Biometrika最新文献

{"title":"A More Robust Approach to Multivariable Mendelian Randomization.","authors":"Yinxiang Wu, Hyunseung Kang, Ting Ye","doi":"10.1093/biomet/asaf053","DOIUrl":"10.1093/biomet/asaf053","url":null,"abstract":"<p><p>Multivariable Mendelian randomization (MVMR) uses genetic variants as instrumental variables to infer the direct effects of multiple exposures on an outcome. However, unlike univariable Mendelian randomization, MVMR often faces greater challenges with many weak instruments, which can lead to bias not necessarily toward zero and inflation of type I errors. In this work, we introduce a new asymptotic regime that allows exposures to have varying degrees of instrument strength, providing a more accurate theoretical framework for studying MVMR estimators. Under this regime, our analysis of the widely used multivariable inverse-variance weighted method shows that it is often biased and tends to produce misleadingly narrow confidence intervals in the presence of many weak instruments. To address this, we propose a simple, closed-form modification to the multivariable inverse-variance weighted estimator to reduce bias from weak instruments, and additionally introduce a novel spectral regularization technique to improve finite-sample performance. We show that the resulting spectral-regularized estimator remains consistent and asymptotically normal under many weak instruments. Through simulations and real data applications, we demonstrate that our proposed estimator and asymptotic framework can enhance the robustness of MVMR analyses.</p>","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12335017/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144815700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integer programming for learning directed acyclic graphs from nonidentifiable Gaussian models.","authors":"Tong Xu, Armeen Taeb, Simge Küçükyavuz, Ali Shojaie","doi":"10.1093/biomet/asaf032","DOIUrl":"10.1093/biomet/asaf032","url":null,"abstract":"<p><p>We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model. State-of-the-art structure learning methods for this setting have at least one of the following shortcomings: (i) they cannot provide optimality guarantees and can suffer from learning suboptimal models; (ii) they rely on the stringent assumption that the noise is homoscedastic, and hence the underlying model is fully identifiable. We overcome these shortcomings and develop a computationally efficient mixed-integer programming framework for learning medium-sized problems that accounts for arbitrary heteroscedastic noise. We present an early stopping criterion under which we can terminate the branch-and-bound procedure to achieve an asymptotically optimal solution and establish the consistency of this approximate solution. In addition, we show via numerical experiments that our method outperforms state-of-the-art algorithms and is robust to noise heteroscedasticity, whereas the performance of some competing methods deteriorates under strong violations of the identifiability assumption. The software implementation of our method is available as the Python package micodag.</p>","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"112 3","pages":"asaf032"},"PeriodicalIF":2.8,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12368277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144941432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A general form of covariate adjustment in clinical trials under covariate-adaptive randomization.","authors":"Marlena S Bannick, Jun Shao, Jingyi Liu, Yu Du, Yanyao Yi, Ting Ye","doi":"10.1093/biomet/asaf029","DOIUrl":"10.1093/biomet/asaf029","url":null,"abstract":"<p><p>In randomized clinical trials, adjusting for baseline covariates can improve credibility and efficiency for demonstrating and quantifying treatment effects. This article studies the augmented inverse propensity weighted estimator, which is a general form of covariate adjustment that uses linear, generalized linear and nonparametric or machine learning models for the conditional mean of the response given covariates. Under covariate-adaptive randomization, we establish general theorems that show a complete picture of the asymptotic normality, efficiency gain and applicability of augmented inverse propensity weighted estimators. In particular, we provide for the first time a rigorous theoretical justification of using machine learning methods with cross-fitting for dependent data under covariate-adaptive randomization. Based on the general theorems, we offer insights on the conditions for guaranteed efficiency gain and universal applicability under different randomization schemes, which also motivate a joint calibration strategy using some constructed covariates after applying augmented inverse propensity weighted estimators.</p>","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"112 3","pages":"asaf029"},"PeriodicalIF":2.8,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12264724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian inference for generalized linear models via quasi-posteriors.","authors":"D Agnoletto, T Rigon, D B Dunson","doi":"10.1093/biomet/asaf022","DOIUrl":"10.1093/biomet/asaf022","url":null,"abstract":"<p><p>Generalized linear models are routinely used for modelling relationships between a response variable and a set of covariates. The simple form of a generalized linear model comes with easy interpretability, but also leads to concerns about model misspecification impacting inferential conclusions. A popular semiparametric solution adopted in the frequentist literature is quasilikelihood, which improves robustness by only requiring correct specification of the first two moments. We develop a robust approach to Bayesian inference in generalized linear models through quasi-posterior distributions. We show that quasi-posteriors provide a coherent generalized Bayes inference method, while also approximating so-called coarsened posteriors. In so doing, we obtain new insights into the choice of coarsening parameter. Asymptotically, the quasi-posterior converges in total variation to a normal distribution and has important connections with the loss-likelihood bootstrap posterior. We demonstrate that it is also well calibrated in terms of frequentist coverage. Moreover, the loss-scale parameter has a clear interpretation as a dispersion, and this leads to a consolidated method-of-moments estimator.