Interval estimation of a population mean using existing knowledge or data on effect sizes.

Abstract:

:Bayes or empirical Bayes methods to improve inferential accuracy for a population mean has been widely adopted in medical research. As the joint prior distribution of both the mean and variance parameters can be difficult to specify or estimate, most of these methods have relied on certain level of simplifications of the joint prior, which could lead to difficulty in the interpretation of the posterior distribution or compromised inferential accuracy. We propose a framework of interval estimation using existing knowledge or data on the effect size to address this difficulty. Our method has two unique characteristics. First, the interpretation of the interval bears the spirit of both Frequentist and Bayesian thinking. For this reason, it will be called FB interval. Second, we define a new quantity, the hybrid effect size, which is a key quantity that mediates the construction of the FB interval when the population variance is unknown. A simulation study and a real data example are presented to evaluate and illustrate our method.

journal_name

Stat Methods Med Res

authors

Shen C

doi

10.1177/0962280218773537

subject

Has Abstract

pub_date

2019-06-01 00:00:00

pages

1703-1715

issue

6

eissn

0962-2802

issn

1477-0334

journal_volume

28

pub_type

杂志文章
  • Measurement error, time lag, unmeasured confounding: Considerations for longitudinal estimation of the effect of a mediator in randomised clinical trials.

    abstract::Clinical trials are expensive and time-consuming and so should also be used to study how treatments work, allowing for the evaluation of theoretical treatment models and refinement and improvement of treatments. These treatment processes can be studied using mediation analysis. Randomised treatment makes some of the a...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280216666111

    authors: Goldsmith KA,Chalder T,White PD,Sharpe M,Pickles A

    更新日期:2018-06-01 00:00:00

  • Penalized count data regression with application to hospital stay after pediatric cardiac surgery.

    abstract::Pediatric cardiac surgery may lead to poor outcomes such as acute kidney injury (AKI) and prolonged hospital length of stay (LOS). Plasma and urine biomarkers may help with early identification and prediction of these adverse clinical outcomes. In a recent multi-center study, 311 children undergoing cardiac surgery we...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280214530608

    authors: Wang Z,Ma S,Zappitelli M,Parikh C,Wang CY,Devarajan P

    更新日期:2016-12-01 00:00:00

  • A unified approach for assessing heterogeneity in age-period-cohort model parameters using random effects.

    abstract::Age-period-cohort models are a popular tool for studying population-level rates; for example, trends in cancer incidence and mortality. Age-period-cohort models decompose observed trends into age effects that correlate with natural history, period effects that reveal factors impacting all ages simultaneously (e.g. inn...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280217713033

    authors: Chernyavskiy P,Little MP,Rosenberg PS

    更新日期:2019-01-01 00:00:00

  • Hybrid test for publication bias in meta-analysis.

    abstract::Publication bias frequently appears in meta-analyses when the included studies' results (e.g., p-values) influence the studies' publication processes. Some unfavorable studies may be suppressed from publication, so the meta-analytic results may be biased toward an artificially favorable direction. Many statistical tes...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280220910172

    authors: Lin L

    更新日期:2020-10-01 00:00:00

  • Correcting for dependent censoring in routine outcome monitoring data by applying the inverse probability censoring weighted estimator.

    abstract::Censored data make survival analysis more complicated because exact event times are not observed. Statistical methodology developed to account for censored observations assumes that patients' withdrawal from a study is independent of the event of interest. However, in practice, some covariates might be associated to b...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280216628900

    authors: Willems S,Schat A,van Noorden MS,Fiocco M

    更新日期:2018-02-01 00:00:00

  • Hierarchical mixture models for longitudinal immunologic data with heterogeneity, non-normality, and missingness.

