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Statistical models for assessing agreement for quantitative data with heterogeneous random raters and replicate measurements Int. J. Biostat. (IF 1.2) Pub Date : 2024-02-21 Claus Thorn Ekstrøm, Bendix Carstensen
Agreement between methods for quantitative measurements are typically assessed by computing limits of agreement between pairs of methods and/or by illustration through Bland–Altman plots. We consider the situation where the observed measurement methods are considered a random sample from a population of possible methods, and discuss how the underlying linear mixed effects model can be extended to this
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Flexible variable selection in the presence of missing data Int. J. Biostat. (IF 1.2) Pub Date : 2024-02-13 Brian D. Williamson, Ying Huang
In many applications, it is of interest to identify a parsimonious set of features, or panel, from multiple candidates that achieves a desired level of performance in predicting a response. This task is often complicated in practice by missing data arising from the sampling design or other random mechanisms. Most recent work on variable selection in missing data contexts relies in some part on a finite-dimensional
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MBPCA-OS: an exploratory multiblock method for variables of different measurement levels. Application to study the immune response to SARS-CoV-2 infection and vaccination Int. J. Biostat. (IF 1.2) Pub Date : 2023-12-12 Martin Paries, Evelyne Vigneau, Adeline Huneau, Olivier Lantz, Stéphanie Bougeard
Studying a large number of variables measured on the same observations and organized in blocks – denoted multiblock data – is becoming standard in several domains especially in biology. To explore the relationships between all these variables – at the block- and the variable-level – several exploratory multiblock methods were proposed. However, most of them are only designed for numeric variables.
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Improving the mixed model for repeated measures to robustly increase precision in randomized trials Int. J. Biostat. (IF 1.2) Pub Date : 2023-11-29 Bingkai Wang, Yu Du
In randomized trials, repeated measures of the outcome are routinely collected. The mixed model for repeated measures (MMRM) leverages the information from these repeated outcome measures, and is often used for the primary analysis to estimate the average treatment effect at the primary endpoint. MMRM, however, can suffer from bias and precision loss when it models intermediate outcomes incorrectly
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Bayesian second-order sensitivity of longitudinal inferences to non-ignorability: an application to antidepressant clinical trial data Int. J. Biostat. (IF 1.2) Pub Date : 2023-11-27 Elahe Momeni Roochi, Samaneh Eftekhari Mahabadi
Incomplete data is a prevalent complication in longitudinal studies due to individuals’ drop-out before intended completion time. Currently available methods via commercial software for analyzing incomplete longitudinal data at best rely on the ignorability of the drop-outs. If the underlying missing mechanism was non-ignorable, potential bias arises in the statistical inferences. To remove the bias
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Prediction-based variable selection for component-wise gradient boosting Int. J. Biostat. (IF 1.2) Pub Date : 2023-11-24 Sophie Potts, Elisabeth Bergherr, Constantin Reinke, Colin Griesbach
Model-based component-wise gradient boosting is a popular tool for data-driven variable selection. In order to improve its prediction and selection qualities even further, several modifications of the original algorithm have been developed, that mainly focus on different stopping criteria, leaving the actual variable selection mechanism untouched. We investigate different prediction-based mechanisms
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Revisiting incidence rates comparison under right censorship Int. J. Biostat. (IF 1.2) Pub Date : 2023-11-13 Pablo Martínez-Camblor, Susana Díaz-Coto
Data description is the first step for understanding the nature of the problem at hand. Usually, it is a simple task that does not require any particular assumption. However, the interpretation of the used descriptive measures can be a source of confusion and misunderstanding. The incidence rate is the quotient between the number of observed events and the sum of time that the studied population was
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Testing for association between ordinal traits and genetic variants in pedigree-structured samples by collapsing and kernel methods Int. J. Biostat. (IF 1.2) Pub Date : 2023-09-25 Li-Chu Chien
