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Explained variation and degrees of necessity and of sufficiency for competing risks survival data Biom. J. (IF 1.7) Pub Date : 2024-02-27 Andreas Gleiss, Michael Gnant, Michael Schemper
In this contribution, the Schemper–Henderson measure of explained variation for survival outcomes is extended to accommodate competing events (CEs) in addition to events of interest. The extension is achieved by moving from the unconditional and conditional survival functions of the original measure to unconditional and conditional cumulative incidence functions, the latter obtained, for example, from
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Nonparametric analysis of delayed treatment effects using single-crossing constraints Biom. J. (IF 1.7) Pub Date : 2024-02-25 Nicholas C. Henderson, Kijoeng Nam, Dai Feng
Clinical trials involving novel immuno-oncology therapies frequently exhibit survival profiles which violate the proportional hazards assumption due to a delay in treatment effect, and, in such settings, the survival curves in the two treatment arms may have a crossing before the two curves eventually separate. To flexibly model such scenarios, we describe a nonparametric approach for estimating the
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A generalized calibrated Bayesian hierarchical modeling approach to basket trials with multiple endpoints Biom. J. (IF 1.7) Pub Date : 2024-02-17 Xiaohan Chi, Ying Yuan, Zhangsheng Yu, Ruitao Lin
A basket trial simultaneously evaluates a treatment in multiple cancer subtypes, offering an effective way to accelerate drug development in multiple indications. Many basket trials are designed and monitored based on a single efficacy endpoint, primarily the tumor response. For molecular targeted or immunotherapy agents, however, a single efficacy endpoint cannot adequately characterize the treatment
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Sparse multiway canonical correlation analysis for multimodal stroke recovery data Biom. J. (IF 1.7) Pub Date : 2024-02-17 Subham Das, Franklin D. West, Cheolwoo Park
Conventional canonical correlation analysis (CCA) measures the association between two datasets and identifies relevant contributors. However, it encounters issues with execution and interpretation when the sample size is smaller than the number of variables or there are more than two datasets. Our motivating example is a stroke-related clinical study on pigs. The data are multimodal and consist of
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Editorial for the special collection “Towards neutral comparison studies in methodological research” Biom. J. (IF 1.7) Pub Date : 2024-02-17 Anne-Laure Boulesteix, Mark Baillie, Dominic Edelmann, Leonhard Held, Tim P. Morris, Willi Sauerbrei
Biomedical researchers are frequently faced with an array of methods they might potentially use for the analysis and/or design of studies. It can be difficult to understand the absolute and relative merits of candidate methods beyond one's own particular interests and expertise. Choosing a method can be difficult even in simple settings but an increase in the volume of data collected, computational
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A review on statistical and machine learning competing risks methods Biom. J. (IF 1.7) Pub Date : 2024-02-13 Karla Monterrubio-Gómez, Nathan Constantine-Cooke, Catalina A. Vallejos
When modeling competing risks (CR) survival data, several techniques have been proposed in both the statistical and machine learning literature. State-of-the-art methods have extended classical approaches with more flexible assumptions that can improve predictive performance, allow high-dimensional data and missing values, among others. Despite this, modern approaches have not been widely employed
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Estimating the proportion of true null hypotheses and adaptive false discovery rate control in discrete paradigm Biom. J. (IF 1.7) Pub Date : 2024-02-14 Aniket Biswas, Gaurangadeb Chattopadhyay
Storey's estimator for the proportion of true null hypotheses, originally proposed under the continuous framework, has been modified in this work under the discrete framework. The modification results in improved estimation of the parameter of interest. The proposed estimator is used to formulate an adaptive version of the Benjamini–Hochberg procedure. Control over the false discovery rate by the proposed
