Sample-weighted semiparametric estimates of cause-specific cumulative incidence using left-/interval censored data from electronic health records
|
|
- Valerie May
- 5 years ago
- Views:
Transcription
1 1 / 22 Sample-weighted semiparametric estimates of cause-specific cumulative incidence using left-/interval censored data from electronic health records Noorie Hyun, Hormuzd A. Katki, Barry I. Graubard Division of Biostatistics, Medical College of Wisconsin July 31, 2018
2 Background 2 / 22
3 3 / 22 Cervical Cancer and Human Papilomavirus (HPV) Causes of cervical cancer 14 oncogenic HPV DNA types Natural history of HPV infection and cervical cancer
4 4 / 22 HPV PaP Cohort Study Kaiser Permanente Northern California (KPNC) large health care provider 1.5 million women screened with the co-test since 2003 HPV PaP (Prevalence and Progression) Cohort Study Collaboration of KPNC and NCI Clinical questions for different treatment Time-to-clearance is different by the 14 HPV DNA types? Time-to-progression is different by the 14 HPV DNA types?
5 Statistical Issues 5 / 22
6 6 / 22 Statistical Issues Competing events: clearance and progression to pre-/cancer Prevalent disease at enrollment (left-censoring) Late-diagnosed prevalent disease Interval-censored incidence Stratified random sample
7 Sample-weighted semiparametric subdistribution hazard models for left-/interval-censored data 7 / 22
8 8 / 22 Notation For subject i, 1 i N, T i : time-to-event K i = 1 if progression; K i = 2 if clearance;k i = 0 if no event (persistence) y i = 1 if T i 0 and K i = 1; 0 otherwise O i = 1 if y i is observed; 0 otherwise v i = {v i1,..., v iqi }: visit times, q i number of visits L i : the last visit time at which subject i is observed as event-free R i : the earliest visit time at which subject i is observed as event-occurrence x i and z i : covariates for the prevalence and incidence
9 8 / 22 Notation For subject i, 1 i N, T i : time-to-event K i = 1 if progression; K i = 2 if clearance;k i = 0 if no event (persistence) y i = 1 if T i 0 and K i = 1; 0 otherwise O i = 1 if y i is observed; 0 otherwise v i = {v i1,..., v iqi }: visit times, q i number of visits L i : the last visit time at which subject i is observed as event-free R i : the earliest visit time at which subject i is observed as event-occurrence x i and z i : covariates for the prevalence and incidence
10 9 / 22 Observed Data For subject i, 1 i N, When O i = 1, D i = {y i, x i, I(y i = 0){L i, R i, K i, z i }} When O i = 0, D i = {x i, L i, R i, K i, z i } Subdistribution For k = 1, 2, F k (t) = Pr(T i t, K i = k) such that F 1 (0) = F 2 = 0. F 1 (t) + F 2 (t) < 1 for t > 0.
11 9 / 22 Observed Data For subject i, 1 i N, When O i = 1, D i = {y i, x i, I(y i = 0){L i, R i, K i, z i }} When O i = 0, D i = {x i, L i, R i, K i, z i } Subdistribution For k = 1, 2, F k (t) = Pr(T i t, K i = k) such that F 1 (0) = F 2 = 0. F 1 (t) + F 2 (t) < 1 for t > 0.
12 9 / 22 Observed Data For subject i, 1 i N, When O i = 1, D i = {y i, x i, I(y i = 0){L i, R i, K i, z i }} When O i = 0, D i = {x i, L i, R i, K i, z i } Subdistribution For k = 1, 2, F k (t) = Pr(T i t, K i = k) such that F 1 (0) = F 2 = 0. F 1 (t) + F 2 (t) < 1 for t > 0.
13 10 / 22 Likelihood of observed data Under missing at random (MAR) assumption for observing {y i }, the likelihood of observed data is ( [ P d (x i ) y i {1 P d (x i )} O i =1 2 {F k (R i ; z i ) F k (L i ; z i )} I(K i=k) k=1 ] 1 yi ) {1 F 1 (L i ; z i ) F 2 (L i ; z i } I(K i=3) [ { P d (x i ) + {1 P d (x i )} {F 1 (R i ; z i ) F 1 (0; z i )} I(K i=1) O i =0 }] {F 2 (R i ; z i ) F 2 (L i ; z i )} I(Ki=2) F 2 (L i ; z i ) I(K i=3).
14 10 / 22 Likelihood of observed data Under missing at random (MAR) assumption for observing {y i }, the likelihood of observed data is ( [ P d (x i ) y i {1 P d (x i )} O i =1 2 {F k (R i ; z i ) F k (L i ; z i )} I(K i=k) k=1 ] 1 yi ) {1 F 1 (L i ; z i ) F 2 (L i ; z i } I(K i=3) [ { P d (x i ) + {1 P d (x i )} {F 1 (R i ; z i ) F 1 (0; z i )} I(K i=1) O i =0 }] {F 2 (R i ; z i ) F 2 (L i ; z i )} I(Ki=2) F 2 (L i ; z i ) I(K i=3).
