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MODULE HANDBOOKSURVIVALANALYSISBACHELOR DEGREE PROGRAMDEPARTEMENT OF STATISTICSFACULTY OF SCIENCE AND DATA ANALYTICSINSTITUT TEKNOLOGI SEPULUH NOPEMBER

ENDORSEMENT PAGEMODULE HANDBOOKSURVIVAL ANALYSISDEPARTMENT OF STATISTICSINSTITUT TEKNOLOGI riksa danPengendalianReview nNamaNameDr. Santi WulanPurnami;Jerry D.P Ph.DDr. Santi WulanPurnami, S.Si,M.Si ; Jerry DwiTrijoyo Purnomo,S.Si. M.Si., Ph.DDr. BambangWidjanarkoOtokDr. KartikaFithriasari, M.SiPenanggung JawabPerson in tureTanggalDateMarch 28, 2019Tim kurikulumCurriculumteamApril 15, 2019KoordinatorRMKCourse ClusterCoordinatorKepalaDepartemenHead ofDepartmentJuly 17, 2019July 30, 2019

MODULE HANDBOOKSURVIVAL ANALYSISModule nameSURVIVAL ANALYSISModule levelUndergraduateCodeKS184824Course (if applicable)SURVIVAL ANALYSISSemesterEighth Semester (Ganjil)Person responsible for Dr. Santi Wulan Purnami;the moduleJerry D.P Ph.DLecturerDr. Santi Wulan Purnami, S.Si, M.Si ; Jerry Dwi Trijoyo Purnomo, S.Si.M.Si., Ph.DLanguageBahasa Indonesia and EnglishRelation to curriculum Undergradute degree program, mandatory, 8th semester.Type of teaching,Lectures, 50 studentscontact hoursWorkload1. Lectures : 3 x 50 150 minutes per week.2. Exercises and Assignments : 3 x 60 180 minutes (3 hours) perweek.3. Private learning : 3 x 60 180 minutes (3 hours) per week.Credit points3 credit points (sks)Requirementsaccording to rning outcomesand theircorresponding PLOsA student must have attended at least 80% of the lectures to sit inthe exams.Mathematical Statistics IICLO.1 Able to explain concepts and apply survival analysistheoryCLO.3Able to analyze data with survival methods andinterpret themCLO.4 Able to identify, formulate and solve problems in thehealth / medical field with survival analysisPLO-01PLO-03PLO-04

ContentStudy andexaminationrequirements andforms of examinationMedia employedReading listSurvival analysis is a statistical method that can be applied in variousfields, one of which is in the health sector. Survival analysis is a statisticalmethod that emphasizes analyzing the time until an event occurs. In thislecture, the basics of survival analysis will be taught such as the KapplanMeier survival function, Hazard function, Hazard ratio, survivalregression with parametric and semiparametric approaches. To betterunderstand this method, applications in real cases will be taughtmanually or using software, especially SPSS, SAS and R. In-class exercises Assignment 1, 2, 3 Mid-term examination Final examinationLCD, whiteboard, websites (myITS Classroom), zoom.1. Cox, D.R. and Oakes, D. 1984. Analysis of Survival Data.Cambridengane : University Printing House2. David, Collet. 2014. Modelling Survival Data in MedicalResearch. 3rd edition, Chapman and Hall/CRC.3. Hosmer, David W., Lemeshow, Stenley. and May, S. 2008.Applied Survival Analysis. Hoboken, New Jersey : John Wileydan Sons, Inc.4. Kleinbaum, David G. and Klein, Mitchel. 2012. Survival Analysis:A self-Learning Text. 3rd edition. Springer, Science BusinenessMedia, LLC.5. Le, C. T. 1997. Applied Survival Analysis. John Wiley dan Sons,Inc.Revisi 01 September 2020

Program StudiMata KuliahKode Mata KuliahSemester/SKSMK PrasyaratRP-S1Bahan KajianStudy MaterialsCPL yang dibebankan MKPLO of the courseCP-MKCLODosen PengampuSarjana, Departemen Statistika, FMKSD-ITSAnalisis SurvivalKS184824VII/3Statistika Matematika I, Analisis Regresi, Analisis Data KualitatifDr. Santi Wulan Purnami, S.Si, M.Si ; Jerry Dwi Trijoyo Purnomo, S.Si. M.Si.,Ph.DDasar Sains, Teori Statistika, Pengumpulan Data, Deskripsi dan Eksplorasi, Komputasi dan Data Processing, Pemodelan, Kesehatan dan LingkunganBasic Science, Statistical Theory, Data Collection, Description and Exploration, Computing and Data Processing, Modeling, Health andEnvironmentCPL 1. Mampu menerapkan pengetahuan sains, teori statistika, matematika, dan komputasi untuk menyelesaikan permasalahan dalam berbagaibidang terapanCPL 3. Mampu menganalisis data dengan metode statistika yang tepat dan mengintepretasikannyaCPL 4. Mampu mengindentifikasi, memformulasi, dan menyelesaikan masalah statistika di berbagai bidang terapanPLO 1. Able to apply knowledge of science, statistical theory, mathematics, and computation to solve problems in various applied fieldsPLO 3. Able to analyze data with appropriate statistical methods and interpret themPLO 4. Able to identify, formulate, and solve statistical problems in various applied fieldsCPMK.1 Mampu menjelaskan konsep dan menerapkan teori analisis survivalCPMK.3 Mampu menganalisis data dengan metode survival dan mengintepretasikannyaCPMK.4 Mampu mengidentifikasi, memformulasi dan menyelesaikan problem dibidang kesehatan/kedokteran dengan analisis survivalCLO.1 Able to explain concepts and apply survival analysis theoryCLO.3 Able to analyze data with survival methods and interpret themCLO.4 Able to identify, formulate and solve problems in the health / medical field with a survival analysisRevisi 01 September 2020