In genome-wide association studies (GWAS), logistic regression is one of the most popular analytics methods for binary traits. Multinomial regression is an extension of binary logistic regression that allows for multiple categories. However, many GWAS methods have been limited application to binary traits. These methods have improperly often been used to account for ordinal traits, which causes inappropriate
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Assessing HIV-infected patient retention in a program of differentiated care in sub-Saharan Africa: a G-estimation approach Int. J. Biostat. (IF 1.2) Pub Date : 2023-09-15 Constantin T. Yiannoutsos, Kara Wools-Kaloustian, Beverly S. Musick, Rose Kosgei, Sylvester Kimaiyo, Abraham Siika
Differentiated care delivery aims to simplify care of people living with HIV, reflect their preferences, reduce burdens on the healthcare system, maintain care quality and preserve resources. However, assessing program effectiveness using observational data is difficult due to confounding by indication and randomized trials may be infeasible. Also, benefits can reach patients directly, through enrollment
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A modified rule of three for the one-sided binomial confidence interval Int. J. Biostat. (IF 1.2) Pub Date : 2023-09-02 Lonnie Turpin, Jeanne-Claire Patin, William Jens, Morgan Turpin
Consider the one-sided binomial confidence interval L , 1 $\left(L,1\right)$ containing the unknown parameter p when all n trials are successful, and the significance level α to be five or one percent. We develop two functions (one for each level) that represent approximations within α / 3 $\alpha /\sqrt{3}$ of the exact lower-bound L = α 1/n . Both the exponential (referred to as a modified rule of
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Agent-based modeling in medical research, virtual baseline generator and change in patients’ profile issue Int. J. Biostat. (IF 1.2) Pub Date : 2023-07-11 Philippe Saint-Pierre, Nicolas Savy
Simulation studies are promising in medical research in particular to improve drug development. For instance, one can aim to develop In Silico Clinical Trial in order to challenge trial’s design parameters in terms of feasibility and probability of success of the trial. Approaches based on agent-based models draw on a particularly useful framework to simulate patients evolution. In this paper, an approach
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Bayesian estimation and prediction for network meta-analysis with contrast-based approach Int. J. Biostat. (IF 1.2) Pub Date : 2023-07-04 Hisashi Noma
Network meta-analysis is gaining prominence in clinical epidemiology and health technology assessments that enable comprehensive assessment of comparative effectiveness for multiple available treatments. In network meta-analysis, Bayesian methods have been one of the standard approaches for the arm-based approach and are widely applied in practical data analyses. Also, for most cases in these applications
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Agent based modeling in health care economics: examples in the field of thyroid cancer Int. J. Biostat. (IF 1.2) Pub Date : 2023-07-01 Romain Demeulemeester, Nicolas Savy, Pascale Grosclaude, Nadège Costa, Philippe Saint-Pierre
Although they remain little used in the field of Health Care Economics, Agent Based Models (ABM) are potentially powerful decision-making tools that open up great prospects. The reasons for this lack of popularity are essentially to be found in a methodology that should be further clarified. This article hence aims to illustrate the methodology by means of two applications to medical examples. The
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Sensitivity of estimands in clinical trials with imperfect compliance Int. J. Biostat. (IF 1.2) Pub Date : 2023-06-27 Heng Chen, Daniel F. Heitjan
In clinical trials that are subject to noncompliance, the commonly used intention-to-treat estimand is valid as a causal effect of treatment assignment but is sensitive to the level of compliance. An alternative estimand, the complier average causal effect (CACE), measures the average effect of treatment received in the latent subset of subjects who would comply with either assigned treatment. Because
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Survival analysis using deep learning with medical imaging Int. J. Biostat. (IF 1.2) Pub Date : 2023-06-14 Samantha Morrison, Constantine Gatsonis, Ani Eloyan, Jon Arni Steingrimsson
There is widespread interest in using deep learning to build prediction models for medical imaging data. These deep learning methods capture the local structure of the image and require no manual feature extraction. Despite the importance of modeling survival in the context of medical data analysis, research on deep learning methods for modeling the relationship of imaging and time-to-event data is
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Right-censored partially linear regression model with error in variables: application with carotid endarterectomy dataset Int. J. Biostat. (IF 1.2) Pub Date : 2023-05-31 Dursun Aydın, Ersin Yılmaz, Nur Chamidah, Budi Lestari
