arXiv subjects

Satoshi Hattori

Publications and source records attributed to Satoshi Hattori.

At least 19 recordsLinked to original sources

arXiv2026

Data-Adaptive Integration with External Summary Data for Outcome Mean Estimation

Combining an internal individual-level study with readily available external summary statistics promises major efficiency gains at minimal additional cost, yet heterogeneity between sources can bias estimates for the internal target population. We develop a generalized entropy-balancing integration strategy that calibrates the internal individual-level sample to externally reported moments while retaining the internal population as the target, explicitly permitting a biased external sample. The weighted-regression version of our estimator is doubly robust: it remains consistent when either the outcome-regression model or the entropy-balancing model is correctly specified. When multiple balancing specifications are plausible, we introduce a data-adaptive entropy-family selection rule. For the final borrowing decision, we propose a bootstrap-based criterion comparing stabilized mean squared error (MSE) estimates for the selected entropy-balancing estimator and the internal sample mean. This criterion is selection consistent under fixed alternatives and reverts to the internal estimator when a nonvanishing bias is detected. Separately, under a linear homoscedastic benchmark, the asymptotic efficiency criteria admit geometric interpretations through the Mahalanobis distance and Pearson chi-squared divergence. The entropy-balancing estimators and numerical-experiment routines are implemented in the R package daisy. Simulations show stable MSE reductions for the weighted-regression estimator across calibrated distributional shifts and the predicted reversion toward the internal estimator under fixed simultaneous misspecification as the sample size increases. An application to nationwide public-access defibrillation records in Japan illustrates the resulting MSE-based borrowing decision.

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arXiv2026

Event-driven type design for clinical trials with recurrent events

It is a common practice in randomized clinical trials with the standard survival outcome to follow patients until a prespecified number of events have been observed, a type of trial known as the event-driven trial. The event-driven design ensures that the target power for a specified type 1 error rate is achieved to detect the target hazard ratio, regardless of the specification of other quantities. To understand the treatment effect for chronic conditions, the analysis of recurrent events has gained popularity in randomized controlled trials, particularly large-scale confirmatory trials. In the absence of within-subject correlation among multiple events, a similar event-driven design can be employed for recurrent event outcomes. On the other hand, in the presence of the within-subject correlation, one needs to model the correlation among recurrent events in evaluating power and setting the sample size. However, information useful in modeling the within-subject correlation is limited at the design stage. Failing to consider the correlation properly may lead to underpowered studies. We propose an event-driven type design for recurrent event outcomes. Our method ensures the target power for the target treatment effect, regardless of the specification of other quantities, by monitoring the robust variance under the marginal rates/means model in a blinded manner. We investigate the operating characteristics of the proposed monitoring procedure in simulation studies. The results of simulation studies showed that the proposed blinded monitoring procedure controlled the power well so that the test possessed the target power and did not lead to serious inflation of the type 1 error rate. Furthermore, we illustrate the proposed method using a real clinical trial dataset.

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arXiv2026

Blinded sample size re-estimation accounting for uncertainty in mid-trial estimation

For randomized controlled trials to be conclusive, it is important to set the target sample size accurately at the design stage. Comparing two normal populations, the sample size calculation requires specification of the variance other than the treatment effect and misspecification can lead to underpowered studies. Blinded sample size re-estimation is an approach to minimize the risk of inconclusive studies. Existing methods proposed to use the total (one-sample) variance that is estimable from blinded data without knowledge of the treatment allocation. We demonstrate that, since the expectation of this estimator is greater than or equal to the true variance, the one-sample variance approach can be regarded as providing an upper bound of the variance in blind reviews. This worst-case evaluation can likely reduce a risk of underpowered studies. However, blinded reviews of small sample size may still lead to underpowered studies. We propose a refined method accounting for estimation error in blind reviews using an upper confidence limit of the variance. A similar idea had been proposed in the setting of external pilot studies. Furthermore, we developed a method to select an appropriate confidence level so that the re-estimated sample size attains the target power. Numerical studies showed that our method works well and outperforms existing methods. The proposed procedure is motivated and illustrated by recent randomized clinical trials.

