Charles E McCulloch, John M Neuhaus, Ross D Boylan
Statistical models incorporating cluster-specific intercepts are commonly used in hierarchical settings, for example, observations clustered within patients or patients clustered within hospitals. Predicted values of these intercepts are often used to identify or "flag" extreme or outlying clusters, such as poorly performing hospitals or patients with rapid declines in their health. We consider a variety of flagging rules, assessing different predictors, and using different accuracy measures. Using theoretical calculations and comprehensive numerical evaluation, we show that previously proposed rules based on the 2 most commonly used predictors, the usual best linear unbiased predictor and fixed effects predictor, perform extremely poorly: the incorrect flagging rates are either unacceptably high (approaching 0.5 in the limit) or overly conservative (eg, much <0.05 for reasonable parameter values, leading to very low correct flagging rates). We develop novel methods for flagging extreme clusters that can control the incorrect flagging rates, including very simple-to-use versions that we call "self-calibrated." The new methods have substantially higher correct flagging rates than previously proposed methods for flagging extreme values, while controlling the incorrect flagging rates. We illustrate their application using data on length of stay in pediatric hospitals for children admitted for asthma diagnoses.
{"title":"Flagging unusual clusters based on linear mixed models using weighted and self-calibrated predictors.","authors":"Charles E McCulloch, John M Neuhaus, Ross D Boylan","doi":"10.1093/biomtc/ujae022","DOIUrl":"10.1093/biomtc/ujae022","url":null,"abstract":"<p><p>Statistical models incorporating cluster-specific intercepts are commonly used in hierarchical settings, for example, observations clustered within patients or patients clustered within hospitals. Predicted values of these intercepts are often used to identify or \"flag\" extreme or outlying clusters, such as poorly performing hospitals or patients with rapid declines in their health. We consider a variety of flagging rules, assessing different predictors, and using different accuracy measures. Using theoretical calculations and comprehensive numerical evaluation, we show that previously proposed rules based on the 2 most commonly used predictors, the usual best linear unbiased predictor and fixed effects predictor, perform extremely poorly: the incorrect flagging rates are either unacceptably high (approaching 0.5 in the limit) or overly conservative (eg, much <0.05 for reasonable parameter values, leading to very low correct flagging rates). We develop novel methods for flagging extreme clusters that can control the incorrect flagging rates, including very simple-to-use versions that we call \"self-calibrated.\" The new methods have substantially higher correct flagging rates than previously proposed methods for flagging extreme values, while controlling the incorrect flagging rates. We illustrate their application using data on length of stay in pediatric hospitals for children admitted for asthma diagnoses.</p>","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140334556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yeseul Jeon, Won Chang, Seonghyun Jeong, Sanghoon Han, Jaewoo Park
Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in prediction accuracy, statistical inference, such as estimating the effects of covariates and quantifying the prediction uncertainty, is not trivial due to the highly complicated model structure and overparameterization. To address this challenge, we propose a new Bayesian approach by embedding CNNs within the generalized linear models (GLMs) framework. We use extracted nodes from the last hidden layer of CNN with Monte Carlo (MC) dropout as informative covariates in GLM. This improves accuracy in prediction and regression coefficient inference, allowing for the interpretation of coefficients and uncertainty quantification. By fitting ensemble GLMs across multiple realizations from MC dropout, we can account for uncertainties in extracting the features. We apply our methods to biological and epidemiological problems, which have both high-dimensional correlated inputs and vector covariates. Specifically, we consider malaria incidence data, brain tumor image data, and fMRI data. By extracting information from correlated inputs, the proposed method can provide an interpretable Bayesian analysis. The algorithm can be broadly applicable to image regressions or correlated data analysis by enabling accurate Bayesian inference quickly.
