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Estimating dynamic treatment regimes for ordinal outcomes with household interference: Application in household smoking cessation Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-04-16 Cong Jiang, Mary Thompson, Michael Wallace
The focus of precision medicine is on decision support, often in the form of dynamic treatment regimes, which are sequences of decision rules. At each decision point, the decision rules determine the next treatment according to the patient’s baseline characteristics, the information on treatments and responses accrued by that point, and the patient’s current health status, including symptom severity
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A simple and robust parametric shared frailty model for recurrent events with the competing risk of death: An application to the Carvedilol Prospective Randomized Cumulative Survival trial Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-04-16 Jiren Sun, Thomas Cook
Many non-fatal events can be considered recurrent in that they can occur repeatedly over time, and some researchers may be interested in the trajectory and relative risk of non-fatal events. With the competing risk of death, the treatment effect on the mean number of recurrent events is non-identifiable since the observed mean is a function of both the recurrent event and terminal event processes.
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The “Why” behind including “Y” in your imputation model Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-04-16 Lucy D’Agostino McGowan, Sarah C Lotspeich, Staci A Hepler
Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. Here, we investigate the scenario where a covariate used in an analysis has missingness and will be imputed. There are recommendations to include the outcome from the analysis model in the imputation model for missing covariates, but it is not necessarily clear if this recommendation
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Non-stationary Bayesian spatial model for disease mapping based on sub-regions Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-04-10 Esmail Abdul-Fattah, Elias Krainski, Janet Van Niekerk, Håvard Rue
This paper aims to extend the Besag model, a widely used Bayesian spatial model in disease mapping, to a non-stationary spatial model for irregular lattice-type data. The goal is to improve the model’s ability to capture complex spatial dependence patterns and increase interpretability. The proposed model uses multiple precision parameters, accounting for different intensities of spatial dependence
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Variable selection for latent class analysis in the presence of missing data with application to record linkage Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-04-09 Huiping Xu, Xiaochun Li, Zuoyi Zhang, Shaun Grannis
The Fellegi-Sunter model is a latent class model widely used in probabilistic linkage to identify records that belong to the same entity. Record linkage practitioners typically employ all available matching fields in the model with the premise that more fields convey greater information about the true match status and hence result in improved match performance. In the context of model-based clustering
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Methods for non-proportional hazards in clinical trials: A systematic review Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-04-09 Maximilian Bardo, Cynthia Huber, Norbert Benda, Jonas Brugger, Tobias Fellinger, Vaidotas Galaune, Judith Heinz, Harald Heinzl, Andrew C Hooker, Florian Klinglmüller, Franz König, Tim Mathes, Martina Mittlböck, Martin Posch, Robin Ristl, Tim Friede
For the analysis of time-to-event data, frequently used methods such as the log-rank test or the Cox proportional hazards model are based on the proportional hazards assumption, which is often debatable. Although a wide range of parametric and non-parametric methods for non-proportional hazards has been proposed, there is no consensus on the best approaches. To close this gap, we conducted a systematic
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An additive-multiplicative model for longitudinal data with informative observation times Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-04-08 Yang Li, Wanzhu Tu
Designed clinical studies often assess outcomes at pre-planned time points. In most situations, standard statistical models, such as generalized linear mixed models and generalized additive models, are sufficient to depict the temporal trends of the outcome and produce valid inference. Complicating factors, however, do exist in practical data analyses. One complication arises when the outcome and observational
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A Bayesian hierarchical model for the analysis of visual analogue scaling tasks Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-04-04 Eldon Sorensen, Jacob Oleson, Ethan Kutlu, Bob McMurray
In psychophysics and psychometrics, an integral method to the discipline involves charting how a person’s response pattern changes according to a continuum of stimuli. For instance, in hearing science, Visual Analog Scaling tasks are experiments in which listeners hear sounds across a speech continuum and give a numeric rating between 0 and 100 conveying whether the sound they heard was more like word
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Isotonic design for single-arm biomarker stratified trials Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-04-04 Lang Li, Anastasia Ivanova
