8,694 results match your criteria Statistics in Medicine [Journal]


Quantile regression and empirical likelihood for the analysis longitudinal data with monotone missing responses due to dropout, with applications to quality of life measurements from clinical trials.

Stat Med 2019 Apr 17. Epub 2019 Apr 17.

Canadian Cancer Trials Group, Queen's University, Kingston, Canada.

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. Read More

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http://dx.doi.org/10.1002/sim.8152DOI Listing
April 2019
3 Reads

Network meta-analysis of rare events using the Mantel-Haenszel method.

Stat Med 2019 Apr 17. Epub 2019 Apr 17.

Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.

The Mantel-Haenszel (MH) method has been used for decades to synthesize data obtained from studies that compare two interventions with respect to a binary outcome. It has been shown to perform better than the inverse-variance method or Peto's odds ratio when data is sparse. Network meta-analysis (NMA) is increasingly used to compare the safety of medical interventions, synthesizing, eg, data on mortality or serious adverse events. Read More

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https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8158
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http://dx.doi.org/10.1002/sim.8158DOI Listing
April 2019
1 Read

One-step validation method for surrogate endpoints using data from multiple randomized cancer clinical trials with failure-time endpoints.

Stat Med 2019 Apr 17. Epub 2019 Apr 17.

INSERM U1219 (Biostatistic), Université Bordeaux Segalen, Bordeaux, France.

A surrogate endpoint can be used instead of the most relevant clinical endpoint to assess the efficiency of a new treatment. Before being used, a surrogate endpoint must be validated based on appropriate methods. Numerous validation approaches have been proposed with the most popular used in a context of meta-analysis, based on a two-step analysis strategy. Read More

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https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8162
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http://dx.doi.org/10.1002/sim.8162DOI Listing
April 2019
4 Reads

Penalized integrative semiparametric interaction analysis for multiple genetic datasets.

Stat Med 2019 Apr 16. Epub 2019 Apr 16.

School of Statistics, Renmin University of China, Beijing, China.

In this article, we consider a semiparametric additive partially linear interaction model for the integrative analysis of multiple genetic datasets. The goals are to identify important genetic predictors and gene-gene interactions and to estimate the nonparametric functions that describe the environmental effects at the same time. To find the similarities and differences of the genetic effects across different datasets, we impose a group structure on the regression coefficients matrix under the homogeneity assumption, ie, models for different datasets share the same sparsity structure, but the coefficients may differ across datasets. Read More

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https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8172
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http://dx.doi.org/10.1002/sim.8172DOI Listing
April 2019
3 Reads

A Bayesian approach for individual-level drug benefit-risk assessment.

Stat Med 2019 Apr 15. Epub 2019 Apr 15.

Biostatistics and Research Decision Sciences, MSD, London, UK.

In existing benefit-risk assessment (BRA) methods, benefit and risk criteria are usually identified and defined separately based on aggregated clinical data and therefore ignore the individual-level differences as well as the association among the criteria. We proposed a Bayesian multicriteria decision-making method for BRA of drugs using individual-level data. We used a multidimensional latent trait model to account for the heterogeneity of treatment effects with latent variables introducing the dependencies among outcomes. Read More

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https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8166
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http://dx.doi.org/10.1002/sim.8166DOI Listing
April 2019
1 Read

Score tests based on a finite mixture model of Markov processes under intermittent observation.

Stat Med 2019 Apr 10. Epub 2019 Apr 10.

Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada.

A mixture model is described, which accommodates different Markov processes governing disease progression in a finite set of latent classes. We give special attention to the setting in which individuals are examined intermittently and transition times are consequently interval censored. A score test is developed to identify genetic markers associated with class membership. Read More

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https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8155
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http://dx.doi.org/10.1002/sim.8155DOI Listing
April 2019
2 Reads

A Bayesian adaptive marker-stratified design for molecularly targeted agents with customized hierarchical modeling.