This paper considers a partially linear regression model relating a right-censored response variable to predictors and an extra covariate with measured error. The main problem here is that censorship and measurement error problems need to be solved to estimate the model correctly. In this sense, we propose three modified semiparametric estimators obtained from local polynomial regression, kernel smoothing
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Bayesian inference for optimal dynamic treatment regimes in practice Int. J. Biostat. (IF 1.2) Pub Date : 2023-05-16 Daniel Rodriguez Duque, Erica E. M. Moodie, David A. Stephens
In this work, we examine recently developed methods for Bayesian inference of optimal dynamic treatment regimes (DTRs). DTRs are a set of treatment decision rules aimed at tailoring patient care to patient-specific characteristics, thereby falling within the realm of precision medicine. In this field, researchers seek to tailor therapy with the intention of improving health outcomes; therefore, they
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Exact correction factor for estimating the OR in the presence of sparse data with a zero cell in 2 × 2 tables Int. J. Biostat. (IF 1.2) Pub Date : 2023-05-10 Malavika Babu, Thenmozhi Mani, Marimuthu Sappani, Sebastian George, Shrikant I. Bangdiwala, Lakshmanan Jeyaseelan
In case-control studies, odds ratios (OR) are calculated from 2 × 2 tables and in some instances, we observe small cell counts or zero counts in one of the cells. The corrections to calculate the ORs in the presence of empty cells are available in literature. Some of these include Yates continuity correction and Agresti and Coull correction. However, the available methods provided different corrections
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Error analysis of the PacBio sequencing CCS reads Int. J. Biostat. (IF 1.2) Pub Date : 2023-05-08 Reza Pourmohammadi, Jamshid Abouei, Alagan Anpalagan
Third generation sequencing technologies such as Pacific Biosciences and Oxford Nanopore provide faster, cost-effective and simpler assembly process generating longer reads than the ones in the next generation sequencing. However, the error rates of these long reads are higher than those of the short reads, resulting in an error correcting process before the assembly such as using the Circular Consensus
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On stochastic dynamic modeling of incidence data Int. J. Biostat. (IF 1.2) Pub Date : 2023-04-29 Emmanouil-Nektarios Kalligeris, Alex Karagrigoriou, Christina Parpoula
In this paper, a Markov Regime Switching Model of Conditional Mean with covariates, is proposed and investigated for the analysis of incidence rate data. The components of the model are selected by both penalized likelihood techniques in conjunction with the Expectation Maximization algorithm, with the goal of achieving a high level of robustness regarding the modeling of dynamic behaviors of epidemiological
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Power of testing for exposure effects under incomplete mediation Int. J. Biostat. (IF 1.2) Pub Date : 2023-04-21 Ruixuan R. Zhou, David M. Zucker, Sihai D. Zhao
Mediation analysis studies situations where an exposure may affect an outcome both directly and indirectly through intervening variables called mediators. It is frequently of interest to test for the effect of the exposure on the outcome, and the standard approach is simply to regress the latter on the former. However, it seems plausible that a more powerful test statistic could be achieved by also
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Approximate reciprocal relationship between two cause-specific hazard ratios in COVID-19 data with mutually exclusive events Int. J. Biostat. (IF 1.2) Pub Date : 2023-03-30 Wentian Li, Sirin Cetin, Ayse Ulgen, Meryem Cetin, Hakan Sivgin, Yaning Yang
COVID-19 survival data presents a special situation where not only the time-to-event period is short, but also the two events or outcome types, death and release from hospital, are mutually exclusive, leading to two cause-specific hazard ratios (csHR d and csHR r ). The eventual mortality/release outcome is also analyzed by logistic regression to obtain odds-ratio (OR). We have the following three
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Heterogeneity in meta-analysis: a comprehensive overview Int. J. Biostat. (IF 1.2) Pub Date : 2023-03-24 Dimitris Stogiannis, Fotios Siannis, Emmanouil Androulakis
In recent years, meta-analysis has evolved to a critically important field of Statistics, and has significant applications in Medicine and Health Sciences. In this work we briefly present existing methodologies to conduct meta-analysis along with any discussion and recent developments accompanying them. Undoubtedly, studies brought together in a systematic review will differ in one way or another.
