8,655 results match your criteria Statistics in Medicine [Journal]


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

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

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 Feb 18. 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
February 2019

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

The mixed model for the analysis of a repeated-measurement multivariate count data.

Stat Med 2019 Feb 13. 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
February 2019

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
1 Read

Robust regression for optimal individualized treatment rules.

Authors:
W Xiao H H Zhang W Lu

Stat Med 2019 Feb 11. 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
February 2019

Risk-adjusted CUSUM charts under model error.

Stat Med 2019 Feb 5. Epub 2019 Feb 5.

Department of Statistics and Applied Probability, National University of Singapore, Singapore.

In recent years, quality control charts have been increasingly applied in the healthcare environment, for example, to monitor surgical performance. Risk-adjusted cumulative (CUSUM) charts that utilize risk scores like the Parsonnet score to estimate the probability of death of a patient from an operation turn out to be susceptible to misfitted risk models causing deterioration of the charts' properties, in particular, the false alarm behavior. Our approach considers the application of power transformations in the logistic regression model to improve the fit to the binary outcome data. Read More

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http://dx.doi.org/10.1002/sim.8104DOI Listing
February 2019

Joint models for longitudinal and time-to-event data in a case-cohort design.

Stat Med 2019 Jan 31. Epub 2019 Jan 31.

Department of Biostatistics, Erasmus MC, Rotterdam, The Netherlands.

Studies with longitudinal measurements are common in clinical research. Particular interest lies in studies where the repeated measurements are used to predict a time-to-event outcome, such as mortality, in a dynamic manner. If event rates in a study are low, however, and most information is to be expected from the patients experiencing the study endpoint, it may be more cost efficient to only use a subset of the data. Read More

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http://doi.wiley.com/10.1002/sim.8113
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http://dx.doi.org/10.1002/sim.8113DOI Listing
January 2019
10 Reads

Gene-based sequential burden association test.

Stat Med 2019 Jan 31. Epub 2019 Jan 31.

Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa.

Detecting the association between a set of variants and a phenotype of interest is the first and important step in genetic and genomic studies. Although it attracted a large amount of attention in the scientific community and several related statistical approaches have been proposed in the literature, powerful and robust statistical tests are still highly desired and yet to be developed in this area. In this paper, we propose a powerful and robust association test, which combines information from each individual single-nucleotide polymorphisms based on sequential independent burden tests. Read More

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http://dx.doi.org/10.1002/sim.8111DOI Listing
January 2019

High-dimensional longitudinal classification with the multinomial fused lasso.

Stat Med 2019 Jan 30. Epub 2019 Jan 30.

Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania.

We study regularized estimation in high-dimensional longitudinal classification problems, using the lasso and fused lasso regularizers. The constructed coefficient estimates are piecewise constant across the time dimension in the longitudinal problem, with adaptively selected change points (break points). We present an efficient algorithm for computing such estimates, based on proximal gradient descent. Read More

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http://dx.doi.org/10.1002/sim.8100DOI Listing
January 2019
1 Read

Evaluating classification accuracy for modern learning approaches.

Stat Med 2019 Jan 30. Epub 2019 Jan 30.

Department of Mathematics and Statistics, Boston University, Boston, Massachusetts.

Deep learning neural network models such as multilayer perceptron (MLP) and convolutional neural network (CNN) are novel and attractive artificial intelligence computing tools. However, evaluation of the performance of these methods is not readily available for practitioners yet. We provide a tutorial for evaluating classification accuracy for various state-of-the-art learning approaches, including familiar shallow and deep learning methods. Read More

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http://dx.doi.org/10.1002/sim.8103DOI Listing
January 2019

Semiparametric probit model for informative current status data.

Stat Med 2019 Jan 30. Epub 2019 Jan 30.

Department of Statistics, University of Missouri, Columbia, Missouri.

Semiparametric probit models have recently attracted some attention for regression analysis of failure time data partly due to the popularity of the normal distribution and its special features. In this paper, we discuss the fitting of such models to informative current status data, which often occur in many areas such as medical studies and whose analysis has also recently attracted a lot of attention. For inference, a sieve maximum likelihood approach is developed and the methodology is further generalized to a class of generalized semiparametric probit models. Read More

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http://dx.doi.org/10.1002/sim.8106DOI Listing
January 2019

Partitioned GMM logistic regression models for longitudinal data.

