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Generalizing Treatment Effect to a Target Population Without Individual Patient Data in a Real-World Setting

PHARMACEUTICAL STATISTICS 2025年第1期24卷 e2435页

作者： Quan, Hui Li, Tong Chen, Xun Li, Gang Sanofi Biostat & Programming Bridgewater NJ 08807 USA Eisai Inc Nutley NJ USA

The innovative use of real-world data (RWD) can answer questions that cannot be addressed using data from randomized clinical trials (RCTs). While the sponsors of RCTs have a central database containing all individual patient data (IPD) collected from trials, analysts of RWD face a challenge: regulations on patient privacy make access to IPD from all regions logistically prohibitive. In this research, we propose a double inverse probability weighting (DIPW) approach for the analysis sponsor to estimate the population average treatment effect (PATE) for a target population without the need to access IPD. One probability weighting is for achieving comparable distributions in confounders across treatment groups;another probability weighting is for generalizing the result from a subpopulation of patients who have data on the endpoint to the whole target population. The likelihood expressions for propensity scores and the DIPW estimator of the PATE can be written to only rely on regional summary statistics that do not require IPD. Our approach hinges upon the positivity and conditional independency assumptions, prerequisites to most RWD analysis approaches. Simulations are conducted to compare the performances of the proposed method against a modified meta-analysis and a regular meta-analysis.

关键词： double inverse probability weighting (DIPW) individual patient data propensity score real-world data real-world evidence

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POD-BIN: A PROBABILITY OF DECISION BAYESIAN INTERVAL DESIGN FOR TIME-TO-EVENT DOSE-FINDING TRIALS WITH MULTIPLE TOXICITY GRADES

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ANNALS OF APPLIED STATISTICS 2025年第1期19卷 147-168页

作者： Liu, Meizi Lin, Ji Mi, Gu Lorenzato, Christelle Chen, Xun Ji, Yuan Takeda Stat & Quantitat Sci Cambridge MA 02142 USA Sanofi Dept Biostat & Programming Bridgewater NJ 08807 USA Univ Chicago Dept Publ Hlth Sci Chicago IL USA

We introduce a Bayesian framework centered on the "probability of decision" for designing dose-finding trials. The proposed PoD-BIN design evaluates the posterior predictive probabilities of up-and-down decisions. In PoDBIN, multiple grades of toxicity, categorized as mild toxicity (MT) and doselimiting toxicity (DLT), are simultaneously modeled, with the primary outcome being time-to-toxicity for both MT and DLT. This approach allows the enrollment of new patients while previously enrolled patients are still being monitored for toxicity, potentially reducing the trial duration. The Bayesian decision rules in PoD-BIN employ the probability of decisions to balance the trade-off between accelerating the trial and the risk of exposing patients to excessively toxic doses. Through numerical examples, we illustrate the trade-off between speed and safety of PoD-BIN and compare it with existing designs. PoD-BIN demonstrates the ability to control the frequency of risky decisions while simultaneously shortening trial duration in simulations.

关键词： Dose limiting toxicity equivalence interval latent probit regression moderate toxici-ties phase I trial time to toxicity

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Variable Selection in High-Dimensional Logistic Regression Models Using a Whitening Approach

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2025年第2期22卷 800-807页

作者： Zhu, Wencan Levy-Leduc, Celine Ternes, Nils Univ Paris Saclay UMR MIA Paris AgroParisTech INRAE F-91190 Gif Sur Yvette France Sanofi R&D Dept Biostat & Programming Bridgewater NJ 08807 USA Univ Paris Cite CNRS Lab Probabil Stat Modelisat LPSM F-75006 Paris France Sanofi R&D Dept Biostat & Programming Bridgewater NJ 08807 USA

In bioinformatics, the rapid development of sequencing technology has enabled us to collect an increasing amount of omics data. Classification based on omics data is one of the central problems in biomedical research. However, omics data usually has a limited sample size but high feature dimensions, and it is assumed that only a few features (biomarkers) are active, i.e. informative to discriminate between different categories. Identifying active biomarkers for classification has therefore become fundamental for omics data analysis. Focusing on binary classification, we propose an innovative feature selection method aiming at dealing with the high correlations between the biomarkers. Our method, WLogit, consists in whitening the design matrix to remove the correlations between biomarkers, then using a penalized criterion adapted to the logistic regression model to select features. The results from numerical experiments suggest that WLogit can identify almost all active biomarkers even in the cases where the biomarkers are highly correlated, while the other methods fail, which consequently leads to higher classification accuracy. The performance of WLogit is also evaluated on two publicly available datasets, and the obtained classifier outperformed other methods in terms of prediction accuracy. Our method is implemented in the WLogit R package available from the Comprehensive R Archive Network (CRAN).