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A Bayesian hierarchical approach to account for evidence and uncertainty in the modeling of infectious diseases: An application to COVID-19 Biom. J. (IF 1.7) Pub Date : 2024-01-28 Raphael Rehms, Nicole Ellenbach, Eva Rehfuess, Jacob Burns, Ulrich Mansmann, Sabine Hoffmann
Infectious disease models can serve as critical tools to predict the development of cases and associated healthcare demand and to determine the set of nonpharmaceutical interventions (NPIs) that is most effective in slowing the spread of an infectious agent. Current approaches to estimate NPI effects typically focus on relatively short time periods and either on the number of reported cases, deaths
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Parametric modal regression with error in covariates Biom. J. (IF 1.7) Pub Date : 2024-01-19 Qingyang Liu, Xianzheng Huang
An inference procedure is proposed to provide consistent estimators of parameters in a modal regression model with a covariate prone to measurement error. A score-based diagnostic tool exploiting parametric bootstrap is developed to assess adequacy of parametric assumptions imposed on the regression model. The proposed estimation method and diagnostic tool are applied to synthetic data generated from
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A comparison of strategies for selecting auxiliary variables for multiple imputation Biom. J. (IF 1.7) Pub Date : 2024-01-23 Rheanna M. Mainzer, Cattram D. Nguyen, John B. Carlin, Margarita Moreno-Betancur, Ian R. White, Katherine J. Lee
Multiple imputation (MI) is a popular method for handling missing data. Auxiliary variables can be added to the imputation model(s) to improve MI estimates. However, the choice of which auxiliary variables to include is not always straightforward. Several data-driven auxiliary variable selection strategies have been proposed, but there has been limited evaluation of their performance. Using a simulation
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Finite mixtures in capture–recapture surveys for modeling residency patterns in marine wildlife populations Biom. J. (IF 1.7) Pub Date : 2024-01-16 Gianmarco Caruso, Pierfrancesco Alaimo Di Loro, Marco Mingione, Luca Tardella, Daniela Silvia Pace, Giovanna Jona Lasinio
This work aims to show how prior knowledge about the structure of a heterogeneous animal population can be leveraged to improve the abundance estimation from capture–recapture survey data. We combine the Open Jolly-Seber model with finite mixtures and propose a parsimonious specification tailored to the residency patterns of the common bottlenose dolphin. We employ a Bayesian framework for our inference
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Mediation analysis with case–control sampling: Identification and estimation in the presence of a binary mediator Biom. J. (IF 1.7) Pub Date : 2024-01-12 Marco Doretti, Minna Genbäck, Elena Stanghellini
With reference to a stratified case–control (CC) procedure based on a binary variable of primary interest, we derive the expression of the distortion induced by the sampling design on the parameters of the logistic model of a secondary variable. This is particularly relevant when performing mediation analysis (possibly in a causal framework) with stratified case–control (SCC) data in settings where
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Neutralise: An open science initiative for neutral comparison of two-sample tests Biom. J. (IF 1.7) Pub Date : 2024-01-12 Leyla Kodalci, Olivier Thas
The two-sample problem is one of the earliest problems in statistics: given two samples, the question is whether or not the observations were sampled from the same distribution. Many statistical tests have been developed for this problem, and many tests have been evaluated in simulation studies, but hardly any study has tried to set up a neutral comparison study. In this paper, we introduce an open
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Estimation of odds ratio from group testing data with misclassified exposure Biom. J. (IF 1.7) Pub Date : 2024-01-12 Surupa Roy, Sumanta Adhya, Subrata Rana
For low prevalence disease, we consider estimation of the odds ratio for two specified groups of individuals using group testing data. Broadly the two groups may be classified as “the exposed” and “the unexposed.” Often in observational studies, the exposure status is not correctly recorded. In addition, diagnostic tests are rarely completely accurate. The proposed model accounts for imperfect sensitivity