Stat Med 2019 Apr 9. Epub 2019 Apr 9.

Center for Computational Biology and Bioinformatics, Indiana University, Indianapolis, Indiana.

It is well known that the treatment effect of a molecularly targeted agent (MTA) may vary dramatically, depending on each patient's biomarker profile. Therefore, for a clinical trial evaluating MTA, it is more reasonable to evaluate its treatment effect within different marker subgroups rather than evaluating the average treatment effect for the overall population. The marker-stratified design (MSD) provides a useful tool to evaluate the subgroup treatment effects of MTAs. Read More

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http://dx.doi.org/10.1002/sim.8159DOI Listing

Prioritized concordance index for hierarchical survival outcomes.

Stat Med 2019 Apr 7. Epub 2019 Apr 7.

Division of Cancer Epidemiology and Genetics, NIH National Cancer Institute, Rockville, MD.

We propose an extension of Harrell's concordance (C) index to evaluate the prognostic utility of biomarkers for diseases with multiple measurable outcomes that can be prioritized. Our prioritized concordance index measures the probability that, given a random subject pair, the subject with the worst disease status as of a time τ has the higher predicted risk. Our prioritized concordance index uses the same approach as the win ratio, by basing generalized pairwise comparisons on the most severe or clinically important comparable outcome. Read More

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http://dx.doi.org/10.1002/sim.8157DOI Listing

Statistical inference for data-adaptive doubly robust estimators with survival outcomes.

Authors:
Iván Díaz

Stat Med 2019 Apr 4. Epub 2019 Apr 4.

Division of Biostatistics, Weill Cornell Medicine, New York, New York.

The consistency of doubly robust estimators relies on the consistent estimation of at least one of two nuisance regression parameters. In moderate-to-large dimensions, the use of flexible data-adaptive regression estimators may aid in achieving this consistency. However, n -consistency of doubly robust estimators is not guaranteed if one of the nuisance estimators is inconsistent. Read More

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http://dx.doi.org/10.1002/sim.8156DOI Listing
April 2019
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Estimation of average treatment effects among multiple treatment groups by using an ensemble approach.

Stat Med 2019 Apr 2. Epub 2019 Apr 2.

Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky.

In observational studies, generalized propensity score (GPS)-based statistical methods, such as inverse probability weighting (IPW) and doubly robust (DR) method, have been proposed to estimate the average treatment effect (ATE) among multiple treatment groups. In this article, we investigate the GPS-based statistical methods to estimate treatment effects from two aspects. The first aspect of our investigation is to obtain an optimal GPS estimation method among four competing GPS estimation methods by using a rank aggregation approach. Read More

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http://dx.doi.org/10.1002/sim.8146DOI Listing
April 2019
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Modeling lottery incentives for daily adherence.

Stat Med 2019 Apr 2. Epub 2019 Apr 2.

Department of Population Health, New York University School of Medicine, New York, New York.

Many health issues require adherence to recommended daily activities, such as taking medication to manage a chronic condition, walking a certain distance to promote weight loss, or measuring weights to assess fluid balance in heart failure. The cost of nonadherence can be high, with respect to both individual health outcomes and the healthcare system. Incentivizing adherence to daily activities can promote better health in patients and populations and potentially provide long-term cost savings. Read More

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http://dx.doi.org/10.1002/sim.8149DOI Listing
April 2019
1 Read

Approaches to treatment effect heterogeneity in the presence of confounding.

Stat Med 2019 Mar 31. Epub 2019 Mar 31.

Department of Statistics and Data Sciences, College of Natural Sciences, The University of Texas at Austin, Austin, Texas.