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arXiv2025

Copas-Jackson-type bounds for publication bias over a general class of selection models

Publication bias (PB) is one of the most vital threats to the accuracy of meta-analysis. Adjustment or sensitivity analysis based on selection models, which describe the probability of a study being published, provide a more objective evaluation of PB than widely-used simple graphical methods such as the trim-and-fill method. Most existing methods rely on parametric selection models. The Copas-Jackson bound (C-J bound) provides a worst-case bound of an analytical form over a nonparametric class of selection models, which would provide more robust conclusions than parametric sensitivity analysis. The nonparametric class of the selection models in the C-J bound is restrictive and only covers parametric selection models monotonic to the standard errors of outcomes. The novelty of this paper is to develop a method that constructs worst-case bounds over a general class of selection models weakening the assumption in the C-J bound. We propose an efficient numerical method to obtain an approximate worst-case bound via tractable nonlinear programming with linear constraints. We substantiate the effectiveness of the proposed bound with extensive simulation studies and show its applicability with two real-world meta-analyses.

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arXiv2025

Copas-Heckman-type sensitivity analysis for publication bias in rare-event meta-analysis under generalized linear mixed models

In systematic reviews and meta-analyses, publication bias (PB) is one of the serious concerns and mainly induced by selective publication of academic literatures. Although many methods have been proposed to deal with PB, almost all the methods are based on the normal-normal (NN) random-effects model assuming that data are normally distributed in both the within-study and the between-study levels. For rare-event meta-analysis where data contain rare occurrences of events, the standard NN random-effects model may perform poorly. Instead, some generalized linear mixed models (GLMMs) which employ the exact distribution for the number of events in within-study level provide alternatives and have been widely used in practice. However, limited methods can be applied to deal with PB in the GLMMs. To address this limitation, we propose a framework of sensitivity analysis for evaluating the impact of PB in various GLMMs. The proposed framework is developed based on the famous Copas-Heckman-type sensitivity analysis methods and can be easily implemented with the standard software with small computational cost. In this paper, we conduct simulation studies to assess the performance of proposed methods in adjusting PB and compare the results with related existing methods. Several real-world examples are also analyzed to show the broad applicability of our proposal in evaluating the potential impact of PB in meta-analysis of odds ratios and proportions with rare-event outcomes.

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arXiv2025

Comment on "Average Hazard as Harmonic Mean" by Chiba

In a recent article published in Pharmaceutical Statistics, Chiba proposed a reinterpretation of the average hazard as a harmonic mean of the hazard function and questioned the validity of the Kaplan-Meier plug-in estimator when the truncation time does not coincide with an observed event time. In this commentary, we examine the arguments presented and highlight several points that warrant clarification. Through simulation studies, we further show that the plug-in estimator provides reliable estimates across a range of truncation times, even in small samples. These support the continued utilization of the Kaplan-Meier plug-in estimator for the average hazard and help clarify its proper interpretation and implementation.

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arXiv2024

Semiparametric Piecewise Accelerated Failure Time Model for the Analysis of Immune-Oncology Clinical Trials

Effectiveness of immune-oncology chemotherapies has been presented in recent clinical trials. The Kaplan-Meier estimates of the survival functions of the immune therapy and the control often suggested the presence of the lag-time until the immune therapy began to act. It implies the use of hazard ratio under the proportional hazards assumption would not be appealing, and many alternatives have been investigated such as the restricted mean survival time. In addition to such overall summary of the treatment contrast, the lag-time is also an important feature of the treatment effect. Identical survival functions up to the lag-time implies patients who are likely to die before the lag-time would not benefit the treatment and identifying such patients would be very important. We propose the semiparametric piecewise accelerated failure time model and its inference procedure based on the semiparametric maximum likelihood method. It provides not only an overall treatment summary, but also a framework to identify patients who have less benefit from the immune-therapy in a unified way. Numerical experiments confirm that each parameter can be estimated with minimal bias. Through a real data analysis, we illustrate the evaluation of the effect of immune-oncology therapy and the characterization of covariates in which patients are unlikely to receive the benefit of treatment.