15 Modeling Subdistribution hazard (Fine and Gray 1999): λ k (t; z) = lim t 0 Pr(t T i < t +, K i = k {T i t or(t t and K k)}, z). Subdistribution and subdistribution hazard F k (t z) = 1 exp{ Λ k (t z)}, where Λ k (t) = λ 0 k (t z)dt. General Transformation Λ k (t; z) = G k {exp(zγ k )Λ k (t)}, for k = 1, 2, Pr(y = 1; x) = g(xβ), where β, γ 1, γ 2 are unknown parameters; Λ 1 (t) and Λ 2 (t) are unknown increasing functions; G k ( ) and g( ) are known functions. 11 / 22
16 Modeling Subdistribution hazard (Fine and Gray 1999): λ k (t; z) = lim t 0 Pr(t T i < t +, K i = k {T i t or(t t and K k)}, z). Subdistribution and subdistribution hazard F k (t z) = 1 exp{ Λ k (t z)}, where Λ k (t) = λ 0 k (t z)dt. General Transformation Λ k (t; z) = G k {exp(zγ k )Λ k (t)}, for k = 1, 2, Pr(y = 1; x) = g(xβ), where β, γ 1, γ 2 are unknown parameters; Λ 1 (t) and Λ 2 (t) are unknown increasing functions; G k ( ) and g( ) are known functions. 11 / 22
17 Inference Procedure 12 / 22
18 13 / 22 Point Estimation Assuming that Λ k (t) is right continuous and a step function with possible discontinuities at unique visit times. Iterative Convex Minorant (ICM) algorithm for Λ k (t) estimation Constrained optimization with restricted support for estimating β, γ 1 and γ 2 log{exp[ G 1 {Λ 1 (L i ) exp(zγ 1 )}]+exp[ G 2 {Λ 2 (L i ) exp(zγ 2 )}] 1}
19 13 / 22 Point Estimation Assuming that Λ k (t) is right continuous and a step function with possible discontinuities at unique visit times. Iterative Convex Minorant (ICM) algorithm for Λ k (t) estimation Constrained optimization with restricted support for estimating β, γ 1 and γ 2 log{exp[ G 1 {Λ 1 (L i ) exp(zγ 1 )}]+exp[ G 2 {Λ 2 (L i ) exp(zγ 2 )}] 1}
20 14 / 22 Weighted Bootstrap N 1/3 (Λ k (t) Λ k0 (t)) d? Weighted Bootstrap (Saegusa 2015): for j = 1,..., J, i.i.d W (1) j,i within stratum j satisfying P(W (1) j,i > 0) = 1, E(W (1) j,i ) = 1 and Var(W (1) j,i ) = p j /(2 p j ) (W (2) j,1,..., W (2) j,n j ): a vector of exchangeable weights following a mixture of the multivariate hypergeometric distribution Bootstrap weight: W (1) j,i W (2) j,i ξ j,i p 1 j
21 Simulation Result 15 / 22
22 Subdistribution hazard estimation 16 / 22
23 Weighted Bootstrap 17 / 22
24 Application to the HPV PaP Cohort Data 18 / 22
25 PIMixture: Progression to pre-cancer or cancer by HPV DNA types 19 / 22
26 Incidence: Progression to pre-cancer or cancer by HPV DNA types 20 / 22
27 Clearance by HPV DNA types 21 / 22
28 22 / 22 Summary Sample-weighted semiparametric subdistribution hazard models for competing risks Prevalence-incidence mixture models for event 1 and incidence model for event 2 Model identification Iterative convex minorant algorithm and constrained optimization Consistent point estimate Weighted bootstrap for variance estimation
A Bayesian Nonparametric Approach to Causal Inference for Semi-competing risks
A Bayesian Nonparametric Approach to Causal Inference for Semi-competing risks Y. Xu, D. Scharfstein, P. Mueller, M. Daniels Johns Hopkins, Johns Hopkins, UT-Austin, UF JSM 2018, Vancouver 1 What are semi-competing
More informationPairwise rank based likelihood for estimating the relationship between two homogeneous populations and their mixture proportion
Pairwise rank based likelihood for estimating the relationship between two homogeneous populations and their mixture proportion Glenn Heller and Jing Qin Department of Epidemiology and Biostatistics Memorial
More informationDirect likelihood inference on the cause-specific cumulative incidence function: a flexible parametric regression modelling approach
Direct likelihood inference on the cause-specific cumulative incidence function: a flexible parametric regression modelling approach Sarwar I Mozumder 1, Mark J Rutherford 1, Paul C Lambert 1,2 1 Biostatistics
More informationSurvival Analysis for Case-Cohort Studies
Survival Analysis for ase-ohort Studies Petr Klášterecký Dept. of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, harles University, Prague, zech Republic e-mail: petr.klasterecky@matfyz.cz
More informationPrerequisite: STATS 7 or STATS 8 or AP90 or (STATS 120A and STATS 120B and STATS 120C). AP90 with a minimum score of 3
University of California, Irvine 2017-2018 1 Statistics (STATS) Courses STATS 5. Seminar in Data Science. 1 Unit. An introduction to the field of Data Science; intended for entering freshman and transfers.
