journal
MENU ▼
Read by QxMD icon Read
search

Statistics in Medicine | Page 2

journal
https://read.qxmd.com/read/31338848/mixed-effects-models-for-slope-based-endpoints-in-clinical-trials-of-chronic-kidney-disease
#21
Edward Vonesh, Hocine Tighiouart, Jian Ying, Hiddo L Heerspink, Julia Lewis, Natalie Staplin, Lesley Inker, Tom Greene
In March of 2018, the National Kidney Foundation, in collaboration with the US Food and Drug Administration and the European Medicines Agency, sponsored a workshop in which surrogate endpoints other than currently established event-time endpoints for clinical trials in chronic kidney disease (CKD) were presented and discussed. One such endpoint is a slope-based parameter describing the rate of decline in the estimated glomerular filtration rate (eGFR) over time. There are a number of challenges that can complicate such slope-based analyses in CKD trials...
July 23, 2019: Statistics in Medicine
https://read.qxmd.com/read/31338847/an-additive-boundary-for-group-sequential-designs-with-connection-to-conditional-error
#22
Dong Xi, Paul Gallo
Group sequential designs allow stopping a clinical trial for meeting its efficacy objectives based on interim evaluation of the accumulating data. Various methods to determine group sequential boundaries that control the probability of crossing the boundary at an interim or the final analysis have been proposed. To monitor trials with uncertainty in group sizes at each analysis, error spending functions are often used to derive stopping boundaries. Although flexible, most spending functions are generic increasing functions with parameters that are difficult to interpret...
July 23, 2019: Statistics in Medicine
https://read.qxmd.com/read/31328285/impact-of-model-misspecification-in-shared-frailty-survival-models
#23
Alessandro Gasparini, Mark S Clements, Keith R Abrams, Michael J Crowther
Survival models incorporating random effects to account for unmeasured heterogeneity are being increasingly used in biostatistical and applied research. Specifically, unmeasured covariates whose lack of inclusion in the model would lead to biased, inefficient results are commonly modeled by including a subject-specific (or cluster-specific) frailty term that follows a given distribution (eg, gamma or lognormal). Despite that, in the context of parametric frailty models, little is known about the impact of misspecifying the baseline hazard or the frailty distribution or both...
July 21, 2019: Statistics in Medicine
https://read.qxmd.com/read/31321806/information-content-of-stepped-wedge-designs-when-treatment-effect-heterogeneity-and-or-implementation-periods-are-present
#24
Jessica Kasza, Monica Taljaard, Andrew B Forbes
Stepped-wedge cluster randomized trials, which randomize clusters of subjects to treatment sequences in which clusters switch from control to intervention conditions, are being conducted with increasing frequency. Due to the real-world nature of this design, methodological and implementation challenges are ubiquitous. To account for such challenges, more complex statistical models to plan studies and analyze data are required. In this paper, we consider stepped-wedge trials that accommodate treatment effect heterogeneity across clusters, implementation periods during which no data are collected, or both treatment effect heterogeneity and implementation periods...
July 18, 2019: Statistics in Medicine
https://read.qxmd.com/read/31321799/modeling-excess-deaths-after-a-natural-disaster-with-application-to-hurricane-maria
#25
Roberto Rivera, Wolfgang Rolke
Estimation of excess deaths due to a natural disaster is an important public health problem. The CDC provides guidelines to fill death certificates to help determine the death toll of such events. But, even when followed by medical examiners, the guidelines cannot guarantee a precise calculation of excess deaths. We propose two models to estimate excess deaths due to an emergency. The first model is simple, permitting excess death estimation with little data through a profile likelihood method. The second model is more flexible, incorporating temporal variation, covariates, and possible population displacement while allowing inference on how the emergency's effect changes with time...
July 18, 2019: Statistics in Medicine
https://read.qxmd.com/read/31317576/reconciling-randomized-trial-evidence-on-proximal-versus-distal-outcomes-with-application-to-trials-of-influenza-vaccination-for-healthcare-workers
#26
Reka Gustafson, Paul Gustafson, Patricia Daly
When synthesizing the body of evidence concerning a clinical intervention, impacts on both proximal and distal outcome variables may be relevant. Assessments will be more defensible if results concerning a proximal outcome align with those concerning a corresponding distal outcome. We present a method to assess the coherence of empirical clinical trial results with biologic and mathematical first principles in situations where the intervention can only plausibly impact the distal outcome indirectly via the proximal outcome...
July 17, 2019: Statistics in Medicine
https://read.qxmd.com/read/31317564/stein-type-shrinkage-estimators-in-gamma-regression-model-with-application-to-prostate-cancer-data
#27
Saumen Mandal, Reza Arabi Belaghi, Akram Mahmoudi, Minoo Aminnejad
Gamma regression is applied in several areas such as life testing, forecasting cancer incidences, genomics, rainfall prediction, experimental designs, and quality control. Gamma regression models allow for a monotone and no constant hazard in survival models. Owing to the broad applicability of gamma regression, we propose some novel and improved methods to estimate the coefficients of gamma regression model. We combine the unrestricted maximum likelihood (ML) estimators and the estimators that are restricted by linear hypothesis, and we present Stein-type shrinkage estimators (SEs)...
