Statistics in Medicine最新文献_第3页

A Novel Approach to Assess the Predictiveness of a Continuous Biomarker in Early Phases of Drug Development.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-02-28 DOI: 10.1002/sim.70026

Alessandra Serra, Julia Geronimi, Sandrine Guilleminot, Hugo Hadjur, Marie-Karelle Riviere, Gaëlle Saint-Hilary, Pavel Mozgunov

{"title":"A Novel Approach to Assess the Predictiveness of a Continuous Biomarker in Early Phases of Drug Development.","authors":"Alessandra Serra, Julia Geronimi, Sandrine Guilleminot, Hugo Hadjur, Marie-Karelle Riviere, Gaëlle Saint-Hilary, Pavel Mozgunov","doi":"10.1002/sim.70026","DOIUrl":"10.1002/sim.70026","url":null,"abstract":"Identifying and quantifying predictive biomarkers is a critical issue of personalized medicine approaches and patient-centric clinical development strategies. In early stages of the development process, significant challenges and numerous uncertainties arise. One of the challenges is the ability to assess the predictive value of a biomarker, i.e., the difference in primary outcomes between experimental and placebo arms above and below a certain threshold of the biomarker. Indeed, when the accumulated information is very limited and the sample size is small, preliminary conclusions about the predictive properties of the biomarker might be misleading. To date, the majority of investigations regarding the predictiveness of biomarkers were in the setting of moderate-to-large sample sizes. In this work, we propose a novel flexible approach inspired by the Kolmogorov-Smirnov Distance in order to assess the predictiveness of a continuous biomarker in a clinical setting where the sample size is small. Via simulations we show that the proposed method allows to achieve a higher power to declare predictiveness compared to the existing methods under a range of scenarios, whilst still maintaining a control of the type I error at a pre-specified level.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 5","pages":"e70026"},"PeriodicalIF":1.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11862805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143504282","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}

引用次数: 0

Network Meta-Analysis With Individual Participant-Level Data of Time-to-Event Outcomes Using Cox Regression.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-02-28 DOI: 10.1002/sim.70027

Kaiyuan Hua, Daniel Wojdyla, Anthony Carnicelli, Christopher Granger, Xiaofei Wang, Hwanhee Hong

{"title":"Network Meta-Analysis With Individual Participant-Level Data of Time-to-Event Outcomes Using Cox Regression.","authors":"Kaiyuan Hua, Daniel Wojdyla, Anthony Carnicelli, Christopher Granger, Xiaofei Wang, Hwanhee Hong","doi":"10.1002/sim.70027","DOIUrl":"10.1002/sim.70027","url":null,"abstract":"The accessibility of individual participant-level data (IPD) enhances the evaluation of moderation effects of patient covariates. It facilitates the provision of accurate estimation of intervention effects and confidence intervals by incorporating covariate correlations across multiple clinical trials. With a time-to-event outcome, Cox regression can be applied for network meta-analysis (NMA) using IPD. However, there lacks comprehensive reviews and comparisons of the specifications and assumptions of these Cox models and their impact on the interpretation of hazard ratios, effect moderation, and trial heterogeneity in IPD-NMA. In this paper, we examine various Cox models for IPD-NMA and compare different approaches to modeling trial, treatment, and covariate effects. We employ multiple graphical tools and statistical tests to assess proportional hazard assumptions and discuss their implications. Additionally, we explore the application of extended Cox models when the proportional hazard assumption is violated. Practical guidance on interpreting and reporting NMA results is provided. A simulation study is conducted to compare the performance of different models. We illustrate the methods to conduct IPD-NMA through a real data example.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 5","pages":"e70027"},"PeriodicalIF":1.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11955150/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143441913","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}

引用次数: 0

Regression-A Means, Not an End.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-02-28 DOI: 10.1002/sim.70000

Robert W Platt

引用次数: 0

Analysis of Cohort Stepped Wedge Cluster-Randomized Trials With Nonignorable Dropout via Joint Modeling.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-02-28 DOI: 10.1002/sim.10347

Alessandro Gasparini, Michael J Crowther, Emiel O Hoogendijk, Fan Li, Michael O Harhay