The literature on causal effect estimation tends to focus on the population mean estimand, which is less informative as medical treatments are becoming more personalized and there is increasing awareness that subpopulations of individuals may experience a group-specific effect that differs from the population average. In fact, it is possible that there is underlying systematic effect heterogeneity that is obscured by focusing on the population mean estimand. In this context, understanding which covariates contribute to this treatment effect heterogeneity (TEH) and how these covariates determine the differential treatment effect (TE) is an important consideration. Read More

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http://dx.doi.org/10.1002/sim.8143DOI Listing

Two-stage analysis for selecting fixed numbers of features in omics association studies.

Stat Med 2019 Mar 31. Epub 2019 Mar 31.

Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya, Japan.

One of main roles of omics-based association studies with high-throughput technologies is to screen out relevant molecular features, such as genetic variants, genes, and proteins, from a large pool of such candidate features based on their associations with the phenotype of interest. Typically, screened features are subject to validation studies using more established or conventional assays, where the number of evaluable features is relatively limited, so that there may exist a fixed number of features measurable by these assays. Such a limitation necessitates narrowing a feature set down to a fixed size, following an initial screening analysis via multiple testing where adjustment for multiplicity is made. Read More

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http://dx.doi.org/10.1002/sim.8150DOI Listing
March 2019
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Optimal design for high-throughput screening via false discovery rate control.

Stat Med 2019 Mar 28. Epub 2019 Mar 28.

Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California.

High-throughput screening (HTS) is a large-scale hierarchical process in which a large number of chemicals are tested in multiple stages. Conventional statistical analyses of HTS studies often suffer from high testing error rates and soaring costs in large-scale settings. This article develops new methodologies for false discovery rate control and optimal design in HTS studies. Read More

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http://dx.doi.org/10.1002/sim.8144DOI Listing

D-optimal designs for multiarm trials with dropouts.

Stat Med 2019 Mar 25. Epub 2019 Mar 25.

Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.

Multiarm trials with follow-up on participants are commonly implemented to assess treatment effects on a population over the course of the studies. Dropout is an unavoidable issue especially when the duration of the multiarm study is long. Its impact is often ignored at the design stage, which may lead to less accurate statistical conclusions. Read More

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http://dx.doi.org/10.1002/sim.8148DOI Listing
March 2019
1 Read

Best linear inverse probability weighted estimation for two-phase designs and missing covariate regression.

Stat Med 2019 Mar 25. Epub 2019 Mar 25.

Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington.

The inverse probability weighted estimator is often applied to two-phase designs and regression with missing covariates. Inverse probability weighted estimators typically are less efficient than likelihood-based estimators but, in general, are more robust against model misspecification. In this paper, we propose a best linear inverse probability weighted estimator for two-phase designs and missing covariate regression. Read More

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http://dx.doi.org/10.1002/sim.8141DOI Listing
March 2019
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How to investigate an accused serial sexual harasser.

Stat Med 2019 Mar 21. Epub 2019 Mar 21.

Converus Inc, Lehi, Utah.

The "MeToo#" movement has been instrumental in delineating the prevalence of alleged sexual harassment complaints in the workplace. In this article, we propose controlled scientific methods for statisticians and credibility assessment experts to jointly collaborate with human resource staff and/or attorneys to help evaluate claims by a class of accusers against an alleged serial harasser. When an accused falsely denies claims as lies, s/he may be guilty of libel/defamation. Read More

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http://dx.doi.org/10.1002/sim.8145DOI Listing

Incorporating single-arm evidence into a network meta-analysis using aggregate level matching: Assessing the impact.

Stat Med 2019 Mar 20. Epub 2019 Mar 20.

National Centre for Pharmacoeconomics, St. James's Hospital, Dublin, Ireland.

Increasingly, single-armed evidence is included in health technology assessment submissions when companies are seeking reimbursement for new drugs. While it is recognized that randomized controlled trials provide a higher standard of evidence, these are not available for many new agents that have been granted licenses in recent years. Therefore, it is important to examine whether alternative strategies for assessing this evidence may be used. Read More

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http://dx.doi.org/10.1002/sim.8139DOI Listing

Two-part models for cost with zeros to decompose effects of covariates on probability of cost, mean nonzero cost, and mean total cost.