In single-arm trials with a predefined subgroup based on baseline biomarkers, it is often assumed that a biomarker defined subgroup, the biomarker positive subgroup, has the same or higher response to treatment compared to its complement, the biomarker negative subgroup. The goal is to determine if the treatment is effective in each of the subgroups or in the biomarker positive subgroup only or not
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A Bayesian quasi-likelihood design for identifying the minimum effective dose and maximum utility dose in dose-ranging studies Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-04-04 Feng Tian, Ruitao Lin, Li Wang, Ying Yuan
Most existing dose-ranging study designs focus on assessing the dose–efficacy relationship and identifying the minimum effective dose. There is an increasing interest in optimizing the dose based on the benefit–risk tradeoff. We propose a Bayesian quasi-likelihood dose-ranging design that jointly considers safety and efficacy to simultaneously identify the minimum effective dose and the maximum utility
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Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-04-03 Bryan S Blette, Scott D Halpern, Fan Li, Michael O Harhay
Understanding whether and how treatment effects vary across subgroups is crucial to inform clinical practice and recommendations. Accordingly, the assessment of heterogeneous treatment effects based on pre-specified potential effect modifiers has become a common goal in modern randomized trials. However, when one or more potential effect modifiers are missing, complete-case analysis may lead to bias
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Sample size estimation for stratified cluster randomization trial with survival endpoint Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-29 Senmiao Ni, Zihang Zhong, Yang Zhao, Feng Chen, Jingwei Wu, Hao Yu, Jianling Bai
Cluster randomization trials with survival endpoint are predominantly used in drug development and clinical care research when drug treatments or interventions are delivered at a group level. Unlike conventional cluster randomization design, stratified cluster randomization design is generally considered more effective in reducing the impacts of imbalanced baseline prognostic factors and varying cluster
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A latent class linear mixed model for monotonic continuous processes measured with error Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-21 Osvaldo Espin-Garcia, Lizbeth Naranjo, Ruth Fuentes-García
Motivated by measurement errors in radiographic diagnosis of osteoarthritis, we propose a Bayesian approach to identify latent classes in a model with continuous response subject to a monotonic, that is, non-decreasing or non-increasing, process with measurement error. A latent class linear mixed model has been introduced to consider measurement error while the monotonic process is accounted for via
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Optimal allocation strategies in platform trials with continuous endpoints Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-20 Marta Bofill Roig, Ekkehard Glimm, Tobias Mielke, Martin Posch
Platform trials are randomized clinical trials that allow simultaneous comparison of multiple interventions, usually against a common control. Arms to test experimental interventions may enter and leave the platform over time. This implies that the number of experimental intervention arms in the trial may change as the trial progresses. Determining optimal allocation rates to allocate patients to the
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Using joint models for longitudinal and time-to-event data to investigate the causal effect of salvage therapy after prostatectomy Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-19 Dimitris Rizopoulos, Jeremy MG Taylor, Grigorios Papageorgiou, Todd M Morgan
Prostate cancer patients who undergo prostatectomy are closely monitored for recurrence and metastasis using routine prostate-specific antigen measurements. When prostate-specific antigen levels rise, salvage therapies are recommended in order to decrease the risk of metastasis. However, due to the side effects of these therapies and to avoid over-treatment, it is important to understand which patients
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Comparisons of various estimates of the I2 statistic for quantifying between-study heterogeneity in meta-analysis Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-19 Yipeng Wang, Natalie DelRocco, Lifeng Lin
Assessing heterogeneity between studies is a critical step in determining whether studies can be combined and whether the synthesized results are reliable. The [Formula: see text] statistic has been a popular measure for quantifying heterogeneity, but its usage has been challenged from various perspectives in recent years. In particular, it should not be considered an absolute measure of heterogeneity
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A matching-based machine learning approach to estimating optimal dynamic treatment regimes with time-to-event outcomes Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-19 Xuechen Wang, Hyejung Lee, Benjamin Haaland, Kathleen Kerrigan, Sonam Puri, Wallace Akerley, Jincheng Shen
Observational data (e.g. electronic health records) has become increasingly important in evidence-based research on dynamic treatment regimes, which tailor treatments over time to patients based on their characteristics and evolving clinical history. It is of great interest for clinicians and statisticians to identify an optimal dynamic treatment regime that can produce the best expected clinical outcome
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Interval estimation in three-class receiver operating characteristic analysis: A fairly general approach based on the empirical likelihood Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-19 Duc-Khanh To, Gianfranco Adimari, Monica Chiogna
