifetimedataanal-
ysis,notanareaIamcomfortablewith,butoneIconfrontonaregularbasis.
DepartmentofStatistics RaymondJ.Carroll
TexasA&MUniversity
CollegeStation,TX77843-3143,USA
and
SchoolofMathematicalandPhysicalSciences
UniversityofTechnologySydney
Broadway,NSW2007,Australia
Preface
Measurement error and misclassification arise ubiquitously and have been a long-
standing concern in statistical analysis. The effects of measurement error and mis-
classificationhavebeenwelldocumentedformanysettingssuchaslinearregression
and nonlinear regression models. Consequences of ignoring measurement error or
misclassificationvaryfromproblemtoproblem;sometimestheeffectsarenegligible
whileothertimestheycanbedrastic.Ageneralconsensusistoconductacase-by-
caseexaminationinordertoreachavalidstatisticalanalysisforerror-contaminated
data.
Overthepastfewdecades,extensiveresearchhasbeendirectedtovariousfields
concerning suchproblems.Researchinterestinmeasurementerrorandmisclassifi-
cationproblemshasbeenrapidlyspurredinawidespectrumofdata,includingevent
historydata(suchassurvivaldataandrecurrenteventdata),correlateddata(suchas
longitudinal data and clustered data), multi-state event data, and data arising from
case–control studies. The literature on this topic is enormous with many methods
scattered diversely. The goal of this monograph is to bring together assorted meth-
ods under the same umbrella and to provide an update on the recent development
for a variety of settings. Measurement error effects and strategies of handling mis-
measurement for different models are to be closely examined in combination with
applicationstospecificproblems.
A number of books concerning measurement error and misclassification have
been published with distinct focuses. An early book by Fuller (1987) summarizes
the development of linear regression models with errors-in-variables. Focusing on
nonlinear measurement error models, Carroll, Ruppert and Stefanski (1995) pro-
vide analysis strategies for regression problems in which covariates are measured
with error; the second edition, Carroll et al. (2006), further documents up-to-date
methods with a comprehensive discussion on many topics on nonlinear measure-
menterrormodels,includingBayesiananalysismethods.Withtheemphasisonthe
use of relatively simple methods, Buonaccorsi (2010) describes methods to correct
IX
X Preface
formeasurementerrorandmisclassificationeffectsforregressionmodels.Underthe
Bayesianparadigm,Gustafson(2004)providesadualtreatmentofmismeasurement
in both continuous and categorical variables. Other relevant books on this topic
include Biemer et al. (1991), Cheng and Van Ness (1999), Wansbeek and Meijer
(2000),andDunn(2004).
This monograph covers the material that complements those books, although
there is overlap in some of the topics. While general principles and strategies may
share certain similarities, this book emphasizes unique features in modeling and
analyzing measurement error and misclassification problems arising from medical
researchandepidemiologicalstudies.Theemphasisisongaininginsightintoprob-
lemscomingfromawiderangeoffields.Thisbookaimstopresentbothstatistical
theory and applications in a self-contained and coherent manner. To increase read-
abilityandeasetheaccessforthereaders,necessarybackgroundandbasicinference
frameworksforerror-freecontextsarepresentedatthebeginningofChapters3–8,in
additiontothediscussioninChapter1.Eachchapterisconcludedwithbibliographic
notesanddiscussion,supplementedwithexerciseproblemswhichmaybeusedfor
graduatecourseteaching.Extensivereferencestorecentdevelopment aregivenfor
thereadersinterestedinresearchonvariousmeasurementerrorandmisclassification
problems.Applicationsandnumericalillustrationsaresupplied.
This monograph is designed for multiple purposes. It can serve as a reference
bookforresearcherswhoareinterestedinstatisticalmethodologyforhandlingdata
withmeasurementerrorormisclassification.Itmaybeusedasatextbookforgrad-
uatestudents,especiallyforthosemajoringinStatisticsandBiostatistics.Thisbook
mayalsobeusedbyappliedstatisticianswhoseinterestfocusesonanalysisoferror-
contaminateddata.
This monograph is intended to be read by readers with diverse backgrounds.
Familiaritywithinferencemethods(suchaslikelihoodandestimatingfunctionthe-
ory)ormodelingschemesinvaryingsettings(suchassurvivalanalysisandlongitu-
dinaldataanalysis)canresultinafullappreciationofthetext,butthisisnotessential.
Readerswhoarenotfamiliarwiththosetopicsmayenjoyreadingthebookbygoing
throughrelevanttopics.Chapters1–2andthefirstsectionofeachfollowingchapter
provide basic inference frameworks and background material which are useful for
unfamiliarreaders.Thebookdoesnothavetobereadaccordingtothesequentialor-
derofthechapters.Readersmaydirectlyreadachapterofinterestbyskippingprior
chapters.Theexercisesattheendofeachchaptersupplementthedevelopmentinthe
text.Someproblemsareorganizedtoprovidejustificationoftheresultsdiscussedin
thetext;someproblemsaremodifiedfromresearchpapersormonographstoserveas
applicationsofthemethodsdiscussedinthetext;andsomeproblemsaredesignedto
bepotentialresearchtopicswhichareworthfurtherexplorations.Referencesatthe
endoftheproblemsindicatethesourcesfromwhichtheproblemsaremodified.