Statistics in Medicine最新文献

{"title":"Exploring the Transitivity Assumption in Network Meta-Analysis: A Novel Approach and Its Implications.","authors":"Loukia M Spineli, Katerina Papadimitropoulou, Chrysostomos Kalyvas","doi":"10.1002/sim.70068","DOIUrl":"https://doi.org/10.1002/sim.70068","url":null,"abstract":"<p><p>The feasibility of network meta-analysis depends on several factors, one of which is the validity of the transitivity assumption that posits no systematic differences in the distribution of effect modifiers across treatment comparisons within a connected network. However, evaluating transitivity is complex for relying on epidemiological grounds. Therefore, establishing a methodological framework to evaluate this assumption is challenging. We propose a novel approach, which involves calculating dissimilarities between treatment comparisons based on study-level aggregate participant and methodological characteristics reported across studies and applying hierarchical clustering to cluster similar comparisons. This approach detects \"hot spots\" of potential intransitivity in the network, enabling empirical exploration of transitivity and semi-objective judgments. Our approach quantifies clinical and methodological (non-statistical) heterogeneity within and between treatment comparisons by computing the dissimilarities across studies in key characteristics acting as effect modifiers. The investigated networks showed varying between-comparison dissimilarities, indicating variability in the clinical and methodological heterogeneity of the networks. Several pairs of treatment comparisons with \"likely concerning\" non-statistical heterogeneity were identified, and some studies were organized into several clusters, suggesting potential intransitivity in the networks. These findings necessitate a closer examination of the evidence base, and such scrutiny becomes pivotal in determining whether concerns about the feasibility of network meta-analysis are justified. Similar to statistical heterogeneity, heterogeneity in clinical and methodological characteristics of the collected studies should be expected and appropriately assessed. Our proposed approach facilitates the evaluation of transitivity using well-established methods and can be applied to newly planned and published systematic reviews.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70068"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11983674/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144010757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frequentist Grouped Weighted Quantile Sum Regression for Correlated Chemical Mixtures.","authors":"Daniel Rud, Md Mostafijur Rahman, Anny H Xiang, Rob McConnell, Fred Lurmann, Michael J Kleeman, Joel Schwartz, Zhanghua Chen, Sandy Eckel, Juan Pablo Lewinger","doi":"10.1002/sim.70078","DOIUrl":"https://doi.org/10.1002/sim.70078","url":null,"abstract":"<p><p>As individuals are exposed to a myriad of potentially harmful pollutants every day, it is important to determine which actors have the greatest influence on health outcomes. However, jointly modeling the associations of multiple pollutant exposures is often hindered by the presence of highly correlated chemicals originating from a common source. A popular approach to analyzing associations between a disease outcome and several highly correlated exposures is Weighted Quantile Sum Regression (WQSR) modeling. WQSR provides increased stability in estimating model parameters but requires data splitting to estimate individual and group effects of chemicals, which reduces the power of the approach. A recent Bayesian implementation of WQSR regression provides a model fitting procedure that avoids data splitting at the cost of high computational expense on large data. In this paper, we introduce a Frequentist Grouped Weighted Quantile Sum Regression (FGWQSR) model that can be fitted efficiently to large datasets without requiring data splitting. FGWQSR produces estimates of the joint effect of mixture groups and of individual chemicals, and likelihood-ratio-based tests that account for FGWQSR's non-standard asymptotics. We demonstrate that FGWQSR is well calibrated for type-I errors while outperforming both Bayesian Grouped Weighted Quantile Sum Regression and Quantile Logistic Regression in terms of statistical power to detect the effects of mixture groups and individual chemicals. In addition, we show that FGWQSR is robust to model misspecification and can be fitted on large datasets in a fraction of the time required for BGWQSR. We apply FGWQSR to a dataset of 317 767 mother-child pairs with exposure profiles generated by chemical transport models to study the associations between several components found in particulate matter with an aerodynamic diameter smaller than 2.5 <math> <semantics><mrow><mi>μ</mi> <mi>m</mi></mrow> <annotation>$$ mu mathrm{m} $$</annotation></semantics> </math> (PM <math> <semantics> <mrow><msub><mo> </mo> <mrow><mn>2</mn> <mo>.</mo> <mn>5</mn></mrow> </msub> </mrow> <annotation>$$ {}_{2.5} $$</annotation></semantics> </math> ) and child Autism Spectrum Disorder (ASD) diagnosis before age 5. PM <math> <semantics> <mrow><msub><mo> </mo> <mrow><mn>2</mn> <mo>.</mo> <mn>5</mn></mrow> </msub> </mrow> <annotation>$$ {}_{2.5} $$</annotation></semantics> </math> copper and PM <math> <semantics> <mrow><msub><mo> </mo> <mrow><mn>2</mn> <mo>.</mo> <mn>5</mn></mrow> </msub> </mrow> <annotation>$$ {}_{2.5} $$</annotation></semantics> </math> crustal material are found to be statistically significantly associated with ASD diagnosis by five years of age.