Stat Med 2019 Mar 20. Epub 2019 Mar 20.

VA Center of Innovation for Veteran-Centered and Value-Driven Care, VA Eastern Colorado Health Care System, Denver, Colorado.

Health care cost data often contain many zero values, for patients who did not use any care. Two-part models with logistic models for part I, probability of use (ie, nonzero cost) and log-link models for part II, mean cost of use (ie, nonzero cost) are often used. Effects of exposures or covariates on total (marginal) cost are often of interest, and recent work has proposed useful methods. Read More

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http://dx.doi.org/10.1002/sim.8140DOI Listing

Composite interaction tree for simultaneous learning of optimal individualized treatment rules and subgroups.

Stat Med 2019 Mar 19. Epub 2019 Mar 19.

Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York.

Treatment response heterogeneity has long been observed in patients affected by chronic diseases. Administering an individualized treatment rule (ITR) offers an opportunity to tailor treatment strategies according to patient-specific characteristics. Overly complex machine learning methods for estimating ITRs may produce treatment rules that have higher benefit but lack transparency and interpretability. Read More

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http://dx.doi.org/10.1002/sim.8105DOI Listing

Randomised trials with provision for early stopping for benefit (or harm): The impact on the estimated treatment effect.

Stat Med 2019 Mar 19. Epub 2019 Mar 19.

Mount Sinai Hospital, Toronto, Canada.

Stopping rules for clinical trials are primarily intended to control Type I error rates if interim analyses are planned, but less is known about the impact that potential stopping has on estimating treatment benefit. In this paper, we derive analytic expressions for (1) the over-estimation of benefit in studies that stop early, (2) the under-estimation of benefit in completed studies, and (3) the overall bias in studies with a stopping rule. We also examine the probability of stopping early and the situation in meta-analyses. Read More

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http://dx.doi.org/10.1002/sim.8142DOI Listing

A model-based multithreshold method for subgroup identification.

Stat Med 2019 Mar 18. Epub 2019 Mar 18.

Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, California.

Thresholding variable plays a crucial role in subgroup identification for personalized medicine. Most existing partitioning methods split the sample based on one predictor variable. In this paper, we consider setting the splitting rule from a combination of multivariate predictors, such as the latent factors, principle components, and weighted sum of predictors. Read More

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http://dx.doi.org/10.1002/sim.8136DOI Listing
March 2019
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A Bayesian nonparametric causal inference model for synthesizing randomized clinical trial and real-world evidence.

Stat Med 2019 Mar 18. Epub 2019 Mar 18.

Oncology Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland.

With the wide availability of various real-world data (RWD), there is an increasing interest in synthesizing information from both randomized clinical trials and RWD for health-care decision makings. The task of addressing study-specific heterogeneities is one of the most difficult challenges in synthesizing data from disparate sources. Bayesian hierarchical models with nonparametric extension provide a powerful and convenient platform that formalizes the information borrowing strength across the sources. Read More

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http://dx.doi.org/10.1002/sim.8134DOI Listing

Emulating a trial of joint dynamic strategies: An application to monitoring and treatment of HIV-positive individuals.

Stat Med 2019 Mar 18. Epub 2019 Mar 18.

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.

Decisions about when to start or switch a therapy often depend on the frequency with which individuals are monitored or tested. For example, the optimal time to switch antiretroviral therapy depends on the frequency with which HIV-positive individuals have HIV RNA measured. This paper describes an approach to use observational data for the comparison of joint monitoring and treatment strategies and applies the method to a clinically relevant question in HIV research: when can monitoring frequency be decreased and when should individuals switch from a first-line treatment regimen to a new regimen? We outline the target trial that would compare the dynamic strategies of interest and then describe how to emulate it using data from HIV-positive individuals included in the HIV-CAUSAL Collaboration and the Centers for AIDS Research Network of Integrated Clinical Systems. Read More

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http://dx.doi.org/10.1002/sim.8120DOI Listing
March 2019
1 Read
2.037 Impact Factor

Structure estimation of binary graphical models on stratified data: Application to the description of injury tables for victims of road accidents.

