Response to Comment on "Censoring Trace-Level Environmental Data: Statistical Analysis Considerations to Limit Bias".
Ontology highlight
ABSTRACT:
SUBMITTER: George BJ
PROVIDER: S-EPMC8796121 | biostudies-literature | 2021 Nov
REPOSITORIES: biostudies-literature
Publications
Response to Comment on "Censoring Trace-Level Environmental Data: Statistical Analysis Considerations to Limit Bias".
George Barbara Jane BJ Thomas Kent W KW Simmons Jane Ellen JE
Environmental science & technology 20211027 22
Similar Datasets
Project description:Trace-level environmental data typically include values near or below detection and quantitation thresholds where health effects may result from low-concentration exposures to one chemical over time or to multiple chemicals. In a cook stove case study, bias in dibenzo[a,h]anthracene concentration means and standard deviations (SDs) was assessed following censoring at thresholds for selected analysis approaches: substituting threshold/2, maximum likelihood estimation, robust regression on order statistics, Kaplan-Meier, and omitting censored observations. Means and SDs for gas chromatography-mass spectrometry-determined concentrations were calculated after censoring at detection and calibration thresholds, 17% and 55% of the data, respectively. Threshold/2 substitution was the least biased. Measurement values were subsequently simulated from two log-normal distributions at two sample sizes. Means and SDs were calculated for 30%, 50%, and 80% censoring levels and compared to known distribution counterparts. Simulation results illustrated (1) threshold/2 substitution to be inferior to modern after-censoring statistical approaches and (2) all after-censoring approaches to be inferior to including all measurement data in analysis. Additionally, differences in stove-specific group means were tested for uncensored samples and after censoring. Group differences of means tests varied depending on censoring and distributional decisions. Investigators should guard against censoring-related bias from (explicit or implicit) distributional and analysis approach decisions.
| S-EPMC8224532 | biostudies-literature
Project description:PurposeThe analysis of Ecological Momentary Assessment (EMA) data can be difficult to conceptualize due to the complexity of how the data are collected. The goal of this tutorial is to provide an overview of statistical considerations for analyzing observational data arising from EMA studies.MethodEMA data are collected in a variety of ways, complicating the statistical analysis. We focus on fundamental statistical characteristics of the data and general purpose statistical approaches to analyzing EMA data. We implement those statistical approaches using a recent study involving EMA.ResultsThe linear or generalized linear mixed-model statistical approach can adequately capture the challenges resulting from EMA collected data if properly set up. Additionally, while sample size depends on both the number of participants and the number of survey responses per participant, having more participants is more important than the number of responses per participant.ConclusionUsing modern statistical methods when analyzing EMA data and adequately considering all of the statistical assumptions being used can lead to interesting and important findings when using EMA.Supplemental materialhttps://doi.org/10.23641/asha.17155961.
| S-EPMC9150728 | biostudies-literature
Project description:This paper describes a method for computing estimates for the location parameter μ > 0 and scale parameter λ > 0 with fixed shape parameter α of the alpha power exponential distribution (APED) under type-II hybrid censored (T-IIHC) samples. We compute the maximum likelihood estimations (MLEs) of (μ, λ) by applying the Newton-Raphson method (NRM) and expectation maximization algorithm (EMA). In addition, the estimate hazard functions and reliability are evaluated by applying the invariance property of MLEs. We calculate the Fisher information matrix (FIM) by applying the missing information rule, which is important in finding the asymptotic confidence interval. Finally, the different proposed estimation methods are compared in simulation studies. A simulation example and real data example are analyzed to illustrate our estimation methods.
| S-EPMC7817043 | biostudies-literature
Project description:(1) Background: Several retrospective observational analyzed treatment outcomes for COVID-19; (2) Methods: Inverse probability of censoring weighting (IPCW) was applied to correct for bias due to informative censoring in database of hospitalized patients who did and did not receive convalescent plasma; (3) Results: When compared with an IPCW analysis, overall mortality was overestimated using an unadjusted Kaplan-Meier curve, and hazard ratios for the older age group compared to the youngest were underestimated using the Cox proportional hazard models and 30-day mortality; (4) Conclusions: An IPCW analysis provided stabilizing weights by hospital admission.