Stat Med 2019 Jan 30. Epub 2019 Jan 30.

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

Correlation is inherent in longitudinal studies due to the repeated measurements on subjects, as well as due to time-dependent covariates in the study. In the National Longitudinal Study of Adolescent to Adult Health (Add Health), data were repeatedly collected on children in grades 7-12 across four waves. Thus, observations obtained on the same adolescent were correlated, while predictors were correlated with current and future outcomes such as obesity status, among other health issues. Read More

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http://dx.doi.org/10.1002/sim.8099DOI Listing
January 2019

Alpha spending for historical versus surveillance Poisson data with CMaxSPRT.

Stat Med 2019 Jan 28. Epub 2019 Jan 28.

Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts.

Sequential analysis hypothesis testing is now an important tool for postmarket drug and vaccine safety surveillance. When the number of adverse events accruing in time is assumed to follow a Poisson distribution, and if the baseline Poisson rate is assessed only with uncertainty, the conditional maximized sequential probability ratio test, CMaxSPRT, is a formal solution. CMaxSPRT is based on comparing monitored data with historical matched data, and it was primarily developed under a flat signaling threshold. Read More

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http://dx.doi.org/10.1002/sim.8097DOI Listing
January 2019

Analysis of combined incident and prevalent cohort data under a proportional mean residual life model.

Stat Med 2019 Jan 24. Epub 2019 Jan 24.

Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.

The Nun Study, a longitudinal study to examine risk factors for the progression of dementia, consists of subjects who were already diagnosed with dementia (ie, prevalent cohort) and those who do not have dementia (ie, incident cohort) at study enrollment. When assessing the risk factors' effects on the survival time from dementia diagnosis until death, utilizing data from both cohorts supports more efficient statistical inference because the two cohorts provide valuable complementary information. A major challenge in analyzing the combined cohort data is that the prevalent cases are not representative of the target population. Read More

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http://dx.doi.org/10.1002/sim.8098DOI Listing
January 2019
1 Read

Bayesian variable selection based on clinical relevance weights in small sample studies-Application to colon cancer.

Stat Med 2019 Jan 22. Epub 2019 Jan 22.

INSERM U1138, Team 22, Centre de Recherche des Cordeliers, University Paris Descartes, University Pierre et Marie Curie, Paris, France.

Using clinical data to model the medical decisions behind sequential treatment actions raises methodological challenges. Physicians often have access to many covariates that may be used when making sequential treatment decisions for individual patients. Statistical variable selection methods may help finding which of these variables are used for this decision in everyday practice. Read More

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http://dx.doi.org/10.1002/sim.8107DOI Listing
January 2019
1 Read

Re-randomization tests in clinical trials.

Stat Med 2019 Jan 22. Epub 2019 Jan 22.

National Institutes of Health, National Institute of Allergy and Infectious Diseases, Rockville, MD.

As randomization methods use more information in more complex ways to assign patients to treatments, analysis of the resulting data becomes challenging. The treatment assignment vector and outcome vector become correlated whenever randomization probabilities depend on data correlated with outcomes. One straightforward analysis method is a re-randomization test that fixes outcome data and creates a reference distribution for the test statistic by repeatedly re-randomizing according to the same randomization method used in the trial. Read More

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http://dx.doi.org/10.1002/sim.8093DOI Listing
January 2019
1 Read

A self-excited threshold autoregressive state-space model for menstrual cycles: Forecasting menstruation and identifying within-cycle stages based on basal body temperature.

Stat Med 2019 Jan 21. Epub 2019 Jan 21.

The Institute of Statistical Mathematics, Tokyo, Japan.

The menstrual cycle is divided into hypothermic and hyperthermic phases based on the periodic shift in the basal body temperature (BBT), reflecting events occurring in the ovary. In the present study, we proposed a state-space model that explicitly incorporates the biphasic nature of the menstrual cycle, in which the probability density distributions for the advancement of the menstrual phase and that for the BBT switch depending on a latent state variable. Our model derives the predictive distribution of the day of the next menstruation onset that is adaptively adjusted by accommodating new observations of the BBT sequentially. Read More

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http://dx.doi.org/10.1002/sim.8096DOI Listing
January 2019
1 Read

Accounting for a decaying correlation structure in cluster randomized trials with continuous recruitment.

Stat Med 2019 Jan 21. Epub 2019 Jan 21.