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arXiv2024

Sensitivity analysis for publication bias in meta-analysis of sparse data based on exact likelihood

Meta-analysis is a powerful tool to synthesize findings from multiple studies. The normal-normal random-effects model is widely used to account for between-study heterogeneity. However, meta-analysis of sparse data, which may arise when the event rate is low for binary or count outcomes, poses a challenge to the normal-normal random-effects model in the accuracy and stability in inference since the normal approximation in the within-study model may not be good. To reduce bias arising from data sparsity, the generalized linear mixed model can be used by replacing the approximate normal within-study model with an exact model. Publication bias is one of the most serious threats in meta-analysis. Several quantitative sensitivity analysis methods for evaluating the potential impacts of selective publication are available for the normal-normal random-effects model. We propose a sensitivity analysis method by extending the likelihood-based sensitivity analysis with the t-statistic selection function of Copas to several generalized linear mixed-effects models. Through applications of our proposed method to several real-world meta-analysis and simulation studies, the proposed method was proven to outperform the likelihood-based sensitivity analysis based on the normal-normal model. The proposed method would give useful guidance to address publication bias in meta-analysis of sparse data.

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arXiv2024

A likelihood-based sensitivity analysis for addressing publication bias in meta-analysis of diagnostic studies using exact likelihood

Publication bias (PB) poses a significant threat to meta-analysis, as studies yielding notable results are more likely to be published in scientific journals. Sensitivity analysis provides a flexible method to address PB and to examine the impact of unpublished studies. A selection model based on t-statistics to sensitivity analysis is proposed by Copas. This t-statistics selection model is interpretable and enables the modeling of biased publication sampling across studies, as indicated by the asymmetry in the funnel-plot. In meta-analysis of diagnostic studies, the summary receiver operating characteristic curve is an essential tool for synthesizing the bivariate outcomes of sensitivity and specificity reported by individual studies. Previous studies address PB upon the bivariate normal model but these methods rely on the normal approximation for the empirical logit-transformed sensitivity and specificity, which is not suitable for sparse data scenarios. Compared to the bivariate normal model, the bivariate binomial model which replaces the normal approximation in the within-study model with the exact within-study model has better finite sample properties. In this study, we applied the Copas t-statistics selection model to the meta-analysis of diagnostic studies using the bivariate binomial model. To our knowledge, this is the first study to apply the Copas t-statistics selection model to the bivariate binomial model. We have evaluated our proposed method through several real-world meta-analyses of diagnostic studies and simulation studies.

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arXiv2024

Publication bias adjustment in network meta-analysis: an inverse probability weighting approach using clinical trial registries

Network meta-analysis (NMA) is a useful tool to compare multiple interventions simultaneously in a single meta-analysis, it can be very helpful for medical decision making when the study aims to find the best therapy among several active candidates. However, the validity of its results is threatened by the publication bias issue. Existing methods to handle the publication bias issue in the standard pairwise meta-analysis are hard to extend to this area with the complicated data structure and the underlying assumptions for pooling the data. In this paper, we aimed to provide a flexible inverse probability weighting (IPW) framework along with several t-type selection functions to deal with the publication bias problem in the NMA context. To solve these proposed selection functions, we recommend making use of the additional information from the unpublished studies from multiple clinical trial registries. A comprehensive numerical study and a real example showed that our methodology can help obtain more accurate estimates and higher coverage probabilities, and improve other properties of an NMA (e.g., ranking the interventions).

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arXiv2024

Nonparametric worst-case bounds for publication bias on the summary receiver operating characteristic curve

The summary receiver operating characteristic (SROC) curve has been recommended as one important meta-analytical summary to represent the accuracy of a diagnostic test in the presence of heterogeneous cutoff values. However, selective publication of diagnostic studies for meta-analysis can induce publication bias (PB) on the estimate of the SROC curve. Several sensitivity analysis methods have been developed to quantify PB on the SROC curve, and all these methods utilize parametric selection functions to model the selective publication mechanism. The main contribution of this article is to propose a new sensitivity analysis approach that derives the worst-case bounds for the SROC curve by adopting nonparametric selection functions under minimal assumptions. The estimation procedures of the worst-case bounds use the Monte Carlo method to obtain the SROC curves along with the corresponding area under the curves in the worst case where the maximum possible PB under a range of marginal selection probabilities is considered. We apply the proposed method to a real-world meta-analysis to show that the worst-case bounds of the SROC curves can provide useful insights for discussing the robustness of meta-analytical findings on diagnostic test accuracy.