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HiPerMAb: a tool for judging the potential of small sample size biomarker pilot studies Int. J. Biostat. (IF 1.2) Pub Date : 2023-03-03 Amani Al-Mekhlafi, Frank Klawonn
Common statistical approaches are not designed to deal with so-called “short fat data” in biomarker pilot studies, where the number of biomarker candidates exceeds the sample size by magnitudes. High-throughput technologies for omics data enable the measurement of ten thousands and more biomarker candidates for specific diseases or states of a disease. Due to the limited availability of study participants
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The EBM+ movement Int. J. Biostat. (IF 1.2) Pub Date : 2023-02-17 Michael Wilde
In this paper, I provide an introduction for biostatisticians and others to some recent work in the philosophy of medicine. Firstly, I give an overview of some philosophical arguments that are thought to create problems for a prominent approach towards establishing causal claims in medicine, namely, the Evidence-Based Medicine (EBM) approach. Secondly, I provide an overview of further recent work in
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Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions Int. J. Biostat. (IF 1.2) Pub Date : 2023-01-06 Annabelle Collin, Boris P. Hejblum, Carole Vignals, Laurent Lehot, Rodolphe Thiébaut, Philippe Moireau, Mélanie Prague
In response to the COVID-19 pandemic caused by SARS-CoV-2, governments have adopted a wide range of non-pharmaceutical interventions (NPI). These include stringent measures such as strict lockdowns, closing schools, bars and restaurants, curfews, and barrier gestures such as mask-wearing and social distancing. Deciphering the effectiveness of each NPI is critical to responding to future waves and outbreaks
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Hierarchical Bayesian bootstrap for heterogeneous treatment effect estimation Int. J. Biostat. (IF 1.2) Pub Date : 2022-12-30 Arman Oganisian, Nandita Mitra, Jason A. Roy
A major focus of causal inference is the estimation of heterogeneous average treatment effects (HTE) – average treatment effects within strata of another variable of interest such as levels of a biomarker, education, or age strata. Inference involves estimating a stratum-specific regression and integrating it over the distribution of confounders in that stratum – which itself must be estimated. Standard
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Unequal allocation of sample/event sizes with considerations of sampling cost for testing equality, non-inferiority/superiority, and equivalence of two Poisson rates Int. J. Biostat. (IF 1.2) Pub Date : 2022-12-30 Wei-Ming Luh, Jiin-Huarng Guo
For non-inferiority/superiority and equivalence tests of two Poisson rates, the determination of the required number of sample sizes has been studied but the studies for the number of events to be observed are very limited. To fill the gap, the present study first is aimed toward determining the number of events to be observed for testing non-inferiority/superiority and equivalence of two Poisson rates
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Bayesianism from a philosophical perspective and its application to medicine Int. J. Biostat. (IF 1.2) Pub Date : 2022-12-09 Jon Williamson
Bayesian philosophy and Bayesian statistics have diverged in recent years, because Bayesian philosophers have become more interested in philosophical problems other than the foundations of statistics and Bayesian statisticians have become less concerned with philosophical foundations. One way in which this divergence manifests itself is through the use of direct inference principles: Bayesian philosophers
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Statistics, philosophy, and health: the SMAC 2021 webconference Int. J. Biostat. (IF 1.2) Pub Date : 2022-12-07 Nicolas Savy, Erica EM Moodie, Isabelle Drouet, Antoine Chambaz, Bruno Falissard, Michael R. Kosorok, Elizabeth F. Krakow, Deborah G. Mayo, Stephen Senn, Mark Van der Laan
SMAC 2021 was a webconference organized in June 2021. The aim of this conference was to bring together data scientists, (bio)statisticians, philosophers, and any person interested in the questions of causality and Bayesian statistics, ranging from technical to philosophical aspects. This webconference consisted of keynote speakers and contributed speakers, and closed with a round-table organized in
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Bayesian learners in gradient boosting for linear mixed models Int. J. Biostat. (IF 1.2) Pub Date : 2022-12-06 Boyao Zhang, Colin Griesbach, Elisabeth Bergherr
