Structural Measurement Error Research Articles

CME Multiple Choice Questions

The Female Sexual Function Index (FSFI) is a patient-reported outcome measure measuring female sexual dysfunction. The FSFI-19 was developed with 6 theoretical subscales in 2000. In 2010, a shortened version became available (FSFI-6).To investigate the measurement properties of the FSFI-19 and FSFI-6.A systematic search was performed of Embase, Medline, and Web of Science for studies that investigated measurement properties of the FSFI-19 or FSFI-6 up to April 2018. Data were extracted and analyzed according to COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines. Evidence was categorized into sufficient, insufficient, inconsistent, or indeterminate, and quality of evidence as very high, high, moderate, or low.The Main Outcome Measure is the evidence of a measurement property, and the quality of evidence based on the COSMIN guidelines.83 studies were included. Concerning the FSFI-19, the evidence for internal consistency was sufficient and of moderate quality. The evidence for reliability was sufficient but of low quality. The evidence for criterion validity was sufficient and of high quality. The evidence for structural validity was inconsistent of low quality. The evidence for construct validity was inconsistent of moderate quality. Concerning the FSFI-6, the evidence for criterion validity was sufficient of moderate quality. The evidence for internal consistency was rated as indeterminate. The evidence for reliability was inconsistent of low quality. The evidence for construct validity was inconsistent of very low quality. No information was available on structural validity of the FSFI-6, and measurement error, responsiveness, and cross-cultural validity of both FSFI-6 and FSFI-19.Conflicting and lack of evidence for some of the measurement properties of the FSFI-19 and FSFI-6 indicates the importance of further research on the validity of these patient-reported outcome measures. We advise researchers who use the FSFI-19 to perform confirmatory factor analyses and report the factor structure found in their sample. Regardless of these concerns, the FSFI-19 and FSFI-6 have strong criterion validity. Pragmatically, they are good screening tools for the current definition of female sexual dysfunction.A strong point of the review is the use of predefined guidelines. A limitation is the use of a precise rather than a sensitive search filter.The FSFI requires more research on structural validity (FSFI-19 and FSFI-6), reliability (FSFI-6), construct validity (FSFI-19), measurement error (FSFI-19 and FSFI-6), and responsiveness (FSFI-19 and FSFI-6). Further corroboration of measurement invariance (both across cultures and across subpopulations) in the factor structure of the FSFI-19 is necessary, as well as tests for the unidimensionality of the FSFI-6.Neijenhuijs KI, Hooghiemstra N, Holtmaat K, et al. The Female Sexual Function Index (FSFI)—A Systematic Review of Measurement Properties. J Sex Med 2019;16:640–660.

Use of the Predictive Sugars Biomarker to Evaluate Self-Reported Total Sugars Intake in the Observing Protein and Energy Nutrition (OPEN) Study

A predictive biomarker for intake of total sugars was recently developed under controlled conditions. We used this biomarker to assess measurement error (ME) structure in self-reported intake of total sugars in free-living individuals. The Observing Protein and Energy Nutrition (OPEN) study involved 484 participants aged 40 to 69 years. Diet was assessed using two administrations of a food frequency questionnaire (FFQ) and two nonconsecutive 24-hour dietary recalls (24HDR). Two 24-hour urine samples checked for completeness were analyzed on sucrose and fructose. We applied the biomarker calibrated in a feeding study to OPEN data to assess the ME structure and the attenuation factors (AF) for intakes of absolute total sugars and sugars density for the FFQ and 24HDR. The AFs for absolute sugars were similar for a single FFQ and 24HDR, but attenuation decreased with repeated 24HDRs. For sugars density, the AFs for FFQ (men: 0.39; women: 0.33) were greater than for single 24HDR (men: 0.30; women: 0.24), and similar to two 24HDRs (men: 0.41; women: 0.35). The attenuation associated with both instruments was greater in women than in men. Both the FFQ and 24HDR were found to be biased; hence, incorporation of the sugars biomarker in calibration studies within the cohorts may be necessary to more reliably estimate associations of sugars and disease. In this article, we propose a new dietary reference instrument based on the recently defined class of predictive biomarkers. Using sugars biomarker, we quantify ME in the FFQ- and 24HDR-reported absolute total sugars and total sugars density.

