Introduction ICH - General Purpose Unification of necessary documentation and its formats for NDA submission E5 Guideline : Extrapolation of foreign clinical data Avoidance of unnecessary clinical trials New GCP Guideline Quality assurance of clinical trial data Simultaneous Global Drug Development Better drugs in a timely fashion
Regulatory Environment Review time A number of approved drugs by application of E5 guideline
Clinical Trial Environment in Japan
Clinical Trial Costs: Numbers of Clinical Trials: Current Situation in Japan Very High Diminishing
Costs of Clinical Trials in Japan Average cost per patient per yearRelative cost per patient Presentation by Dr. Uden at 3 rd Kitasato-Harvard Symposium, 2002
No. of Initial Clinical Trial Notifications
Location of Clinical Trials conducted by Japanese Companies Even Japanese companies conduct clinical trials in foreign countries
Speed of Clinical Trials in Japan
Hollowing out of Clinical Trials High cost to conduct clinical trials Domestic companies conduct their clinical trials outside of Japan Slow speed of clinical trials
Recent R&D Trend From bridging to global studies Importance of basic science
Concept: Avoidance of Unnecessary Clinical Trials US ASIAEU Foreign data New Regions Bridging studies Simultaneous global studies
Issues to be shown Intrinsic factors Extrinsic factors Intra variability >> Inter variability Conduct of a proposed clinical trial among regions Difference in Medical Practice - Different study design - Different adverse event reporting system
Intrinsic factors (Influence of Genotype) Fukuda et. al.(2000) investigated whether the disposition of venlafaxine was affected by the CYP2D6 genotype. # subject=36 blue(*10/*10) = 6 red(*1/*10,*2/*10)=13 orange(*1/*1,*1/*2,*2/*2)=16 green(others)=1 may affect efficacy and safety – adjustment of dosage
Mixture of Target Disease Population DNA micro array: NEJM, Target Population: diffuse large-B-cell lymphoma - Efficacy anthracycline chemotherapy -35% - 40% -mixture of target disease population -Gene expression: - grouped target population - clearly defined target disease population
DNA micro array: NEJM,2002 Cox regression Gene-expression signatures: 4 distinct gene-expression signatures score by the combination of the 4 signatures Mixture of Target Disease Population
Extrinsic factors US and EU: Placebo Controlled Trial Japan: Non-inferiority Trial or Placebo Controlled Relapse Trial Different medical practice Ex: Depression Trials
3 Major Studies DrugSourceIndicationType of Study Tolterodine Presentation by Dr.Kong Gans at the 3 rd K-H Sympo. Overactive Bladder Asian Study (Japan and Korea) Irresa Review report by PMDEC Non-small Lung Cancer Global phase II study (Japanese vs. Non- Japanese) Losartan NEJMRenal DiseaseGlobal study
Lessons Intrinsic factors: design (phase I and II) Importance of basic science Clear definition of a target population - P450 information: investigate individual variation w.r.t. efficacy and safety - pharmacogenomics: possibly identified individual characteristics - surrogate markers: quick detection of efficacy different angles of profile - PPK analysis: investigation of possible factors
Lessons Extrinsic factors Realization of conductivity of a planned trial Regulatory aspects: New GCP implementation regulatory science practice – depends on structure of a review system Design aspects: study design: different medical practice independent data monitoring committee Simulation studies probably play an important role for future prediction
Statistical Issues and Potential Approaches How can statistics play a role in extrapolation of foreign clinical data?
Statistical Issues Intrinsic factors Clearly defined target population intra-variability >> inter-variability Randomization Scheme Statistical Issues: - Definition of similarity -Statistical test vs point estimation -Variability within a region -Required sample size?
Practical Issues Extrinsic factors Conductivity of a proposed clinical trial -Regulatory agencies -Different medical practice Statistical Issues: - What should be shown? -Similarity: dose response, efficacy Regulatory science -Placebo response: how to estimate Different medical practice
Kitasato-Harvard-Pfizer-Hitachi project Under various settings, using real data sets and simulation techniques, we are trying to figure out how to deal with the important issues concerning design and analysis of global clinical trials. Project team member [Kitasato] M. Takeuchi, X. M. Fang, F. Takahashi, H. Uno [Harvard] LJ Wei [Pfizer] C. Balagtas, Y. Ii, M. Beltangady, I. Marschner [Hitachi] J. Mehegan The 6 th Kitasato-Harvard Symposium, Oct 24-25, 2005, Tokyo, Japan
Global/Multi-national Trials Global trials involve many regions/countries. Global trials provide us information about investigational drug worldwide simultaneously. As to getting new drug approval, there is the fact that each region/country has its own regulatory policy. A lot of statistical issues for DESIGN, ANALYSIS and MONITORING of global trials still remain. we are trying to figure out how to deal with these issues, using real data sets. Todays talk is concerning with the analysis issues regarding local inference.