Selection of relevant fixed and random effects without prior choices made from possibly insufficient theory is important in mixed models. Inference with current boosting techniques suffers from biased estimates of random effects and the inflexibility of random effects selection. This paper proposes a new inference method “BayesBoost” that integrates a Bayesian learner into gradient boosting with simultaneous
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Highly robust causal semiparametric U-statistic with applications in biomedical studies Int. J. Biostat. (IF 1.2) Pub Date : 2022-11-26 Anqi Yin, Ao Yuan, Ming T. Tan
With our increased ability to capture large data, causal inference has received renewed attention and is playing an ever-important role in biomedicine and economics. However, one major methodological hurdle is that existing methods rely on many unverifiable model assumptions. Thus robust modeling is a critically important approach complementary to sensitivity analysis, where it compares results under
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Penalized logistic regression with prior information for microarray gene expression classification Int. J. Biostat. (IF 1.2) Pub Date : 2022-11-25 Murat Genç
Cancer classification and gene selection are important applications in DNA microarray gene expression data analysis. Since DNA microarray data suffers from the high-dimensionality problem, automatic gene selection methods are used to enhance the classification performance of expert classifier systems. In this paper, a new penalized logistic regression method that performs simultaneous gene coefficient
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The balanced discrete triplet Lindley model and its INAR(1) extension: properties and COVID-19 applications Int. J. Biostat. (IF 1.2) Pub Date : 2022-11-24 Masoumeh Shirozhan, Naushad A. Mamode Khan, Célestin C. Kokonendji
This paper proposes a new flexible discrete triplet Lindley model that is constructed from the balanced discretization principle of the extended Lindley distribution. This model has several appealing statistical properties in terms of providing exact and closed form moment expressions and handling all forms of dispersion. Due to these, this paper explores further the usage of the discrete triplet Lindley
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Application of the patient-reported outcomes continual reassessment method to a phase I study of radiotherapy in endometrial cancer Int. J. Biostat. (IF 1.2) Pub Date : 2022-11-17 Nolan A. Wages, Bailey Nelson, Jordan Kharofa, Teresa Meier
This article considers the concept of designing Phase I clinical trials using both clinician- and patient-reported outcomes to adaptively allocate study participants to tolerable doses and determine the maximum tolerated dose (MTD) at the study conclusion. We describe an application of a Bayesian form of the patient-reported outcomes continual reassessment method (PRO-CRMB) in an ongoing Phase I study
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Potential application of elastic nets for shared polygenicity detection with adapted threshold selection Int. J. Biostat. (IF 1.2) Pub Date : 2022-11-03 Majnu John, Todd Lencz
Current research suggests that hundreds to thousands of single nucleotide polymorphisms (SNPs) with small to modest effect sizes contribute to the genetic basis of many disorders, a phenomenon labeled as polygenicity. Additionally, many such disorders demonstrate polygenic overlap, in which risk alleles are shared at associated genetic loci. A simple strategy to detect polygenic overlap between two
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Estimating risk and rate ratio in rare events meta-analysis with the Mantel–Haenszel estimator and assessing heterogeneity Int. J. Biostat. (IF 1.2) Pub Date : 2022-10-28 Dankmar Böhning, Patarawan Sangnawakij, Heinz Holling
Meta-analysis of binary outcome data faces often a situation where studies with a rare event are part of the set of studies to be considered. These studies have low occurrence of event counts to the extreme that no events occur in one or both groups to be compared. This raises issues how to estimate validly the summary risk or rate ratio across studies. A preferred choice is the Mantel–Haenszel estimator
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Statistical modelling of COVID-19 and drug data via an INAR(1) process with a recent thinning operator and cosine Poisson innovations Int. J. Biostat. (IF 1.2) Pub Date : 2022-10-27 Zohreh Mohammadi, Hassan S. Bakouch, Maryam Sharafi
In this paper, we propose the first-order stationary integer-valued autoregressive process with the cosine Poisson innovation, based on the negative binomial thinning operator. It can be equi-dispersed, under-dispersed and over-dispersed. Therefore, it is flexible for modelling integer-valued time series. Some statistical properties of the process are derived. The parameters of the process are estimated