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Decoupling power and type I error rate considerations when incorporating historical control data using a test-then-pool approach Biom. J. (IF 1.7) Pub Date : 2024-01-09 Kazufumi Okada, Shiro Tanaka, Jun Matsubayashi, Keita Takahashi, Isao Yokota
To accelerate a randomized controlled trial, historical control data may be used after ensuring little heterogeneity between the historical and current trials. The test-then-pool approach is a simple frequentist borrowing method that assesses the similarity between historical and current control data using a two-sided test. A limitation of the conventional test-then-pool method is the inability to
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Online false discovery rate control for LORD++ and SAFFRON under positive, local dependence Biom. J. (IF 1.7) Pub Date : 2023-12-16 Aaron Fisher
Online testing procedures assume that hypotheses are observed in sequence, and allow the significance thresholds for upcoming tests to depend on the test statistics observed so far. Some of the most popular online methods include alpha investing, LORD++, and SAFFRON. These three methods have been shown to provide online control of the “modified” false discovery rate (mFDR) under a condition known as
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Addressing unmeasured confounders in cohort studies: Instrumental variable method for a time-fixed exposure on an outcome trajectory Biom. J. (IF 1.7) Pub Date : 2023-12-14 Kateline Le Bourdonnec, Cécilia Samieri, Christophe Tzourio, Thibault Mura, Aniket Mishra, David-Alexandre Trégouët, Cécile Proust-Lima
Instrumental variable methods, which handle unmeasured confounding by targeting the part of the exposure explained by an exogenous variable not subject to confounding, have gained much interest in observational studies. We consider the very frequent setting of estimating the unconfounded effect of an exposure measured at baseline on the subsequent trajectory of an outcome repeatedly measured over time
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A new method for clustered survival data: Estimation of treatment effect heterogeneity and variable selection Biom. J. (IF 1.7) Pub Date : 2023-12-10 Liangyuan Hu
We recently developed a new method random-intercept accelerated failure time model with Bayesian additive regression trees (riAFT-BART) to draw causal inferences about population treatment effect on patient survival from clustered and censored survival data while accounting for the multilevel data structure. The practical utility of this method goes beyond the estimation of population average treatment
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Robust incorporation of historical information with known type I error rate inflation Biom. J. (IF 1.7) Pub Date : 2023-12-08 Silvia Calderazzo, Manuel Wiesenfarth, Annette Kopp-Schneider
Bayesian clinical trials can benefit from available historical information through the specification of informative prior distributions. Concerns are however often raised about the potential for prior-data conflict and the impact of Bayes test decisions on frequentist operating characteristics, with particular attention being assigned to inflation of type I error (TIE) rates. This motivates the development
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Bayesian optimal stepped wedge design Biom. J. (IF 1.7) Pub Date : 2023-12-06 Satya Prakash Singh
Recently, there has been a growing interest in designing cluster trials using stepped wedge design (SWD). An SWD is a type of cluster–crossover design in which clusters of individuals are randomized unidirectional from a control to an intervention at certain time points. The intraclass correlation coefficient (ICC) that measures the dependency of subject within a cluster plays an important role in
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Bayesian dose escalation with overdose and underdose control utilizing all toxicities in Phase I/II clinical trials Biom. J. (IF 1.7) Pub Date : 2023-12-04 Jieqi Tu, Zhengjia Chen
Escalation with overdose control (EWOC) is a commonly used Bayesian adaptive design, which controls overdosing risk while estimating maximum tolerated dose (MTD) in cancer Phase I clinical trials. In 2010, Chen and his colleagues proposed a novel toxicity scoring system to fully utilize patients’ toxicity information by using a normalized equivalent toxicity score (NETS) in the range 0 to 1 instead