当输入变量为图像或空间数据时,卷积神经网络(CNN)可为各种应用提供灵活的函数近似。虽然卷积神经网络在预测准确性上往往优于传统统计模型,但由于模型结构非常复杂且参数过多,统计推断(如估计协变量的影响和量化预测的不确定性)并非易事。为了应对这一挑战,我们提出了一种新的贝叶斯方法,即在广义线性模型(GLM)框架内嵌入 CNN。我们将从 CNN 最后一个隐藏层提取的节点与蒙特卡罗(MC)剔除作为广义线性模型中的信息协变量。这提高了预测和回归系数推断的准确性,允许对系数进行解释和不确定性量化。通过拟合来自 MC 丢失的多个变现的集合 GLM,我们可以考虑提取特征时的不确定性。我们将我们的方法应用于生物和流行病学问题,这些问题既有高维相关输入,也有向量协变量。具体来说,我们考虑了疟疾发病率数据、脑肿瘤图像数据和 fMRI 数据。通过从相关输入中提取信息,所提出的方法可以提供可解释的贝叶斯分析。通过快速实现准确的贝叶斯推理,该算法可广泛应用于图像回归或相关数据分析。
{"title":"A Bayesian convolutional neural network-based generalized linear model.","authors":"Yeseul Jeon, Won Chang, Seonghyun Jeong, Sanghoon Han, Jaewoo Park","doi":"10.1093/biomtc/ujae057","DOIUrl":"https://doi.org/10.1093/biomtc/ujae057","url":null,"abstract":"<p><p>Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in prediction accuracy, statistical inference, such as estimating the effects of covariates and quantifying the prediction uncertainty, is not trivial due to the highly complicated model structure and overparameterization. To address this challenge, we propose a new Bayesian approach by embedding CNNs within the generalized linear models (GLMs) framework. We use extracted nodes from the last hidden layer of CNN with Monte Carlo (MC) dropout as informative covariates in GLM. This improves accuracy in prediction and regression coefficient inference, allowing for the interpretation of coefficients and uncertainty quantification. By fitting ensemble GLMs across multiple realizations from MC dropout, we can account for uncertainties in extracting the features. We apply our methods to biological and epidemiological problems, which have both high-dimensional correlated inputs and vector covariates. Specifically, we consider malaria incidence data, brain tumor image data, and fMRI data. By extracting information from correlated inputs, the proposed method can provide an interpretable Bayesian analysis. The algorithm can be broadly applicable to image regressions or correlated data analysis by enabling accurate Bayesian inference quickly.</p>","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141417569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It has become consensus that mild cognitive impairment (MCI), one of the early symptoms onset of Alzheimer's disease (AD), may appear 10 or more years after the emergence of neuropathological abnormalities. Therefore, understanding the progression of AD biomarkers and uncovering when brain alterations begin in the preclinical stage, while patients are still cognitively normal, are crucial for effective early detection and therapeutic development. In this paper, we develop a Bayesian semiparametric framework that jointly models the longitudinal trajectory of the AD biomarker with a changepoint relative to the occurrence of symptoms onset, which is subject to left truncation and right censoring, in a heterogeneous population. Furthermore, unlike most existing methods assuming that everyone in the considered population will eventually develop the disease, our approach accounts for the possibility that some individuals may never experience MCI or AD, even after a long follow-up time. We evaluate the proposed model through simulation studies and demonstrate its clinical utility by examining an important AD biomarker, ptau181, using a dataset from the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) study.