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A SIMEX approach for meta-analysis of diagnostic accuracy studies with attention to ROC curves Int. J. Biostat. (IF 1.2) Pub Date : 2022-10-26 Annamaria Guolo, Tania Erika Pesantez Cabrera
Bivariate random-effects models represent an established approach for meta-analysis of accuracy measures of a diagnostic test, which are typically given by sensitivity and specificity. A recent formulation of the classical model describes the test accuracy in terms of study-specific Receiver Operating Characteristics curves. In this way, the resulting summary curve can be thought of as an average of
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Detection of atypical response trajectories in biomedical longitudinal databases Int. J. Biostat. (IF 1.2) Pub Date : 2022-10-22 Lucio José Pantazis, Rafael Antonio García
Many health care professionals and institutions manage longitudinal databases, involving follow-ups for different patients over time. Longitudinal data frequently manifest additional complexities such as high variability, correlated measurements and missing data. Mixed effects models have been widely used to overcome these difficulties. This work proposes the use of linear mixed effects models as a
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A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes Int. J. Biostat. (IF 1.2) Pub Date : 2022-10-22 Julien St-Pierre, Karim Oualkacha
In genome wide association studies (GWAS), researchers are often dealing with dichotomous and non-normally distributed traits, or a mixture of discrete-continuous traits. However, most of the current region-based methods rely on multivariate linear mixed models (mvLMMs) and assume a multivariate normal distribution for the phenotypes of interest. Hence, these methods are not applicable to disease or
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Causal inference for oncology: past developments and current challenges Int. J. Biostat. (IF 1.2) Pub Date : 2022-09-02 Erica E. M. Moodie
In this paper, we review some important early developments on causal inference in medical statistics and epidemiology that were inspired by questions in oncology. We examine two classical examples from the literature and point to a current area of ongoing methodological development, namely the estimation of optimal adaptive treatment strategies. While causal approaches to analysis have become more
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Robust statistical boosting with quantile-based adaptive loss functions Int. J. Biostat. (IF 1.2) Pub Date : 2022-08-11 Jan Speller, Christian Staerk, Andreas Mayr
We combine robust loss functions with statistical boosting algorithms in an adaptive way to perform variable selection and predictive modelling for potentially high-dimensional biomedical data. To achieve robustness against outliers in the outcome variable (vertical outliers), we consider different composite robust loss functions together with base-learners for linear regression. For composite loss
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Review and comparison of treatment effect estimators using propensity and prognostic scores Int. J. Biostat. (IF 1.2) Pub Date : 2022-08-09 Myoung-Jae Lee, Sanghyeok Lee
In finding effects of a binary treatment, practitioners use mostly either propensity score matching (PSM) or inverse probability weighting (IPW). However, many new treatment effect estimators are available now using propensity score and “prognostic score”, and some of these estimators are much better than PSM and IPW in several aspects. In this paper, we review those recent treatment effect estimators
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Efficient estimation of pathwise differentiable target parameters with the undersmoothed highly adaptive lasso Int. J. Biostat. (IF 1.2) Pub Date : 2022-07-15 Mark J. van der Laan, David Benkeser, Weixin Cai
We consider estimation of a functional parameter of a realistically modeled data distribution based on observing independent and identically distributed observations. The highly adaptive lasso estimator of the functional parameter is defined as the minimizer of the empirical risk over a class of cadlag functions with finite sectional variation norm, where the functional parameter is parametrized in
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Modeling sign concordance of quantile regression residuals with multiple outcomes Int. J. Biostat. (IF 1.2) Pub Date : 2022-07-09 Silvia Columbu, Paolo Frumento, Matteo Bottai
