Optimisation of statistical procedures to assess the diagnostic accuracy of cervical cancer ...

136 Pages·2017·2.28 MB·English

2017 | Faculty of Sciences
Doctoral dissertation submitted to obtain the degree of
Doctor of Sciences: Statistics, to be defended by
Victoria Nyawira Nyaga
DOCTORAL DISSERTATION
Optimisation of statistical
procedures to assess the
diagnostic accuracy of cervical
cancer screening tests
Promoter: Prof. Dr Marc Aerts | UHasselt D/2017/2451/57

Co-promoter: Dr Marc Arbyn | Wetenschappelijk Instituut
Volksgezondheid

TomydaugtherNoni.
Apparently,thankstoyouthearchitectureofmybrainhaschangedforgood. The
volumeofmygraymatterhasalsosubstantiallyreducedtherebymaturingfurther
myneuralnetworksub-servingsocialcognition[1].

Acknowledgments
TheworkpresentedinthisthesisisaresultofcollaborativeeffortsbetweenHasselt
university (UHasselt) and Scientific Institute of Public Health (ISP). Through this
collaboration, I have interacted with statistician and non-statistician professionals
consequentlygainingexperiencefromacademiaaswellasindustry.
Firstandforemost,IamveryhonoredtohavehadthesupervisionofProf. Marc
Aerts (UHasselt). Despite being busy, he still managed to set aside time for stimu-
latingdiscussions,mostofthemoverskype,wheneverIneededhisvaluableadvice
andguidanceonthetheoreticalpartofthisthesis. Iwillbeforevergratefulforhis
suggestions, contributions and immense knowledge on which the successful com-
pletionofmyPh.Drest.
I am also very grateful to Dr. Marc Arbyn (ISP), my co-supervisor and at the
same time my immediate supervisor. Using data which he has collected over the
pastyearsoncervicalcancerprecursorsscreening,diagnosisandtreatment,ourre-
searchwastranslatedfromtheorytopractice. Beinganepidemiologistandaclinical
doctor, he was an important part of the bridge between mathematical models de-
velopedinthisthesisandpublichealth. Hisexpertisewasinvaluableinsuggesting
possibleapplicationareasofourwork. Hispersistentsupportandsincerityoffered
me an environment to experiment within safe borders. By engaging me in other
‘side’projectsIhavelearnthowtomulti-taskandcollaboratewithnon-statisticians.
Thanks to my colleagues at ISP who were very friendly and kind. You were
fantasticcolleaguesanddespitecomingfromdifferentpartsoftheworld,youhave
becomefamilytome. IhaveimmenselyenjoyedthecompanyofRenataandMartin
and wish them well in their plans to move to Czech in the near future. Frank and
Lan, thanksforlettingmehaveatasteofChina. Iwishyoutoosuccessandallthe
bestinyourplanstomovebacktoChina.
I would like to thank the support staff in ISP for their logistical support when-
everIhadtotraveltoUHasseltforactivitiesrelatedtodoctoralschoolortonation-
ally/internationallyorganizedscientificmeetingandtrainings. Ialsoacknowledge
thestaffatUHasseltfortheirinvolvementinmydoctoralstudyingeneralandhelp
completingthenecessaryformalitiesformydefense.
Specialgratitudetomypartner,DriesNollet. Coincidentally,Igottoknowyou
justwhenIwasbeginningmydoctoralstudyandsincethenIhavelearntsomuch
about Europe and travelled to places I never ever imagined or knew of. My view
andknowledgeoftheworldhassincebeenexponentiallyamplified. Youhavebeen
there during the happy and difficult moments and have made the journey of my
Ph.Dmemorable. Yourfamilyisveryopen,kindandwelcoming. Theyhavereally
helped me integrate into the Belgian culture and made ‘Belgium’ feel warm even
duringwinter.
IamalsoverygratefultomyfamilyinKenya. Icanneverfindenoughwordsto
sayhowgratefulIamtomymotherandfather. Theyalwayspushedmenotonlyto
dreammorebutalsotodomoreinordertorealizethosedreams. Thoughveryfar,I
alwaysfeeltheirtrust,warmthandlove. IamwhoIambecauseoftheirupbringing,
persistent support and guidance. Thank you to my grandfather and grandmother
(whomIamnamedafter)fortheirendlessconcernandsteadfastencouragement.
Finally, Itakethisopportunitytothankeveryoneelsenotmentionedinthisac-