School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.

A requirement for calculating sample sizes for cluster randomized trials (CRTs) conducted over multiple periods of time is the specification of a form for the correlation between outcomes of subjects within the same cluster, encoded via the within-cluster correlation structure. Previously proposed within-cluster correlation structures have made strong assumptions; for example, the usual assumption is that correlations between the outcomes of all pairs of subjects are identical ("uniform correlation"). More recently, structures that allow for a decay in correlation between pairs of outcomes measured in different periods have been suggested. Read More

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http://dx.doi.org/10.1002/sim.8089DOI Listing
January 2019
2 Reads

Statistical methods for building better biomarkers of chronic kidney disease.

Stat Med 2019 Jan 21. Epub 2019 Jan 21.

Division of Kidney, Urologic and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland.

The last two decades have witnessed an explosion in research focused on the development and assessment of novel biomarkers for improved prognosis of diseases. As a result, best practice standards guiding biomarker research have undergone extensive development. Currently, there is great interest in the promise of biomarkers to enhance research efforts and clinical practice in the setting of chronic kidney disease, acute kidney injury, and glomerular disease. Read More

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http://dx.doi.org/10.1002/sim.8091DOI Listing
January 2019
3 Reads

A sequential density-based empirical likelihood ratio test for treatment effects.

Stat Med 2019 Jan 20. Epub 2019 Jan 20.

Department of Biostatistics, The State University of New York at Buffalo, Buffalo, New York.

In health-related experiments, treatment effects can be identified using paired data that consist of pre- and posttreatment measurements. In this framework, sequential testing strategies are widely accepted statistical tools in practice. Since performances of parametric sequential testing procedures vitally depend on the validity of the parametric assumptions regarding underlying data distributions, we focus on distribution-free mechanisms for sequentially evaluating treatment effects. Read More

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http://dx.doi.org/10.1002/sim.8095DOI Listing
January 2019
3 Reads

Using simulation studies to evaluate statistical methods.

Stat Med 2019 Jan 16. Epub 2019 Jan 16.

Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, United Kingdom.

Simulation studies are computer experiments that involve creating data by pseudo-random sampling. A key strength of simulation studies is the ability to understand the behavior of statistical methods because some "truth" (usually some parameter/s of interest) is known from the process of generating the data. This allows us to consider properties of methods, such as bias. Read More

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http://dx.doi.org/10.1002/sim.8086DOI Listing
January 2019
1 Read

The use of prognostic scores for causal inference with general treatment regimes.

Stat Med 2019 Jan 16. Epub 2019 Jan 16.

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

In nonrandomised studies, inferring causal effects requires appropriate methods for addressing confounding bias. Although it is common to adopt propensity score analysis to this purpose, prognostic score analysis has recently been proposed as an alternative strategy. While both approaches were originally introduced to estimate causal effects for binary interventions, the theory of propensity score has since been extended to the case of general treatment regimes. Read More

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http://dx.doi.org/10.1002/sim.8084DOI Listing
January 2019
2 Reads

Bayesian hierarchical modeling of substate area estimates from the Medicare CAHPS survey.

Stat Med 2019 Jan 15. Epub 2019 Jan 15.

Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts.

Each year, surveys are conducted to assess the quality of care for Medicare beneficiaries, using instruments from the Consumer Assessment of Healthcare Providers and Systems (CAHPS®) program. Currently, survey measures presented for Fee-for-Service beneficiaries are either pooled at the state level or unpooled for smaller substate areas nested within the state; the choice in each state is based on statistical tests of measure heterogeneity across areas within state. We fit spatial-temporal Bayesian random-effects models using a flexible parameterization to estimate mean scores for each of the domains formed by 94 areas in 32 states measured over 5 years. Read More

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http://doi.wiley.com/10.1002/sim.8068
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http://dx.doi.org/10.1002/sim.8068DOI Listing
January 2019
4 Reads

Modeling the spatial variability in the spread and correlation of childhood malnutrition in Nigeria.

Stat Med 2019 Jan 15. Epub 2019 Jan 15.

Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

The average nutritional status of children in Nigeria is, just as in most developing countries, still in an alarmingly bad condition. Prior studies have shown that this status relies on a series of different influences and can be measured by three anthropometric variables for stunting, wasting, and underweight. Different regression modeling techniques have been adopted over the years to explain the determinants and spatial clustering. Read More

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http://dx.doi.org/10.1002/sim.8077DOI Listing
January 2019
1 Read

A new approach for sizing trials with composite binary endpoints using anticipated marginal values and accounting for the correlation between components.