The empirical likelihood is a powerful nonparametric tool, that emulates its parametric counterpart—the parametric likelihood—preserving many of its large-sample properties. This article tackles the problem of assessing the discriminatory power of three-class diagnostic tests from an empirical likelihood perspective. In particular, we concentrate on interval estimation in a three-class receiver operating
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Statistical inference for diagnostic test accuracy studies with multiple comparisons Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-15 Max Westphal, Antonia Zapf
Diagnostic accuracy studies assess the sensitivity and specificity of a new index test in relation to an established comparator or the reference standard. The development and selection of the index test are usually assumed to be conducted prior to the accuracy study. In practice, this is often violated, for instance, if the choice of the (apparently) best biomarker, model or cutpoint is based on the
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Simultaneous inference procedures for the comparison of multiple characteristics of two survival functions Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-11 Robin Ristl, Heiko Götte, Armin Schüler, Martin Posch, Franz König
Survival time is the primary endpoint of many randomized controlled trials, and a treatment effect is typically quantified by the hazard ratio under the assumption of proportional hazards. Awareness is increasing that in many settings this assumption is a priori violated, for example, due to delayed onset of drug effect. In these cases, interpretation of the hazard ratio estimate is ambiguous and statistical
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Simulation models for aggregated data meta-analysis: Evaluation of pooling effect sizes and publication biases Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-10 Edwin R van den Heuvel, Osama Almalik, Zhuozhao Zhan
Simulation studies are commonly used to evaluate the performance of newly developed meta-analysis methods. For methodology that is developed for an aggregated data meta-analysis, researchers often resort to simulation of the aggregated data directly, instead of simulating individual participant data from which the aggregated data would be calculated in reality. Clearly, distributional characteristics
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Combining multiple biomarkers linearly to minimize the Euclidean distance of the closest point on the receiver operating characteristic surface to the perfection corner in trichotomous settings Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-06 Brian R Mosier, Leonidas E Bantis
The performance of individual biomarkers in discriminating between two groups, typically the healthy and the diseased, may be limited. Thus, there is interest in developing statistical methodologies for biomarker combinations with the aim of improving upon the individual discriminatory performance. There is extensive literature referring to biomarker combinations under the two-class setting. However
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BOIN-ETC: A Bayesian optimal interval design considering efficacy and toxicity to identify the optimal dose combinations Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-06 Tomoyuki Kakizume, Kentaro Takeda, Masataka Taguri, Satoshi Morita
One of the primary objectives of a dose-finding trial for novel anti-cancer agent combination therapies, such as molecular targeted agents and immune-oncology therapies, is to identify optimal dose combinations that are tolerable and therapeutically beneficial for subjects in subsequent clinical trials. The goal differs from that of a dose-finding trial for traditional cytotoxic agents, in which the
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Bayesian framework for multi-source data integration-Application to human extrapolation from preclinical studies Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-06 Sandrine Boulet, Moreno Ursino, Robin Michelet, Linda BS Aulin, Charlotte Kloft, Emmanuelle Comets, Sarah Zohar
In preclinical investigations, for example, in in vitro, in vivo, and in silico studies, the pharmacokinetic, pharmacodynamic, and toxicological characteristics of a drug are evaluated before advancing to first-in-man trial. Usually, each study is analyzed independently and the human dose range does not leverage the knowledge gained from all studies. Taking into account all preclinical data through
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Cross-validation approaches for penalized Cox regression Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-06 Biyue Dai, Patrick Breheny
Cross-validation is the most common way of selecting tuning parameters in penalized regression, but its use in penalized Cox regression models has received relatively little attention in the literature. Due to its partial likelihood construction, carrying out cross-validation for Cox models is not straightforward, and there are several potential approaches for implementation. Here, we propose a new
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Weight calibration in the joint modelling of medical cost and mortality Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-06 Seong Hoon Yoon, Alain Vandal, Claudia Rivera-Rodriguez
Joint modelling of longitudinal and time-to-event data is a method that recognizes the dependency between the two data types, and combines the two outcomes into a single model, which leads to more precise estimates. These models are applicable when individuals are followed over a period of time, generally to monitor the progression of a disease or a medical condition, and also when longitudinal covariates