July 17, 2019: Statistics in Medicine
https://read.qxmd.com/read/31313376/controlling-false-discovery-proportion-in-identification-of-drug-related-adverse-events-from-multiple-system-organ-classes
#28
Xianming Tan, Guanghan F Liu, Donglin Zeng, William Wang, Guoqing Diao, Joseph F Heyse, Joseph G Ibrahim
Analyzing safety data from clinical trials to detect safety signals worth further examination involves testing multiple hypotheses, one for each observed adverse event (AE) type. There exists certain hierarchical structure for these hypotheses due to the classification of the AEs into system organ classes, and these AEs are also likely correlated. Many approaches have been proposed to identify safety signals under the multiple testing framework and tried to achieve control of false discovery rate (FDR). The FDR control concerns the expectation of the false discovery proportion (FDP)...
July 17, 2019: Statistics in Medicine
https://read.qxmd.com/read/31313349/assessing-pharmacokinetic-marker-correlates-of-outcome-with-application-to-antibody-prevention-efficacy-trials
#29
Peter B Gilbert, Yuanyuan Zhang, Erika Rudnicki, Yunda Huang
The Antibody Mediated Prevention efficacy trials are the first studies to evaluate whether passive administration of a broadly neutralizing monoclonal antibody can prevent human immunodeficiency virus (HIV) acquisition. The trials randomize 4600 HIV-negative volunteers to receive 10 infusions of the monoclonal antibody VRC01 or placebo. The primary objective compares the incidence of HIV infection between the study groups. The secondary objective assesses whether and how a marker defined as the serum concentration of VRC01 over time associates with the instantaneous rate of HIV infection, using a two-phase sampling design, a pharmacokinetic model for the time-concentration curve, and an estimator of HIV infection times...
July 17, 2019: Statistics in Medicine
https://read.qxmd.com/read/31313344/accounting-for-established-predictors-with-the-multistep-elastic-net
#30
Elizabeth C Chase, Philip S Boonstra
Multivariable models for prediction or estimating associations with an outcome are rarely built in isolation. Instead, they are based upon a mixture of covariates that have been evaluated in earlier studies (eg, age, sex, or common biomarkers) and covariates that were collected specifically for the current study (eg, a panel of novel biomarkers or other hypothesized risk factors). For that context, we present the multistep elastic net (MSN), which considers penalized regression with variables that can be qualitatively grouped based upon their degree of prior research support: established predictors vs unestablished predictors...
July 17, 2019: Statistics in Medicine
https://read.qxmd.com/read/31313337/exposure-density-sampling-dynamic-matching-with-respect-to-a-time-dependent-exposure
#31
Kristin Ohneberg, Jan Beyersmann, Martin Schumacher
Estimating the potential risk associated with an exposure occurring over time requires complex statistical techniques, since ignoring the time from study entry until the exposure leads to potentially seriously biased effect estimates. A prominent example is estimating the effect of hospital-acquired infections on adverse outcomes in patients admitted to the intensive care unit. Exposure density sampling has been proposed as an approach to dynamic matching with respect to a time-dependent exposure. Firstly, exposure density sampling can be useful to reduce the workload of study follow up, as it includes all exposed but only a subset of the not yet exposed individuals...
July 17, 2019: Statistics in Medicine
https://read.qxmd.com/read/31304619/identifying-and-interpreting-subgroups-in-health-care-utilization-data-with-count-mixture-regression-models
#32
Christoph F Kurz, Laura A Hatfield
Inpatient care is a large share of total health care spending, making analysis of inpatient utilization patterns an important part of understanding what drives health care spending growth. Common features of inpatient utilization measures such as length of stay and spending include zero inflation, overdispersion, and skewness, all of which complicate statistical modeling. Moreover, latent subgroups of patients may have distinct patterns of utilization and relationships between that utilization and observed covariates...
July 15, 2019: Statistics in Medicine
https://read.qxmd.com/read/31304613/decomposition-feature-selection-with-applications-in-detecting-correlated-biomarkers-of-bipolar-disorders
#33
Hailin Huang, Yuanzhang Li, Hua Liang, Colin O Wu
Feature selection is an important initial step of exploratory analysis in biomedical studies. Its main objective is to eliminate the covariates that are uncorrelated with the outcome. For highly correlated covariates, traditional feature selection methods, such as the Lasso, tend to select one of them and eliminate the others, although some of the eliminated ones are still scientifically valuable. To alleviate this drawback, we propose a feature selection method based on covariate space decomposition, referred herein as the "Decomposition Feature Selection" (DFS), and show that this method can lead to scientifically meaningful results in studies with correlated high dimensional data...