knowledgmentwhohascontributedtheoreticallyorotherwisetothesuccessofmy
doctoralstudy.
vi
ListofPublications
Manuscripts
Thisthesiscorrespondstoacollectionofthefollowingoriginalpublications.
1. Chapter 2: Nyaga VN, Arbyn M and Aerts M. Metaprop: a Stata command
to perform meta-analysis of binomial data. Archives of Public Health, 72(1):39,
2014.
2. Chapter3: NyagaVN,ArbynMandAertsM.CopulaDTA:Copulabasedbi-
variatebeta-binomialmodelsfordiagnostictestaccuracystudiesinaBayesian
framework.JournalofStatisticalSoftware,2015.Conditionallyacceptedforpub-
lication.
3. Chapter 4: Nyaga VN, Aerts M and Arbyn M. ANOVA model for network
meta-analysis of diagnostic test accuracy data. Statistical Methods in Medical
Research,2016. FirstpublishedSep20,2016;DOI:10.1177/0962280216669182
4. Chapter5: NyagaVN,ArbynMandAertsM.Beta-binomialanalysisofvari-
ance model for network meta-analysis of diagnostic test accuracy data. Sta-
tistical Methods in Medical Research, 2016. First published Jan 1, 2016; DOI:
10.1177/0962280216682532
Contributedmanuscripts
Koliopoulos G, Nyaga VN, Santesso N, et al. Cytology versus HPV testing for cer-
vicalcancerscreeninginthegeneralpopulation. CochraneDatabaseofSystematicRe-
views,2017. PrebublisedAug10,2017;DOI:10.1002/14651858.CD008587.pub2.
Softwaredevelopment
Aspartofthedoctoralproject,thefollowingsoftwarehavebeendevelopedwiththe
aimtoprovidetoolsformeta-analysistothescientificcommunity.
1. NyagaVN,ArbynMandAertsM.METAPROP:Statamoduletoperformfixed
andrandomeffectsmeta-analysisofproportions. 2017.
https://ideas.repec.org/c/boc/bocode/s457781.html
2. NyagaVN,ArbynMandAertsM.METAPROP ONE:Statamoduletoperform
fixedandrandomeffectsmeta-analysisofproportions. 2017.
https://ideas.repec.org/c/boc/bocode/s457861.html
3. Nyaga VN. CopulaDTA: Copula based bivariate beta-binomial model for di-
agnostictestaccuracystudies. Rpackageversion0.0.5,2017.
https://cran.r-project.org/package=CopulaDTA
4. Nyaga VN. MADAREG: Meta-analysis and meta-regression of diagnostic ac-
curacystudiesinSAS.2017. https://github.com/VNyaga/Madareg
5. NyagaVN.NMADAS:Networkmeta-analysisofdiagnostictestaccuracystud-
ies. Rpackageversion0.0.1,2017. https://github.com/VNyaga/NMADAS
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ListofAbbreviations
AB Arm-Based
ABC ApproximateBayesianComputation
AIC AkaikeInformationCriterion
ANOVA ANalysisOfVAriance
ASC-US AtypicalSquamousCellsofUndeterminedSignificance
BRMA BivariateRandom-effectsMeta-Analysis
CB Contrast-Based
CC ConventionalCytology
CCD CentralCompositeDesign
CIN CervicalIntraepithelialNeoplasia
CIN2+ CervicalIntraepithelialNeoplasialesionofgrade2orworse
CIN3+ CervicalIntraepithelialNeoplasialesionofgrade3orworse
CRAN ComprehensiveRArchiveNetwork
DIC DevianceInformationCriterion
DLL DynamicLinkLibrary
DNA DeoxyriboNucleicAcid
DOR DiagnosticOddsRatio
ESS EffectiveSampleSize
FGM Farlie-Gumbel-Morgenstern
FPR FalsePositiveRate
GLMM GeneralizedLinearMixedModel
HC2 HybridCapture2
HMC HarmiltonMonteCarlo
HPV HumanPapillomaVirus
HSROC HierarchicalSummaryROC
INLA IntegratedNestedLaplaceApproximation
IPD IndividualPatientData
ISP ScientificInstituteofPublicHealth
ITT IntentionToTreat
JAGS JustAnotherGibbsSampler
LBC LiquidBasedCytology
LCM LatentClassModel
LKJ LewandowskiKurowickaandJoe[2]
LR- NegativeLikelihoodRatio
LR+ PositiveLikelihoodRatio
LSIL Low-gradeSquamousIntraepithelialLesion
LSM LeastSquaresMethod
MA Meta-Analysis
MAR MissingAtRandom
MCAR MissingCompletelyAtRandom
MCMC MarkovChainMonteCarlo
ML MaximumLikelihood
MOM MethodOfMoments
NMA NetworkMeta-Analysis
NPV NegativePredictiveValue
NUTS No-U-TurnSampler
OR OddsRatio
OSPADAC OptimisationofStatisticalProceduresto
AssesstheDiagnosticAccuracyofCervicalcancerscreeningtests
PPV PositivePredictiveValue
REML REstrictedMaximumLikelihood
RNA RiboNucleicAcid
TPR TruePositiveRate
WAIC Watanabe-AlkaikeInformationCriterion
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