More informationAnalysis of competing risks data and simulation of data following predened subdistribution hazards
Analysis of competing risks data and simulation of data following predened subdistribution hazards Bernhard Haller Institut für Medizinische Statistik und Epidemiologie Technische Universität München 27.05.2013
More informationFirst Aid Kit for Survival. Hypoxia cohort. Goal. DFS=Clinical + Marker 1/21/2015. Two analyses to exemplify some concepts of survival techniques
First Aid Kit for Survival Melania Pintilie pintilie@uhnres.utoronto.ca Two analyses to exemplify some concepts of survival techniques Checking linearity Checking proportionality of hazards Predicted curves:
More informationStatistical methods for panel data from a semi-markov process, with application to HPV
Biostatistics (2007), 8, 2, pp. 252 264 doi:10.1093/biostatistics/kxl006 Advance Access publication on June 1, 2006 Statistical methods for panel data from a semi-markov process, with application to HPV
More informationSurvival Analysis. Lu Tian and Richard Olshen Stanford University
1 Survival Analysis Lu Tian and Richard Olshen Stanford University 2 Survival Time/ Failure Time/Event Time We will introduce various statistical methods for analyzing survival outcomes What is the survival
More informationApplication of Time-to-Event Methods in the Assessment of Safety in Clinical Trials
Application of Time-to-Event Methods in the Assessment of Safety in Clinical Trials Progress, Updates, Problems William Jen Hoe Koh May 9, 2013 Overview Marginal vs Conditional What is TMLE? Key Estimation
More informationPubH 7470: STATISTICS FOR TRANSLATIONAL & CLINICAL RESEARCH
PubH 7470: STATISTICS FOR TRANSLATIONAL & CLINICAL RESEARCH The First Step: SAMPLE SIZE DETERMINATION THE ULTIMATE GOAL The most important, ultimate step of any of clinical research is to do draw inferences;
More informationModels for Multivariate Panel Count Data
Semiparametric Models for Multivariate Panel Count Data KyungMann Kim University of Wisconsin-Madison kmkim@biostat.wisc.edu 2 April 2015 Outline 1 Introduction 2 3 4 Panel Count Data Motivation Previous
More informationA Regression Model For Recurrent Events With Distribution Free Correlation Structure
A Regression Model For Recurrent Events With Distribution Free Correlation Structure J. Pénichoux(1), A. Latouche(2), T. Moreau(1) (1) INSERM U780 (2) Université de Versailles, EA2506 ISCB - 2009 - Prague
More informationDynamic Prediction of Disease Progression Using Longitudinal Biomarker Data
Dynamic Prediction of Disease Progression Using Longitudinal Biomarker Data Xuelin Huang Department of Biostatistics M. D. Anderson Cancer Center The University of Texas Joint Work with Jing Ning, Sangbum
More informationEnsemble estimation and variable selection with semiparametric regression models
Ensemble estimation and variable selection with semiparametric regression models Sunyoung Shin Department of Mathematical Sciences University of Texas at Dallas Joint work with Jason Fine, Yufeng Liu,
More informationA Regression Model for the Copula Graphic Estimator
Discussion Papers in Economics Discussion Paper No. 11/04 A Regression Model for the Copula Graphic Estimator S.M.S. Lo and R.A. Wilke April 2011 2011 DP 11/04 A Regression Model for the Copula Graphic
More informationConstrained Maximum Likelihood Estimation for Model Calibration Using Summary-level Information from External Big Data Sources
Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-level Information from External Big Data Sources Yi-Hau Chen Institute of Statistical Science, Academia Sinica Joint with Nilanjan
More informationCASE STUDY: Bayesian Incidence Analyses from Cross-Sectional Data with Multiple Markers of Disease Severity. Outline:
CASE STUDY: Bayesian Incidence Analyses from Cross-Sectional Data with Multiple Markers of Disease Severity Outline: 1. NIEHS Uterine Fibroid Study Design of Study Scientific Questions Difficulties 2.