Quantile regression permits describing how quantiles of a scalar response variable depend on a set of predictors. Because a unique definition of multivariate quantiles is lacking, extending quantile regression to multivariate responses is somewhat complicated. In this paper, we describe a simple approach based on a two-step procedure: in the first step, quantile regression is applied to each response
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A varying-coefficient partially linear transformation model for length-biased data with an application to HIV vaccine studies Int. J. Biostat. (IF 1.2) Pub Date : 2022-07-08 Alan T. K. Wan, Wei Zhao, Peter Gilbert, Yong Zhou
Prevalent cohort studies in medical research often give rise to length-biased survival data that require special treatments. The recently proposed varying-coefficient partially linear transformation (VCPLT) model has the virtue of providing a more dynamic content of the effects of the covariates on survival times than the well-known partially linear transformation (PLT) model by allowing flexible interactions
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Two-sample t α -test for testing hypotheses in small-sample experiments Int. J. Biostat. (IF 1.2) Pub Date : 2022-06-24 Yuan-De Tan
It has been reported that about half of biological discoveries are irreproducible. These irreproducible discoveries were partially attributed to poor statistical power. The poor powers are majorly owned to small sample sizes. However, in molecular biology and medicine, due to the limit of biological resources and budget, most molecular biological experiments have been conducted with small samples.
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The optimal dynamic treatment rule superlearner: considerations, performance, and application to criminal justice interventions Int. J. Biostat. (IF 1.2) Pub Date : 2022-06-16 Lina M. Montoya, Mark J. van der Laan, Alexander R. Luedtke, Jennifer L. Skeem, Jeremy R. Coyle, Maya L. Petersen
The optimal dynamic treatment rule (ODTR) framework offers an approach for understanding which kinds of patients respond best to specific treatments – in other words, treatment effect heterogeneity. Recently, there has been a proliferation of methods for estimating the ODTR. One such method is an extension of the SuperLearner algorithm – an ensemble method to optimally combine candidate algorithms
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Estimators for the value of the optimal dynamic treatment rule with application to criminal justice interventions Int. J. Biostat. (IF 1.2) Pub Date : 2022-06-03 Lina M. Montoya, Mark J. van der Laan, Jennifer L. Skeem, Maya L. Petersen
Given an (optimal) dynamic treatment rule, it may be of interest to evaluate that rule – that is, to ask the causal question: what is the expected outcome had every subject received treatment according to that rule? In this paper, we study the performance of estimators that approximate the true value of: (1) an a priori known dynamic treatment rule (2) the true, unknown optimal dynamic treatment rule
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A quantile regression estimator for interval-censored data Int. J. Biostat. (IF 1.2) Pub Date : 2022-06-02 Paolo Frumento
We describe an estimating equation that can be used to fit quantile regression models to interval-censored data. The proposed estimator presents important advantages over the existing methods, and can be applied when the data are a mixture of interval-censored, left-censored, and right-censored observations. We describe estimation and inference, report simulation results, and apply the proposed method
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Variable selection for bivariate interval-censored failure time data under linear transformation models Int. J. Biostat. (IF 1.2) Pub Date : 2022-06-02 Rong Liu, Mingyue Du, Jianguo Sun
Variable selection is needed and performed in almost every field and a large literature on it has been established, especially under the context of linear models or for complete data. Many authors have also investigated the variable selection problem for incomplete data such as right-censored failure time data. In this paper, we discuss variable selection when one faces bivariate interval-censored
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Multivariate small area modelling of undernutrition prevalence among under-five children in Bangladesh. Int. J. Biostat. (IF 1.2) Pub Date : 2022-05-30 Saurav Guha,Sumonkanti Das,Bernard Baffour,Hukum Chandra
District-representative data are rarely collected in the surveys for identifying localised disparities in Bangladesh, and so district-level estimates of undernutrition indicators - stunting, wasting and underweight - have remained largely unexplored. This study aims to estimate district-level prevalence of these indicators by employing a multivariate Fay-Herriot (MFH) model which accounts for the underlying
