arXiv subjects

Deukwoo Kwon

Publications and source records attributed to Deukwoo Kwon.

6 recordsLinked to original sources

arXiv2026

Continuous monitoring of delayed outcomes in basket trials

Precision medicine has led to a paradigm shift allowing the development of targeted drugs that are agnostic to the tumor location. In this context, basket trials aim to identify which tumor types - or baskets - would benefit from the targeted therapy among patients with the same molecular marker or mutation. We propose the implementation of continuous monitoring for basket trials to increase the likelihood of early identification of non-promising baskets. Although the current Bayesian trial designs available in the literature can incorporate more than one interim analysis, most of them have high computational cost, and none of them handle delayed outcomes that are expected for targeted treatments such as immunotherapies. We leverage the Bayesian empirical approach proposed by Fujiwara et al., which has low computational cost. We also extend ideas of Cai et al to address the practical challenge of performing interim analysis with delayed outcomes using multiple imputation. Operating characteristics of four different strategies to handle delayed outcomes in basket trials are compared in an extensive simulation study with the benchmark strategy where trial accrual is put on hold until complete data is observed to make a decision. The optimal handling of missing data at interim analyses is trial-dependent. With slow accrual, missingness is minimal even with continuous monitoring, favoring simpler approaches over computationally intensive methods. Although individual sample-size savings are small, multiple imputation becomes more appealing when sample size savings scale with the number of baskets and agents tested.

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arXiv2025

Intra-Class Correlation Coefficient Ignorable Clustered Randomized Trials for Detecting Treatment Effect Heterogeneity

Accurately estimating the intra-class correlation coefficient (ICC) is crucial for adequately powering clustered randomized trials (CRTs). Challenges arise due to limited prior data on the specific outcome within the target population, making accurate ICC estimation difficult. Furthermore, ICC can vary significantly across studies, even for the same outcome, influenced by factors like study design, participant characteristics, and the specific intervention. Power calculations are extremely sensitive to ICC assumptions. Minor variation in the assumed ICC can lead to large differences in the number of clusters needed, potentially impacting trial feasibility and cost. This paper identifies a special class of CRTs aiming to detect the treatment effect heterogeneity, wherein the ICC can be completely disregarded in calculation of power and sample size. This result offers a solution for research projects lacking preliminary estimates of the ICC or facing challenges in their estimate. Moreover, this design facilitates power improvement through increasing the cluster sizes rather than the number of clusters, making it particular advantageous in the situations where expanding the number of clusters is difficult or costly. This paper provides a rigorous theoretical foundation for this class of ICC-ignorable CRTs, including mathematical proofs and practical guidance for implementation. We also present illustrative examples to demonstrate the practical implications of this approach in various research contexts in healthcare delivery.

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arXiv2020

ABCMETAapp: R Shiny Application for Simulation-based Estimation of Mean and Standard Deviation for Meta-analysis via Approximate Bayesian Computation (ABC)

Background and Objective: In meta-analysis based on continuous outcome, estimated means and corresponding standard deviations from the selected studies are key inputs to obtain a pooled estimate of the mean and its confidence interval. We often encounter the situation that these quantities are not directly reported in the literatures. Instead, other summary statistics are reported such as median, minimum, maximum, quartiles, and study sample size. Based on available summary statistics, we need to estimate estimates of mean and standard deviation for meta-analysis. Methods: We developed a R Shiny code based on approximate Bayesian computation (ABC), ABCMETA, to deal with this situation. Results: In this article, we present an interactive and user-friendly R Shiny application for implementing the proposed method (named ABCMETAapp). In ABCMETAapp, users can choose an underlying outcome distribution other than the normal distribution when the distribution of the outcome variable is skewed or heavy tailed. We show how to run ABCMETAapp with examples. Conclusions: ABCMETAapp provides a R Shiny implementation. This method is more flexible than the existing analytical methods since estimation can be based on five different distribution (Normal, Lognormal, Exponential, Weibull, and Beta) for the outcome variable.