More informationMultilevel Statistical Models: 3 rd edition, 2003 Contents
Multilevel Statistical Models: 3 rd edition, 2003 Contents Preface Acknowledgements Notation Two and three level models. A general classification notation and diagram Glossary Chapter 1 An introduction
More informationSurvival Analysis Math 434 Fall 2011
Survival Analysis Math 434 Fall 2011 Part IV: Chap. 8,9.2,9.3,11: Semiparametric Proportional Hazards Regression Jimin Ding Math Dept. www.math.wustl.edu/ jmding/math434/fall09/index.html Basic Model Setup
More informationAttributable Risk Function in the Proportional Hazards Model
UW Biostatistics Working Paper Series 5-31-2005 Attributable Risk Function in the Proportional Hazards Model Ying Qing Chen Fred Hutchinson Cancer Research Center, yqchen@u.washington.edu Chengcheng Hu
More informationLecture 3. Truncation, length-bias and prevalence sampling
Lecture 3. Truncation, length-bias and prevalence sampling 3.1 Prevalent sampling Statistical techniques for truncated data have been integrated into survival analysis in last two decades. Truncation in
More informationScore test for random changepoint in a mixed model
Score test for random changepoint in a mixed model Corentin Segalas and Hélène Jacqmin-Gadda INSERM U1219, Biostatistics team, Bordeaux GDR Statistiques et Santé October 6, 2017 Biostatistics 1 / 27 Introduction
More informationDiscussion of Identifiability and Estimation of Causal Effects in Randomized. Trials with Noncompliance and Completely Non-ignorable Missing Data
Biometrics 000, 000 000 DOI: 000 000 0000 Discussion of Identifiability and Estimation of Causal Effects in Randomized Trials with Noncompliance and Completely Non-ignorable Missing Data Dylan S. Small
More informationEmpirical Processes & Survival Analysis. The Functional Delta Method
STAT/BMI 741 University of Wisconsin-Madison Empirical Processes & Survival Analysis Lecture 3 The Functional Delta Method Lu Mao lmao@biostat.wisc.edu 3-1 Objectives By the end of this lecture, you will
More informationMixture modelling of recurrent event times with long-term survivors: Analysis of Hutterite birth intervals. John W. Mac McDonald & Alessandro Rosina
Mixture modelling of recurrent event times with long-term survivors: Analysis of Hutterite birth intervals John W. Mac McDonald & Alessandro Rosina Quantitative Methods in the Social Sciences Seminar -
More informationLecture 12: Application of Maximum Likelihood Estimation:Truncation, Censoring, and Corner Solutions
Econ 513, USC, Department of Economics Lecture 12: Application of Maximum Likelihood Estimation:Truncation, Censoring, and Corner Solutions I Introduction Here we look at a set of complications with the
More informationPackage crrsc. R topics documented: February 19, 2015
Package crrsc February 19, 2015 Title Competing risks regression for Stratified and Clustered data Version 1.1 Author Bingqing Zhou and Aurelien Latouche Extension of cmprsk to Stratified and Clustered
More informationComparative effectiveness of dynamic treatment regimes
Comparative effectiveness of dynamic treatment regimes An application of the parametric g- formula Miguel Hernán Departments of Epidemiology and Biostatistics Harvard School of Public Health www.hsph.harvard.edu/causal
More informationCTDL-Positive Stable Frailty Model
CTDL-Positive Stable Frailty Model M. Blagojevic 1, G. MacKenzie 2 1 Department of Mathematics, Keele University, Staffordshire ST5 5BG,UK and 2 Centre of Biostatistics, University of Limerick, Ireland
More informationDynamic Scheduling of the Upcoming Exam in Cancer Screening
Dynamic Scheduling of the Upcoming Exam in Cancer Screening Dongfeng 1 and Karen Kafadar 2 1 Department of Bioinformatics and Biostatistics University of Louisville 2 Department of Statistics University
More informationLongitudinal + Reliability = Joint Modeling
Longitudinal + Reliability = Joint Modeling Carles Serrat Institute of Statistics and Mathematics Applied to Building CYTED-HAROSA International Workshop November 21-22, 2013 Barcelona Mainly from Rizopoulos,
More informationOn the Use of the Bross Formula for Prioritizing Covariates in the High-Dimensional Propensity Score Algorithm
On the Use of the Bross Formula for Prioritizing Covariates in the High-Dimensional Propensity Score Algorithm Richard Wyss 1, Bruce Fireman 2, Jeremy A. Rassen 3, Sebastian Schneeweiss 1 Author Affiliations:
More informationSpecial Topic: Bayesian Finite Population Survey Sampling
Special Topic: Bayesian Finite Population Survey Sampling Sudipto Banerjee Division of Biostatistics School of Public Health University of Minnesota April 2, 2008 1 Special Topic Overview Scientific survey
More informationPerson-Time Data. Incidence. Cumulative Incidence: Example. Cumulative Incidence. Person-Time Data. Person-Time Data
Person-Time Data CF Jeff Lin, MD., PhD. Incidence 1. Cumulative incidence (incidence proportion) 2. Incidence density (incidence rate) December 14, 2005 c Jeff Lin, MD., PhD. c Jeff Lin, MD., PhD. Person-Time