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Extended excess hazard models for spatially dependent survival data Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-06 André Victor Ribeiro Amaral, Francisco Javier Rubio, Manuela Quaresma, Francisco J Rodríguez-Cortés, Paula Moraga
Relative survival represents the preferred framework for the analysis of population cancer survival data. The aim is to model the survival probability associated with cancer in the absence of information about the cause of death. Recent data linkage developments have allowed for incorporating the place of residence into the population cancer databases; however, modeling this spatial information has
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Predicting absolute risk for a person with missing risk factors Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-03-01 Bang Wang, Yu Cheng, Mitchell H Gail, Jason Fine, Ruth M Pfeiffer
We compared methods to project absolute risk, the probability of experiencing the outcome of interest in a given projection interval accommodating competing risks, for a person from the target population with missing predictors. Without missing data, a perfectly calibrated model gives unbiased absolute risk estimates in a new target population, even if the predictor distribution differs from the training
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Partly linear single-index cure models with a nonparametric incidence link function Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-02-24 Chun Yin Lee, Kin Yau Wong, Dipankar Bandyopadhyay
In cancer studies, it is commonplace that a fraction of patients participating in the study are cured, such that not all of them will experience a recurrence, or death due to cancer. Also, it is plausible that some covariates, such as the treatment assigned to the patients or demographic characteristics, could affect both the patients’ survival rates and cure/incidence rates. A common approach to accommodate
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Regression analysis of longitudinal data with random change point Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-02-24 Peng Zhang, Xuerong Chen, Jianguo Sun
A great deal of literature has been established for regression analysis of longitudinal data and in particular, many methods have been proposed for the situation where there exist some change points. However, most of these methods only apply to continuous response and focus on the situations where the change point only occurs on the response or the trend of the individual trajectory. In this article
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Improved semi-parametric inference for a mixture model of responses from a control versus treatment group trial Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-02-24 Bradley Lubich, Daniel R Jeske
The mixture of a distribution of responses from untreated patients and a shift of that distribution is a useful model for the responses from a group of treated patients. The mixture model accounts for the fact that not all the patients in the treated group will respond to the treatment and consequently their responses follow the same distribution as the responses from untreated patients. The treatment
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A framework for testing non-inferiority in a three-arm, sequential, multiple assignment randomized trial Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-02-24 Erina Paul, Bibhas Chakraborty, Alla Sikorskii, Samiran Ghosh
Sequential multiple assignment randomized trial design is becoming increasingly used in the field of precision medicine. This design allows comparisons of sequences of adaptive interventions tailored to the individual patient. Superiority testing is usually the initial goal in order to determine which embedded adaptive intervention yields the best primary outcome on average. When direct superiority
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Exact interval estimation for the linear combination of binomial proportions Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-02-13 Shuiyun Lu, Weizhen Wang, Tianfa Xie
The weighted sum of binomial proportions and the interaction effect are two important cases of the linear combination of binomial proportions. Existing confidence intervals for these two parameters are approximate. We apply the [Formula: see text]-function method to a given approximate interval and obtain an exact interval. The process is repeated multiple times until the final-improved interval (exact)
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Heterogeneous treatment effect estimation for observational data using model-based forests Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-02-09 Susanne Dandl, Andreas Bender, Torsten Hothorn
The estimation of heterogeneous treatment effects has attracted considerable interest in many disciplines, most prominently in medicine and economics. Contemporary research has so far primarily focused on continuous and binary responses where heterogeneous treatment effects are traditionally estimated by a linear model, which allows the estimation of constant or heterogeneous effects even under certain
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A diagnostic phase III/IV seamless design to investigate the diagnostic accuracy and clinical effectiveness using the example of HEDOS and HEDOS II Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-02-08 Amra Pepić, Maria Stark, Tim Friede, Annette Kopp-Schneider, Silvia Calderazzo, Maria Reichert, Michael Wolf, Ulrich Wirth, Stefan Schopf, Antonia Zapf