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arXiv2016

Approximate Bayesian computation (ABC) coupled with Bayesian model averaging method for estimating mean and standard deviation

Background: We proposed approximate Bayesian computation with single distribution selection (ABC-SD) for estimating mean and standard deviation from other reported summary statistics. The ABC-SD generates pseudo data from a single parametric distribution thought to be the true distribution of underlying study data. This single distribution is either an educated guess, or it is selected via model selection using posterior probability criterion for testing two or more candidate distributions. Further analysis indicated that when model selection is used, posterior model probabilities are sensitive to the prior distribution(s) for parameter(s) and dependable on the type of reported summary statistics. Method: We propose ABC with Bayesian model averaging (ABC-BMA) methodology to estimate mean and standard deviation based on various sets of other summary statistics reported in published studies. We conduct a Monte Carlo simulation study to compare the new proposed ABC-BMA method with our previous ABC-SD method. Results: In the estimation of standard deviation, ABC-BMA has smaller average relative errors (AREs) than that of ABC-SD for normal, lognormal, beta, and exponential distributions. For Weibull distribution, ARE of ABC-BMA is larger than that of ABC-SD but <0.05 in small sample sizes and moves toward zero as sample size increases. When underlying distribution is highly skewed and available summary statistics are only quartiles and sample size, ABC-BMA is recommended but it should be used with caution. Comparison of mean estimation between ABC-BMA and ABC-SD shows similar patterns of results as for standard deviation estimation. Conclusion: ABC-BMA is easy to implement and it performs even better than our previous ABC-SD method for estimation of mean and standard deviation.

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arXiv2015

Simulation-based Estimation of Mean and Standard Deviation for Meta-analysis via Approximate Bayesian Computation (ABC)

Background: When conducting a meta-analysis of a continuous outcome, estimated means and standard deviations from the selected studies are required in order to obtain an overall estimate of the mean effect and its confidence interval. If these quantities are not directly reported in the publications, they need to must be estimated from other reported summary statistics, such as the median, the minimum, the maximum, and quartiles. Methods: We propose a simulation-based estimation approach using the Approximate Bayesian Computation (ABC) technique for estimating mean and standard deviation based on various sets of summary statistics found in published studies. We conduct a simulation study to compare the proposed ABC method with the existing methods of Hozo et al. (2005), Bland (2015), and Wan et al. (2014). Results: In the estimation of the standard deviation, our ABC method performs best in skewed or heavy-tailed distributions. The average relative error (ARE) approaches zero as sample size increases. In the normal distribution, our ABC performs well. However, the Wan et al. method is best since it is based on the normal distribution assumption. When the distribution is skewed or heavy-tailed, the ARE of Wan et al. moves away from zero even as sample size increases. In the estimation of the mean, our ABC method is best since the AREs converge to zero. Conclusion: ABC is a flexible method for estimating the study-specific mean and standard deviation for meta-analysis, especially with underlying skewed or heavy-tailed distributions. The ABC method can be applied using other reported summary statistics such as the posterior mean and 95% credible interval when Bayesian analysis has been employed.

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arXiv2014

Bayesian dose-response analysis for epidemiological studies with complex uncertainty in dose estimation

Most conventional risk analysis methods rely on a single best estimate of exposure per person which does not allow for adjustment for exposure-related uncertainty. Here, we propose a Bayesian model averaging method to properly quantify the relationship between radiation dose and disease outcomes by accounting for shared and unshared uncertainty in estimated dose. Our Bayesian risk analysis method utilizes multiple realizations of sets (vectors) of doses generated by a two-dimensional Monte Carlo simulation method that properly separates shared and unshared errors in dose estimation. The exposure model used in this work is taken from a study of the risk of thyroid nodules among a cohort of 2,376 subjects following exposure to fallout resulting from nuclear testing in Kazakhstan. We assessed the performance of our method through an extensive series of simulation tests and comparisons against conventional regression risk analysis methods. We conclude that when estimated doses contain relatively small amounts of uncertainty, the Bayesian method using multiple realizations of possibly true dose vectors gave similar results to the conventional regression-based methods of dose-response analysis. However, when large and complex mixtures of shared and unshared uncertainties are present, the Bayesian method using multiple dose vectors had significantly lower relative bias than conventional regression-based risk analysis methods as well as a markedly increased capability to include the pre-established 'true' risk coefficient within the credible interval of the Bayesian-based risk estimate. An evaluation of the dose-response using our method is presented for an epidemiological study of thyroid disease following radiation exposure.

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