| S-EPMC9865049 | biostudies-literature
Project description:BackgroundMeasures of patient satisfaction are increasingly used to measure patient experience. Most satisfaction measures have notable ceiling effects, which limits our ability to learn from variation among relatively satisfied patients. This study tested a variety of single-question satisfaction measures for their mean overall score, ceiling and floor effect, and data distribution. In addition, we assessed the correlation between satisfaction and psychological factors and assessed how the various methods for measuring satisfaction affected net promoter scores (NPSs).MethodologyA total of 212 patients visiting orthopedic offices were enrolled in this randomized controlled trial. Patients were randomized to 1 of 5 newly designed, single-question satisfaction scales: (a) a helpfulness 11-point ordinal scale from 0 to 10, (b) a helpfulness ordinal 11-point scale from 0 to 5 (ie, with 1.5, 2.5, etc), (c) a helpfulness 100-point slider, (d) a satisfaction 11-point ordinal scale from 0 to 10, and (e) a willingness to recommend 11-point ordinal scale from 0 to 10. Additionally, patients completed the 2-item Pain Self-Efficacy Questionnaire (PSEQ-2), 5-item Short Health Anxiety Inventory (SHAI-5) Scale, and Patient-Reported Outcomes Measurement Information System (PROMIS) Depression. We assessed mean and median score, ceiling and floor effect, and skewness and kurtosis for each scale. Spearman's correlation tests were used to test correlations between satisfaction and psychological status. Finally, we assessed the NPS for the various scales.ResultsCeiling effects ranged from 29% to 68%. The 11-point ordinal helpfulness scale from 0 to 10 had the least ceiling effect (29%). All of the scales were asymmetrically distributed, with the 11-point ordinal scale from 0 to 5 having the most Gaussian distribution (skew = 0.64 and kurtosis = 2.3). Satisfaction scores did not correlate with psychological factors: PSEQ-2 (r = 0.04; P = .57), SHAI-5 (r = 0.01; P = .93), and PROMIS Depression (r = -0.04; P = .61). Net promoter scores varied substantially by scale design, with higher scores corresponding with greater ceiling effects.ConclusionsVariations in scale types, text anchors, and lead-in statements do not eliminate the ceiling effect of single-question measures of satisfaction with a visit to an orthopedic specialist. Further studies might test other scale designs and labels.Level of evidenceDiagnostic; Level II.
| S-EPMC7786736 | biostudies-literature
Project description:Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring ("motion scrubbing"). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal-to-noise ratio. Motion censoring decreased variance in parameter estimates within- and across-subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement.
| S-EPMC3895106 | biostudies-literature
Project description:Noncoding DNA contains gene regulatory elements that alter gene expression, and the function of these elements can be modified by genetic variation. Massively parallel reporter assays (MPRA) enable high-throughput identification and characterization of functional genetic variants, but the statistical methods to identify allelic effects in MPRA data have not been fully developed. In this study, we demonstrate how the baseline allelic imbalance in MPRA libraries can produce biased results, and we propose a novel, nonparametric, adaptive testing method that is robust to this bias. We compare the performance of this method with other commonly used methods, and we demonstrate that our novel adaptive method controls Type I error in a wide range of scenarios while maintaining excellent power. We have implemented these tests along with routines for simulating MPRA data in the Analysis Toolset for MPRA (@MPRA), an R package for the design and analyses of MPRA experiments. It is publicly available at http://github.com/redaq/atMPRA.
| S-EPMC7722129 | biostudies-literature
Project description:Measures of substance concentration in urine, serum or other biological matrices often have an assay limit of detection. When concentration levels fall below the limit, the exact measures cannot be obtained. Instead, the measures are censored as only partial information that the levels are under the limit is known. Assuming the concentration levels are from a single population with a normal distribution or follow a normal distribution after some transformation, Tobit regression models, or censored normal regression models, are the standard approach for analyzing such data. However, in practice, it is often the case that the data can exhibit more censored observations than what would be expected under the Tobit regression models. One common cause is the heterogeneity of the study population, caused by the existence of a latent group of subjects who lack the substance measured. For such subjects, the measurements will always be under the limit. If a censored normal regression model is appropriate for modeling the subjects with the substance, the whole population follows a mixture of a censored normal regression model and a degenerate distribution of the latent class. While there are some studies on such mixture models, a fundamental question about testing whether such mixture modeling is necessary, i.e. whether such a latent class exists, has not been studied yet. In this paper, three tests including Wald test, likelihood ratio test and score test are developed for testing the existence of such latent class. Simulation studies are conducted to evaluate the performance of the tests, and two real data examples are employed to illustrate the tests.
| S-EPMC7231674 | biostudies-literature
Project description:In this paper, we investigate a step-stress partially accelerated lifetime test for the four-component hybrid systems with Type-II progressive hybrid censoring scheme while the life time of system component follows exponential failure rate. In many cases, the exact component causing the system failure cannot be identified and the cause of failure is masked. Based on Type-II progressively hybrid censored and masked data, the maximum likelihood estimations for unknown parameters and acceleration factor are obtained. In addition, approximate confidence interval and bootstrap confidence interval are presented by using the asymptotic distributions of the maximum likelihood estimations for unknown parameters and bootstrap method, respectively. Finally, the proposed method is illustrated through the simulation studies.
| S-EPMC5653305 | biostudies-literature
Project description:BackgroundWhen data are collected subject to a detection limit, observations below the detection limit may be considered censored. In addition, the domain of such observations may be restricted; for example, values may be required to be non-negative.MethodsWe propose a method for estimating population mean and variance from censored observations that accounts for known domain restriction. The method finds maximum likelihood estimates assuming an underlying truncated normal distribution.ResultsWe show that our method, tcensReg, has lower bias, Type I error rates, and mean squared error than other methods commonly used for data with detection limits such as Tobit regression and single imputation under a range of simulation settings from mild to heavy censoring and truncation. We further demonstrate the consistency of the maximum likelihood estimators. We apply our method to analyze vision quality data collected from ophthalmology clinical trials comparing different types of intraocular lenses implanted during cataract surgery. All of the methods yield similar conclusions regarding non-inferiority, but estimates from the tcensReg method suggest that there may be greater mean differences and overall variability.ConclusionsIn the presence of detection limits, our new method tcensReg provides a way to incorporate known domain restrictions in dependent variables that substantially improves inferences.
| S-EPMC7322891 | biostudies-literature