</p>","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"112 2","pages":"asaf022"},"PeriodicalIF":2.4,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12206450/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sample splitting and assessing goodness-of-fit of time series.","authors":"Richard A Davis, Leon Fernandes","doi":"10.1093/biomet/asaf017","DOIUrl":"10.1093/biomet/asaf017","url":null,"abstract":"<p><p>A fundamental and often final step in time series modelling is to assess the quality of fit of a proposed model to the data. Since the underlying distribution of the innovations that generate a model is often not prescribed, goodness-of-fit tests typically take the form of testing the fitted residuals for serial independence. However, these fitted residuals are intrinsically dependent since they are based on the same parameter estimates, and thus standard tests of serial independence, such as those based on the autocorrelation function or auto-distance correlation function of the fitted residuals, need to be adjusted. The sample-splitting procedure of Pfister et al. (2018) is one such fix for the case of models for independent data, but fails to work in the dependent setting. In this article, sample splitting is leveraged in the time series setting to perform tests of serial dependence of fitted residuals using the autocorrelation function and auto-distance correlation function. The first [Formula: see text] of the data points are used to estimate the parameters of the model and then, using these parameter estimates, the last [Formula: see text] of the data points are used to compute the estimated residuals. Tests for serial independence are then based on these [Formula: see text] residuals. As long as the overlap between the [Formula: see text] and [Formula: see text] data splits is asymptotically [Formula: see text], the autocorrelation function and auto-distance correlation function tests of serial independence often have the same limit distributions as when the underlying residuals are indeed independent and identically distributed. In particular, if the first half of the data is used to estimate the parameters and the estimated residuals are computed for the entire dataset based on these parameter estimates, then the autocorrelation function and auto-distance correlation function can have the same limit distributions as if the residuals were independent and identically distributed. This procedure ameliorates the need for adjustment in the construction of confidence bounds for both the autocorrelation function and the auto-distance correlation function in goodness-of-fit testing.</p>","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"112 2","pages":"asaf017"},"PeriodicalIF":2.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12206451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consistency of common spatial estimators under spatial confounding.","authors":"Brian Gilbert, Elizabeth L Ogburn, Abhirup Datta","doi":"10.1093/biomet/asae070","DOIUrl":"10.1093/biomet/asae070","url":null,"abstract":"<p><p>This article addresses the asymptotic performance of popular spatial regression estimators of the linear effect of an exposure on an outcome under spatial confounding, the presence of an unmeasured spatially structured variable influencing both the exposure and the outcome. We first show that the estimators from ordinary least squares and restricted spatial regression are asymptotically biased under spatial confounding. We then prove a novel result on the infill consistency of the generalized least squares estimator using a working covariance matrix from a Matérn or squared exponential kernel, in the presence of spatial confounding. The result holds under very mild assumptions, accommodating any exposure with some nonspatial variation, any spatially continuous fixed confounder function, and non-Gaussian errors in both the exposure and the outcome. Finally, we prove that spatial estimators from generalized least squares, Gaussian process regression and spline models that are consistent under confounding by a fixed function will also be consistent under endogeneity or confounding by a random function, i.e., a stochastic process. We conclude that, contrary to some claims in the literature on spatial confounding, traditional spatial estimators are capable of estimating linear exposure effects under spatial confounding as long as there is some noise in the exposure. We support our theoretical arguments with simulation studies.</p>","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"112 2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12411883/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145013761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving efficiency in transporting average treatment effects.","authors":"K E Rudolph, N T Williams, E A Stuart, I Díaz","doi":"10.1093/biomet/asaf027","DOIUrl":"10.1093/biomet/asaf027","url":null,"abstract":"<p><p>We develop flexible, semiparametric estimators of the average treatment effect (ATE) transported to a new target population that offer potential efficiency gains. Transport may be of value when the ATE may differ across populations. We consider the setting where differences in the ATE are due to differences in the distribution of effect modifiers, baseline covariates that modify the treatment effect. First, we propose a collaborative one-step semiparametric estimator that can improve efficiency. This approach does not require researchers to have knowledge about which covariates are effect modifiers and which differ in distribution between the populations, but does require all covariates to be measured in the target population. Second, we propose two one-step semiparametric estimators that assume knowledge of which covariates are effect modifiers and which are both effect modifiers and differentially distributed between the populations. These estimators can be used even when not all covariates are observed in the target population; one requires that only effect modifiers are observed, and the other requires that only those modifiers that are also differentially distributed are observed. We use simulation to compare finite sample performance across our proposed estimators and an existing semiparametric estimator of the transported ATE, including in the presence of practical violations of the positivity assumption. Lastly, we apply our proposed estimators to a large-scale housing trial.