The development process of medical devices can be streamlined by combining different study phases. Here, for a diagnostic medical device, we present the combination of confirmation of diagnostic accuracy (phase III) and evaluation of clinical effectiveness regarding patient-relevant endpoints (phase IV) using a seamless design. This approach is used in the Thyroid HEmorrhage DetectOr Study (HEDOS &
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Covariate adjustment in Bayesian adaptive randomized controlled trials Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-02-08 James Willard, Shirin Golchi, Erica EM Moodie
In conventional randomized controlled trials, adjustment for baseline values of covariates known to be at least moderately associated with the outcome increases the power of the trial. Recent work has shown a particular benefit for more flexible frequentist designs, such as information adaptive and adaptive multi-arm designs. However, covariate adjustment has not been characterized within the more
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Accounting for informative observation process in transition models of binary longitudinal outcome: Application to medical record data Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-02-02 Joe Bible, Madeleine St. Ville, Paul S Albert, Danping Liu
When extracting medical record data to form a retrospective cohort, investigators typically focus on a pre-specified study window, and select subjects who had hospital visits during that study window. However, such data extraction may suffer from an informative observation process, since sicker patients may have hospital visits more frequently. For example, Consecutive Pregnancy Study is a retrospective
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A statistical framework for planning and analysing test–retest studies of repeatability Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-02-01 Moritz Fabian Danzer, Maria Eveslage, Dennis Görlich, Benjamin Noto
There is an increasing number of potential quantitative biomarkers that could allow for early assessment of treatment response or disease progression. However, measurements of such biomarkers are subject to random variability. Hence, differences of a biomarker in longitudinal measurements do not necessarily represent real change but might be caused by this random measurement variability. Before utilizing
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Optimal design for inference on the threshold of a biomarker Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-02-01 Alessandro Baldi Antognini, Rosamarie Frieri, William F Rosenberger, Maroussa Zagoraiou
Enrichment designs with a continuous biomarker require the estimation of a threshold to determine the subpopulation benefitting from the treatment. This article provides the optimal allocation for inference in a two-stage enrichment design for treatment comparisons when a continuous biomarker is suspected to affect patient response. Several design criteria, associated with different trial objectives
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Cure models with adaptive activation for modeling cancer survival Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-02-01 Qi Jiang, Sanjib Basu
We propose a class of cure rate models motivated by analysis of colon cancer and triple-negative breast cancer survival data. This class is indexed by an adaptive activation parameter and a function. We establish that the class is stochastically ordered in the activation parameter and also establish two identifiability results for this class. The first- and last-activation models are members of this
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Determination of correlations in multivariate count data with informative observation times Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-02-01 Chia-Hui Huang
We consider there are various types of recurrent events and the total number of occurrences are collected at the random observation times. It has concerned that the observation process may not be independent to the multivariate event processes, hence the total counts and observation times may be correlated and the dependence may exist among different types of the event processes as well. Many methods
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Regression analysis of multivariate recurrent event data allowing time-varying dependence with application to stroke registry data Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-01-24 Wen Li, Mohammad H. Rahbar, Sean I. Savitz, Jing Zhang, Sori Kim Lundin, Amirali Tahanan, Jing Ning
In multivariate recurrent event data, each patient may repeatedly experience more than one type of event. Analysis of such data gets further complicated by the time-varying dependence structure among different types of recurrent events. The available literature regarding the joint modeling of multivariate recurrent events assumes a constant dependency over time, which is strict and often violated in
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A comparison of model-free phase I dose escalation designs for dual-agent combination therapies Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-01-24 Helen Barnett, Matthew George, Donia Skanji, Gaelle Saint-Hilary, Thomas Jaki, Pavel Mozgunov
It is increasingly common for therapies in oncology to be given in combination. In some cases, patients can benefit from the interaction between two drugs, although often at the risk of higher toxicity. A large number of designs to conduct phase I trials in this setting are available, where the objective is to select the maximum tolerated dose combination. Recently, a number of model-free (also called