Program StudiMata KuliahKode Mata KuliahSemester/SKSMK PrasyaratRP-S1PertemuanMeetingDosen PengampuKemampuan AkhirSub CP-MKFinal Ability1.11Dapat menjelaskankonsep dan tujuananalisis survival1.1 Can explain theconcept and purpose ofsurvival analysisKeluasan (materipembelajaran)Extent (learning WaktuDurationPengantar analisissurvival: konsep dasaranalisis survival, censoreddataIntroduction to survivalanalysis: the basicconcepts of survivalanalysis, censored dataCeramahinteraktifDiskusi (CID)InteractivelectureDiscussion (CID)150menit150minutesFungsi survival: fungsisurvival (parametrik), kurvasurvival Kaplan Meier,hazard rateSurvival function: survivalfunction (parametric),Kaplan Meier survivalcurve, hazard rateCeramahinteraktifDiskusiLatihan tions (CIDL)150menit150minutesBentuk EvaluasiEvaluation TypeObservasi Aktifitasdi kelasObservation nmembuat grafik fungsisurvival menggunakanmetode parametrikdan Kaplan Meier1.2 Can calculateestimates and graphsurvival functions usingthe parametric methodand Kaplan MeierSarjana, Departemen Statistika, FMKSD-ITSAnalisis SurvivalKS184824VII/3Statistika Matematika I, Analisis Regresi, Analisis Data KualitatifDr. Santi Wulan Purnami, S.Si, M.Si ; Jerry Dwi Trijoyo Purnomo, S.Si. M.Si.,Ph.DObservasi Aktifitasdi kelasObservation ofclassroomactivitiesKriteria dan Indikator PenilaianAssessment Criteria and Indicators1.1 Dapat menjelaskan konsep analisissurvival1.2 Dapat menjelaskan tujuan analisissurvival1.1. Dapat menjelaskan censored data1.1 Can explain the concept of survivalanalysis1.2 Can explain the purpose of thesurvival analysis1.3 Can explain censored data2.1 Dapat menghitung probabilitassurvival2.2 Dapat membuat kurva Kaplan Meier(KM)2.1. Dapat mengidentifikasi bentukmodel survival (parametrik)2.1 Can calculate the probability ofsurvival2.2 Can create a Kaplan Meier (KM)curve2.3 Can identify the shape of thesurvival model isi 01 September 2020

Program StudiMata KuliahKode Mata KuliahSemester/SKSMK PrasyaratRP-S1PertemuanMeetingDosen PengampuKemampuan AkhirSub CP-MKFinal Ability1.33,45,6,7Dapatmelakukanpengujian perbedaandua atau lebih kurvasurvival1.3 Can test the differencebetween two or moresurvival curves3.1 Dapatmengidentifikasi danmelakukkan estimasiparameterregresisurvival untuk datalengkapmaupuntersensorSarjana, Departemen Statistika, FMKSD-ITSAnalisis SurvivalKS184824VII/3Statistika Matematika I, Analisis Regresi, Analisis Data KualitatifDr. Santi Wulan Purnami, S.Si, M.Si ; Jerry Dwi Trijoyo Purnomo, S.Si. M.Si.,Ph.DKeluasan (materipembelajaran)Extent (learning material)The log rank (LR) test: LRtest untuk 2 group danlebih dari 2 groupThe log rank (LR) test: LRtest for 2 groups andmore than 2 groupsRegresi survivalparametrik: RegresiEksponensial, Weibull,LoglogistikParametric survivalregression: ExponentialRegression, CeramahInteraktifDiskusiPraktikumLatihan ifDiskusiPraktikumLatihan 300Minutes450menit450MinutesKriteria dan Indikator PenilaianAssessment Criteria and IndicatorsBobotPenilaianScoringTugas 1Task 13.1 Dapat melakukan uji LR untuk 2group3.1. Dapat melakukan uji LR untukbeberapa group (lebih dari 2 group)3.1 Can do LR test for 2 groups3.2 Can perform LR tests for severalgroups (more than 2 groups)10%/30%Tugas 2Task 24.1 Dapat mengidentifikasi regresi yangsesuai (regresi eksponensial,weibull, loglogistik)4.2 Dapat melakukan estimasi MLE dariparameter regres yang sesuai baikuntuk data lengkap maupuntersensor4.1. 4.3 Dapat menganalisis modelregresi20%/50%Bentuk EvaluasiEvaluation TypeRevisi 01 September 2020