Stat Med 2019 Mar 14. Epub 2019 Mar 14.

Nutritional Methodology and Biostatistics Group, International Agency for Research on Cancer, Lyon, France.

Graphical models are used in many applications such as medical diagnostics and computer security. Increasingly often, the estimation of such models has to be performed on several predefined strata of the whole population. For instance, in epidemiology and clinical research, strata are often defined according to age, gender, treatment, or disease type. Read More

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http://dx.doi.org/10.1002/sim.8138DOI Listing

Level of evidence for promising subgroup findings: The case of trends and multiple subgroups.

Stat Med 2019 Mar 13. Epub 2019 Mar 13.

Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, The Netherlands.

Subgroup analyses are an essential part of fully understanding the complete results from confirmatory clinical trials. However, they come with substantial methodological challenges. In case no statistically significant overall treatment effect is found in a clinical trial, this does not necessarily indicate that no patients will benefit from treatment. Read More

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http://doi.wiley.com/10.1002/sim.8133
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http://dx.doi.org/10.1002/sim.8133DOI Listing
March 2019
4 Reads

Bayesian estimation of the average treatment effect on the treated using inverse weighting.

Stat Med 2019 Mar 11. Epub 2019 Mar 11.

Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Canada.

We develop a Bayesian approach to estimate the average treatment effect on the treated in the presence of confounding. The approach builds on developments proposed by Saarela et al in the context of marginal structural models, using importance sampling weights to adjust for confounding and estimate a causal effect. The Bayesian bootstrap is adopted to approximate posterior distributions of interest and avoid the issue of feedback that arises in Bayesian causal estimation relying on a joint likelihood. Read More

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http://dx.doi.org/10.1002/sim.8121DOI Listing

The power-integrated discriminant improvement: An accurate measure of the incremental predictive value of additional biomarkers.

Stat Med 2019 Mar 12. Epub 2019 Mar 12.

The Institute of Mathematical Science, Tokyo, Japan.

The predictive performance of biomarkers is a central concern in biomedical research. This is often evaluated by comparing two statistical models: a "new" model incorporating additional biomarkers and an "old" model without them. In 2008, the integrated discrimination improvement (IDI) was proposed for cases when the response variable is binary, and it is now widely applied as a promising alternative to conventional measures, such as the difference of the area under the receiver operating characteristic curve. Read More

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http://dx.doi.org/10.1002/sim.8135DOI Listing

A test for the correct specification of marginal structural models.

Stat Med 2019 Mar 11. Epub 2019 Mar 11.

Unité Santé des Populations et Pratiques Optimales en Santé, CHU de Québec - Université Laval Research Center, Québec City, Canada.

Marginal structural models (MSMs) allow estimating the causal effect of a time-varying exposure on an outcome in the presence of time-dependent confounding. The parameters of MSMs can be estimated utilizing an inverse probability of treatment weight estimator under certain assumptions. One of these assumptions is that the proposed causal model relating the outcome to exposure history is correctly specified. Read More

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http://dx.doi.org/10.1002/sim.8132DOI Listing
March 2019
2 Reads

MEBoost: Variable selection in the presence of measurement error.

Stat Med 2019 Mar 11. Epub 2019 Mar 11.

Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota.

We present a novel method for variable selection in regression models when covariates are measured with error. The iterative algorithm we propose, Measurement Error Boosting (MEBoost), follows a path defined by estimating equations that correct for covariate measurement error. We illustrate the use of MEBoost in practice by analyzing data from the Box Lunch Study, a clinical trial in nutrition where several variables are based on self-report and, hence, measured with error, where we are interested in performing model selection from a large data set to select variables that are related to the number of times a subject binge ate in the last 28 days. Read More

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http://dx.doi.org/10.1002/sim.8130DOI Listing
March 2019
2 Reads

Assisted graphical model for gene expression data analysis.