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Comparison between inverse-probability weighting and multiple imputation in Cox model with missing failure subtype Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-01-23 Fuyu Guo, Benjamin Langworthy, Shuji Ogino, Molin Wang
Identifying and distinguishing risk factors for heterogeneous disease subtypes has been of great interest. However, missingness in disease subtypes is a common problem in those data analyses. Several methods have been proposed to deal with the missing data, including complete-case analysis, inverse-probability weighting, and multiple imputation. Although extant literature has compared these methods
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Dynamic prediction of survival using multivariate functional principal component analysis: A strict landmarking approach Stat. Methods Med. Res. (IF 2.3) Pub Date : 2024-01-10 Daniel Gomon, Hein Putter, Marta Fiocco, Mirko Signorelli
Dynamically predicting patient survival probabilities using longitudinal measurements has become of great importance with routine data collection becoming more common. Many existing models utilize a multi-step landmarking approach for this problem, mostly due to its ease of use and versatility but unfortunately most fail to do so appropriately. In this article we make use of multivariate functional
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Modeling and estimating a threshold effect: An application to improving cardiac surgery practices. Stat. Methods Med. Res. (IF 2.3) Pub Date : 2023-11-30 Guangyu Yang,Baqun Zhang,Jonathan W Haft,Robert B Hawkins,David Sturmer,Donald S Likosky,Min Zhang
Estimating thresholds when a threshold effect exists has important applications in biomedical research. However, models/methods commonly used in the biomedical literature may lead to a biased estimate. For patients undergoing coronary artery bypass grafting (CABG), it is thought that exposure to low oxygen delivery (DO2) contributes to an increased risk of avoidable acute kidney injury. This research
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The staircase cluster randomised trial design: A pragmatic alternative to the stepped wedge. Stat. Methods Med. Res. (IF 2.3) Pub Date : 2023-11-30 Kelsey L Grantham,Andrew B Forbes,Richard Hooper,Jessica Kasza
This article introduces the 'staircase' design, derived from the zigzag pattern of steps along the diagonal of a stepped wedge design schematic where clusters switch from control to intervention conditions. Unlike a complete stepped wedge design where all participating clusters must collect and provide data for the entire trial duration, clusters in a staircase design are only required to be involved
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Correlational analyses of biomarkers that are harmonized through a bridging study due to measurement errors. Stat. Methods Med. Res. (IF 2.3) Pub Date : 2023-11-22 Chengjie Xiong,Suzanne E Schindler,Rachel L Henson,David A Wolk,Leslie M Shaw,Folasade Agboola,John C Morris,Ruijin Lu,Jingqin Luo
Evaluating correlations between disease biomarkers and clinical outcomes is crucial in biomedical research. During the early stages of many chronic diseases, changes in biomarkers and clinical outcomes are often subtle. A major challenge to detecting subtle correlations is that studies with large sample sizes are usually needed to achieve sufficient statistical power. This challenge is even greater
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Multiply robust causal inference of the restricted mean survival time difference. Stat. Methods Med. Res. (IF 2.3) Pub Date : 2023-11-15 Di Shu,Sagori Mukhopadhyay,Hajime Uno,Jeffrey S Gerber,Douglas E Schaubel
The hazard ratio (HR) remains the most frequently employed metric in assessing treatment effects on survival times. However, the difference in restricted mean survival time (RMST) has become a popular alternative to the HR when the proportional hazards assumption is considered untenable. Moreover, independent of the proportional hazards assumption, many comparative effectiveness studies aim to base
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Estimating drug concentration-response relationships by applying causal inference methods for continuous point exposures and time-to-event outcomes. Stat. Methods Med. Res. (IF 2.3) Pub Date : 2023-11-15 Sean Yiu,Qing Wang,Francois Mercier,Marianna Manfrini,Harold Koendgen,Heidemarie Kletzl,Fabian Model
In clinical development, it is useful to characterize the causal relationship between individual drug concentrations and clinical outcomes in large phase III trials of new therapeutic agents because it can provide insights on whether increasing the currently administered drug dose may lead to better outcomes. However, estimating causal effects of drug concentration is complicated by the fact that drug
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A support vector machine-based cure rate model for interval censored data. Stat. Methods Med. Res. (IF 2.3) Pub Date : 2023-11-08 Suvra Pal,Yingwei Peng,Wisdom Aselisewine,Sandip Barui
The mixture cure rate model is the most commonly used cure rate model in the literature. In the context of mixture cure rate model, the standard approach to model the effect of covariates on the cured or uncured probability is to use a logistic function. This readily implies that the boundary classifying the cured and uncured subjects is linear. In this article, we propose a new mixture cure rate model
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Improving transportability of randomized controlled trial inference using robust predictionmethods. Stat. Methods Med. Res. (IF 2.3) Pub Date : 2023-11-07 Michael R Elliott,Orlagh Carroll,Richard Grieve,James Carpenter