Questions Can we think of the treatment difference derived from pooled analysis as that in Japan? Should we believe the results derived from by-country analysis ? Can we borrow the information from other countries? How to borrow information? Although a single summary of the treatment difference across countries is important, but local inference is also desirable. What can we say about the treatment difference in one country, for example, in Japan (with ONLY 14 subjects)? One of the challenging statistical issues
Analysis model for local inference Fit the Cox model to each country One extreme Pooled Analysis (borrowing directly) another extreme By-country Analysis (borrowing NO info) Compromised approaches in between (borrowing information) Suppose Cox-model Fit the stratified Cox model (strata=country) An empirical Bayes approach -Fit Cox model to each country -Normal approximation of MLE for the treatment difference -Fit a Normal-Normal hierarchical model (next page) -Get the posterior distribution of and Confidence Set. : treatment difference : covariate 1=treatment group 0=control group : baseline hazard function for k-th country : treatment difference for k-th country Get CI for
Individual Sampling Density Distribution of random parameter of interest A normal-normal hierarchical model True treatment Difference in each country
Individual Sampling Density A normal-normal hierarchical model Normal Approx. of MLE True treatment difference In each country Distribution of random parameter of interest
Individual Sampling Density A normal-normal hierarchical model Normal Approx. of MLE Empirical Bayes: Estimating UNKOWN hyper parameter using observed data True treatment difference In each country Distribution of random parameter of interest
Naive EBCI is constructed from the posterior distribution of with plugging-in the estimates to unknown A reason why we picked a N-N model on EB There is a well-known issue on EBCI: Naive EBCI fails to attain their nominal coverage probability. There are a lot of literature concerning EB for a N-N model. Some theories are available to correct Naive EBCI especially for a N-N model. (Morris (1983), Laird & Louis (1987), Carlin & Gelfand (1990), Datta et al (2002), etc.) We applied the Morris correction in the following analysis. However, since are random, the posterior variance should be The term under the square root is just an approximation of the first term of RHS in above equation.
Approximated likelihood / Posterior distribution Pooled AnalysisBy-Country AnalysisEmpirical Bayes
A small simulation study was conducted to evaluate the performance of this approach under the Cox model. The number of countries and the sample size in each country were fixed, evaluated the coverage probability and average length of confidence interval were evaluated based on 10,000 iterations., the coverage probability of 95% CI is calculated Simulation scheme: Parameter of interest (treatment difference): Survival time of group A: Survival time of group B: Censoring time of both groups: Simulation studies Thus, generated data for group A: generated data for group B:
Conclusion This empirical Bayes approach (Normal-Normal hierarchical model coupled with normal approximation of the estimator of the treatment difference) can be used in a wide variety of situations. From a simulation study, the performance of this approach was not bad in terms of both coverage probability and length of CIs. As to RALES data, this analysis provides shorter CIs and suggests that the treatment differences among each country are toward the same direction. In global clinical trials, performing this kind of intermediate analysis can be encouraged as a planned sensitivity analysis in addition to the pooled analysis and by-country analysis for better understanding of the treatment difference in a specific country.
References Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis, 2nd ed. New York: Springer-Verlag. Carlin, B. & Gelfand, A. (1990). Approaches for empirical Bayes confidence intervals. JASA 85, Carlin, B. & Louis, T. (2000). Bayes and Empirical Bayes Methods for Data Analysis, 2nd ed. London: Chapman & Hall/CRC. Datta, G et al (2002). On an asymptotic theory of conditional and unconditional coverage probabilities on empirical Bayes confidence intervals. Scand. J. Statist 29, Laird, N. & Louis, T. (1987). Empirical Bayes confidence intervals based on bootstrap samples. JASA 82, Morris, C. (1983a). Parametric empirical Bayes inference: theory and applications. JASA 78, Morris, C. (1983b). Parametric empirical Bayes confidence intervals. In Scientific inference, data analysis, and robustness, 2550, New York: Academic Press. Pitt, B et al. (1999) The effect of spironolactone on morbidity and mortality in patients with severe heart failure. NEJM 341,
Safety Issues Intrinsic/Extrinsic factors How can we ensure the safety of the drug if a drug is approved based on a small clinical data in a region? Need a type of a phase IV study after a approval, i.e., electronic data capturing system, and how can we analyze the data and what is a appropriate interpretation.
Safety Issues Network system among Hospitals Research Grant from MHLW Network system among hospitals by EDC to monitor patients Detection of unexpected AEs Build data base regarding pats` background for signal detection, pharmacoepidemiology
Overall Picture Medical Facility 2Medical Facility N Medical Facility 3 Medical Facility 5 Medical Facility 4 Medical Facility 1 Data Center Step 1 Step 2
Step 1: Within a MF Unification of Medical Records per Patient regarding -Patient`s background - Dosage and duration -Efficacy -Safety Connect Necessary Medical Records per Patient
Step 2: Among MFs Medical Facility 2Medical Facility N Medical Facility 1 Data Center Step 2 (i) Unification of Data base from different MFs and Establishment of Patients` data base at Data Center (ii) Detect unexpected AEs and analyze safety profile according to actual dosage and duration
Conclusion Asian and Global Studies are a future direction Design and Statistical Issues must cope with basic science Phase IV studies based on EDC are necessary for assurance of safety