    abstract::It is a common practice to analyze longitudinal data frequently arisen in medical studies using various mixed-effects models in the literature. However, the following issues may standout in longitudinal data analysis: (i) In clinical practice, the profile of each subject's response from a longitudinal study may follow...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280214544207

    authors: Huang Y,Chen J,Yin P

    更新日期:2017-02-01 00:00:00

  • Maximum likelihood estimation of time to first event in the presence of data gaps and multiple events.

    abstract::We propose a novel likelihood method for analyzing time-to-event data when multiple events and multiple missing data intervals are possible prior to the first observed event for a given subject. This research is motivated by data obtained from a heart monitor used to track the recovery process of subjects experiencing...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280212466089

    authors: Green CL,Brownie C,Boos DD,Lu JC,Krucoff MW

    更新日期:2016-04-01 00:00:00

  • Linear time-dependent reference intervals where there is measurement error in the time variable-a parametric approach.

    abstract::This article re-examines parametric methods for the calculation of time specific reference intervals where there is measurement error present in the time covariate. Previous published work has commonly been based on the standard ordinary least squares approach, weighted where appropriate. In fact, this is an incorrect...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280211426617

    authors: Gillard J

    更新日期:2015-12-01 00:00:00

  • Optimal vaccination schemes for epidemics among a population of households, with application to variola minor in Brazil.

    abstract::This paper is concerned with stochastic models for the spread of an epidemic among a community of households, in which individuals mix uniformly within households and, in addition, uniformly at a much lower rate within the population at large. This two-level mixing structure has important implications for the threshol...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280206071643

    authors: Ball F,Lyne O

    更新日期:2006-10-01 00:00:00

  • The impact of covariance misspecification in group-based trajectory models for longitudinal data with non-stationary covariance structure.

    abstract::One purpose of a longitudinal study is to gain a better understanding of how an outcome of interest changes among a given population over time. In what follows, a trajectory will be taken to mean the series of measurements of the outcome variable for an individual. Group-based trajectory modelling methods seek to iden...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280215598806

    authors: Davies CE,Glonek GF,Giles LC

    更新日期:2017-08-01 00:00:00

  • Statistical designs for familial aggregation.

    abstract::In the past two decades, it has become increasingly clear that genetic factors contribute to the aetiology of many common diseases including cancers, coronary disease, allergy and psychiatric disorders. While one goal of genetic epidemiological studies is to locate susceptibility genes for these complex diseases, it i...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章,评审

    doi:10.1177/096228020000900603

    authors: Liang KY,Beaty TH

    更新日期:2000-12-01 00:00:00

  • Time-dependent efficacy of longitudinal biomarker for clinical endpoint.

    abstract::Joint modelling of longitudinal biomarker and event-time processes has gained its popularity in recent years as they yield more accurate and precise estimates. Considering this modelling framework, a new methodology for evaluating the time-dependent efficacy of a longitudinal biomarker for clinical endpoint is propose...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280216673084

    authors: Kolamunnage-Dona R,Williamson PR

    更新日期:2018-06-01 00:00:00

  • Small sample sizes: A big data problem in high-dimensional data analysis.

    abstract::In many experiments and especially in translational and preclinical research, sample sizes are (very) small. In addition, data designs are often high dimensional, i.e. more dependent than independent replications of the trial are observed. The present paper discusses the applicability of max t-test-type statistics (mu...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280220970228

    authors: Konietschke F,Schwab K,Pauly M

    更新日期:2020-11-24 00:00:00

  • Adjustment for treatment changes in epilepsy trials: A comparison of causal methods for time-to-event outcomes.

    abstract:BACKGROUND:When trials are subject to departures from randomised treatment, simple statistical methods that aim to estimate treatment efficacy, such as per protocol or as treated analyses, typically introduce selection bias. More appropriate methods to adjust for departure from randomised treatment are rarely employed,...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280217735560

    authors: Dodd S,Williamson P,White IR

    更新日期:2019-03-01 00:00:00

  • Efficient estimation of a linear transformation model for current status data via penalized splines.