More informationHarvard University. Harvard University Biostatistics Working Paper Series. Survival Analysis with Change Point Hazard Functions
Harvard University Harvard University Biostatistics Working Paper Series Year 2006 Paper 40 Survival Analysis with Change Point Hazard Functions Melody S. Goodman Yi Li Ram C. Tiwari Harvard University,
More informationOnline supplement. Absolute Value of Lung Function (FEV 1 or FVC) Explains the Sex Difference in. Breathlessness in the General Population
Online supplement Absolute Value of Lung Function (FEV 1 or FVC) Explains the Sex Difference in Breathlessness in the General Population Table S1. Comparison between patients who were excluded or included
More informationIntegrated likelihoods in survival models for highlystratified
Working Paper Series, N. 1, January 2014 Integrated likelihoods in survival models for highlystratified censored data Giuliana Cortese Department of Statistical Sciences University of Padua Italy Nicola
More informationPower and Sample Size Calculations with the Additive Hazards Model
Journal of Data Science 10(2012), 143-155 Power and Sample Size Calculations with the Additive Hazards Model Ling Chen, Chengjie Xiong, J. Philip Miller and Feng Gao Washington University School of Medicine
More informationApproximate Bayesian Computation
Approximate Bayesian Computation Michael Gutmann https://sites.google.com/site/michaelgutmann University of Helsinki and Aalto University 1st December 2015 Content Two parts: 1. The basics of approximate
More informationA general mixed model approach for spatio-temporal regression data
A general mixed model approach for spatio-temporal regression data Thomas Kneib, Ludwig Fahrmeir & Stefan Lang Department of Statistics, Ludwig-Maximilians-University Munich 1. Spatio-temporal regression
More informationGROUPED SURVIVAL DATA. Florida State University and Medical College of Wisconsin
FITTING COX'S PROPORTIONAL HAZARDS MODEL USING GROUPED SURVIVAL DATA Ian W. McKeague and Mei-Jie Zhang Florida State University and Medical College of Wisconsin Cox's proportional hazard model is often
More informationFrailty Probit model for multivariate and clustered interval-censor
Frailty Probit model for multivariate and clustered interval-censored failure time data University of South Carolina Department of Statistics June 4, 2013 Outline Introduction Proposed models Simulation
More informationSession 9: Introduction to Sieve Analysis of Pathogen Sequences, for Assessing How VE Depends on Pathogen Genomics Part I
Session 9: Introduction to Sieve Analysis of Pathogen Sequences, for Assessing How VE Depends on Pathogen Genomics Part I Peter B Gilbert Vaccine and Infectious Disease Division, Fred Hutchinson Cancer
More informationf X (y, z; θ, σ 2 ) = 1 2 (2πσ2 ) 1 2 exp( (y θz) 2 /2σ 2 ) l c,n (θ, σ 2 ) = i log f(y i, Z i ; θ, σ 2 ) (Y i θz i ) 2 /2σ 2
Chapter 7: EM algorithm in exponential families: JAW 4.30-32 7.1 (i) The EM Algorithm finds MLE s in problems with latent variables (sometimes called missing data ): things you wish you could observe,
More informationModular Program Report
Modular Program Report Disclaimer The following report(s) provides findings from an FDA initiated query using Sentinel. While Sentinel queries may be undertaken to assess potential medical product safety
More informationDouble-Sampling Designs for Dropouts
Double-Sampling Designs for Dropouts Dave Glidden Division of Biostatistics, UCSF 26 February 2010 http://www.epibiostat.ucsf.edu/dave/talks.html Outline Mbarra s Dropouts Future Directions Inverse Prob.
More informationPart IV Extensions: Competing Risks Endpoints and Non-Parametric AUC(t) Estimation
Part IV Extensions: Competing Risks Endpoints and Non-Parametric AUC(t) Estimation Patrick J. Heagerty PhD Department of Biostatistics University of Washington 166 ISCB 2010 Session Four Outline Examples
More informationRESEARCH ARTICLE. Detecting Multiple Change Points in Piecewise Constant Hazard Functions
Journal of Applied Statistics Vol. 00, No. 00, Month 200x, 1 12 RESEARCH ARTICLE Detecting Multiple Change Points in Piecewise Constant Hazard Functions Melody S. Goodman a, Yi Li b and Ram C. Tiwari c
More informationRegression analysis of interval censored competing risk data using a pseudo-value approach
Communications for Statistical Applications and Methods 2016, Vol. 23, No. 6, 555 562 http://dx.doi.org/10.5351/csam.2016.23.6.555 Print ISSN 2287-7843 / Online ISSN 2383-4757 Regression analysis of interval
More informationJOINT REGRESSION MODELING OF TWO CUMULATIVE INCIDENCE FUNCTIONS UNDER AN ADDITIVITY CONSTRAINT AND STATISTICAL ANALYSES OF PILL-MONITORING DATA
JOINT REGRESSION MODELING OF TWO CUMULATIVE INCIDENCE FUNCTIONS UNDER AN ADDITIVITY CONSTRAINT AND STATISTICAL ANALYSES OF PILL-MONITORING DATA by Martin P. Houze B. Sc. University of Lyon, 2000 M. A.