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Drug combinations screening using a Bayesian ranking approach based on dose–response models Biom. J. (IF 1.7) Pub Date : 2023-11-20 Luana Boumendil, Morgane Fontaine, Vincent Lévy, Kim Pacchiardi, Raphaël Itzykson, Lucie Biard
Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose–response matrix of candidate combinations over a large number of drugs
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Multivariate joint model under competing risks to predict death of hospitalized patients for SARS-CoV-2 infection Biom. J. (IF 1.7) Pub Date : 2023-11-01 Alexandra Lavalley-Morelle, Nathan Peiffer-Smadja, Simon B. Gressens, Bérénice Souhail, Alexandre Lahens, Agathe Bounhiol, François-Xavier Lescure, France Mentré, Jimmy Mullaert
During the coronavirus disease 2019 (COVID-19) pandemic, several clinical prognostic scores have been proposed and evaluated in hospitalized patients, relying on variables available at admission. However, capturing data collected from the longitudinal follow-up of patients during hospitalization may improve prediction accuracy of a clinical outcome. To answer this question, 327 patients diagnosed with
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A scalable approach for short-term disease forecasting in high spatial resolution areal data Biom. J. (IF 1.7) Pub Date : 2023-10-27 Erick Orozco-Acosta, Andrea Riebler, Aritz Adin, Maria D. Ugarte
Short-term disease forecasting at specific discrete spatial resolutions has become a high-impact decision-support tool in health planning. However, when the number of areas is very large obtaining predictions can be computationally intensive or even unfeasible using standard spatiotemporal models. The purpose of this paper is to provide a method for short-term predictions in high-dimensional areal
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Incorporation of healthy volunteers data on receptor occupancy into a phase II proof-of-concept trial using a Bayesian dynamic borrowing design Biom. J. (IF 1.7) Pub Date : 2023-10-27 Fulvio Di Stefano, Christelle Rodrigues, Stephanie Galtier, Sandrine Guilleminot, Veronique Robert, Mauro Gasparini, Gaelle Saint-Hilary
Receptor occupancy in targeted tissues measures the proportion of receptors occupied by a drug at equilibrium and is sometimes used as a surrogate of drug efficacy to inform dose selection in clinical trials. We propose to incorporate data on receptor occupancy from a phase I study in healthy volunteers into a phase II proof-of-concept study in patients, with the objective of using all the available
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Multiple testing of composite null hypotheses for discrete data using randomized p-values Biom. J. (IF 1.7) Pub Date : 2023-10-19 Daniel Ochieng, Anh-Tuan Hoang, Thorsten Dickhaus
P-values that are derived from continuously distributed test statistics are typically uniformly distributed on (0,1) under least favorable parameter configurations (LFCs) in the null hypothesis. Conservativeness of a p-value P (meaning that P is under the null hypothesis stochastically larger than uniform on (0,1)) can occur if the test statistic from which P is derived is discrete, or if the true
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A Bayesian approach for mixed effects state-space models under skewness and heavy tails Biom. J. (IF 1.7) Pub Date : 2023-10-18 Lina L. Hernandez-Velasco, Carlos A. Abanto-Valle, Dipak K. Dey, Luis M. Castro
Human immunodeficiency virus (HIV) dynamics have been the focus of epidemiological and biostatistical research during the past decades to understand the progression of acquired immunodeficiency syndrome (AIDS) in the population. Although there are several approaches for modeling HIV dynamics, one of the most popular is based on Gaussian mixed-effects models because of its simplicity from the implementation
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Comment on Oberman & Vink: Should we fix or simulate the complete data in simulation studies evaluating missing data methods? Biom. J. (IF 1.7) Pub Date : 2023-10-12 Tim P. Morris, Ian R. White, Suzie Cro, Jonathan W. Bartlett, James R. Carpenter, Tra My Pham
For simulation studies that evaluate methods of handling missing data, we argue that generating partially observed data by fixing the complete data and repeatedly simulating the missingness indicators is a superficially attractive idea but only rarely appropriate to use.