    abstract::We propose a flexible and computationally efficient penalized estimation method for a semi-parametric linear transformation model with current status data. To facilitate model fitting, the unknown monotone function is approximated by monotone B-splines, and a computationally efficient hybrid algorithm involving the Fi...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280218820406

    authors: Lu M,Liu Y,Li CS

    更新日期:2020-01-01 00:00:00

  • Joint modeling of longitudinal zero-inflated count and time-to-event data: A Bayesian perspective.

    abstract::Longitudinal zero-inflated count data are encountered frequently in substance-use research when assessing the effects of covariates and risk factors on outcomes. Often, both the time to a terminal event such as death or dropout and repeated measure count responses are collected for each subject. In this setting, the l...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280216659312

    authors: Zhu H,DeSantis SM,Luo S

    更新日期:2018-04-01 00:00:00

  • Prospective analysis of infectious disease surveillance data using syndromic information.

    abstract::In this paper, we describe a Bayesian hierarchical Poisson model for the prospective analysis of data for infectious diseases. The proposed model consists of two components. The first component describes the behavior of disease during nonepidemic periods and the second component represents the increase in disease coun...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280214527385

    authors: Corberán-Vallet A,Lawson AB

    更新日期:2014-12-01 00:00:00

  • Inferences about population means of health care costs.

    abstract::The analysis of health care costs is complicated by the skewed and heteroscedastic nature of their distribution with possibly additional zero values. Statistical methods that do not adjust for these features can lead to incorrect conclusions. This paper reviews recent developments in statistical methods for the analys...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章,评审

    doi:10.1191/0962280202sm290ra

    authors: Zhou XH

    更新日期:2002-08-01 00:00:00

  • Flexible and structured survival model for a simultaneous estimation of non-linear and non-proportional effects and complex interactions between continuous variables: Performance of this multidimensional penalized spline approach in net survival trend ana

    abstract::Cancer survival trend analyses are essential to describe accurately the way medical practices impact patients' survival according to the year of diagnosis. To this end, survival models should be able to account simultaneously for non-linear and non-proportional effects and for complex interactions between continuous v...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280218779408

    authors: Remontet L,Uhry Z,Bossard N,Iwaz J,Belot A,Danieli C,Charvat H,Roche L,CENSUR Working Survival Group.

    更新日期:2019-08-01 00:00:00

  • A Bayesian semiparametric approach with change points for spatial ordinal data.

    abstract::The change-point model has drawn much attention over the past few decades. It can accommodate the jump process, which allows for changes of the effects before and after the change point. Intellectual disability is a long-term disability that impacts performance in cognitive aspects of life and usually has its onset pr...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280212463415

    authors: Cai B,Lawson AB,McDermott S,Aelion CM

    更新日期:2016-04-01 00:00:00

  • Extending backcalculation to analyse BSE data.

    abstract::We review the origins of backcalculation (or back projection) methods developed for the analysis of AIDS (acquired immunodeficiency syndrome) incidence data. These techniques have been used extensively for >15 years to deconvolute clinical case incidence, given knowledge of the incubation period distribution, to obtai...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章,评审

    doi:10.1191/0962280203sm337ra

    authors: Donnelly CA,Ferguson NM,Ghani AC,Anderson RM

    更新日期:2003-06-01 00:00:00

  • A quick and accurate method for the estimation of covariate effects based on empirical Bayes estimates in mixed-effects modeling: Correction of bias due to shrinkage.

    abstract::Nonlinear mixed-effects modeling is a popular approach to describe the temporal trajectory of repeated measurements of clinical endpoints collected over time in clinical trials, to distinguish the within-subject and the between-subject variabilities, and to investigate clinically important risk factors (covariates) th...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280218812595

    authors: Yuan M,Xu XS,Yang Y,Xu J,Huang X,Tao F,Zhao L,Zhang L,Pinheiro J

    更新日期:2019-12-01 00:00:00

  • Receiver operating characteristic curve estimation for time to event with semicompeting risks and interval censoring.

