Faculty Research Interest Seminar Series Graduate School of Public Health, UNIVERSITY of PITTSBURGH Methodological Research and Collaboration Lan Kong Assistant professor Department of Biostatistics October 20, 2015

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Outline Introduction Methodological Research Survival analysis Clinical trials Collaboration Projects Motivated problems

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Introduction Joined the Biostat dept in August 2003 PhD in Biostat from UNC-Chapel Hill In collaboration with critical care medicine dept. On doctoral exam committee Courses intend to teach: survival analysis, advanced categorical data analysis, or estimating equation method.

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Methodological research Survival analysis Study design: case-cohort Model: semiparametric transformation models and accelerated failure time model. Approach: estimating equation method, inverse of probability weighting technique Statistical theory: U-statistics, finite population sampling, martingales, empirical/stochastic process

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Case-cohort design (Prentice, 1986) Subcohort Full cohort Complete data available for Subcohort + Additional Cases outside the subcohort Full cohort Failures/cases

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Semiparametric Survival Models Cox model λ (t)= λ 0 (t)exp(β’Z), λ 0 (t) unspecified Transformation models h (T)= -β’Z+ε OR g{S z (t)}=h(t)+ β’Z h unknown, ε has known CDF F(.), g -1 =1-F. Accelerated failure time model log (T)= -β’Z+ε, ε has unspecified CDF.

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Methodological Research (cont.) Clinical trials Problem: Multiplicity issues Example: Multi-dose clinical trials--correlations among multiple comparisons Statistical concerns: familywise Type I error and power

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Family-Wise Error (FWE) Probability that at least one hypothesis is incorrectly declared significant. Strongly control of FWE: FWE is protected for any composite null hypothesis Closed testing procedures strongly control the FWE

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Multiple testing procedures Analysis strategies: Perform a sequence of tests in a pre-specified order through the closed testing principle : 1. Global assessment of any dose effect 2. Comparisons of doses to Placebo 3. Other comparisons among doses Manage multiplicity within the respective steps by Hochberg method (BKA, 1988), closed testing procedure.

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Vaccine trials Problem: Inferiority/Equivalence assessment Multiplicity due to multiple endpoints (immune responses) Statistical concerns: How correlations among endpoints affect study design of inferiority/EQ trials (power, sample size)?

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH -K 0 -K 0 K T-C H 0 (inf) H 1 (noninf) H 0 (noneq) H 1 (eq) H 0 (noneq) 0 T-C H 0 (inf) H 1 (sup) Hypotheses T: treatment group, C: control group

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Further questions of interest How to handle multiple endpoints in multi-dose clinical trials? How to simultaneously assess inferiority/EQ on some endpoints and superiority on the others?

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Collaboration Projects mainly involved: Genetic and inflammatory markers of Sepsis (GenIMS) Economic Analysis of the Pulmonary Artery Catheter Use (EA-PAC) Prolonged Outcomes of Nitric Oxide for Ventilated Premature Babies (PRONOX)

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Statistical problems motivated from collaborative projects

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Genetic data Explore how the candidate markers interactively affect the outcome (Selection of subset from a list of candidate genetic markers) Statistical learning method Pattern recognition approach Haplotype analysis for survival data to accommodate missing genotypes, case- cohort design

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Missing and/or truncated data Examples: inflammatory marker data are below detectable limit, the measurement on the severity of sepsis are heavily missing Longitudinal analysis of truncated inflammatory marker (extension of Tobit model) Jointly modeling several truncated inflammatory markers Testing whether the missing is informative in the non-monotone missing pattern Jointly modeling the longitudinal outcome and longitudinal covariates

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Quality of Life and Cost data Various types of outcomes: survival data, cost data, quality of life data. Informative censoring Modeling quality adjusted survival Jointly modeling QOL with survival data Cost-effective analysis in presence of repeated measures and missing data

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Related papers Kong L, Cai J, Sen PK. Weighted estimating equations for semiparametric transformation models with censored data from a case-cohort design. Biometrika 2004; 91(2): Kong L, Cai J, Sen PK. Asymptotic results for fitting semiparametric transformation models to failure time data from a case-cohort design. Statistica Sinica 2004, in press. Kong L. Analysis of case-cohort data with accelerated failure time model, in preparation

Dept of Biostatistics, GSPH, UNIVERSITY of PITTSBRGH Related papers (cont.) Kong L, Kohberger R, Koch G. Type I error and power in non-inferiority/equivalence trials with correlated multiple endpoints: an example from vaccine development trials. Journal of Biopharmaceutical Statistics 2004; 14(4): Kong L, Koch G, Liu T, Wang H. Performance of some multiple testing procedures to compare three doses of a test drug and placebo. Pharmaceutical Statistics 2004, in press. Kong L, Kohberger R, Koch G. Equivalence/non- inferiority assessment on multiple proportion outcomes. In preparation.