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Statistics in Medicine | Page 2

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https://read.qxmd.com/read/31049998/bayesian-nonparametric-estimation-of-roc-surface-under-verification-bias
#21
Rui Zhu, Subhashis Ghosal
The receiver operating characteristic (ROC) surface, as a generalization of the ROC curve, has been widely used to assess the accuracy of a diagnostic test for three categories. A common problem is verification bias, referring to the situation where not all subjects have their true classes verified. In this paper, we consider the problem of estimating the ROC surface under verification bias. We adopt a Bayesian nonparametric approach by directly modeling the underlying distributions of the three categories by Dirichlet process mixture priors...
May 3, 2019: Statistics in Medicine
https://read.qxmd.com/read/31049978/cloud-based-simulation-studies-in-r-a-tutorial-on-using-doredis-with-amazon-spot-fleets
#22
G Hirschfeld, C Thiele
Simulation studies are helpful in testing novel statistical methods. From a computational perspective, they constitute embarrassingly parallel tasks. We describe parallelization techniques in the programming language R that can be used on Amazon's cloud-based infrastructure. After a short conceptual overview of the parallelization techniques in R, we provide a hands-on tutorial on how the doRedis package in conjunction with the Redis server can be used on Amazon Web Services, specifically running spot fleets...
May 3, 2019: Statistics in Medicine
https://read.qxmd.com/read/31037749/quantifying-risk-stratification-provided-by-diagnostic-tests-and-risk-predictions-comparison-to-auc-and-decision-curve-analysis
#23
Hormuzd A Katki
A property of diagnostic tests and risk models deserving more attention is risk stratification, defined as the ability of a test or model to separate those at high absolute risk of disease from those at low absolute risk. Risk stratification fills a gap between measures of classification (ie, area under the curve (AUC)) that do not require absolute risks and decision analysis that requires not only absolute risks but also subjective specification of costs and utilities. We introduce mean risk stratification (MRS) as the average change in risk of disease (posttest-pretest) revealed by a diagnostic test or risk model dichotomized at a risk threshold...
April 30, 2019: Statistics in Medicine
https://read.qxmd.com/read/31033011/sample-size-considerations-for-stratified-cluster-randomization-design-with-binary-outcomes-and-varying-cluster-size
#24
Xiaohan Xu, Hong Zhu, Chul Ahn
Stratified cluster randomization trials (CRTs) have been frequently employed in clinical and healthcare research. Comparing with simple randomized CRTs, stratified CRTs reduce the imbalance of baseline prognostic factors among different intervention groups. Due to the popularity, there has been a growing interest in methodological development on sample size estimation and power analysis for stratified CRTs; however, existing work mostly assumes equal cluster size within each stratum and uses multilevel models...
April 29, 2019: Statistics in Medicine
https://read.qxmd.com/read/31032999/variable-selection-in-semiparametric-nonmixture-cure-model-with-interval-censored-failure-time-data-an-application-to-the-prostate-cancer-screening-study
#25
Liuquan Sun, Shuwei Li, Lianming Wang, Xinyuan Song
Censored failure time data with a cured subgroup is frequently encountered in many scientific areas including the cancer screening research, tumorigenicity studies, and sociological surveys. Meanwhile, one may also encounter an extraordinary large number of risk factors in practice, such as patient's demographic characteristics, clinical measurements, and medical history, which makes variable selection an emerging need in the data analysis. Motivated by a medical study on prostate cancer screening, we develop a variable selection method in the semiparametric nonmixture or promotion time cure model when interval-censored data with a cured subgroup are present...
April 29, 2019: Statistics in Medicine
https://read.qxmd.com/read/31025411/targeted-learning-with-daily-ehr-data
#26
Oleg Sofrygin, Zheng Zhu, Julie A Schmittdiel, Alyce S Adams, Richard W Grant, Mark J van der Laan, Romain Neugebauer
Electronic health records (EHR) data provide a cost- and time-effective opportunity to conduct cohort studies of the effects of multiple time-point interventions in the diverse patient population found in real-world clinical settings. Because the computational cost of analyzing EHR data at daily (or more granular) scale can be quite high, a pragmatic approach has been to partition the follow-up into coarser intervals of pre-specified length (eg, quarterly or monthly intervals). The feasibility and practical impact of analyzing EHR data at a granular scale has not been previously evaluated...
April 25, 2019: Statistics in Medicine
https://read.qxmd.com/read/31020670/integrating-multiple-domain-rules-for-disease-classification
#27
Christine Mauro, M Katherine Shear, Yuanjia Wang
In psychiatry, clinicians use criteria sets from the Diagnostic and Statistical Manual of Mental Disorders to diagnose mental disorders. Most criteria sets have several symptom domains, and in order to be diagnosed, an individual must meet the minimum number of symptoms required by each domain. Some efforts are now focused on adding biomarkers to these symptom domains to facilitate the detection of and highlight the neurobiological basis of psychiatric disorders. Thus, a new criteria set may consist of both clinical symptom counts in several domains and continuous biomarkers...