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arXiv2023

A simple sensitivity analysis method for unmeasured confounders via linear programming with estimating equation constraints

In estimating the average treatment effect in observational studies, the influence of confounders should be appropriately addressed. To this end, the propensity score is widely used. If the propensity scores are known for all the subjects, bias due to confounders can be adjusted by using the inverse probability weighting (IPW) by the propensity score. Since the propensity score is unknown in general, it is usually estimated by the parametric logistic regression model with unknown parameters estimated by solving the score equation under the strongly ignorable treatment assignment (SITA) assumption. Violation of the SITA assumption and/or misspecification of the propensity score model can cause serious bias in estimating the average treatment effect. To relax the SITA assumption, the IPW estimator based on the outcome-dependent propensity score has been successfully introduced. However, it still depends on the correctly specified parametric model and its identification. In this paper, we propose a simple sensitivity analysis method for unmeasured confounders. In the standard practice, the estimating equation is used to estimate the unknown parameters in the parametric propensity score model. Our idea is to make inference on the average causal effect by removing restrictive parametric model assumptions while still utilizing the estimating equation. Using estimating equations as constraints, which the true propensity scores asymptotically satisfy, we construct the worst-case bounds for the average treatment effect with linear programming. Different from the existing sensitivity analysis methods, we construct the worst-case bounds with minimal assumptions. We illustrate our proposal by simulation studies and a real-world example.

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arXiv2023

Sensitivity analysis for publication bias on the time-dependent summary ROC analysis in meta-analysis of prognosis studies

In the analysis of prognosis studies with time-to-event outcomes, dichotomization of patients is often made. As the evaluations of prognostic capacity, the survivals of groups with high/low expression of the biomarker are often estimated by the Kaplan-Meier method, and the difference between groups is summarized via the hazard ratio (HR). The high/low expressions are usually determined by study-specific cutoff values, which brings heterogeneity over multiple prognosis studies and difficulty to synthesizing the results in a simple way. In meta-analysis of diagnostic studies with binary outcomes, the summary receiver operating characteristics (SROC) analysis provides a useful cutoff-free summary over studies. Recently, this methodology has been extended to the time-dependent SROC analysis for time-to-event outcomes in meta-analysis of prognosis studies. In this paper, we propose a sensitivity analysis method for evaluating the impact of publication bias on the time-dependent SROC analysis. Our proposal extends the recently introduced sensitivity analysis method for meta-analysis of diagnostic studies based on the bivariate normal model on sensitivity and specificity pairs. To model the selective publication process specific to prognosis studies, we introduce a trivariate model on the time-dependent sensitivity and specificity and the log-transformed HR. Based on the proved asymptotic property of the trivariate model, we introduce a likelihood based sensitivity analysis method based on the conditional likelihood constrained by the expected proportion of published studies. We illustrate the proposed sensitivity analysis method through the meta-analysis of Ki67 for breast cancer. Simulation studies are conducted to evaluate the performance of the proposed method.

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arXiv2023

On a fundamental problem in the analysis of cancer registry data

In epidemiology research with cancer registry data, it is often of primary interest to make inference on cancer death, not overall survival. Since cause of death is not easy to collect or is not necessarily reliable in cancer registries, some special methodologies have been introduced and widely used by using the concepts of the relative survival ratio and the net survival. In making inference of those measures, external life tables of the general population are utilized to adjust the impact of non-cancer death on overall survival. The validity of this adjustment relies on the assumption that mortality in the external life table approximates non-cancer mortality of cancer patients. However, the population used to calculate a life table may include cancer death and cancer patients. Sensitivity analysis proposed by Talbäck and Dickman to address it requires additional information which is often not easily available. We propose a method to make inference on the net survival accounting for potential presence of cancer patients and cancer death in the life table for the general population. The idea of adjustment is to consider correspondence of cancer mortality in the life table and that in the cancer registry. We realize a novel method to adjust cancer mortality in the cancer registry without any additional information to the standard analyses of cancer registries. Our simulation study revealed that the proposed method successfully removed the bias. We illustrate the proposed method with the cancer registry data in England.

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arXiv2023

DTAmetasa: an R shiny application for meta-analysis of diagnostic test accuracy and sensitivity analysis of publication bias

Meta-analysis of diagnostic test accuracy (DTA) is the powerful statistical method for synthesizing and evaluating the diagnostic capacity of the medical tests and has been extensively used by clinical physicians and healthcare decision-makers. However, publication bias (PB) threatens the validity of meta-analysis of DTA. Some statistical methods have been developed to deal with PB in meta-analysis of DTA, but implementing these methods requires high-level statistical knowledge and programming skill. To assist non-technical users in running most routines in meta-analysis of DTA and handling with PB, we developed an interactive application, DTAmetasa. DTAmetasa is developed with the web-based graphical user interface based on the R shiny framework. It allows users to upload data and conduct meta-analysis of DTA by "point and click" operations. Moreover, DTAmetasa provides the sensitivity analysis of PB and presents the graphical results to evaluate the magnitude of the PB under various publication mechanisms. In this study, we introduce the functionalities of DTAmetasa and use the real-world meta-analysis to show its capacity for dealing with PB.