Stat Med 2019 Jan 13. Epub 2019 Jan 13.

Departament d'Estadística i Investigació Operativa, Universitat Politècnica de Catalunya, Barcelona, Spain.

Composite binary endpoints are increasingly used as primary endpoints in clinical trials. When designing a trial, it is crucial to determine the appropriate sample size for testing the statistical differences between treatment groups for the primary endpoint. As shown in this work, when using a composite binary endpoint to size a trial, one needs to specify the event rates and the effect sizes of the composite components as well as the correlation between them. Read More

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http://dx.doi.org/10.1002/sim.8092DOI Listing
January 2019
1 Read

Identifying gene-environment interactions incorporating prior information.

Stat Med 2019 Jan 13. Epub 2019 Jan 13.

Department of Biostatistics, Yale University, New Haven, Connecticut.

For many complex diseases, gene-environment (G-E) interactions have independent contributions beyond the main G and E effects. Despite extensive effort, it still remains challenging to identify G-E interactions. With the long accumulation of experiments and data, for many biomedical problems of common interest, there are existing studies that can be relevant and informative for the identification of G-E interactions and/or main effects. Read More

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http://doi.wiley.com/10.1002/sim.8064
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http://dx.doi.org/10.1002/sim.8064DOI Listing
January 2019
9 Reads

Estimating seasonal onsets and peaks of bronchiolitis with spatially and temporally uncertain data.

Stat Med 2019 Jan 13. Epub 2019 Jan 13.

Naval Medical Research Unit Dayton, Wright-Patterson Air Force Base, Dayton, Ohio.

RSV bronchiolitis (an acute lower respiratory tract viral infection in infants) is the most common cause of infant hospitalizations in the United States (US). The only preventive intervention currently available is monthly injections of immunoprophylaxis. However, this treatment is expensive and needs to be administered simultaneously with seasonal bronchiolitis cycles in order to be effective. Read More

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http://dx.doi.org/10.1002/sim.8081DOI Listing
January 2019
2 Reads

Extreme learning machine Cox model for high-dimensional survival analysis.

Authors:
Hong Wang Gang Li

Stat Med 2019 Jan 10. Epub 2019 Jan 10.

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

Some interesting recent studies have shown that neural network models are useful alternatives in modeling survival data when the assumptions of a classical parametric or semiparametric survival model such as the Cox (1972) model are seriously violated. However, to the best of our knowledge, the plausibility of adapting the emerging extreme learning machine (ELM) algorithm for single-hidden-layer feedforward neural networks to survival analysis has not been explored. In this paper, we present a kernel ELM Cox model regularized by an L -based broken adaptive ridge (BAR) penalization method. Read More

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http://dx.doi.org/10.1002/sim.8090DOI Listing
January 2019
1 Read

A modified CUSUM test to control postoutbreak false alarms.

Stat Med 2019 Jan 9. Epub 2019 Jan 9.

Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, Colorado.

The cumulative sum (CUSUM) control chart is a method for detecting whether the mean of a time series process has shifted beyond some tolerance (ie, is out of control). Originally developed in an industrial process control setting, the CUSUM statistic is typically reset to zero once a process is discovered to be out of control since the industrial process is then recalibrated to be in control. The CUSUM method is also used to detect disease outbreaks in prospective disease surveillance, with a disease outbreak coinciding with an out-of-control process. Read More

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http://doi.wiley.com/10.1002/sim.8088
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http://dx.doi.org/10.1002/sim.8088DOI Listing
January 2019
3 Reads

Assessing health care interventions via an interrupted time series model: Study power and design considerations.

Stat Med 2019 Jan 7. Epub 2019 Jan 7.

Department of Statistics, University of California, Irvine, California.

The delivery and assessment of quality health care is complex with many interacting and interdependent components. In terms of research design and statistical analysis, this complexity and interdependency makes it difficult to assess the true impact of interventions designed to improve patient health care outcomes. Interrupted time series (ITS) is a quasi-experimental design developed for inferring the effectiveness of a health policy intervention while accounting for temporal dependence within a single system or unit. Read More

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http://dx.doi.org/10.1002/sim.8067DOI Listing
January 2019
1 Read

Sample size considerations and predictive performance of multinomial logistic prediction models.