Measurement Error Correction by Exploiting Gene–Environment Independence in Family‐Based Case–Control Studies

Abstract. Family‐based case–control designs are commonly used in epidemiological studies for evaluating the role of genetic susceptibility and environmental exposure to risk factors in the etiology of rare diseases. Within this framework, it is often reasonable to assume genetic susceptibility and environmental exposure being conditionally independent of each other within families in the source population. We focus on this setting to explore the situation of measurement error affecting the assessment of the environmental exposure. We correct for measurement error through a likelihood‐based method. We exploit a conditional likelihood approach to relate the probability of disease to the genetic and the environmental risk factors. We show that this approach provides less biased and more efficient results than that based on logistic regression. Regression calibration, instead, provides severely biased estimators of the parameters. The comparison of the correction methods is performed through simulation, under common measurement error structures.

Measurement error in the timing of events: effect on survival analyses in randomized clinical trials

Presence of measurement error in the outcome can complicate the interpretation of a randomized clinical trial. The Oncologic Drugs Advisory Committee of the US Food and Drug Administration voted against recommending approval of bevacizumab for the initial treatment of metastatic breast cancer; one of their major concerns was the presence of a large amount of nondifferential measurement error in the evaluation of progression-free survival, the primary outcome of the randomized clinical trial E2100. To investigate the effects of nondifferential measurement error in time-to-event outcomes on the conclusions of a proportional hazards analysis of a randomized clinical trial. Simulations were performed showing effects of measurement error on the estimated treatment effect (hazard ratio) in a clinical trial. In some simulations, the measurement error structure from E2100 data was approximated; for other simulations, larger or smaller measurement error was considered. The bias in estimating the hazard ratio was very small using measurement error and a hazard ratio similar to E2100. Even with a larger nondifferential measurement error, the bias remained small when the hazard ratio was in a range commonly seen in clinical trials. There was no or little effect on the variability of the estimated treatment effect. Because of censoring issues, retrospective evaluation of the measurement error structure from a completed trial is difficult. Although our simulations cover a range of plausible measurement error values, in theory, a trial could have much larger measurement error than we considered. Differential measurement error is only briefly considered. Nondifferential measurement error due to variability in estimating time-to-event outcomes will typically not be a major concern in randomized clinical trials.

Slope estimation in structural line‐segment heteroscedastic measurement error models

This paper extends the line-segment parametrization of the structural measurement error (ME) model to situations in which the error variance on both variables is not constant over all observations. Under these conditions, we develop a method-of-moments estimate of the slope, and derive its asymptotic variance. We further derive an accurate estimator of the variability of the slope estimate based on sample data in a rather general setting. We perform simulations that validate our results and demonstrate that our estimates are more precise than estimates under a different model when the ME variance is not small. Finally, we illustrate our estimation approach using real data involving heteroscedastic ME, and compare its performance with that of earlier models.

Structural measurement errors in nonseparable models

This paper considers measurement error from a new perspective. In surveys, response errors are often caused by the fact that respondents recall past events and quantities imperfectly. We explore the consequences of limited recall for the identification of marginal effects. Our identification approach is entirely nonparametric, using Matzkin-type nonseparable models that nest a large class of potential structural models. We show that measurement error due to limited recall will generally exhibit nonstandard behavior, in particular be nonclassical and differential, even for left-hand side variables in linear models. We establish that information reduction by individuals is the critical issue for the severity of recall measurement error. In order to detect information reduction, we propose a nonparametric test statistic. Finally, we propose bounds to address identification problems resulting from recall errors. We illustrate our theoretical findings using real-world data on food consumption.

On a Method to Reduce Uncertainties in Bulk Property Measurements of Two-Component Composites

For two-component composites, we address the inverse problem of estimating the structural parameters and decrease measurement errors in bulk property measurements. A measurement of the effective permittivity at one frequency gives microstructural information about the composite that is used in cross-property bounds to estimate the effective permittivity at other frequencies. We use this information and inverse bounds on microstructural parameters to tighten error bars on permittivity measurements at microwave frequencies. The method can be used in the design of random and periodic composite materials for a large variety of applications. We apply the method to a composite material used in radar applications.

Robust statistical methods for analysis of biomarkers measured with batch/experiment‐specific errors

In many biological studies, biomarkers are measured with errors. In addition, study samples are often divided and measured in separate batches, and data collected from different experiments are used in a single analysis. Generally speaking, the structure of the measurement error is unknown and is not easy to ascertain. While the conditions under which the measurements are taken vary from one batch/experiment to another, they are often held steady within each batch/experiment. Thus, the measurement error can be considered batch/experiment specific, that is, fixed within each batch/experiment, which results into a rank-preserving property within each batch/experiment. Under this condition, we study robust statistical methods for analyzing the association between an outcome variable and predictors measured with error, and evaluating the diagnostic or predictive accuracy of these biomarkers. Our methods require no assumptions on the structure and distribution of the measurement error, which are often unrealistic. Compared with existing methods that are predicated on normality and additive structure of measurement errors, our methods still yield valid inferences under departure from these assumptions. The proposed methods are easy to implement using off-shelf software. Simulation studies show that under various measurement error structures, the performance of the proposed methods is satisfactory even for a fairly small sample size, whereas existing methods under misspecified structures and a naive approach exhibited substantial bias. Our methods are illustrated using a biomarker validation case-control study for colorectal neoplasms.