</p>","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"112 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12338304/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144833907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"With random regressors, least squares inference is robust to correlated errors with unknown correlation structure.","authors":"Zifeng Zhang, Peng Ding, Wen Zhou, Haonan Wang","doi":"10.1093/biomet/asae054","DOIUrl":"10.1093/biomet/asae054","url":null,"abstract":"<p><p>Linear regression is arguably the most widely used statistical method. With fixed regressors and correlated errors, the conventional wisdom is to modify the variance-covariance estimator to accommodate the known correlation structure of the errors. We depart from existing literature by showing that with random regressors, linear regression inference is robust to correlated errors with unknown correlation structure. The existing theoretical analyses for linear regression are no longer valid because even the asymptotic normality of the least squares coefficients breaks down in this regime. We first prove the asymptotic normality of the <math><mi>t</mi></math> statistics by establishing their Berry-Esseen bounds based on a novel probabilistic analysis of self-normalized statistics. We then study the local power of the corresponding <math><mi>t</mi></math> tests and show that, perhaps surprisingly, error correlation can even enhance power in the regime of weak signals. Overall, our results show that linear regression is applicable more broadly than the conventional theory suggests, and they further demonstrate the value of randomization for ensuring robustness of inference.</p>","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"112 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12320931/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144783434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving randomized controlled trial analysis via data-adaptive borrowing.","authors":"Chenyin Gao, Shu Yang, Mingyang Shan, Wenyu Ye, Ilya Lipkovich, Douglas Faries","doi":"10.1093/biomet/asae069","DOIUrl":"10.1093/biomet/asae069","url":null,"abstract":"<p><p>In recent years, real-world external controls have grown in popularity as a tool to empower randomized placebo-controlled trials, particularly in rare diseases or cases where balanced randomization is unethical or impractical. However, as external controls are not always comparable to the trials, direct borrowing without scrutiny may heavily bias the treatment effect estimator. Our paper proposes a data-adaptive integrative framework capable of preventing unknown biases of the external controls. The adaptive nature is achieved by dynamically sorting out a comparable subset of external controls via bias penalization. Our proposed method can simultaneously achieve (a) the semiparametric efficiency bound when the external controls are comparable and (b) selective borrowing that mitigates the impact of the existence of incomparable external controls. Furthermore, we establish statistical guarantees, including consistency, asymptotic distribution and inference, providing Type-I error control and good power. Extensive simulations and two real-data applications show that the proposed method leads to improved performance over the trial-only estimator across various bias-generating scenarios.</p>","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"112 2","pages":"asae069"},"PeriodicalIF":2.4,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11972012/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143794582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes.","authors":"Yichen Zhu, Michele Peruzzi, Cheng Li, David B Dunson","doi":"10.1093/biomet/asae029","DOIUrl":"10.1093/biomet/asae029","url":null,"abstract":"<p><p>In geostatistical problems with massive sample size, Gaussian processes can be approximated using sparse directed acyclic graphs to achieve scalable <math><mi>O</mi> <mo>(</mo> <mi>n</mi> <mo>)</mo></math> computational complexity. In these models, data at each location are typically assumed conditionally dependent on a small set of parents which usually include a subset of the nearest neighbors. These methodologies often exhibit excellent empirical performance, but the lack of theoretical validation leads to unclear guidance in specifying the underlying graphical model and sensitivity to graph choice. We address these issues by introducing radial neighbors Gaussian processes (RadGP), a class of Gaussian processes based on directed acyclic graphs in which directed edges connect every location to all of its neighbors within a predetermined radius. We prove that any radial neighbors Gaussian process can accurately approximate the corresponding unrestricted Gaussian process in Wasserstein-2 distance, with an error rate determined by the approximation radius, the spatial covariance function, and the spatial dispersion of samples. We offer further empirical validation of our approach via applications on simulated and real world data showing excellent performance in both prior and posterior approximations to the original Gaussian process.</p>","PeriodicalId":9001,"journal":{"name":"Biometrika","volume":"111 4","pages":"1151-1167"},"PeriodicalIF":2.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11993192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143967374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}