轻度认知障碍(MCI)是阿尔茨海默病(AD)的早期症状之一,可能在神经病理学异常出现 10 年或更长时间后才出现,这一点已成为共识。因此,了解阿尔茨海默病生物标志物的发展过程,并在患者认知能力正常的情况下揭示大脑改变何时开始于临床前阶段,对于有效的早期检测和治疗开发至关重要。在本文中,我们开发了一种贝叶斯半参数框架,在异质性人群中,该框架可联合建模AD生物标志物的纵向轨迹与相对于症状发作的变化点,该变化点会受到左截断和右删减的影响。此外,与大多数现有方法假设所考虑人群中的每个人最终都会发病不同,我们的方法考虑到了某些个体即使经过长时间随访也可能从未出现 MCI 或 AD 的可能性。我们通过模拟研究对所提出的模型进行了评估,并利用正常人认知能力下降生物标志物(BIOCARD)研究的数据集检测了一个重要的注意力缺失症生物标志物 ptau181,从而证明了该模型的临床实用性。
{"title":"A Bayesian semi-parametric model for learning biomarker trajectories and changepoints in the preclinical phase of Alzheimer's disease.","authors":"Kunbo Wang, William Hua, MeiCheng Wang, Yanxun Xu","doi":"10.1093/biomtc/ujae048","DOIUrl":"10.1093/biomtc/ujae048","url":null,"abstract":"<p><p>It has become consensus that mild cognitive impairment (MCI), one of the early symptoms onset of Alzheimer's disease (AD), may appear 10 or more years after the emergence of neuropathological abnormalities. Therefore, understanding the progression of AD biomarkers and uncovering when brain alterations begin in the preclinical stage, while patients are still cognitively normal, are crucial for effective early detection and therapeutic development. In this paper, we develop a Bayesian semiparametric framework that jointly models the longitudinal trajectory of the AD biomarker with a changepoint relative to the occurrence of symptoms onset, which is subject to left truncation and right censoring, in a heterogeneous population. Furthermore, unlike most existing methods assuming that everyone in the considered population will eventually develop the disease, our approach accounts for the possibility that some individuals may never experience MCI or AD, even after a long follow-up time. We evaluate the proposed model through simulation studies and demonstrate its clinical utility by examining an important AD biomarker, ptau181, using a dataset from the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) study.</p>","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11110494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141074619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In mobile health, tailoring interventions for real-time delivery is of paramount importance. Micro-randomized trials have emerged as the "gold-standard" methodology for developing such interventions. Analyzing data from these trials provides insights into the efficacy of interventions and the potential moderation by specific covariates. The "causal excursion effect," a novel class of causal estimand, addresses these inquiries. Yet, existing research mainly focuses on continuous or binary data, leaving count data largely unexplored. The current work is motivated by the Drink Less micro-randomized trial from the UK, which focuses on a zero-inflated proximal outcome, i.e., the number of screen views in the subsequent hour following the intervention decision point. To be specific, we revisit the concept of causal excursion effect, specifically for zero-inflated count outcomes, and introduce novel estimation approaches that incorporate nonparametric techniques. Bidirectional asymptotics are established for the proposed estimators. Simulation studies are conducted to evaluate the performance of the proposed methods. As an illustration, we also implement these methods to the Drink Less trial data.
{"title":"Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes.","authors":"Xueqing Liu, Tianchen Qian, Lauren Bell, Bibhas Chakraborty","doi":"10.1093/biomtc/ujae054","DOIUrl":"https://doi.org/10.1093/biomtc/ujae054","url":null,"abstract":"<p><p>In mobile health, tailoring interventions for real-time delivery is of paramount importance. Micro-randomized trials have emerged as the \"gold-standard\" methodology for developing such interventions. Analyzing data from these trials provides insights into the efficacy of interventions and the potential moderation by specific covariates. The \"causal excursion effect,\" a novel class of causal estimand, addresses these inquiries. Yet, existing research mainly focuses on continuous or binary data, leaving count data largely unexplored. The current work is motivated by the Drink Less micro-randomized trial from the UK, which focuses on a zero-inflated proximal outcome, i.e., the number of screen views in the subsequent hour following the intervention decision point. To be specific, we revisit the concept of causal excursion effect, specifically for zero-inflated count outcomes, and introduce novel estimation approaches that incorporate nonparametric techniques. Bidirectional asymptotics are established for the proposed estimators. Simulation studies are conducted to evaluate the performance of the proposed methods. As an illustration, we also implement these methods to the Drink Less trial data.</p>","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141260409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fish growth models are crucial for fisheries stock assessments and are commonly estimated using fish length-at-age data. This data is widely collected using length-stratified age sampling (LSAS), a cost-effective two-phase response-selective sampling method. The data may contain age measurement errors (MEs). We propose a methodology that accounts for both LSAS and age MEs to accurately estimate fish growth. The proposed methods use empirical proportion likelihood methodology for LSAS and the structural errors in variables methodology for age MEs. We provide a measure of uncertainty for parameter estimates and standardized residuals for model validation. To model the age distribution, we employ a continuation ratio-logit model that is consistent with the random nature of the true age distribution. We also apply a discretization approach for age and length distributions, which significantly improves computational efficiency and is consistent with the discrete age and length data typically encountered in practice. Our simulation study shows that neglecting age MEs can lead to significant bias in growth estimation, even with small but non-negligible age MEs. However, our new approach performs well regardless of the magnitude of age MEs and accurately estimates SEs of parameter estimators. Real data analysis demonstrates the effectiveness of the proposed model validation device. Computer codes to implement the methodology are provided.