关键词： Biomarkers Correlation Feature extraction Biological system modeling Bioinformatics Logistic regression Integrated circuit modeling Diseases Computational modeling Computational biology Feature selection highly correlated predictors binary classification regularized approches

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Utilization of treatment effect on a surrogate endpoint for planning a study to evaluate treatment effect on a final endpoint

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PHARMACEUTICAL STATISTICS 2023年第4期22卷 633-649页

作者： Quan, Hui Xu, Zhixing Luo, Junxiang Paux, Gautier Cho, Meehyung Chen, Xun Sanofi Biostat & Programming Bridgewater NJ USA Moderna Biostat & Programming Cambridge MA USA Sanofi Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA

To design a phase III study with a final endpoint and calculate the required sample size for the desired probability of success, we need a good estimate of the treatment effect on the endpoint. It is prudent to fully utilize all available information including the historical and phase II information of the treatment as well as external data of the other treatments. It is not uncommon that a phase II study may use a surrogate endpoint as the primary endpoint and has no or limited data for the final endpoint. On the other hand, external information from the other studies for the other treatments on the surrogate and final endpoints may be available to establish a relationship between the treatment effects on the two endpoints. Through this relationship, making full use of the surrogate information may enhance the estimate of the treatment effect on the final endpoint. In this research, we propose a bivariate Bayesian analysis approach to comprehensively deal with the problem. A dynamic borrowing approach is considered to regulate the amount of historical data and surrogate information borrowing based on the level of consistency. A much simpler frequentist method is also discussed. Simulations are conducted to compare the performances of different approaches. An example is used to illustrate the applications of the methods.

关键词： bivariate Bayesian analysis dynamic data borrowing posterior distribution power prior probability of success

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COMPARISON OF STATISTICAL MODELS ADJUSTING FOR BASELINE IN THE ANALYSIS OF PARALLEL-GROUP THOROUGH QT/QTc STUDIES

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2012年第3期22卷 438-462页

作者： Sun, Guowen (Gordon) Quan, Hui Kringle, Robert Meng, Zhaoling Celgene Corp Biostat & Programming Summit NJ USA Sanofi Aventis Biostat & Programming Bridgewater NJ USA

The ICH E14 guidance recommends the use of a time-matched baseline, while others recommend alternative baseline definitions including a day-averaged baseline. In this article we consider six models adjusting for baselines. We derive the explicit covariances and compare their power under various conditions. Simulation results are provided. We conclude that type I error rates are controlled. However, one model outperforms the others on statistical power under certain conditions. In general, the analysis of covariance (ANCOVA) model using a day-averaged baseline is preferred. If the time-matched baseline has to be used as per requests from regulatory agencies, the analysis by time point using ANCOVA model should be recommended.

关键词： Day-averaged baseline Parallel studies Statistical power Time-matched baseline Type I error

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A generalized weighted combination test of treatment effect for clinical trials with a sequential parallel comparison design and binary endpoint

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STATISTICS IN MEDICINE 2022年第15期41卷 2725-2744页

作者： Quan, Hui Chen, Xiaofei Luo, Junxiang Chen, Xun Sanofi Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA Moderna Biostat & Programming Cambridge MA USA

To address the issue of a large placebo effect in certain therapeutic areas, rather than the application of the traditional gold standard parallel group placebo-controlled design, different versions of the sequential parallel comparison design have been advocated. In general, the design consists of two consecutive stages and three treatment groups. Stage 1 placebo nonresponders potentially form a prespecified patient subgroup for formal between-treatment comparison at the final analysis. In this research, a version of the design is considered for a binary endpoint. To fully utilize all available data, a generalized weighted combination test is proposed in case placebo has a relatively small effect for some of the study endpoints. The weighted combination of the test based on stage 1 data and the test based on stage 2 data of stage 1 placebo nonresponders suggested in the literature uses only a part of the study data and is a special case of this generalized weighted combination test. A multiple imputation approach is outlined for handling missing not at random data. Simulation is conducted to evaluate the performances of the methods and a data example is employed to illustrate the applications of the methods.