More informationA Sampling of IMPACT Research:
A Sampling of IMPACT Research: Methods for Analysis with Dropout and Identifying Optimal Treatment Regimes Marie Davidian Department of Statistics North Carolina State University http://www.stat.ncsu.edu/
More informationMultivariate Survival Analysis
Multivariate Survival Analysis Previously we have assumed that either (X i, δ i ) or (X i, δ i, Z i ), i = 1,..., n, are i.i.d.. This may not always be the case. Multivariate survival data can arise in
More informationDynamic Models Part 1
Dynamic Models Part 1 Christopher Taber University of Wisconsin December 5, 2016 Survival analysis This is especially useful for variables of interest measured in lengths of time: Length of life after
More informationStatistical Analysis of Randomized Experiments with Nonignorable Missing Binary Outcomes
Statistical Analysis of Randomized Experiments with Nonignorable Missing Binary Outcomes Kosuke Imai Department of Politics Princeton University July 31 2007 Kosuke Imai (Princeton University) Nonignorable
More information1. Introduction In many biomedical studies, the random survival time of interest is never observed and is only known to lie before an inspection time
ASYMPTOTIC PROPERTIES OF THE GMLE WITH CASE 2 INTERVAL-CENSORED DATA By Qiqing Yu a;1 Anton Schick a, Linxiong Li b;2 and George Y. C. Wong c;3 a Dept. of Mathematical Sciences, Binghamton University,
More informationREGRESSION ANALYSIS FOR TIME-TO-EVENT DATA THE PROPORTIONAL HAZARDS (COX) MODEL ST520
REGRESSION ANALYSIS FOR TIME-TO-EVENT DATA THE PROPORTIONAL HAZARDS (COX) MODEL ST520 Department of Statistics North Carolina State University Presented by: Butch Tsiatis, Department of Statistics, NCSU
More informationUnit 3. Discrete Distributions
PubHlth 640 3. Discrete Distributions Page 1 of 39 Unit 3. Discrete Distributions Topic 1. Proportions and Rates in Epidemiological Research.... 2. Review - Bernoulli Distribution. 3. Review - Binomial
More informationAnalysing geoadditive regression data: a mixed model approach
Analysing geoadditive regression data: a mixed model approach Institut für Statistik, Ludwig-Maximilians-Universität München Joint work with Ludwig Fahrmeir & Stefan Lang 25.11.2005 Spatio-temporal regression
More informationDefinitions and examples Simple estimation and testing Regression models Goodness of fit for the Cox model. Recap of Part 1. Per Kragh Andersen
Recap of Part 1 Per Kragh Andersen Section of Biostatistics, University of Copenhagen DSBS Course Survival Analysis in Clinical Trials January 2018 1 / 65 Overview Definitions and examples Simple estimation
More informationProbability and Probability Distributions. Dr. Mohammed Alahmed
Probability and Probability Distributions 1 Probability and Probability Distributions Usually we want to do more with data than just describing them! We might want to test certain specific inferences about
More informationMultistate models in survival and event history analysis
Multistate models in survival and event history analysis Dorota M. Dabrowska UCLA November 8, 2011 Research supported by the grant R01 AI067943 from NIAID. The content is solely the responsibility of the
More informationFrailty Modeling for clustered survival data: a simulation study
Frailty Modeling for clustered survival data: a simulation study IAA Oslo 2015 Souad ROMDHANE LaREMFiQ - IHEC University of Sousse (Tunisia) souad_romdhane@yahoo.fr Lotfi BELKACEM LaREMFiQ - IHEC University
More information,..., θ(2),..., θ(n)
Likelihoods for Multivariate Binary Data Log-Linear Model We have 2 n 1 distinct probabilities, but we wish to consider formulations that allow more parsimonious descriptions as a function of covariates.