鱼类生长模型对渔业资源评估至关重要,通常使用鱼类的年龄长度数据进行估算。这些数据广泛采用长度分层年龄取样法(LSAS)收集,这是一种具有成本效益的两阶段反应选择取样法。这些数据可能包含年龄测量误差(ME)。我们提出了一种既考虑 LSAS 又考虑年龄测量误差的方法,以准确估计鱼类的生长情况。建议的方法对 LSAS 采用经验比例似然法,对年龄 ME 采用变量结构误差法。我们为参数估计提供了不确定性度量,并为模型验证提供了标准化残差。为了建立年龄分布模型,我们采用了与真实年龄分布的随机性相一致的延续比对数模型。我们还对年龄和身长分布采用了离散化方法,这大大提高了计算效率,并与实践中通常遇到的离散年龄和身长数据相一致。我们的模拟研究表明,忽略年龄 ME 会导致生长估计出现明显偏差,即使年龄 ME 较小但不可忽略。然而,无论年龄中位数的大小如何,我们的新方法都能表现出色,并能准确估计参数估计值的 SE。实际数据分析证明了所提出的模型验证方法的有效性。本文还提供了实现该方法的计算机代码。
{"title":"Addressing age measurement errors in fish growth estimation from length-stratified samples.","authors":"Nan Zheng, Atefeh Kheirollahi, Yildiz Yilmaz","doi":"10.1093/biomtc/ujae029","DOIUrl":"https://doi.org/10.1093/biomtc/ujae029","url":null,"abstract":"<p><p>Fish growth models are crucial for fisheries stock assessments and are commonly estimated using fish length-at-age data. This data is widely collected using length-stratified age sampling (LSAS), a cost-effective two-phase response-selective sampling method. The data may contain age measurement errors (MEs). We propose a methodology that accounts for both LSAS and age MEs to accurately estimate fish growth. The proposed methods use empirical proportion likelihood methodology for LSAS and the structural errors in variables methodology for age MEs. We provide a measure of uncertainty for parameter estimates and standardized residuals for model validation. To model the age distribution, we employ a continuation ratio-logit model that is consistent with the random nature of the true age distribution. We also apply a discretization approach for age and length distributions, which significantly improves computational efficiency and is consistent with the discrete age and length data typically encountered in practice. Our simulation study shows that neglecting age MEs can lead to significant bias in growth estimation, even with small but non-negligible age MEs. However, our new approach performs well regardless of the magnitude of age MEs and accurately estimates SEs of parameter estimators. Real data analysis demonstrates the effectiveness of the proposed model validation device. Computer codes to implement the methodology are provided.</p>","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140849395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Evan Kwiatkowski, Jiawen Zhu, Xiao Li, Herbert Pang, Grazyna Lieberman, Matthew A Psioda
We develop a method for hybrid analyses that uses external controls to augment internal control arms in randomized controlled trials (RCTs) where the degree of borrowing is determined based on similarity between RCT and external control patients to account for systematic differences (e.g., unmeasured confounders). The method represents a novel extension of the power prior where discounting weights are computed separately for each external control based on compatibility with the randomized control data. The discounting weights are determined using the predictive distribution for the external controls derived via the posterior distribution for time-to-event parameters estimated from the RCT. This method is applied using a proportional hazards regression model with piecewise constant baseline hazard. A simulation study and a real-data example are presented based on a completed trial in non-small cell lung cancer. It is shown that the case weighted power prior provides robust inference under various forms of incompatibility between the external controls and RCT population.