关键词： expectation-maximization algorithm missing data imputation placebo effect power

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PLANNING FOR PRECISION IN SURVIVAL STUDIES

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JOURNAL OF CLINICAL EPIDEMIOLOGY 1994年第11期47卷 1277-1285页

作者： BORENSTEIN, M BIOSTAT PROGRAMMING ASSOCIATES TEANECK NJ USA

If the purpose of a clinical study is not only to test the null hypothesis but also to estimate the magnitude of the treatment effect, the study design should ensure not only that the study will have adequate power but also that it will enable the researcher to report the relevant parameters with an appropriate level of precision. This paper discusses the factors that control precision in survival studies and shows how a computer program may be used to address these issues. The program allows the user to systematically modify assumptions about the population (e.g. the magnitude of the hazard ratio or the attrition rate) and elements of the study design (e.g. sample size and trial duration), quickly identify the impact of these factors on the study's precision, and modify the study design accordingly. The program may also be used to compute power for a planned study, and confidence intervals for a completed study.

关键词： SURVIVAL ANALYSIS LIFE TABLE CONFIDENCE INTERVALS CONFIDENCE LIMITS STATISTICAL POWER STATISTICAL PRECISION CLINICAL IMPORTANCE biostatISTICAL METHODS

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Algorithms for imputing partially observed recurrent events with applications to multiple imputation in pattern mixture models

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2018年第3期28卷 518-533页

作者： Tang, Yongqiang Dept Biostat & Programming Lexington MA USA

Five algorithms are described for imputing partially observed recurrent events modeled by a negative binomial process, or more generally by a mixed Poisson process when the mean function for the recurrent events is continuous over time. We also discuss how to perform the imputation when the mean function of the event process has jump discontinuities. The validity of these algorithms is assessed by simulations. These imputation algorithms are potentially very useful in the implementation of pattern mixture models, which have been popularly used as sensitivity analysis under the non-ignorability assumption in clinical trials. A chronic granulomatous disease trial is analyzed for illustrative purposes.

关键词： Mixed poisson process negative binomial process overdispersion pattern mixture models rejection sampling

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Sample size for comparing negative binomial rates in noninferiority and equivalence trials with unequal follow-up times

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2018年第3期28卷 475-491页

作者： Tang, Yongqiang Dept Biostat & Programming Shire MA USA

We derive the sample size formulae for comparing two negative binomial rates based on both the relative and absolute rate difference metrics in noninferiority and equivalence trials with unequal follow-up times, and establish an approximate relationship between the sample sizes required for the treatment comparison based on the two treatment effect metrics. The proposed method allows the dispersion parameter to vary by treatment groups. The accuracy of these methods is assessed by simulations. It is demonstrated that ignoring the between-subject variation in the follow-up time by setting the follow-up time for all individuals to be the mean follow-up time may greatly underestimate the required size, resulting in underpowered studies. Methods are provided for back-calculating the dispersion parameter based on the published summary results.

关键词： Fixed margin approach mixed Poisson distribution negative binomial distribution noninferiority margin overdispersion unequal follow-up

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A MCP-Mod approach to designing and analyzing survival trials with potential non-proportional hazards

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PHARMACEUTICAL STATISTICS 2022年第6期21卷 1294-1308页

作者： Luo, Xiaodong Sun, Yuan Xu, Zhixing Sanofi Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA Sanofi Biostat & Programming Beijing Peoples R China

Non-proportional hazards have been observed in many studies especially in immuno-oncology clinical trials. Traditional analysis using the combined approach with log-rank test as the significance test and Cox model for treatment effect estimation becomes questionable as this approach relies heavily on the proportional hazards assumption. Inspired by the MCP-Mod (multiple comparisons and modeling approach) that has been widely used in dose-finding studies, we propose a similar approach to handle non-proportional hazards. Using this approach, efficacy signal is first established by a max-combo test, after which hazard ratios across time will be estimated using a logically nested splines model. Simulations studies and real-data examples are used to illustrate the use of this approach.

关键词： max-combo test MCP-Mod non-proportional hazards ridge regression survival trials

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