Stat Med 2019 Jan 6. Epub 2019 Jan 6.

Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.

Multinomial Logistic Regression (MLR) has been advocated for developing clinical prediction models that distinguish between three or more unordered outcomes. We present a full-factorial simulation study to examine the predictive performance of MLR models in relation to the relative size of outcome categories, number of predictors and the number of events per variable. It is shown that MLR estimated by Maximum Likelihood yields overfitted prediction models in small to medium sized data. Read More

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http://dx.doi.org/10.1002/sim.8063DOI Listing
January 2019
2 Reads

Estimation of the distribution of longitudinal biomarker trajectories prior to disease progression.

Stat Med 2019 Jan 6. Epub 2019 Jan 6.

Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Most studies characterize longitudinal biomarker trajectories by looking forward at them from a commonly used time origin, such as the initial treatment time. For a better understanding of the relationship between biomarkers and disease progression, we propose to align all subjects by using their disease progression time as the origin and then looking backward at the biomarker distributions prior to that event. We demonstrate that such backward-looking plots are much more informative than forward-looking plots when the research goal is to understand the shape of the trajectory leading up to the event of interest. Read More

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http://dx.doi.org/10.1002/sim.8085DOI Listing
January 2019
2 Reads
2.037 Impact Factor

Single-number summary and decision analytic measures can happily coexist.

Stat Med 2019 Feb;38(3):499-500

Department of Mathematics and Statistics, Boston University, Boston, Massachusetts.

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http://dx.doi.org/10.1002/sim.8031DOI Listing
February 2019
1 Read

Comments on "Net reclassification index at event rate: Properties and relationships".

Authors:
Andrew J Vickers

Stat Med 2019 Feb;38(3):497-498

Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10021, USA.

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http://dx.doi.org/10.1002/sim.7631DOI Listing
February 2019
1 Read

Semiparametric linear transformation models: Effect measures, estimators, and applications.

Stat Med 2019 Jan 4. Epub 2019 Jan 4.

Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark.

Semiparametric linear transformation models form a versatile class of regression models with the Cox proportional hazards model being the most well-known member. These models are well studied for right censored outcomes and are typically used in survival analysis. We consider transformation models as a tool for situations with uncensored continuous outcomes where linear regression is not appropriate. Read More

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http://dx.doi.org/10.1002/sim.8078DOI Listing
January 2019
1 Read

Evaluating center-specific long-term outcomes through differences in mean survival time: Analysis of national kidney transplant data.

Stat Med 2019 Jan 4. Epub 2019 Jan 4.

Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan.

Center-specific survival outcomes of kidney transplant recipients are an important quality measure, with several challenges. Existing methods based on restricted mean lifetime tend to focus on short- and medium-term clinical outcomes and may fail to capture long-term effects associated with quality of follow-up care. In this report, we propose methods that combine a lognormal frailty model and piecewise exponential baseline rates to compare the mean survival time across centers. Read More

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http://dx.doi.org/10.1002/sim.8076DOI Listing
January 2019
1 Read

Weighted causal inference methods with mismeasured covariates and misclassified outcomes.

Authors:
Di Shu Grace Y Yi

Stat Med 2019 Jan 4. Epub 2019 Jan 4.

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

Inverse probability weighting (IPW) estimation has been widely used in causal inference. Its validity relies on the important condition that the variables are precisely measured. This condition, however, is often violated, which distorts the IPW method and thus yields biased results. Read More

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http://dx.doi.org/10.1002/sim.8073DOI Listing
January 2019
1 Read

Classification using ensemble learning under weighted misclassification loss.

Stat Med 2019 Jan 4. Epub 2019 Jan 4.

Department of Biostatistics, Brown University, Providence, RI.

Binary classification rules based on covariates typically depend on simple loss functions such as zero-one misclassification. Some cases may require more complex loss functions. For example, individual-level monitoring of HIV-infected individuals on antiretroviral therapy requires periodic assessment of treatment failure, defined as having a viral load (VL) value above a certain threshold. Read More

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http://dx.doi.org/10.1002/sim.8082DOI Listing
January 2019
1 Read
2.037 Impact Factor

Optimal probability weights for estimating causal effects of time-varying treatments with marginal structural Cox models.