{"title":"Case weighted power priors for hybrid control analyses with time-to-event data.","authors":"Evan Kwiatkowski, Jiawen Zhu, Xiao Li, Herbert Pang, Grazyna Lieberman, Matthew A Psioda","doi":"10.1093/biomtc/ujae019","DOIUrl":"10.1093/biomtc/ujae019","url":null,"abstract":"<p><p>We develop a method for hybrid analyses that uses external controls to augment internal control arms in randomized controlled trials (RCTs) where the degree of borrowing is determined based on similarity between RCT and external control patients to account for systematic differences (e.g., unmeasured confounders). The method represents a novel extension of the power prior where discounting weights are computed separately for each external control based on compatibility with the randomized control data. The discounting weights are determined using the predictive distribution for the external controls derived via the posterior distribution for time-to-event parameters estimated from the RCT. This method is applied using a proportional hazards regression model with piecewise constant baseline hazard. A simulation study and a real-data example are presented based on a completed trial in non-small cell lung cancer. It is shown that the case weighted power prior provides robust inference under various forms of incompatibility between the external controls and RCT population.</p>","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10968526/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140304678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The marginal structure quantile model (MSQM) provides a unique lens to understand the causal effect of a time-varying treatment on the full distribution of potential outcomes. Under the semiparametric framework, we derive the efficiency influence function for the MSQM, from which a new doubly robust estimator is proposed for point estimation and inference. We show that the doubly robust estimator is consistent if either of the models associated with treatment assignment or the potential outcome distributions is correctly specified, and is semiparametric efficient if both models are correct. To implement the doubly robust MSQM estimator, we propose to solve a smoothed estimating equation to facilitate efficient computation of the point and variance estimates. In addition, we develop a confounding function approach to investigate the sensitivity of several MSQM estimators when the sequential ignorability assumption is violated. Extensive simulations are conducted to examine the finite-sample performance characteristics of the proposed methods. We apply the proposed methods to the Yale New Haven Health System Electronic Health Record data to study the effect of antihypertensive medications to patients with severe hypertension and assess the robustness of the findings to unmeasured baseline and time-varying confounding.
{"title":"Doubly robust estimation and sensitivity analysis for marginal structural quantile models.","authors":"Chao Cheng, Liangyuan Hu, Fan Li","doi":"10.1093/biomtc/ujae045","DOIUrl":"https://doi.org/10.1093/biomtc/ujae045","url":null,"abstract":"<p><p>The marginal structure quantile model (MSQM) provides a unique lens to understand the causal effect of a time-varying treatment on the full distribution of potential outcomes. Under the semiparametric framework, we derive the efficiency influence function for the MSQM, from which a new doubly robust estimator is proposed for point estimation and inference. We show that the doubly robust estimator is consistent if either of the models associated with treatment assignment or the potential outcome distributions is correctly specified, and is semiparametric efficient if both models are correct. To implement the doubly robust MSQM estimator, we propose to solve a smoothed estimating equation to facilitate efficient computation of the point and variance estimates. In addition, we develop a confounding function approach to investigate the sensitivity of several MSQM estimators when the sequential ignorability assumption is violated. Extensive simulations are conducted to examine the finite-sample performance characteristics of the proposed methods. We apply the proposed methods to the Yale New Haven Health System Electronic Health Record data to study the effect of antihypertensive medications to patients with severe hypertension and assess the robustness of the findings to unmeasured baseline and time-varying confounding.</p>","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141330357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To leverage the advancements in genome-wide association studies (GWAS) and quantitative trait loci (QTL) mapping for traits and molecular phenotypes to gain mechanistic understanding of the genetic regulation, biological researchers often investigate the expression QTLs (eQTLs) that colocalize with QTL or GWAS peaks. Our research is inspired by 2 such studies. One aims to identify the causal single nucleotide polymorphisms that are responsible for the phenotypic variation and whose effects can be explained by their impacts at the transcriptomic level in maize. The other study in mouse focuses on uncovering the cis-driver genes that induce phenotypic changes by regulating trans-regulated genes. Both studies can be formulated as mediation problems with potentially high-dimensional exposures, confounders, and mediators that seek to estimate the overall indirect effect (IE) for each exposure. In this paper, we propose MedDiC, a novel procedure to estimate the overall IE based on difference-in-coefficients approach. Our simulation studies find that MedDiC offers valid inference for the IE with higher power, shorter confidence intervals, and faster computing time than competing methods. We apply MedDiC to the 2 aforementioned motivating datasets and find that MedDiC yields reproducible outputs across the analysis of closely related traits, with results supported by external biological evidence. The code and additional information are available on our GitHub page (https://github.com/QiZhangStat/MedDiC).