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arXiv2023

Frequentist analysis of basket trials with one-sample Mantel-Haenszel procedures

Recent substantial advances of molecular targeted oncology drug development is requiring new paradigms for early-phase clinical trial methodologies to enable us to evaluate efficacy of several subtypes simultaneously and efficiently. The concept of the basket trial is getting of much attention to realize this requirement borrowing information across subtypes, which are called baskets. Bayesian approach is a natural approach to this end and indeed the majority of the existing proposals relies on it. On the other hand, it required complicated modeling and may not necessarily control the type 1 error probabilities at the nominal level. In this paper, we develop a purely frequentist approach for basket trials based on one-sample Mantel-Haenszel procedure relying on a very simple idea for borrowing information under the common treatment effect assumption over baskets. We show that the proposed estimator is consistent under two limiting models of the large strata and sparse data limiting models (dually consistent) and propose dually consistent variance estimators. The proposed Mantel-Haenszel estimators are interpretable even if the common treatment assumptions are violated. Then, we can design basket trials in a confirmatory matter. We also propose an information criterion approach to identify effective subclass of baskets.

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arXiv2022

On sample size determination for restricted mean survival time-based tests in randomized clinical trials

Restricted mean survival time (RMST) is gaining attention as a measure to quantify the treatment effect on survival outcomes in randomized clinical trials. Several methods to determine sample size based on the RMST-based tests have been proposed. However, to the best of our knowledge, there is no discussion about the power and sample size regarding the augmented version of RMST-based tests, which utilize baseline covariates for a gain in estimation efficiency and in power for testing the no treatment effect. The conventional event-driven study design based on the log-rank test allows us to calculate the power for a given hazard ratio without specifying the survival functions. In contrast, the existing sample size determination methods for the RMST-based tests relies on the adequacy of the assumptions of the entire survival curves of two groups. Furthermore, to handle the augmented test, the correlation between the baseline covariates and the martingale residuals must be handled. To address these issues, we propose an approximated sample size formula for the augmented version of the RMST-based test, which does not require specifying the entire survival curve in the treatment group, and also a sample size recalculation approach to update the correlations between the baseline covariates and the martingale residuals with the blinded data. The proposed procedure will enable the studies to have the target power for a given RMST difference even when correct survival functions cannot be specified at the design stage.

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arXiv2022

A likelihood based sensitivity analysis for publication bias on summary ROC in meta-analysis of diagnostic test accuracy

In meta-analysis of diagnostic test accuracy, summary receiver operating characteristic (SROC) is a recommended method to summarize the discriminant capacity of a diagnostic test in the presence of study-specific cutoff values and the area under the SROC (SAUC) gives the aggregate measure of test accuracy. SROC or SAUC can be estimated by bivariate modelling of pairs of sensitivity and specificity over the primary diagnostic studies. However, publication bias is a major threat to the validity of estimates in meta-analysis. To address this issue, we propose to adopt sensitivity analysis to make an objective inference for the impact of publication bias on SROC or SAUC. We extend Copas likelihood based sensitivity analysis to the bivariate normal model used for meta-analysis of diagnostic test accuracy to evaluate how much SROC or SAUC would change with different selection probabilities under several selective publication mechanisms dependent on sensitivity and/or specificity. The selection probability is modelled by a selection function on $t$-type statistic for the linear combination of logit-transformed sensitivity and specificity, allowing the selective publication of each study to be influenced by the cutoff-dependent $p$-value for sensitivity, specificity, or diagnostic odds ratio. By embedding the selection function into the bivariate normal model, the conditional likelihood is proposed and the bias-corrected SROC or SAUC can be estimated by maximizing the likelihood. We illustrate the proposed sensitivity analysis by reanalyzing a meta-analysis of test accuracy for intravascular device related infection. Simulation studies are conducted to investigate the performance of proposed methods.

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