{"title":"Analysis of Cohort Stepped Wedge Cluster-Randomized Trials With Nonignorable Dropout via Joint Modeling.","authors":"Alessandro Gasparini, Michael J Crowther, Emiel O Hoogendijk, Fan Li, Michael O Harhay","doi":"10.1002/sim.10347","DOIUrl":"10.1002/sim.10347","url":null,"abstract":"Stepped wedge cluster-randomized trial (CRTs) designs randomize clusters of individuals to intervention sequences, ensuring that every cluster eventually transitions from a control period to receive the intervention under study by the end of the study period. The analysis of stepped wedge CRTs is usually more complex than parallel-arm CRTs due to more complex intra-cluster correlation structures. A further challenge in the analysis of closed-cohort stepped wedge CRTs, which follow groups of individuals enrolled in each period longitudinally, is the occurrence of dropout. This is particularly problematic in studies of individuals at high risk for mortality, which causes nonignorable missing outcomes. If not appropriately addressed, missing outcomes from death will erode statistical power, at best, and bias treatment effect estimates, at worst. Joint longitudinal-survival models can accommodate informative dropout and missingness patterns in longitudinal studies. Specifically, within the joint longitudinal-survival modeling framework, one directly models the dropout process via a time-to-event submodel together with the longitudinal outcome of interest. The two submodels are then linked using a variety of possible association structures. This work extends linear mixed-effects models by jointly modeling the dropout process to accommodate informative missing outcome data in closed-cohort stepped wedge CRTs. We focus on constant intervention and general time-on-treatment effect parametrizations for the longitudinal submodel and study the performance of the proposed methodology using Monte Carlo simulation under several data-generating scenarios. We illustrate the joint modeling methodology in practice by reanalyzing data from the \"Frail Older Adults: Care in Transition\" (ACT) trial, a stepped wedge CRT of a multifaceted geriatric care model versus usual care in 35 primary care practices in the Netherlands.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 5","pages":"e10347"},"PeriodicalIF":1.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143441880","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}

引用次数: 0

A General Framework to Assess Complex Heterogeneity in the Strength of a Surrogate Marker.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-02-28 DOI: 10.1002/sim.70001

Rebecca Knowlton, Lu Tian, Layla Parast

{"title":"A General Framework to Assess Complex Heterogeneity in the Strength of a Surrogate Marker.","authors":"Rebecca Knowlton, Lu Tian, Layla Parast","doi":"10.1002/sim.70001","DOIUrl":"10.1002/sim.70001","url":null,"abstract":"A surrogate marker is a biological measurement in a clinical trial that aims to replace the primary outcome in evaluating the treatment effect, and can be measured earlier, with less cost, or with less patient burden. In theory, once a surrogate is validated, future studies can evaluate treatment efficacy using only the surrogate. While there are many methods to evaluate a surrogate, these methods rarely account for heterogeneity in surrogacy, that is, when a surrogate is valid for only certain people. We propose a general framework for the assessment of complex heterogeneity in the strength of a surrogate marker, as well as corresponding parametric and semiparametric estimation procedures. Our framework defines the proportion of the treatment effect on the primary outcome that is explained by the treatment effect on the surrogate, as a function of multiple baseline covariates, <math> <semantics><mrow><mtext>W</mtext></mrow> <annotation>$$ mathbf{W} $$</annotation></semantics> </math> . We additionally propose a formal test of heterogeneity and a method to identify a region of the covariate space where the surrogate is sufficiently strong. We examine the performance of our methods via a simulation study featuring varying levels of heterogeneity and use our methods to examine potential heterogeneity in the strength of a surrogate in an AIDS clinical trial.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 5","pages":"e70001"},"PeriodicalIF":1.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11835199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143365859","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}

引用次数: 0

Estimating Mean Viral Load Trajectory From Intermittent Longitudinal Data and Unknown Time Origins.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-02-28 DOI: 10.1002/sim.70033