Stat Med 2019 Mar 10. Epub 2019 Mar 10.

Department of Statistics, School of Economics, Xiamen University, Xiamen, China.

The analysis of gene expression data has been playing a pivotal role in recent biomedical research. For gene expression data, network analysis has been shown to be more informative and powerful than individual-gene and geneset-based analysis. Despite promising successes, with the high dimensionality of gene expression data and often low sample sizes, network construction with gene expression data is still often challenged. Read More

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http://dx.doi.org/10.1002/sim.8112DOI Listing
March 2019
6 Reads

On identification of agonistic interaction: Hepatitis B and C interaction on hepatocellular carcinoma.

Stat Med 2019 Mar 6. Epub 2019 Mar 6.

Genomics Research Center, Academia Sinica, Taipei, Taiwan.

Agonistic interaction is one of the most important types of mechanistic interaction, which is difficult to be distinguished from synergistic interaction by empirical data. In this study, we propose four approaches that suffice to identify and estimate the agonistic interaction: (1) to make a strong assumption that synergism does not exist; (2) to exploit information from a third factor by assuming that this factor is a necessary component for the background condition of synergistic interaction but is not involved in other mechanisms; (3) to consider a third factor necessary for the background condition of agonistic interaction but not involved in other mechanisms; and (4) similar to (3) but to allow flexibility that the third factor may have a main effect on the outcome and/or a synergistic effect with the two risk factors of interest. We applied the proposed methods to quantify the agonism of Hepatitis B and C viruses (HBV and HCV) infections on liver cancer using a Taiwanese cohort study (n = 23 820; HBV carrier n = 4149 (17. Read More

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http://dx.doi.org/10.1002/sim.8123DOI Listing
March 2019
1 Read

Assessing health insurance coverage in Florida using the behavioral risk factor surveillance system.

Stat Med 2019 Mar 5. Epub 2019 Mar 5.

Joint Program in Survey Methodology, University of Maryland, College Park, Maryland.

We use data from the Behavioral Risk Factor Surveillance System, BRFSS, to investigate the important topic of health insurance coverage. Here, our investigation is about coverage in Florida at the county level and for important subpopulations defined by age, gender, and race. As large US government administered surveys are designed to provide reliable estimates of finite population characteristics for large geographical areas such as the entire US or for individual states, they are not designed to make direct inferences for small geographical regions and/or subpopulations. Read More

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http://dx.doi.org/10.1002/sim.8108DOI Listing

Prediction intervals for overdispersed binomial data with application to historical controls.

Stat Med 2019 Mar 5. Epub 2019 Mar 5.

Abteilung Biostatistik, Institut für Zellbiologie und Biophysik, Leibniz Universität Hannover, Hannover, Germany.

Bioassays are highly standardized trials for assessing the impact of a chemical compound on a model organism. In that context, it is standard to compare several treatment groups with an untreated control. If the same type of bioassay is carried out several times, the amount of information about the historical controls rises with every new study. Read More

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http://dx.doi.org/10.1002/sim.8124DOI Listing
March 2019
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Online estimation of the case fatality rate using a run-off triangle data approach: An application to the Korean MERS outbreak in 2015.

Stat Med 2019 Mar 5. Epub 2019 Mar 5.

Department of Statistics, Seoul National University, Seoul, South Korea.

This work is motivated by the recent Korean Middle East respiratory syndrome outbreak. We propose an easy online estimation procedure for the case fatality rate, ie, the proportion of deaths among the total cases during the course of an epidemic disease, which is an important indicator of the severity of a disease. The key step in our procedure is representing the data with the run-off triangle, which simultaneously takes into account two time axes, namely, the calendar and disease-duration times. Read More

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http://dx.doi.org/10.1002/sim.8125DOI Listing

Comments on "A general approach for sample size calculation for the three-arm 'gold standard' non-inferiority design".

