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Methodological Research and Collaboration

Methodological Research and Collaboration. Lan Kong Assistant professor Department of Biostatistics June 5, 2014. Outline. Introduction Methodological Research Survival analysis Clinical trials Collaboration Projects Motivated problems. Introduction.

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Methodological Research and Collaboration

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  1. Methodological Research and Collaboration Lan Kong Assistant professor Department of Biostatistics June 5, 2014

  2. Outline • Introduction • Methodological Research • Survival analysis • Clinical trials • Collaboration • Projects • Motivated problems

  3. 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.

  4. 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

  5. Case-cohort design (Prentice, 1986) Full cohort Failures/cases Subcohort Full cohort Complete data available for Subcohort + Additional Cases outside the subcohort

  6. Semiparametric Survival Models • Cox model λ(t)=λ0(t)exp(β’Z), λ0(t) unspecified • Transformation models h(T)= -β’Z+εOR g{Sz(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.

  7. 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

  8. 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

  9. 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.

  10. 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)?

  11. H0 (inf) H1(sup) T-C 0 Hypotheses H0 (inf) H1(noninf) T-C -K 0 H0 (noneq) H1(eq) H0 (noneq) T-C -K 0 K T: treatment group, C: control group

  12. 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?

  13. 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)

  14. Statistical problems motivated from collaborative projects

  15. 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

  16. 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

  17. 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

  18. 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):305-319. 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

  19. 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):893-907. 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.

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