Yonatan Woodbridge, Micha Mandel, Yair Goldberg, Amit Huppert

{"title":"Estimating Mean Viral Load Trajectory From Intermittent Longitudinal Data and Unknown Time Origins.","authors":"Yonatan Woodbridge, Micha Mandel, Yair Goldberg, Amit Huppert","doi":"10.1002/sim.70033","DOIUrl":"10.1002/sim.70033","url":null,"abstract":"Viral load (VL) in the respiratory tract is the leading proxy for assessing infectiousness potential. Understanding the dynamics of disease-related VL within the host is of great importance, as it helps to determine different policies and health recommendations. However, normally the VL is measured on individuals only once, in order to confirm infection, and furthermore, the infection date is unknown. It is therefore necessary to develop statistical approaches to estimate the typical VL trajectory. We show here that, under plausible parametric assumptions, two measures of VL on infected individuals can be used to accurately estimate the VL mean function. Specifically, we consider a discrete-time likelihood-based approach to modeling and estimating partial observed longitudinal samples. We study a multivariate normal model for a function of the VL that accounts for possible correlation between measurements within individuals. We derive an expectation-maximization (EM) algorithm which treats the unknown time origins and the missing measurements as latent variables. Our main motivation is the reconstruction of the daily mean VL, given measurements on patients whose VLs were measured multiple times on different days. Such data should and can be obtained at the beginning of a pandemic with the specific goal of estimating the VL dynamics. For demonstration purposes, the method is applied to SARS-Cov-2 cycle-threshold-value data collected in Israel.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 5","pages":"e70033"},"PeriodicalIF":1.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851093/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493468","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}

引用次数: 0

Bayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data. 利用遗传变量辅助病理影像数据的癌症预后贝叶斯模型。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-02-10 DOI: 10.1002/sim.10350

Yunju Im, Rong Li, Shuangge Ma

{"title":"Bayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data.","authors":"Yunju Im, Rong Li, Shuangge Ma","doi":"10.1002/sim.10350","DOIUrl":"10.1002/sim.10350","url":null,"abstract":"With the increasing maturity of genetic profiling, an essential and routine task in cancer research is to model disease outcomes/phenotypes using genetic variables. Many methods have been successfully developed. However, oftentimes, empirical performance is unsatisfactory because of a \"lack of information.\" In cancer research and clinical practice, a source of information that is broadly available and highly cost-effective comes from pathological images, which are routinely collected for definitive diagnosis and staging. In this article, we consider a Bayesian approach for selecting relevant genetic variables and modeling their relationships with a cancer outcome/phenotype. We propose borrowing information from (manually curated, low-dimensional) pathological imaging features via reinforcing the same selection results for the cancer outcome and imaging features. We further develop a weighting strategy to accommodate the scenario where information borrowing may not be equally effective for all subjects. Computation is carefully examined. Simulations demonstrate competitive performance of the proposed approach. We analyze TCGA (The Cancer Genome Atlas) LUAD (lung adenocarcinoma) data, with overall survival and gene expressions being the outcome and genetic variables, respectively. Findings different from the alternatives and with sound properties are made.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 3-4","pages":"e10350"},"PeriodicalIF":1.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11774474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143011847","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}

引用次数: 0

Non-Parametric Estimation for Semi-Competing Risks Data With Event Misascertainment.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-02-10 DOI: 10.1002/sim.10332

Ruiqian Wu, Ying Zhang, Giorgos Bakoyannis

{"title":"Non-Parametric Estimation for Semi-Competing Risks Data With Event Misascertainment.","authors":"Ruiqian Wu, Ying Zhang, Giorgos Bakoyannis","doi":"10.1002/sim.10332","DOIUrl":"10.1002/sim.10332","url":null,"abstract":"The semi-competing risks data model is a special type of disease-state model that focuses on studying the association between an intermediate event and a terminal event and proves to be a useful tool in modeling disease progression. The study of the semi-competing risk data model not only allows us to evaluate whether a disease episode is related to death but also provides a toolkit to predict death, given that the episode occurred at a certain time. However, the computation of the semi-competing risk models is a numerically challenging task. The Gamma-Frailty conditional Markov model has been shown to be an efficient computation model for studying semi-competing risks data. Building on recent advances in studying semi-competing risks data, this work proposes a non-parametric pseudo-likelihood method equipped with an EM-like algorithm to study semi-competing risks data with event misascertainment under the restricted Gamma-Frailty conditional Markov model. A thorough simulation study is conducted to demonstrate the inference validity of the proposed method and its numerical stability. The proposed method is applied to a large HIV cohort study, EA-IeDEA, that has a severe death under-reporting issue to assess the degree of adverse impact of the interruption of ART care on HIV mortality.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 3-4","pages":"e10332"},"PeriodicalIF":1.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11758483/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034101","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}