April 24, 2019: Statistics in Medicine
https://read.qxmd.com/read/31016792/how-to-analyze-and-interpret-recurrent-events-data-in-the-presence-of-a-terminal-event-an-application-on-readmission-after-colorectal-cancer-surgery
#28
Anaïs Charles-Nelson, Sandrine Katsahian, Catherine Schramm
Recurrent events arise when an event occurs many times for a subject. Many models have been developed to analyze these kind of data: the Andersen-Gill's model is one of them as well as the Prentice-William and the Peterson's model, the Wei Lee and Weissfeld's model, or even frailty models, all assuming an independent and noninformative censoring. However, in practice, these assumptions may be violated by the existence of a terminal event that permanently stops the recurrent process (eg, death). Indeed, a patient who experiences an early terminal event is more likely to have a lower number of recurrent events than a patient who experiences a terminal event later...
April 23, 2019: Statistics in Medicine
https://read.qxmd.com/read/30997691/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
#29
Yang Lv, Guoyou Qin, Zhongyi Zhu, Dongsheng Tu
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. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator...
April 17, 2019: Statistics in Medicine
https://read.qxmd.com/read/30997687/network-meta-analysis-of-rare-events-using-the-mantel-haenszel-method
#30
Orestis Efthimiou, Gerta Rücker, Guido Schwarzer, Julian P T Higgins, Matthias Egger, Georgia Salanti
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. In this setting, sparse data occur often and yet there is to-date, no extension of the MH method for the case of NMA...
April 17, 2019: Statistics in Medicine
https://read.qxmd.com/read/30997685/one-step-validation-method-for-surrogate-endpoints-using-data-from-multiple-randomized-cancer-clinical-trials-with-failure-time-endpoints
#31
Casimir Ledoux Sofeu, Takeshi Emura, Virginie Rondeau
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. For two failure-time endpoints, two association measurements are usually used, Kendall's τ at the individual level and the adjusted coefficient of determination ( <mml:math xmlns:mml="http://www...
April 17, 2019: Statistics in Medicine
https://read.qxmd.com/read/30993736/penalized-integrative-semiparametric-interaction-analysis-for-multiple-genetic-datasets
#32
Yang Li, Rong Li, Cunjie Lin, Yichen Qin, Shuangge Ma
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...
April 16, 2019: Statistics in Medicine
https://read.qxmd.com/read/30989691/a-bayesian-approach-for-individual-level-drug-benefit-risk-assessment
#33
Kan Li, Sheng Luo, Sammy Yuan, Shahrul Mt-Isa
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...
April 15, 2019: Statistics in Medicine
https://read.qxmd.com/read/30972787/score-tests-based-on-a-finite-mixture-model-of-markov-processes-under-intermittent-observation
#34
Shu Jiang, Richard J Cook
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. Simulation studies are conducted to validate the algorithm, assess the finite sample properties of the estimators, and assess the frequency properties of the score tests...
April 10, 2019: Statistics in Medicine
https://read.qxmd.com/read/30968435/a-bayesian-adaptive-marker-stratified-design-for-molecularly-targeted-agents-with-customized-hierarchical-modeling
#35
Yong Zang, Beibei Guo, Yan Han, Sha Cao, Chi Zhang
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. Under the Bayesian framework, the beta-binomial model is conventionally used under the MSD to estimate the response rate and test the hypothesis...
April 9, 2019: Statistics in Medicine
https://read.qxmd.com/read/30957257/prioritized-concordance-index-for-hierarchical-survival-outcomes
#36
Li C Cheung, Qing Pan, Noorie Hyun, Hormuzd A Katki
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...
April 7, 2019: Statistics in Medicine
https://read.qxmd.com/read/30950107/statistical-inference-for-data-adaptive-doubly-robust-estimators-with-survival-outcomes
#37
Iván Díaz
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, n1/2 -consistency of doubly robust estimators is not guaranteed if one of the nuisance estimators is inconsistent. In this paper, we present a doubly robust estimator for survival analysis with the novel property that it converges to a Gaussian variable at an n1/2 -rate for a large class of data-adaptive estimators of the nuisance parameters, under the only assumption that at least one of them is consistently estimated at an n1/4 -rate...
April 4, 2019: Statistics in Medicine
https://read.qxmd.com/read/30941812/estimation-of-average-treatment-effects-among-multiple-treatment-groups-by-using-an-ensemble-approach
#38
Xiaofang Yan, Younathan Abdia, Somnath Datta, K B Kulasekera, Beatrice Ugiliweneza, Maxwell Boakye, Maiying Kong
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...
April 2, 2019: Statistics in Medicine
https://read.qxmd.com/read/30941805/modeling-lottery-incentives-for-daily-adherence
#39
Colman H Humphrey, Dylan S Small, Shane T Jensen, Kevin G Volpp, David A Asch, Jingsan Zhu, Andrea B Troxel
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. Multiple incentive structures are possible...
April 2, 2019: Statistics in Medicine
https://read.qxmd.com/read/30931547/approaches-to-treatment-effect-heterogeneity-in-the-presence-of-confounding
#40
Sarah C Anoke, Sharon-Lise Normand, Corwin M Zigler
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...
March 31, 2019: Statistics in Medicine
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