{"title":"Dissecting the colocalized GWAS and eQTLs with mediation analysis for high-dimensional exposures and confounders.","authors":"Qi Zhang, Zhikai Yang, Jinliang Yang","doi":"10.1093/biomtc/ujae050","DOIUrl":"https://doi.org/10.1093/biomtc/ujae050","url":null,"abstract":"<p><p>To leverage the advancements in genome-wide association studies (GWAS) and quantitative trait loci (QTL) mapping for traits and molecular phenotypes to gain mechanistic understanding of the genetic regulation, biological researchers often investigate the expression QTLs (eQTLs) that colocalize with QTL or GWAS peaks. Our research is inspired by 2 such studies. One aims to identify the causal single nucleotide polymorphisms that are responsible for the phenotypic variation and whose effects can be explained by their impacts at the transcriptomic level in maize. The other study in mouse focuses on uncovering the cis-driver genes that induce phenotypic changes by regulating trans-regulated genes. Both studies can be formulated as mediation problems with potentially high-dimensional exposures, confounders, and mediators that seek to estimate the overall indirect effect (IE) for each exposure. In this paper, we propose MedDiC, a novel procedure to estimate the overall IE based on difference-in-coefficients approach. Our simulation studies find that MedDiC offers valid inference for the IE with higher power, shorter confidence intervals, and faster computing time than competing methods. We apply MedDiC to the 2 aforementioned motivating datasets and find that MedDiC yields reproducible outputs across the analysis of closely related traits, with results supported by external biological evidence. The code and additional information are available on our GitHub page (https://github.com/QiZhangStat/MedDiC).</p>","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep learning has continuously attained huge success in diverse fields, while its application to survival data analysis remains limited and deserves further exploration. For the analysis of current status data, a deep partially linear Cox model is proposed to circumvent the curse of dimensionality. Modeling flexibility is attained by using deep neural networks (DNNs) to accommodate nonlinear covariate effects and monotone splines to approximate the baseline cumulative hazard function. We establish the convergence rate of the proposed maximum likelihood estimators. Moreover, we derive that the finite-dimensional estimator for treatment covariate effects is $sqrt{n}$-consistent, asymptotically normal, and attains semiparametric efficiency. Finally, we demonstrate the performance of our procedures through extensive simulation studies and application to real-world data on news popularity.
{"title":"Deep partially linear cox model for current status data.","authors":"Qiang Wu, Xingwei Tong, Xingqiu Zhao","doi":"10.1093/biomtc/ujae024","DOIUrl":"10.1093/biomtc/ujae024","url":null,"abstract":"<p><p>Deep learning has continuously attained huge success in diverse fields, while its application to survival data analysis remains limited and deserves further exploration. For the analysis of current status data, a deep partially linear Cox model is proposed to circumvent the curse of dimensionality. Modeling flexibility is attained by using deep neural networks (DNNs) to accommodate nonlinear covariate effects and monotone splines to approximate the baseline cumulative hazard function. We establish the convergence rate of the proposed maximum likelihood estimators. Moreover, we derive that the finite-dimensional estimator for treatment covariate effects is $sqrt{n}$-consistent, asymptotically normal, and attains semiparametric efficiency. Finally, we demonstrate the performance of our procedures through extensive simulation studies and application to real-world data on news popularity.</p>","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140334555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.
{"title":"Causal inference for time-to-event data with a cured subpopulation.","authors":"Yi Wang, Yuhao Deng, Xiao-Hua Zhou","doi":"10.1093/biomtc/ujae028","DOIUrl":"https://doi.org/10.1093/biomtc/ujae028","url":null,"abstract":"<p><p>When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, we propose 2 causal estimands, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow us to study the treatment effects on failure times in the always-uncured subpopulation. We show the identifiability using a substitutional variable for the potential cure status under ignorable treatment assignment mechanism, these 2 estimands are identifiable. We also provide estimation methods using mixture cure models. We applied our approach to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. Our proposed approach yielded insightful results that can be used to inform future treatment decisions.</p>","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140856016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}