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70078"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11987061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143985973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Instrumental Variable Methods to Target Hypothetical Estimands With Longitudinal Repeated Measures Data: Application to the STEP 1 Trial.","authors":"Jack Bowden, Jesper Madsen, Bryan Goldman, Aske Thorn Iversen, Xiaoran Liang, Stijn Vansteelandt","doi":"10.1002/sim.70076","DOIUrl":"https://doi.org/10.1002/sim.70076","url":null,"abstract":"<p><p>The STEP 1 randomized trial evaluated the effect of taking semaglutide versus placebo on body weight over a 68-week duration. As with any study evaluating an intervention delivered over a sustained period, nonadherence was observed. This was addressed in the original trial analysis within the Estimand Framework by viewing nonadherence as an intercurrent event. The primary analysis applied a treatment policy strategy which viewed it as an aspect of the treatment regimen, and thus made no adjustment for its presence. A supplementary analysis used a hypothetical strategy, targeting an estimand that would have been realized had all participants adhered, under the assumption that no post-baseline variables confounded adherence and change in body weight. In this article, we propose an alternative instrumental variable (IV) method to adjust for nonadherence which does not rely on the same \"unconfoundedness\" assumption and is less vulnerable to positivity violations (e.g., it can give valid results even under conditions where nonadherence is guaranteed). Unlike many previous IV approaches, it makes full use of the repeatedly measured outcome data, and allows for a time-varying effect of treatment adherence on a participant's weight. We show that it provides a natural vehicle for defining two distinct hypothetical estimands: the treatment effect if all participants would have adhered to semaglutide, and the treatment effect if all participants would have adhered to both semaglutide and placebo. When applied to the STEP 1 study, they suggest a sustained, slowly decaying weight loss effect of semaglutide treatment.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70076"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11987489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144062221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Stratified Analysis Method for Testing and Estimating Overall Treatment Effects on Time-To-Event Outcomes Using Average Hazard With Survival Weight.","authors":"Zihan Qian, Lu Tian, Miki Horiguchi, Hajime Uno","doi":"10.1002/sim.70056","DOIUrl":"https://doi.org/10.1002/sim.70056","url":null,"abstract":"<p><p>Given the limitations of using the Cox hazard ratio to summarize the magnitude of the treatment effect, alternative measures that do not have these limitations are gaining attention. One of the recently proposed alternative methods uses the average hazard with survival weight (AH). This population quantity can be interpreted as the average intensity of the event occurrence in a given time window that does not involve study-specific censoring. Inference procedures for the ratio of AH and difference in AH have already been proposed in simple randomized controlled trial settings to compare two groups. However, methods with stratification factors have not been well discussed, although stratified analysis is often used in practice to adjust for confounding factors and increase the power to detect a between-group difference. The conventional stratified analysis or meta-analysis approach, which integrates stratum-specific treatment effects using an optimal weight, directly applies to the ratio of AH and difference in AH. However, this conventional approach has significant limitations similar to the Cochran-Mantel-Haenszel method for a binary outcome and the stratified Cox procedure for a time-to-event outcome. To address this, we propose a new stratified analysis method for AH using standardization. With the proposed method, one can summarize the between-group treatment effect in both absolute difference and relative terms, adjusting for stratification factors. This can be a valuable alternative to the traditional stratified Cox procedure to estimate and report the magnitude of the treatment effect on time-to-event outcomes using hazard.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70056"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144027233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BayTetra: A Bayesian Semiparametric Approach for Testing Trajectory Differences.","authors":"Wei Jin, Qiuxin Gao, Yanxun Xu","doi":"10.1002/sim.70071","DOIUrl":"https://doi.org/10.1002/sim.70071","url":null,"abstract":"<p><p>Testing differences in longitudinal trajectories among distinct groups of population is an important task in many biomedical applications. Motivated by an application in Alzheimer's disease, we develop BayTetra, an innovative Bayesian semiparametric approach for estimating and testing group differences in multivariate longitudinal trajectories. BayTetra jointly models multivariate longitudinal data by directly accounting for correlations among different responses, and uses a semiparametric framework based on B-splines to capture the non-linear trajectories with great flexibility. To avoid overfitting, BayTetra encourages