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Response-adaptive randomization for multiarm clinical trials using context-dependent information measures Biom. J. (IF 1.7) Pub Date : 2023-10-10 Ksenia Kasianova, Mark Kelbert, Pavel Mozgunov
Theoretical-information approach applied to the clinical trial designs appeared to bring several advantages when tackling a problem of finding a balance between power and expected number of successes (ENS). In particular, it was shown that the built-in parameter of the weight function allows finding the desired trade-off between the statistical power and number of treated patients in the context of
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On “Reflections on the concept of optimality of single decision point treatment regimes” Biom. J. (IF 1.7) Pub Date : 2023-10-05 Erica E. M. Moodie, Denis Talbot
This is a discussion of “Reflections on the concept of optimality of single decision point treatment regimes” by Trung Dung Tran, Ariel Alonso Abad, Geert Verbeke, Geert Molenberghs, and Iven Van Mechelen. The authors propose a thoughtful consideration of optimization targets and the implications of such targets for the resulting optimal treatment rule. However, we contest the assertation that targets
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Multiplicity adjustments for the Dunnett procedure under heterokcedasticity Biom. J. (IF 1.7) Pub Date : 2023-10-03 Ajit C. Tamhane, Dong Xi
We give a simulation-based method for computing the multiplicity adjusted p-values and critical constants for the Dunnett procedure for comparing treatments with a control under heteroskedasticity. The Welch–Satterthwaite test statistics used in this procedure do not have a simple multivariate t-distribution because their denominators are mixtures of chi-squares and are correlated because of the common
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Estimation in optimal treatment regimes based on mean residual lifetimes with right-censored data Biom. J. (IF 1.7) Pub Date : 2023-10-03 Zhishuai Liu, Zishu Zhan, Cunjie Lin, Baqun Zhang
An optimal individualized treatment regime (ITR) is a decision rule in allocating the best treatment to each patient and, hence, maximizing overall benefits. In this paper, we propose a novel framework based on nonparametric inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW) estimators of the value function when the data are subject to right censoring. In contrast
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The marginality principle revisited: Should “higher-order” terms always be accompanied by “lower-order” terms in regression analyses? Biom. J. (IF 1.7) Pub Date : 2023-09-29 Tim P. Morris, Maarten van Smeden, Tra My Pham
The marginality principle guides analysts to avoid omitting lower-order terms from models in which higher-order terms are included as covariates. Lower-order terms are viewed as “marginal” to higher-order terms. We consider how this principle applies to three cases: regression models that may include the ratio of two measured variables; polynomial transformations of a measured variable; and factorial
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Regularized parametric survival modeling to improve risk prediction models Biom. J. (IF 1.7) Pub Date : 2023-09-29 J. Hoogland, T. P. A. Debray, M. J. Crowther, R. D. Riley, J. IntHout, J. B. Reitsma, A. H. Zwinderman
We propose to combine the benefits of flexible parametric survival modeling and regularization to improve risk prediction modeling in the context of time-to-event data. Thereto, we introduce ridge, lasso, elastic net, and group lasso penalties for both log hazard and log cumulative hazard models. The log (cumulative) hazard in these models is represented by a flexible function of time that may depend
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Surrogacy validation for time-to-event outcomes with illness-death frailty models Biom. J. (IF 1.7) Pub Date : 2023-09-29 Emily K. Roberts, Michael R. Elliott, Jeremy M. G. Taylor
A common practice in clinical trials is to evaluate a treatment effect on an intermediate outcome when the true outcome of interest would be difficult or costly to measure. We consider how to validate intermediate outcomes in a causally-valid way when the trial outcomes are time-to-event. Using counterfactual outcomes, those that would be observed if the counterfactual treatment had been given, the
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Designing multicenter individually randomized group treatment trials Biom. J. (IF 1.7) Pub Date : 2023-09-28 Guangyu Tong, Jiaqi Tong, Fan Li