Randomized trials have been the gold standard for assessing causal effects since their introduction by Fisher in the 1920s, since they can eliminate both observed and unobserved confounding. Estimates of causal effects at the population level from randomized controlled trials can still be biased if there are both effect modification and systematic differences between the trial sample and the ultimate
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Clustering minimal inhibitory concentration data through Bayesian mixture models: An application to detect Mycobacteriumtuberculosis resistance mutations. Stat. Methods Med. Res. (IF 2.3) Pub Date : 2023-11-03 Clara Grazian
Antimicrobial resistance is becoming a major threat to public health throughout the world. Researchers are attempting to contrast it by developing both new antibiotics and patient-specific treatments. In the second case, whole-genome sequencing has had a huge impact in two ways: first, it is becoming cheaper and faster to perform whole-genome sequencing, and this makes it competitive with respect to
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Regression analysis of a future state entry time distribution conditional on a past state occupation in a progressive multistate model. Stat. Methods Med. Res. (IF 2.3) Pub Date : 2023-10-27 Yuting Yang,Samuel Wu,Somnath Datta
We present a nonparametric method for estimating the conditional future state entry probabilities and distributions of state entry time conditional on a past state visit when data are subject to dependent censorings in a progressive multistate model where Markovianity of the system is not assumed. These estimators are constructed using the competing risk techniques with risk sets consisting of fractional
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Random survival forests with multivariate longitudinal endogenous covariates. Stat. Methods Med. Res. (IF 2.3) Pub Date : 2023-10-27 Anthony Devaux,Catherine Helmer,Robin Genuer,Cécile Proust-Lima
Predicting the individual risk of clinical events using the complete patient history is a major challenge in personalized medicine. Analytical methods have to account for a possibly large number of time-dependent predictors, which are often characterized by irregular and error-prone measurements, and are truncated early by the event. In this work, we extended the competing-risk random survival forests
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Multivariate control charts for monitoring a bivariate correlated count process with application to meningococcal disease. Stat. Methods Med. Res. (IF 2.3) Pub Date : 2023-10-25 Hanhan Li,Cong Li
In recent years, with the increasing number and complexity of infectious diseases, the idea of using control charts to monitor public health and disease has been proposed. In this paper, we study multivariate control charts for monitoring a bivariate integer-valued autocorrelation process with bivariate Poisson distribution and select the optimal control scheme by comparing the performance of control
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Modified interactive Q-learning for attenuating the impact of model misspecification with treatment effect heterogeneity. Stat. Methods Med. Res. (IF 2.3) Pub Date : 2023-10-20 Yuan Zhang,David M Vock,Megan E Patrick,Thomas A Murray
A sequential multiple assignment randomized trial, which incorporates multiple stages of randomization, is a popular approach for collecting data to inform personalized and adaptive treatments. There is an extensive literature on statistical methods to analyze data collected in sequential multiple assignment randomized trials and estimate the optimal dynamic treatment regime. Q-learning with linear
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Functional proportional hazards mixture cure model with applications in cancer mortality in NHANES and post ICU recovery. Stat. Methods Med. Res. (IF 2.3) Pub Date : 2023-10-19 Rahul Ghosal,Marcos Matabuena,Jiajia Zhang
We develop a functional proportional hazards mixture cure model with scalar and functional covariates measured at the baseline. The mixture cure model, useful in studying populations with a cure fraction of a particular event of interest is extended to functional data. We employ the expectation-maximization algorithm and develop a semiparametric penalized spline-based approach to estimate the dynamic
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A novel rare variants association test for binary traits in family-based designs via copulas. Stat. Methods Med. Res. (IF 2.3) Pub Date : 2023-10-13 Houssou R G Dossa,Alexandre Bureau,Michel Maziade,Lajmi Lakhal-Chaieb,Karim Oualkacha
With the cost-effectiveness technology in whole-genome sequencing, more sophisticated statistical methods for testing genetic association with both rare and common variants are being investigated to identify the genetic variation between individuals. Several methods which group variants, also called gene-based approaches, are developed. For instance, advanced extensions of the sequence kernel association
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SIGHR: Side information guided high-dimensional regression. Stat. Methods Med. Res. (IF 2.3) Pub Date : 2023-10-12 Yuan Yang,Christopher S McMahan,Yu-Bo Wang,James W Baurley,Sung-Shim Park
In this work, we develop a novel Bayesian regression framework that can be used to complete variable selection in high dimensional settings. Unlike existing techniques, the proposed approach can leverage side information to inform about the sparsity structure of the regression coefficients. This is accomplished by replacing the usual inclusion probability in the spike and slab prior with a binary regression