parsimonious trajectory estimation by imposing penalties on the smoothness of the spline functions. The proposed method converts the challenging task of hypothesis testing for longitudinal trajectories into a more manageable equivalent form based on hypothesis testing for spline coefficients. More importantly, by leveraging posterior inference with natural uncertainty quantification, our Bayesian method offers a more robust and straightforward hypothesis testing procedure than frequentist methods. Extensive simulations demonstrate BayTetra's superior performance over alternatives. Applications to the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) study yield interpretable and valuable clinical insights. A major contribution of this paper is that we have developed an R package BayTetra, which implements the proposed Bayesian semiparametric approach and is the first publicly available software for hypothesis testing on trajectory differences based on a flexible modeling framework.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70071"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal Multistate Models to Evaluate Treatment Delay.","authors":"Ilaria Prosepe, Saskia le Cessie, Hein Putter, Nan van Geloven","doi":"10.1002/sim.70061","DOIUrl":"10.1002/sim.70061","url":null,"abstract":"<p><p>Multistate models allow for the study of scenarios where individuals experience different events over time. While effective for descriptive and predictive purposes, multistate models are not typically used for causal inference. We propose an estimator that combines a multistate model with g-computation to estimate the causal effect of treatment delay strategies. In particular, we estimate the impact of strategies such as awaiting natural recovery for 3 months, on the marginal probability of recovery. We use an illness-death model, where illness and death represent, respectively, treatment and recovery. We formulate the causal assumptions needed for identification and the modeling assumptions needed to estimate the quantities of interest. In a simulation study, we present scenarios where the proposed method can make more efficient use of data compared to an alternative approach using cloning-censoring-reweighting. We then showcase the proposed methodology on real data by estimating the effect of treatment delay on a cohort of 1896 couples with unexplained subfertility who seek intrauterine insemination.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70061"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11978571/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Generalized Linear Models for Analyzing Compositional and Sub-Compositional Microbiome Data via EM Algorithm.","authors":"Li Zhang, Zhenying Ding, Jinhong Cui, Xiaoxiao Zhou, Nengjun Yi","doi":"10.1002/sim.70084","DOIUrl":"https://doi.org/10.1002/sim.70084","url":null,"abstract":"<p><p>The study of compositional microbiome data is critical for exploring the functional roles of microbial communities in human health and disease. Recent advances have shifted from traditional log-ratio transformations of compositional covariates to zero constraint on the sum of the corresponding coefficients. Various approaches, including penalized regression and Markov Chain Monte Carlo (MCMC) algorithms, have been extended to enforce this sum-to-zero constraint. However, these methods exhibit limitations: penalized regression yields only point estimates, limiting uncertainty assessment, while MCMC methods, although reliable, are computationally intensive, particularly in high-dimensional data settings. To address the challenges posed by existing methods, we proposed Bayesian generalized linear models for analyzing compositional and sub-compositional microbiome data. Our model employs a spike-and-slab double-exponential prior on the microbiome coefficients, inducing weak shrinkage on large coefficients and strong shrinkage on irrelevant ones, making it ideal for high-dimensional microbiome data. The sum-to-zero constraint is handled through soft-centers by applying prior distribution on the sum of compositional or subcompositional coefficients. To alleviate computational intensity, we have developed a fast and stable algorithm incorporating expectation-maximization (EM) steps into the routine iteratively weighted least squares (IWLS) algorithm for fitting GLMs. The performance of the proposed method was assessed by extensive simulation studies. The simulation results show that our approach outperforms existing methods with higher accuracy of coefficient estimates and lower prediction error. We also applied the proposed method to one microbiome study to find microorganisms linked to inflammatory bowel disease (IBD). The methods have been implemented in a freely available R package BhGLM https://github.com/nyiuab/BhGLM.