Stat Med 2018 Dec 27. Epub 2018 Dec 27.

Unit of Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Marginal structural Cox models have been used to estimate the causal effect of a time-varying treatment on a survival outcome in the presence of time-dependent confounders. These methods rely on the positivity assumption, which states that the propensity scores are bounded away from zero and one. Practical violations of this assumption are common in longitudinal studies, resulting in extreme weights that may yield erroneous inferences. Read More

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http://dx.doi.org/10.1002/sim.8080DOI Listing
December 2018
1 Read

Combining biomarker trajectories to improve diagnostic accuracy in prospective cohort studies with verification bias.

Stat Med 2018 Dec 27. Epub 2018 Dec 27.

Department of Biostatistics, Brown University, Providence, Rhode Island.

In this paper, we develop methods to combine multiple biomarker trajectories into a composite diagnostic marker using functional data analysis (FDA) to achieve better diagnostic accuracy in monitoring disease recurrence in the setting of a prospective cohort study. In such studies, the disease status is usually verified only for patients with a positive test result in any biomarker and is missing in patients with negative test results in all biomarkers. Thus, the test result will affect disease verification, which leads to verification bias if the analysis is restricted only to the verified cases. Read More

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http://dx.doi.org/10.1002/sim.8079DOI Listing
December 2018
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Selection of nonlinear interactions by a forward stepwise algorithm: Application to identifying environmental chemical mixtures affecting health outcomes.

Stat Med 2018 Dec 26. Epub 2018 Dec 26.

Department of Environmental Health Sciences, University of Michigan, Ann Arbor, Michigan.

In this paper, we propose a stepwise forward selection algorithm for detecting the effects of a set of correlated exposures and their interactions on a health outcome of interest when the underlying relationship could potentially be nonlinear. Though the proposed method is very general, our application in this paper remains to be on analysis of multiple pollutants and their interactions. Simultaneous exposure to multiple environmental pollutants could affect human health in a multitude of complex ways. Read More

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http://dx.doi.org/10.1002/sim.8059DOI Listing
December 2018
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Should a propensity score model be super? The utility of ensemble procedures for causal adjustment.

Stat Med 2018 Dec 26. Epub 2018 Dec 26.

Department of Mathematics and Statistics, McGill University, Montreal, Canada.

In investigations of the effect of treatment on outcome, the propensity score is a tool to eliminate imbalance in the distribution of confounding variables between treatment groups. Recent work has suggested that Super Learner, an ensemble method, outperforms logistic regression in nonlinear settings; however, experience with real-data analyses tends to show overfitting of the propensity score model using this approach. We investigated a wide range of simulated settings of varying complexities including simulations based on real data to compare the performances of logistic regression, generalized boosted models, and Super Learner in providing balance and for estimating the average treatment effect via propensity score regression, propensity score matching, and inverse probability of treatment weighting. Read More

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http://dx.doi.org/10.1002/sim.8075DOI Listing
December 2018
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Two-stage residual inclusion for survival data and competing risks-An instrumental variable approach with application to SEER-Medicare linked data.

Stat Med 2018 Dec 26. Epub 2018 Dec 26.

Department of Radiation Medicine and Applied Sciences, University of California, San Diego, California.

Instrumental variable is an essential tool for addressing unmeasured confounding in observational studies. Two-stage predictor substitution (2SPS) estimator and two-stage residual inclusion (2SRI) are two commonly used approaches in applying instrumental variables. Recently, 2SPS was studied under the additive hazards model in the presence of competing risks of time-to-events data, where linearity was assumed for the relationship between the treatment and the instrument variable. Read More

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http://dx.doi.org/10.1002/sim.8071DOI Listing
December 2018
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The STRAND Chart: A survival time control chart.

Authors:
Olivia Aj Grigg

Stat Med 2018 Dec 26. Epub 2018 Dec 26.

MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

The STRAND Chart (Survival Time, Risk-Adjusted, N-Division Chart) is a new tool for online risk-adjusted (RA) monitoring of survival outcomes. The chart is drawn in continuous time, making it responsive to change in the process of interest, for example, performance over time of a surgical unit and the procedures that they employ. Though it is difficult to achieve with charts designed for the purpose described, we show that our suggested chart keeps patient ordering intact. Read More

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http://dx.doi.org/10.1002/sim.8065DOI Listing
December 2018
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