    abstract::Semicompeting risks and interval censoring are frequent in medical studies, for instance when a disease may be diagnosed only at times of visit and disease onset is in competition with death. To evaluate the ability of markers to predict disease onset in this context, estimators of discrimination measures must account...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280214531691

    authors: Jacqmin-Gadda H,Blanche P,Chary E,Touraine C,Dartigues JF

    更新日期:2016-12-01 00:00:00

  • Multilevel models for censored and latent responses.

    abstract::Multilevel models were originally developed to allow linear regression or ANOVA models to be applied to observations that are not mutually independent. This lack of independence commonly arises due to clustering of the units of observations into 'higher level units' such as patients in hospitals. In linear mixed model...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/096228020101000604

    authors: Rabe-Hesketh S,Yang S,Pickles A

    更新日期:2001-12-01 00:00:00

  • Letter to the editor: Fitting truncated normal distributions.

    abstract::I comment here on a recent paper in this journal, on the fitting of truncated normal distributions by the EM algorithm. I show that the fitting of such distributions by direct numerical maximization of likelihood (rather than EM) is straightforward, contrary to an assertion made by the authors of that paper. ...

    journal_title:Statistical methods in medical research

    pub_type: 评论,信件

    doi:10.1177/0962280217712089

    authors: MacDonald IL

    更新日期:2018-12-01 00:00:00

  • Controlling false positive selections in high-dimensional regression and causal inference.

    abstract::Guarding against false positive selections is important in many applications. We discuss methods based on subsampling and sample splitting for controlling the expected number of false positives and assigning p-values. They are generic and especially useful for high-dimensional settings. We review encouraging results f...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280211428371

    authors: Bühlmann P,Rütimann P,Kalisch M

    更新日期:2013-10-01 00:00:00

  • A mixed-effects, spatially varying coefficients model with application to multi-resolution functional magnetic resonance imaging data.

    abstract::Spatial resolution plays an important role in functional magnetic resonance imaging studies as the signal-to-noise ratio increases linearly with voxel volume. In scientific studies, where functional magnetic resonance imaging is widely used, the standard spatial resolution typically used is relatively low which ensure...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280217752378

    authors: Liu Z,Bartsch AJ,Berrocal VJ,Johnson TD

    更新日期:2019-04-01 00:00:00

  • Bayesian variable selection in the accelerated failure time model with an application to the surveillance, epidemiology, and end results breast cancer data.

    abstract::Accelerated failure time model is a popular model to analyze censored time-to-event data. Analysis of this model without assuming any parametric distribution for the model error is challenging, and the model complexity is enhanced in the presence of large number of covariates. We developed a nonparametric Bayesian met...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280215626947

    authors: Zhang Z,Sinha S,Maiti T,Shipp E

    更新日期:2018-04-01 00:00:00

  • Predicting brain activity using a Bayesian spatial model.

    abstract::Increasing the clinical applicability of functional neuroimaging technology is an emerging objective, e.g. for diagnostic and treatment purposes. We propose a novel Bayesian spatial hierarchical framework for predicting follow-up neural activity based on an individual's baseline functional neuroimaging data. Our appro...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280212448972

    authors: Derado G,Bowman FD,Zhang L,Alzheimer's Disease Neuroimaging Initiative Investigators.

    更新日期:2013-08-01 00:00:00

  • Measuring continuous baseline covariate imbalances in clinical trial data.

    abstract::This paper presents and compares several methods of measuring continuous baseline covariate imbalance in clinical trial data. Simulations illustrate that though the t-test is an inappropriate method of assessing continuous baseline covariate imbalance, the test statistic itself is a robust measure in capturing imbalan...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280211416038

    authors: Ciolino JD,Martin RH,Zhao W,Hill MD,Jauch EC,Palesch YY

    更新日期:2015-04-01 00:00:00