引用次数: 0

Adjusting for Ascertainment Bias in Meta-Analysis of Penetrance for Cancer Risk.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-02-10 DOI: 10.1002/sim.10323

Thanthirige Lakshika M Ruberu, Danielle Braun, Giovanni Parmigiani, Swati Biswas

{"title":"Adjusting for Ascertainment Bias in Meta-Analysis of Penetrance for Cancer Risk.","authors":"Thanthirige Lakshika M Ruberu, Danielle Braun, Giovanni Parmigiani, Swati Biswas","doi":"10.1002/sim.10323","DOIUrl":"10.1002/sim.10323","url":null,"abstract":"Multi-gene panel testing allows efficient detection of pathogenic variants in cancer susceptibility genes including moderate-risk genes such as ATM and PALB2. A growing number of studies examine the risk of breast cancer (BC) conferred by pathogenic variants of these genes. A meta-analysis combining the reported risk estimates can provide an overall estimate of age-specific risk of developing BC, that is, penetrance for a gene. However, estimates reported by case-control studies often suffer from ascertainment bias. Currently, there is no method available to adjust for such bias in this setting. We consider a Bayesian random effect meta-analysis method that can synthesize different types of risk measures and extend it to incorporate studies with ascertainment bias. This is achieved by introducing a bias term in the model and assigning appropriate priors. We validate the method through a simulation study and apply it to estimate BC penetrance for carriers of pathogenic variants in the ATM and PALB2 genes. Our simulations show that the proposed method results in more accurate and precise penetrance estimates compared to when no adjustment is made for ascertainment bias or when such biased studies are discarded from the analysis. The overall estimated BC risk for individuals with pathogenic variants are (1) 5.77% (3.22%-9.67%) by age 50 and 26.13% (20.31%-32.94%) by age 80 for ATM; (2) 12.99% (6.48%-22.23%) by age 50, and 44.69% (34.40%-55.80%) by age 80 for PALB2. The proposed method allows meta-analyses to include studies with ascertainment bias, resulting in inclusion of more studies and thereby more accurate estimates.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 3-4","pages":"e10323"},"PeriodicalIF":1.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11881752/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143047829","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}

引用次数: 0

Allocation Predictability of Individual Assignments in Restricted Randomization Designs for Two-Arm Equal Allocation Trials.

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-02-10 DOI: 10.1002/sim.10343

Wenle Zhao, Sherry Livingston

{"title":"Allocation Predictability of Individual Assignments in Restricted Randomization Designs for Two-Arm Equal Allocation Trials.","authors":"Wenle Zhao, Sherry Livingston","doi":"10.1002/sim.10343","DOIUrl":"10.1002/sim.10343","url":null,"abstract":"This manuscript derives the allocation predictability measured by the correct guess probability and the probability of being deterministic for individual treatment assignments, as well as the averages of a randomization sequence, based on the treatment imbalance transition matrix and the conditional allocation probability. The methods described are applicable to restricted randomization designs that satisfy the following criteria: (1) two-arm equal allocation, (2) restriction of maximum tolerated imbalance, and (3) conditional allocation probability fully determined by the observed current treatment imbalance. Analytical results indicate that, for two-arm equal allocation trials, allocation predictability alternates by the odd/even sequence order of the treatment assignment. Additionally, the sequence average allocation predictability converges to its asymptotic value significantly more slowly than the allocation predictability for individual assignment does. Consequently, comparisons of allocation predictability between different randomization designs based on sequence averages are sensitive to sequence length. Using sequence average allocation predictability may underestimate the risk of selection bias for individual assignment. This discrepancy is particularly pronounced for short sequence lengths, where individual assignment predictability can be substantially higher than the sequence average.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 3-4","pages":"e10343"},"PeriodicalIF":1.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11810053/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034090","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}

引用次数: 0