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70084"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144012662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Generalized Phase I/II Dose Optimization Trial Design With Multi-Categorical and Multi-Graded Outcomes.","authors":"Yichen Yan, Ruitao Lin, Tianyu Guan, Haolun Shi, Xiaolei Lin","doi":"10.1002/sim.70049","DOIUrl":"https://doi.org/10.1002/sim.70049","url":null,"abstract":"<p><p>Pursuing accurate observations and rational assumptions always drives advances in clinical trial design. In recent years, more trials have begun to collect multi-graded outcomes for more informative analyses. At the same time, assumptions other than the traditional monotonicity relationship have been considered in the dose-efficacy curve to be more realistic. Inspired by these two trends, we propose a phase I/II design that simultaneously considers multi-categorical toxicity and efficacy with multi-graded outcomes, measured as quasi-continuous probability based on prespecified weight matrices of clinical significance. Following keyboard design, our approach aims to screen out overly toxic doses by the toxicity probability intervals and adaptively makes dose escalation or de-escalation decisions by comparing the posterior distributions of dose desirability (utility) among the adjacent levels of the current dose. It helps to more accurately identify the OBD in a non-monotonically increasing dose-efficacy relationship. We also comprehensively present the safety, accuracy and reliability performance through numerical simulations in multiple scenarios and compare the results with several already available designs. The benchmarking results of multiple operating characteristics convincingly support that our design leads in overall performance while ensuring robustness.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70049"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144000121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring the Performance of Survival Models to Personalize Treatment Choices.","authors":"Orestis Efthimiou, Jeroen Hoogland, Thomas P A Debray, Valerie Aponte Ribero, Wilma Knol, Huiberdina L Koek, Matthias Schwenkglenks, Séverine Henrard, Matthias Egger, Nicolas Rodondi, Ian R White","doi":"10.1002/sim.70050","DOIUrl":"https://doi.org/10.1002/sim.70050","url":null,"abstract":"<p><p>Various statistical and machine learning algorithms can be used to predict treatment effects at the patient level using data from randomized clinical trials (RCTs). Such predictions can facilitate individualized treatment decisions. Recently, a range of methods and metrics were developed for assessing the accuracy of such predictions. Here, we extend these methods, focusing on the case of survival (time-to-event) outcomes. We start by providing alternative definitions of the participant-level treatment benefit; subsequently, we summarize existing and propose new measures for assessing the performance of models estimating participant-level treatment benefits. We explore metrics assessing discrimination and calibration for benefit and decision accuracy. These measures can be used to assess the performance of statistical as well as machine learning models and can be useful during model development (i.e., for model selection or for internal validation) or when testing a model in new settings (i.e., in an external validation). We illustrate methods using simulated data and real data from the OPERAM trial, an RCT in multimorbid older people, which randomized participants to either standard care or a pharmacotherapy optimization intervention. We provide R codes for implementing all models and measures.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70050"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11983264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144000124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining Straight-Line and Map-Based Distances to Investigate the Connection Between Proximity to Healthy Foods and Disease.","authors":"Sarah C Lotspeich, Ashley E Mullan, Lucy D'Agostino McGowan, Staci A Hepler","doi":"10.1002/sim.70054","DOIUrl":"https://doi.org/10.1002/sim.70054","url":null,"abstract":"<p><p>Healthy foods are essential for a healthy life, but accessing healthy food can be more challenging for some people than others. This disparity in food access may lead to disparities in well-being, potentially with disproportionate rates of diseases in communities that face more challenges in accessing healthy food (i.e., low-access communities). Identifying low-access, high-risk communities for targeted interventions is a public health priority, but current methods to quantify food access rely on distance measures that are either computationally simple (like the length of the shortest straight-line route) or accurate (like the length of the shortest map-based driving route), but not both. We propose a multiple imputation approach to combine these distance measures, allowing researchers to harness the computational ease of one with the accuracy of the other. The approach incorporates straight-line distances for all neighborhoods and map-based distances for just a subset, offering comparable estimates to the \"gold standard\" model using map-based distances for all neighborhoods and improved efficiency over the \"complete case\" model using map-based distances for just the subset. Through the adoption of a measurement error framework, information from the straight-line distances can be leveraged to compute informative placeholders (i.e., impute) for any neighborhoods without map-based distances. Using simulations and data for the Piedmont Triad region of North Carolina, we quantify and compare the associations between two health outcomes (diabetes and obesity) and neighborhood-level access to healthy foods. The imputation procedure also makes it possible to predict the full landscape of food access in an area without requiring map-based measurements for all neighborhoods.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 7","pages":"e70054"},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11995689/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}