Stat Med 2019 Mar;38(7):1300-1302

Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, UK.

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http://dx.doi.org/10.1002/sim.8049DOI Listing
March 2019
2 Reads

Comments on "A spatial scan statistic for compound Poisson data".

Stat Med 2019 Mar;38(7):1297-1299

Department of Statistics, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.

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http://dx.doi.org/10.1002/sim.8006DOI Listing
March 2019
1 Read

Adjusting for unmeasured confounding using validation data: Simplified two-stage calibration for survival and dichotomous outcomes.

Stat Med 2019 Mar 3. Epub 2019 Mar 3.

Department of Chronic Diseases and Ageing, Norwegian Institute of Public Health, Oslo, Norway.

In epidemiology, one typically wants to estimate the risk of an outcome associated with an exposure after adjusting for confounders. Sometimes, outcome and exposure and maybe some confounders are available in a large data set, whereas some important confounders are only available in a validation data set that is typically a subset of the main data set. A generally applicable method in this situation is the two-stage calibration (TSC) method. Read More

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http://dx.doi.org/10.1002/sim.8131DOI Listing

Estimation of sensitivity and specificity of multiple repeated binary tests without a gold standard.

Stat Med 2019 Feb 27. Epub 2019 Feb 27.

Medtronic Inc, Minneapolis, Minnesota.

A model for multiple diagnostic tests, applied repeatedly over time on each subject, is proposed; gold standard data are not required. The model is identifiable with as few as three tests, and correlation among tests at each time point in the diseased and nondiseased populations, as well as across time points, is explicitly included. An efficient Markov chain Monte Carlo scheme allows for straightforward posterior inference; sample R code is available in the Supporting Web Materials for this paper. Read More

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http://dx.doi.org/10.1002/sim.8114DOI Listing
February 2019
3 Reads

Correlation-adjusted regression survival scores for high-dimensional variable selection.

Stat Med 2019 Feb 22. Epub 2019 Feb 22.

Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany.

Background: The development of classification methods for personalized medicine is highly dependent on the identification of predictive genetic markers. In survival analysis, it is often necessary to discriminate between influential and noninfluential markers. It is common to perform univariate screening using Cox scores, which quantify the associations between survival and each of the markers to provide a ranking. Read More

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http://dx.doi.org/10.1002/sim.8116DOI Listing
February 2019

Optimising the two-stage randomised trial design when some participants are indifferent in their treatment preferences.

Stat Med 2019 Feb 22. Epub 2019 Feb 22.

Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.

Outcomes in a clinical trial can be affected by any underlying preferences that its participants have for the treatments under comparison and by whether they actually receive their preferred treatment. These effects cannot be evaluated in standard trial designs but are estimable in the alternative two-stage randomised trial design, in which some patients can choose their treatment, while the rest are randomly assigned. We have previously shown that, when all two-stage trial participants have a preferred treatment, the preference effects can be evaluated, in addition to the usual direct effect of treatment. Read More

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http://dx.doi.org/10.1002/sim.8119DOI Listing
February 2019
1 Read

Bias-reduced and separation-proof GEE with small or sparse longitudinal binary data.

Stat Med 2019 Feb 22. Epub 2019 Feb 22.

Institute of Statistical Research and Training, University of Dhaka, Dhaka, Bangladesh.

Generalized estimating equation (GEE) is a popular approach for analyzing correlated binary data. However, the problems of separation in GEE are still unknown. The separation created by a covariate often occurs in small correlated binary data and even in large data with rare outcome and/or high intra-cluster correlation and a number of influential covariates. Read More

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http://dx.doi.org/10.1002/sim.8126DOI Listing
February 2019
2.037 Impact Factor

Sensitivity analyses for average treatment effects when outcome is censored by death in instrumental variable models.