In an individually randomized group treatment (IRGT) trial, participant outcomes can be positively correlated due to, for example, shared therapists in treatment delivery. Oftentimes, because of limited treatment resources or participants at one location, an IRGT trial can be carried out across multiple centers. This design can be subject to potential correlations in the participant outcomes between
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Spearman-like correlation measure adjusting for covariates in bivariate survival data Biom. J. (IF 1.7) Pub Date : 2023-09-27 Svetlana K. Eden, Chun Li, Bryan E. Shepherd
We propose an extension of Spearman's correlation for censored continuous and discrete data that permits covariate adjustment. Previously proposed nonparametric and semiparametric Spearman's correlation estimators require either nonparametric estimation of the bivariate survival surface or parametric assumptions about the dependence structure. In practice, nonparametric estimation of the bivariate
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M-quantile regression shrinkage and selection via the Lasso and Elastic Net to assess the effect of meteorology and traffic on air quality Biom. J. (IF 1.7) Pub Date : 2023-09-24 M. Giovanna Ranalli, Nicola Salvati, Lea Petrella, Francesco Pantalone
In this work, we intersect data on size-selected particulate matter (PM) with vehicular traffic counts and a comprehensive set of meteorological covariates to study the effect of traffic on air quality. To this end, we develop an M-quantile regression model with Lasso and Elastic Net penalizations. This allows (i) to identify the best proxy for vehicular traffic via model selection, (ii) to investigate
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CITIES: Clinical trials with intercurrent events simulator Biom. J. (IF 1.7) Pub Date : 2023-09-22 Ahmad Hakeem Abdul Wahab, Yongming Qu, Hege Michiels, Junxiang Luo, Run Zhuang, Dominique McDaniel, Dong Xi, Elena Polverejan, Steven Gilbert, Stephen Ruberg, Arman Sabbaghi
Although clinical trials are often designed with randomization and well-controlled protocols, complications will inevitably arise in the presence of intercurrent events (ICEs) such as treatment discontinuation. These can lead to missing outcome data and possibly confounding causal inference when the missingness is a function of a latent stratification of patients defined by intermediate outcomes. The
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A joint Bayesian framework for missing data and measurement error using integrated nested Laplace approximations Biom. J. (IF 1.7) Pub Date : 2023-09-22 Emma Skarstein, Sara Martino, Stefanie Muff
Measurement error (ME) and missing values in covariates are often unavoidable in disciplines that deal with data, and both problems have separately received considerable attention during the past decades. However, while most researchers are familiar with methods for treating missing data, accounting for ME in covariates of regression models is less common. In addition, ME and missing data are typically
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Reflections on the concept of optimality of single decision point treatment regimes Biom. J. (IF 1.7) Pub Date : 2023-09-21 Trung Dung Tran, Ariel Alonso Abad, Geert Verbeke, Geert Molenberghs, Iven Van Mechelen
In many areas, applied researchers as well as practitioners have to choose between different solutions for a problem at hand; this calls for optimal decision rules to settle the choices involved. As a key example, one may think of the search for optimal treatment regimes (OTRs) in clinical research, that specify which treatment alternative should be administered to each patient under study. Motivated
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Trial arm outcome variance difference after dropout as an indicator of missing-not-at-random bias in randomized controlled trials Biom. J. (IF 1.7) Pub Date : 2023-09-20 Audinga-Dea Hazewinkel, Kate Tilling, Kaitlin H. Wade, Tom Palmer
Randomized controlled trials (RCTs) are vulnerable to bias from missing data. When outcomes are missing not at random (MNAR), estimates from complete case analysis (CCA) and multiple imputation (MI) may be biased. There is no statistical test for distinguishing between outcomes missing at random (MAR) and MNAR. Current strategies rely on comparing dropout proportions and covariate distributions, and
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A fiducial-based confidence interval for the linear combination of multinomial probabilities Biom. J. (IF 1.7) Pub Date : 2023-09-11 Katherine A. Batterton, Christine M. Schubert, Richard L. Warr