Program StudiMata KuliahKode Mata KuliahSemester/SKSMK PrasyaratRP-S1PertemuanMeetingDosen PengampuKemampuan AkhirSub CP-MKFinal AbilitySarjana, Departemen Statistika, FMKSD-ITSAnalisis SurvivalKS184824VII/3Statistika Matematika I, Analisis Regresi, Analisis Data KualitatifDr. Santi Wulan Purnami, S.Si, M.Si ; Jerry Dwi Trijoyo Purnomo, S.Si. M.Si.,Ph.DKeluasan (materipembelajaran)Extent (learning material)3.1 Can identify andestimate survivalregression parametersfor complete andcensored )89-10EstimasiWaktuDurationBentuk EvaluasiEvaluation TypeKriteria dan Indikator PenilaianAssessment Criteria and IndicatorsBobotPenilaianScoring4.1 Can identify suitable regressions(exponential regression, weibull,logistic)4.2 Can estimate MLE from theappropriateregressionparameters for both complete andcensored data4.3 Can analyze the regression modelETS4.4 Dapatmengidentifikasi danmerumuskan bentukumum dari modelCox PH4.4 Can identify andformulate thegeneral form of theCox PH modelThe Model Coxproportional Hazard (PH)model:Estimasi model cox PH,Hazard ratio model cox PH,interval estimationThe Model Coxproportional Hazard (PH)model:Cox PH model estimation,Hazard ratio cox PHmodel, inutesObservasi Aktifitasdi kelasObservation ofclassroomactivities5.1 Dapat merumuskan bentuk spesifikdari model Cox PH yang sesuai5.2 Dapat merumuskan bentuk dansifat-sifat fungsi hazard model CoxPH5.3 Dapat menginterpretasikan modelCox PH5.1 Can formulate a specific form ofthe appropriate Cox PH model5.2 Can formulate the form andcharacteristics of the hazardfunction 5.3 Cox PH model5.3 Can interpret the Cox PH model10%/60%Revisi 01 September 2020

Program StudiMata KuliahKode Mata KuliahSemester/SKSMK PrasyaratRP-S1Dosen PengampuPertemuanMeetingKemampuan AkhirSub CP-MKFinal Ability11-124.1 Dapatmelakukandan menganalisis ujiasumsi dari modelCoxPHdenganmetode grafik dan ujigoodness of fit (GOF)4.1 Can perform andanalyze theassumption test ofthe Cox PH modelwith the graphmethod and thegoodness of fit(GOF) test13-145.2 DapatmelakukanpemodelanmenggunakanStratified Cox Model4.2 Can performmodeling using theStratified Cox ModelSarjana, Departemen Statistika, FMKSD-ITSAnalisis SurvivalKS184824VII/3Statistika Matematika I, Analisis Regresi, Analisis Data KualitatifDr. Santi Wulan Purnami, S.Si, M.Si ; Jerry Dwi Trijoyo Purnomo, S.Si. M.Si.,Ph.DKeluasan (materipembelajaran)Extent (learning material)Evaluasi asumsiproportional hazards:pendekatan grafik(log-logplots, nilai aktual dengannilai prediksi)- - pendekatan ujigoodness of fitEvaluation ofproportional hazardsassumptions: graphicalapproach (log-log plots,actual values withpredicted values)- - Goodness of fit testapproachStratified CoxregressionStratified urationBentuk EvaluasiEvaluation TypeKriteria dan Indikator PenilaianAssessment Criteria and inutesTugas 3Makalah 1Presentasi 1Task 3Paper 1Presentation 16.1 Dapat melakukan uji asumsi modelCox PH dengan metode:- Grafik- Uji GOF6.2 Dapat menganalisis danmenginterptretasikan hasil evaluasiasumsi5.1 Can test the assumptions of theCox PH model using the followingmethods:GraphicsGOF test6.2 Can analyze and interpret theresults of evaluation assumptions300menit300MinutesFinal ProjectMakalah 2Presentasi 2Final ProjectPaper 2Presentation 28.1 Dapat melakukan pemodelanStratified Cox regression8.2. Dapat menganalisis danmenginterptretasikan modelStratified Cox regression8.1 Can perform Stratifiedregression modelingCoxRevisi 01 September 2020