Stat Med 2019 Feb 20. Epub 2019 Feb 20.

Department of Statistics, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania.

Two problems that arise in making causal inferences for nonmortality outcomes such as bronchopulmonary dysplasia (BPD) are unmeasured confounding and censoring by death, ie, the outcome is observed only when subjects survive. In randomized experiments with noncompliance and no censoring by death, instrumental variable (IV) methods can be used to control for the unmeasured confounding. But, when there is censoring by death, the average causal treatment effect cannot be identified under usual assumptions but can be studied for a specific subpopulation by using sensitivity analysis with additional assumptions. Read More

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http://dx.doi.org/10.1002/sim.8117DOI Listing
February 2019

Split bootstrap hierarchical modeling of antibiotics abuse in China.

Stat Med 2019 May 18;38(12):2282-2291. Epub 2019 Feb 18.

Department of Economics, Arizona State University, Tempe, Arizona.

In the 1990s, China experienced a high degree of antibiotics abuse, which resulted in increased drug resistance. As a result, the World Health Organization introduced a program for children under the age of 5 years who had an acute respiratory tract infection. We analyze the data pertaining to the treatment provided by doctors in several hospitals in China in order to understand the relationships in the data. Read More

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http://dx.doi.org/10.1002/sim.8118DOI Listing
May 2019
1 Read

Comment on "Under-reported data analysis with INAR-hidden Markov chains".

Authors:
Johannes Bracher

Stat Med 2019 Feb;38(5):893-898

Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.

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http://dx.doi.org/10.1002/sim.8032DOI Listing
February 2019

Response to the letter of "Under-reported data analysis with INAR-hidden Markov chains".

Stat Med 2019 Feb;38(5):899-900

Barcelona Graduate School of Mathematics (BGSMath), Departament de Matemàtiques, Universitat Autònoma de Barcelona, Barcelona, Spain.

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http://dx.doi.org/10.1002/sim.8033DOI Listing
February 2019
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The mixed model for the analysis of a repeated-measurement multivariate count data.

Stat Med 2019 May 13;38(12):2248-2268. Epub 2019 Feb 13.

Department of Biostatistics and Research Support, UMC Utrecht, Utrecht, The Netherlands.

Clustered overdispersed multivariate count data are challenging to model due to the presence of correlation within and between samples. Typically, the first source of correlation needs to be addressed but its quantification is of less interest. Here, we focus on the correlation between time points. Read More

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http://dx.doi.org/10.1002/sim.8101DOI Listing

Regression to the mean for the bivariate binomial distribution.

Stat Med 2019 Feb 11. Epub 2019 Feb 11.

School of Mathematics and Statistics, The University of New South Wales, Sydney, Australia.

Regression to the mean (RTM) occurs when subjects having relatively high or low measurements are remeasured and found closer to the population mean. This phenomenon can potentially lead to an inaccurate conclusion in a pre-post study design. Expressions are available for quantifying RTM when the distribution of pre and post observations are bivariate normal and bivariate Poisson. Read More

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http://dx.doi.org/10.1002/sim.8115DOI Listing
February 2019
3 Reads

Robust regression for optimal individualized treatment rules.

Authors:
W Xiao H H Zhang W Lu

Stat Med 2019 May 11;38(11):2059-2073. Epub 2019 Feb 11.

Department of Statistics, North Carolina State University, Raleigh, North Carolina.

Because different patients may respond quite differently to the same drug or treatment, there is an increasing interest in discovering individualized treatment rules. In particular, there is an emerging need to find optimal individualized treatment rules, which would lead to the "best" clinical outcome. In this paper, we propose a new class of loss functions and estimators based on robust regression to estimate the optimal individualized treatment rules. Read More

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http://dx.doi.org/10.1002/sim.8102DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6449186PMC