More informationSTAT331. Cox s Proportional Hazards Model
STAT331 Cox s Proportional Hazards Model In this unit we introduce Cox s proportional hazards (Cox s PH) model, give a heuristic development of the partial likelihood function, and discuss adaptations
More informationLarge sample theory for merged data from multiple sources
Large sample theory for merged data from multiple sources Takumi Saegusa University of Maryland Division of Statistics August 22 2018 Section 1 Introduction Problem: Data Integration Massive data are collected
More informationSTA6938-Logistic Regression Model
Dr. Ying Zhang STA6938-Logistic Regression Model Topic 6-Logistic Regression for Case-Control Studies Outlines: 1. Biomedical Designs 2. Logistic Regression Models for Case-Control Studies 3. Logistic
More informationParametric Maximum Likelihood Estimation of Cure Fraction Using Interval-Censored Data
Columbia International Publishing Journal of Advanced Computing (2013) 1: 43-58 doi:107726/jac20131004 Research Article Parametric Maximum Likelihood Estimation of Cure Fraction Using Interval-Censored
More informationMarginal Structural Cox Model for Survival Data with Treatment-Confounder Feedback
University of South Carolina Scholar Commons Theses and Dissertations 2017 Marginal Structural Cox Model for Survival Data with Treatment-Confounder Feedback Yanan Zhang University of South Carolina Follow
More informationDynamic analysis of binary longitudinal data
Dynamic analysis of binary longitudinal data Ørnulf Borgan Department of Mathematics University of Oslo Based on joint work with Rosemeire L. Fiaccone, Robin Henderson and Mauricio L. Barreto 1 Outline:
More informationDirect likelihood inference on the cause-specific cumulative incidence function: a flexible parametric regression modelling approach
Research Article Received XXXX (www.interscience.wiley.com) DOI: 10.1002/sim.0000 Direct likelihood inference on the cause-specific cumulative incidence function: a flexible parametric regression modelling
More informationDescription Syntax for predict Menu for predict Options for predict Remarks and examples Methods and formulas References Also see
Title stata.com stcrreg postestimation Postestimation tools for stcrreg Description Syntax for predict Menu for predict Options for predict Remarks and examples Methods and formulas References Also see
More informationEstimation of Conditional Kendall s Tau for Bivariate Interval Censored Data
Communications for Statistical Applications and Methods 2015, Vol. 22, No. 6, 599 604 DOI: http://dx.doi.org/10.5351/csam.2015.22.6.599 Print ISSN 2287-7843 / Online ISSN 2383-4757 Estimation of Conditional
More informationNONPARAMETRIC ADJUSTMENT FOR MEASUREMENT ERROR IN TIME TO EVENT DATA: APPLICATION TO RISK PREDICTION MODELS
BIRS 2016 1 NONPARAMETRIC ADJUSTMENT FOR MEASUREMENT ERROR IN TIME TO EVENT DATA: APPLICATION TO RISK PREDICTION MODELS Malka Gorfine Tel Aviv University, Israel Joint work with Danielle Braun and Giovanni
More informationApproximation of Survival Function by Taylor Series for General Partly Interval Censored Data
Malaysian Journal of Mathematical Sciences 11(3): 33 315 (217) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES Journal homepage: http://einspem.upm.edu.my/journal Approximation of Survival Function by Taylor
More informationFaculty of Health Sciences. Regression models. Counts, Poisson regression, Lene Theil Skovgaard. Dept. of Biostatistics
Faculty of Health Sciences Regression models Counts, Poisson regression, 27-5-2013 Lene Theil Skovgaard Dept. of Biostatistics 1 / 36 Count outcome PKA & LTS, Sect. 7.2 Poisson regression The Binomial
More informationUniversity of California, Berkeley
University of California, Berkeley U.C. Berkeley Division of Biostatistics Working Paper Series Year 2002 Paper 122 Construction of Counterfactuals and the G-computation Formula Zhuo Yu Mark J. van der
More informationSpatio-Temporal Threshold Models for Relating UV Exposures and Skin Cancer in the Central United States
Spatio-Temporal Threshold Models for Relating UV Exposures and Skin Cancer in the Central United States Laura A. Hatfield and Bradley P. Carlin Division of Biostatistics School of Public Health University
More informationAFT Models and Empirical Likelihood
AFT Models and Empirical Likelihood Mai Zhou Department of Statistics, University of Kentucky Collaborators: Gang Li (UCLA); A. Bathke; M. Kim (Kentucky) Accelerated Failure Time (AFT) models: Y = log(t
More informationMulti-state models: prediction
Department of Medical Statistics and Bioinformatics Leiden University Medical Center Course on advanced survival analysis, Copenhagen Outline Prediction Theory Aalen-Johansen Computational aspects Applications
More informationExtensions of Cox Model for Non-Proportional Hazards Purpose
PhUSE Annual Conference 2013 Paper SP07 Extensions of Cox Model for Non-Proportional Hazards Purpose Author: Jadwiga Borucka PAREXEL, Warsaw, Poland Brussels 13 th - 16 th October 2013 Presentation Plan
More informationMissing covariate data in matched case-control studies: Do the usual paradigms apply?