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Multivariate small area modelling of undernutrition prevalence among under-five children in Bangladesh Int. J. Biostat. (IF 1.2) Pub Date : 2022-05-28 Saurav Guha, Sumonkanti Das, Bernard Baffour, Hukum Chandra
District-representative data are rarely collected in the surveys for identifying localised disparities in Bangladesh, and so district-level estimates of undernutrition indicators – stunting, wasting and underweight – have remained largely unexplored. This study aims to estimate district-level prevalence of these indicators by employing a multivariate Fay–Herriot (MFH) model which accounts for the underlying
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A comparison of joint dichotomization and single dichotomization of interacting variables to discriminate a disease outcome Int. J. Biostat. (IF 1.2) Pub Date : 2022-05-10 Sybil Prince Nelson, Viswanathan Ramakrishnan, Paul Nietert, Diane Kamen, Paula Ramos, Bethany Wolf
Dichotomization is often used on clinical and diagnostic settings to simplify interpretation. For example, a person with systolic and diastolic blood pressure above 140 over 90 may be prescribed medication. Blood pressure as well as other factors such as age and cholesterol and their interactions may lead to increased risk of certain diseases. When using a dichotomized variable to determine a diagnosis
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A comparison of joint dichotomization and single dichotomization of interacting variables to discriminate a disease outcome. Int. J. Biostat. (IF 1.2) Pub Date : 2022-05-10 Sybil Prince Nelson,Viswanathan Ramakrishnan,Paul Nietert,Diane Kamen,Paula Ramos,Bethany Wolf
Dichotomization is often used on clinical and diagnostic settings to simplify interpretation. For example, a person with systolic and diastolic blood pressure above 140 over 90 may be prescribed medication. Blood pressure as well as other factors such as age and cholesterol and their interactions may lead to increased risk of certain diseases. When using a dichotomized variable to determine a diagnosis
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Borrowing historical information for non-inferiority trials on Covid-19 vaccines. Int. J. Biostat. (IF 1.2) Pub Date : 2022-04-27 Fulvio De Santis,Stefania Gubbiotti
Non-inferiority vaccine trials compare new candidates to active controls that provide clinically significant protection against a disease. Bayesian statistics allows to exploit pre-experimental information available from previous studies to increase precision and reduce costs. Here, historical knowledge is incorporated into the analysis through a power prior that dynamically regulates the degree of
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Borrowing historical information for non-inferiority trials on Covid-19 vaccines Int. J. Biostat. (IF 1.2) Pub Date : 2022-04-26 Fulvio De Santis, Stefania Gubbiotti
Non-inferiority vaccine trials compare new candidates to active controls that provide clinically significant protection against a disease. Bayesian statistics allows to exploit pre-experimental information available from previous studies to increase precision and reduce costs. Here, historical knowledge is incorporated into the analysis through a power prior that dynamically regulates the degree of
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Regression trees and ensembles for cumulative incidence functions Int. J. Biostat. (IF 1.2) Pub Date : 2022-03-25 Youngjoo Cho, Annette M. Molinaro, Chen Hu, Robert L. Strawderman
The use of cumulative incidence functions for characterizing the risk of one type of event in the presence of others has become increasingly popular over the past two decades. The problems of modeling, estimation and inference have been treated using parametric, nonparametric and semi-parametric methods. Efforts to develop suitable extensions of machine learning methods, such as regression trees and
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Regression trees and ensembles for cumulative incidence functions. Int. J. Biostat. (IF 1.2) Pub Date : 2022-03-25 Youngjoo Cho,Annette M Molinaro,Chen Hu,Robert L Strawderman
The use of cumulative incidence functions for characterizing the risk of one type of event in the presence of others has become increasingly popular over the past two decades. The problems of modeling, estimation and inference have been treated using parametric, nonparametric and semi-parametric methods. Efforts to develop suitable extensions of machine learning methods, such as regression trees and