Across a broad set of applications, system outcomes may be summarized as probabilities in confusion or contingency tables. In settings with more than two outcomes (e.g., stages of cancer), these outcomes represent multinomial experiments. Measures to summarize system performance have been presented as linear combinations of the resulting multinomial probabilities. Statistical inference on the linear
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Estimating causal effects in observational studies for survival data with a cure fraction using propensity score adjustment Biom. J. (IF 1.7) Pub Date : 2023-09-06 Ziwen Wang, Chenguang Wang, Xiaoguang Wang
In observational studies, covariates are often confounding factors for treatment assignment. Such covariates need to be adjusted to estimate the causal treatment effect. For observational studies with survival outcomes, it is usually more challenging to adjust for the confounding covariates for causal effect estimation because of censoring. The challenge becomes even thornier when there exists a nonignorable
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Sample size planning for multiple contrast tests Biom. J. (IF 1.7) Pub Date : 2023-09-04 Anna Pöhlmann, Frank Konietschke
Sample size calculations for two (independent) samples are well established and applied in (pre-)clinical research. When planning several samples, which is common in, for example, preclinical studies, sample size planning tools based on analysis of variance methods are available. Since the underlying effect sizes of these methods are often hard to interpret and to provide for the sample size planning
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Variable selection in linear regression models: Choosing the best subset is not always the best choice Biom. J. (IF 1.7) Pub Date : 2023-08-29 Moritz Hanke, Louis Dijkstra, Ronja Foraita, Vanessa Didelez
We consider the question of variable selection in linear regressions, in the sense of identifying the correct direct predictors (those variables that have nonzero coefficients given all candidate predictors). Best subset selection (BSS) is often considered the “gold standard,” with its use being restricted only by its NP-hard nature. Alternatives such as the least absolute shrinkage and selection operator
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Opportunities and challenges with decentralized trials in Neuroscience Biom. J. (IF 1.7) Pub Date : 2023-08-23 Hans Ulrich Burger, Tom Van de Casteele, Khadija Rerhou Rantell, Patricia Corey-Lisle, Nikolaos Sfikas, Markus Abt
Decentralized clinical trials (DCTs), that is, studies integrating elements of telemedicine and mobile/local healthcare providers allowing for home-based assessments, are an important concept to make studies more resilient and more patient-centric by taking into consideration participant's views and shifting trial activities to better meet the needs of trial participants. There are however, not only
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Unbiased and efficient estimation of causal treatment effects in crossover trials Biom. J. (IF 1.7) Pub Date : 2023-08-08 Jeppe Ekstrand Halkjær Madsen, Thomas Scheike, Christian Pipper
We introduce causal inference reasoning to crossover trials, with a focus on thorough QT (TQT) studies. For such trials, we propose different sets of assumptions and consider their impact on the modeling strategy and estimation procedure. We show that unbiased estimates of a causal treatment effect are obtained by a g-computation approach in combination with weighted least squares predictions from
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Editorial: Special Issue 42nd ISCB Conference Biom. J. (IF 1.7) Pub Date : 2023-08-02 Hélène Jacqmin-Gadda
This special issue originated from the 42nd Annual Conference of the International Society for Clinical Biostatistics (ISCB), which took place virtually in Lyon, France, in August 2021. Despite the inability to organize an in-person conference due to the COVID-19 crisis, the conference was a success, drawing approximately 650 participants from around the globe. The conference covered a wide range of
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Using real-world data to predict health outcomes—The prediction design: Application and sample size planning Biom. J. (IF 1.7) Pub Date : 2023-07-26 Stella Erdmann, Dominic Edelmann, Meinhard Kieser
The gold standard for investigating the efficacy of a new therapy is a (pragmatic) randomized controlled trial (RCT). This approach is costly, time-consuming, and not always practicable. At the same time, huge quantities of available patient-level control condition data in analyzable format of (former) RCTs or real-world data (RWD) are neglected. Therefore, alternative study designs are desirable.