Missing covariate data in matched case-control studies: Do the usual paradigms apply? Bryan Langholz USC Department of Preventive Medicine Joint work with Mulugeta Gebregziabher Larry Goldstein Mark Huberman
More informationA simple bivariate count data regression model. Abstract
A simple bivariate count data regression model Shiferaw Gurmu Georgia State University John Elder North Dakota State University Abstract This paper develops a simple bivariate count data regression model
More informationJoint Modeling of Longitudinal Item Response Data and Survival
Joint Modeling of Longitudinal Item Response Data and Survival Jean-Paul Fox University of Twente Department of Research Methodology, Measurement and Data Analysis Faculty of Behavioural Sciences Enschede,
More informationLecture 3: Measures of effect: Risk Difference Attributable Fraction Risk Ratio and Odds Ratio
Lecture 3: Measures of effect: Risk Difference Attributable Fraction Risk Ratio and Odds Ratio Dankmar Böhning Southampton Statistical Sciences Research Institute University of Southampton, UK March 3-5,
More informationLecture 12. Multivariate Survival Data Statistics Survival Analysis. Presented March 8, 2016
Statistics 255 - Survival Analysis Presented March 8, 2016 Dan Gillen Department of Statistics University of California, Irvine 12.1 Examples Clustered or correlated survival times Disease onset in family
More informationDETERMINATION OF THE SAMPLE SIZE AND THE NUMBER OF FOLLOW-UP TIMES BY USING LINEAR PROGRAMMING
Journal of Statistics: Advances in Theory and Applications Volume 14, Number 1, 2015, Pages 27-35 Available at http://scientificadvances.co.in DOI: http://dx.doi.org/10.18642/jsata_7100121529 DETERMINATION
More informationMulti-state Models: An Overview
Multi-state Models: An Overview Andrew Titman Lancaster University 14 April 2016 Overview Introduction to multi-state modelling Examples of applications Continuously observed processes Intermittently observed
More informationSurvival Distributions, Hazard Functions, Cumulative Hazards
BIO 244: Unit 1 Survival Distributions, Hazard Functions, Cumulative Hazards 1.1 Definitions: The goals of this unit are to introduce notation, discuss ways of probabilistically describing the distribution
More informationApproaches to Modeling Menstrual Cycle Function
Approaches to Modeling Menstrual Cycle Function Paul S. Albert (albertp@mail.nih.gov) Biostatistics & Bioinformatics Branch Division of Epidemiology, Statistics, and Prevention Research NICHD SPER Student
More informationTMA 4275 Lifetime Analysis June 2004 Solution
TMA 4275 Lifetime Analysis June 2004 Solution Problem 1 a) Observation of the outcome is censored, if the time of the outcome is not known exactly and only the last time when it was observed being intact,
More informationStatistical Methods for Alzheimer s Disease Studies
Statistical Methods for Alzheimer s Disease Studies Rebecca A. Betensky, Ph.D. Department of Biostatistics, Harvard T.H. Chan School of Public Health July 19, 2016 1/37 OUTLINE 1 Statistical collaborations
More informationConstrained estimation for binary and survival data
Constrained estimation for binary and survival data Jeremy M. G. Taylor Yong Seok Park John D. Kalbfleisch Biostatistics, University of Michigan May, 2010 () Constrained estimation May, 2010 1 / 43 Outline
More informationWeighted likelihood estimation under two-phase sampling
Weighted likelihood estimation under two-phase sampling Takumi Saegusa Department of Biostatistics University of Washington Seattle, WA 98195-7232 e-mail: tsaegusa@uw.edu and Jon A. Wellner Department
More informationOne-stage dose-response meta-analysis
One-stage dose-response meta-analysis Nicola Orsini, Alessio Crippa Biostatistics Team Department of Public Health Sciences Karolinska Institutet http://ki.se/en/phs/biostatistics-team 2017 Nordic and
More informationTutorial on Approximate Bayesian Computation
Tutorial on Approximate Bayesian Computation Michael Gutmann https://sites.google.com/site/michaelgutmann University of Helsinki Aalto University Helsinki Institute for Information Technology 16 May 2016
More informationCOMPETING RISKS: MODELLING CRUDE CUMULATIV INCIDENCE FUNCTIONS
Statistica Applicata Vol. 17, n. 1, 2005 25 COMPETING RISKS: MODELLING CRUDE CUMULATIV INCIDENCE FUNCTIONS Patrizia Boracchi 1, LauraAntolini 2, Elia Biganzoli 2, Ettore Marubini 1 1 Istituto di Statistica
More informationOn Two-Stage Hypothesis Testing Procedures Via Asymptotically Independent Statistics
UW Biostatistics Working Paper Series 9-8-2010 On Two-Stage Hypothesis Testing Procedures Via Asymptotically Independent Statistics James Dai FHCRC, jdai@fhcrc.org Charles Kooperberg fred hutchinson cancer
More informationDisease mapping with Gaussian processes
EUROHEIS2 Kuopio, Finland 17-18 August 2010 Aki Vehtari (former Helsinki University of Technology) Department of Biomedical Engineering and Computational Science (BECS) Acknowledgments Researchers - Jarno
More informationPart III Measures of Classification Accuracy for the Prediction of Survival Times
Part III Measures of Classification Accuracy for the Prediction of Survival Times Patrick J Heagerty PhD Department of Biostatistics University of Washington 102 ISCB 2010 Session Three Outline Examples
More information