July 15, 2019: Statistics in Medicine
https://read.qxmd.com/read/31297869/regression-analysis-and-variable-selection-for-two-stage-multiple-infection-group-testing-data
#34
Juexin Lin, Dewei Wang, Qi Zheng
Group testing, as a cost-effective strategy, has been widely used to perform large-scale screening for rare infections. Recently, the use of multiplex assays has transformed the goal of group testing from detecting a single disease to diagnosing multiple infections simultaneously. Existing research on multiple-infection group testing data either exclude individual covariate information or ignore possible retests on suspicious individuals. To incorporate both, we propose a new regression model. This new model allows us to perform a regression analysis for each infection using multiple-infection group testing data...
July 11, 2019: Statistics in Medicine
https://read.qxmd.com/read/31297847/nonparametric-estimation-of-the-spatio-temporal-covariance-structure
#35
Kai Yang, Peihua Qiu
Spatio-temporal modeling is an active research problem with broad applications. In this problem, proper description and estimation of the data covariance structure plays an important role. In the literature, most available methods assume that the data covariance is stationary and follows a specific parametric form. In practice, however, such assumptions are hardly valid or difficult to verify. In this paper, we propose a new and flexible method for estimating the underlying covariance structure. Our proposed method does not require the covariance to be stationary or follow a parametric form...
July 11, 2019: Statistics in Medicine
https://read.qxmd.com/read/31297825/bayesian-consensus-based-sample-size-criteria-for-binomial-proportions
#36
Lawrence Joseph, Patrick Bélisle
Many sample size criteria exist. These include power calculations and methods based on confidence interval widths from a frequentist viewpoint, and Bayesian methods based on credible interval widths or decision theory. Bayesian methods account for the inherent uncertainty of inputs to sample size calculations through the use of prior information rather than the point estimates typically used by frequentist methods. However, the choice of prior density can be problematic because there will almost always be different appreciations of the past evidence...
July 11, 2019: Statistics in Medicine
https://read.qxmd.com/read/31292995/a-modelling-approach-for-correcting-reporting-delays-in-disease-surveillance-data
#37
Leonardo S Bastos, Theodoros Economou, Marcelo F C Gomes, Daniel A M Villela, Flavio C Coelho, Oswaldo G Cruz, Oliver Stoner, Trevor Bailey, Claudia T Codeço
One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistical problems, infrastructure difficulties, and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty...
July 10, 2019: Statistics in Medicine
https://read.qxmd.com/read/31290191/design-and-analysis-of-nested-case-control-studies-for-recurrent-events-subject-to-a-terminal-event
#38
Ina Jazić, Sebastien Haneuse, Benjamin French, Gaëtan MacGrogan, Virginie Rondeau
The process by which patients experience a series of recurrent events, such as hospitalizations, may be subject to death. In cohort studies, one strategy for analyzing such data is to fit a joint frailty model for the intensities of the recurrent event and death, which estimates covariate effects on the two event types while accounting for their dependence. When certain covariates are difficult to obtain, however, researchers may only have the resources to subsample patients on whom to collect complete data: one way is using the nested case-control (NCC) design, in which risk set sampling is performed based on a single outcome...
July 9, 2019: Statistics in Medicine
https://read.qxmd.com/read/31290184/estimation-and-prediction-for-a-mechanistic-model-of-measles-transmission-using-particle-filtering-and-maximum-likelihood-estimation
#39
Kirsten E Eilertson, John Fricks, Matthew J Ferrari
Disease incidence reported directly within health systems frequently reflects a partial observation relative to the true incidence in the population. State-space models present a general framework for inferring both the dynamics of infectious disease processes and the unobserved burden of disease in the population. Here, we present a state-space model of measles transmission and vaccine-based interventions at the country-level and a particle filter-based estimation procedure. Our dynamic transmission model builds on previous work by incorporating population age-structure to allow explicit representation of age-targeted vaccine interventions...
July 9, 2019: Statistics in Medicine
https://read.qxmd.com/read/31286537/clinical-heterogeneity-in-random-effect-meta-analysis-between-study-boundary-estimate-problem
#40
Daisuke Yoneoka, Masayuki Henmi
Random-effect meta-analysis is commonly applied to estimate overall effects with unexplained heterogeneity across studies. However, standard methods, including (restricted) maximum likelihood (ML or REML), frequently produce (near) zero estimates for between-study variance parameters. Consequently, these methods are reduced to simple and unrealistic fixed-effect models, resulting in an ignorance of the substantial clinical heterogeneity and sometimes leading to incorrect conclusions. To solve the boundary estimate problem, we propose (1) an adjusted maximum likelihood method for the between-study variance that maximizes a likelihood defined as a product of a standard likelihood and a Gaussian class of adjustment factor and (2) a framework using sensitivity analysis by developing a new criterion to check for the occurrence of the boundary estimate...
July 8, 2019: Statistics in Medicine
journal
journal
28444
2
3
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"