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Letter to the editor on "New weighting methods when cases are only a subset of events in a nested case-control study" by Qian M. Zhou, Xuan Wang, Yingye Zheng, and Tianxi Cai. Biom. J. (IF 1.7) Pub Date : 2023-07-20 Francesca Graziano,Maria Grazia Valsecchi,Paola Rebora
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Stability of clinical prediction models developed using statistical or machine learning methods Biom. J. (IF 1.7) Pub Date : 2023-07-19 Richard D. Riley, Gary S. Collins
Clinical prediction models estimate an individual's risk of a particular health outcome. A developed model is a consequence of the development dataset and model-building strategy, including the sample size, number of predictors, and analysis method (e.g., regression or machine learning). We raise the concern that many models are developed using small datasets that lead to instability in the model and
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A zero-inflated endemic–epidemic model with an application to measles time series in Germany Biom. J. (IF 1.7) Pub Date : 2023-07-13 Junyi Lu, Sebastian Meyer
Count data with an excess of zeros are often encountered when modeling infectious disease occurrence. The degree of zero inflation can vary over time due to nonepidemic periods as well as by age group or region. A well-established approach to analyze multivariate incidence time series is the endemic–epidemic modeling framework, also known as the HHH approach. However, it assumes Poisson or negative
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State-space prior distribution for parameter of nonhomogeneous Poisson spatiotemporal models Biom. J. (IF 1.7) Pub Date : 2023-07-09 Fidel Ernesto Castro Morales
This article proposes a new class of nonhomogeneous Poisson spatiotemporal model. In this approach, we use a state-space model-based prior distribution to handle the scale and shape parameters of the Weibull intensity function. The proposed prior distribution enables the inclusion of changes in the behavior of the intensity function over time. In defining the spatial correlation function of the model
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A neutral comparison of algorithms to minimize L0 penalties for high-dimensional variable selection Biom. J. (IF 1.7) Pub Date : 2023-07-08 Florian Frommlet
Variable selection methods based on L0 penalties have excellent theoretical properties to select sparse models in a high-dimensional setting. There exist modifications of the Bayesian Information Criterion (BIC) which either control the familywise error rate (mBIC) or the false discovery rate (mBIC2) in terms of which regressors are selected to enter a model. However, the minimization of L0 penalties
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On near-redundancy and identifiability of parametric hazard regression models under censoring Biom. J. (IF 1.7) Pub Date : 2023-07-02 Francisco J. Rubio, Jorge A. Espindola, José A. Montoya
We study parametric inference on a rich class of hazard regression models in the presence of right-censoring. Previous literature has reported some inferential challenges, such as multimodal or flat likelihood surfaces, in this class of models for some particular data sets. We formalize the study of these inferential problems by linking them to the concepts of near-redundancy and practical nonidentifiability
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Sample size calculation for one-armed clinical trials with clustered data and binary outcome Biom. J. (IF 1.7) Pub Date : 2023-06-28 Maximilian Pilz
The formula of Fleiss and Cuzick (1979) to estimate the intraclass correlation coefficient is applied to reduce the task of sample size calculation for clustered data with binary outcome. It is demonstrated that this approach reduces the complexity of sample size calculation to the determination of the null and alternative hypothesis and the formulation of the quantitative influence of the belonging
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Multivariate reference and tolerance regions based on conditional transformation models: Application to glycemic markers Biom. J. (IF 1.7) Pub Date : 2023-06-25 Óscar Lado-Baleato, Carmen Cadarso-Suárez, Thomas Kneib, Francisco Gude
The reference interval is the most widely used medical decision-making, constituting a central tool in determining whether an individual is healthy or not. When the results of several continuous diagnostic tests are available for the same patient, their clinical interpretation is more reliable if a multivariate reference region (MVR) is available rather than multiple univariate reference intervals
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Causal decomposition maps: An exploratory tool for designing area-level interventions aimed at reducing health disparities Biom. J. (IF 1.7) Pub Date : 2023-06-20 Melissa J. Smith, Mary E. Charlton, Jacob J. Oleson
Methods for decomposition analyses have been developed to partition between-group differences into explained and unexplained portions. In this paper, we introduce the concept of causal decomposition maps, which allow researchers to test the effect of area-level interventions on disease maps before implementation. These maps quantify the impact of interventions that aim to reduce differences in health
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TITE-gBOIN-ET: Time-to-event generalized Bayesian optimal interval design to accelerate dose-finding accounting for ordinal graded efficacy and toxicity outcomes Biom. J. (IF 1.7) Pub Date : 2023-06-12 Kentaro Takeda, Yusuke Yamaguchi, Masataka Taguri, Satoshi Morita
One of the primary objectives of an oncology dose-finding trial for novel therapies, such as molecular-targeted agents and immune-oncology therapies, is to identify an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. These new therapeutic agents appear more likely to induce multiple low or moderate-grade toxicities than dose-limiting toxicities