307 Talking about sex: identifying psycho-sexual concerns in the clinic

The International Index of Erectile Function (IIEF) is a patient-reported outcome measure to evaluate erectile dysfunction and other sexual problems in men.To perform a systematic review of the measurement properties of the 15-item patient-reported outcome measure (IIEF-15) and the shortened 5-item version (IIEF-5).A systematic search of scientific literature up to April 2018 was performed. Data were extracted and analyzed according to COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) guidelines for structural validity, internal consistency, reliability, measurement error, hypothesis testing for construct validity, and responsiveness. Evidence of measurement properties was categorized into sufficient, insufficient, inconsistent, or indeterminate, and quality of evidence as very high, high, moderate, or low.40 studies were included. The evidence for criterion validity (of the Erectile Function subscale), and responsiveness of the IIEF-15 was sufficient (high quality), but inconsistent (moderate quality) for structural validity, internal consistency, construct validity, and test-retest reliability. Evidence for structural validity, test-retest reliability, construct validity, and criterion validity of the IIEF-5 was sufficient (moderate quality) but indeterminate for internal consistency, measurement error, and responsiveness.Lack of evidence for and evidence not supporting some of the measurement properties of the IIEF-15 and IIEF-5 shows the importance of further research on the validity of these questionnaires in clinical research and clinical practice.A strength of the current review is the use of predefined guidelines (COSMIN). A limitation of this review is the use of a precise rather than a sensitive search filter regarding measurement properties to identify studies to be included.The IIEF requires more research on structural validity (IIEF-15), internal consistency (IIEF-15 and IIEF-5), construct validity (IIEF-15), measurement error (IIEF-15 and IIEF-5), and responsiveness (IIEF-5). The most pressing matter for future research is determining the unidimensionality of the IIEF-5 and the exact factor structure of the IIEF-15.Neijenhuijs KI, Holtmaat K, Aaronson NK, et al. The International Index of Erectile Function (IIEF)—A Systematic Review of Measurement Properties. J Sex Med 2019;16:1078–1091.

Quantile Regression With Measurement Error

Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure.

On Latent‐Variable Model Misspecification in Structural Measurement Error Models for Binary Response

We consider structural measurement error models for a binary response. We show that likelihood-based estimators obtained from fitting structural measurement error models with pooled binary responses can be far more robust to covariate measurement error in the presence of latent-variable model misspecification than the corresponding estimators from individual responses. Furthermore, despite the loss in information, pooling can provide improved parameter estimators in terms of mean-squared error. Based on these and other findings, we create a new diagnostic method to detect latent-variable model misspecification in structural measurement error models with individual binary response. We use simulation and data from the Framingham Heart Study to illustrate our methods.

An improved test of latent‐variable model misspecification in structural measurement error models for group testing data

We consider structural measurement error models for group testing data. Likelihood inference based on structural measurement error models requires one to specify a model for the latent true predictors. Inappropriate specification of this model can lead to erroneous inference. We propose a new method tailored to detect latent-variable model misspecification in structural measurement error models for group testing data. Compared with the existing diagnostic methods developed for the same purpose, our method shows vast improvement in the power to detect latent-variable model misspecification in group testing design. We illustrate the implementation and performance of the proposed method via simulation and application to a real data example.

A Bayesian hierarchical framework for calibrating aquatic biogeochemical models

Model practitioners increasingly place emphasis on rigorous quantitative error analysis in aquatic biogeochemical models and the existing initiatives range from the development of alternative metrics for goodness of fit, to data assimilation into operational models, to parameter estimation techniques. However, the treatment of error in many of these efforts is arguably selective and/or ad hoc. A Bayesian hierarchical framework enables the development of robust probabilistic analysis of error and uncertainty in model predictions by explicitly accommodating measurement error, parameter uncertainty, and model structure imperfection. This paper presents a Bayesian hierarchical formulation for simultaneously calibrating aquatic biogeochemical models at multiple systems (or sites of the same system) with differences in their trophic conditions, prior precisions of model parameters, available information, measurement error or inter-annual variability. Our statistical formulation also explicitly considers the uncertainty in model inputs (model parameters, initial conditions), the analytical/sampling error associated with the field data, and the discrepancy between model structure and the natural system dynamics (e.g., missing key ecological processes, erroneous formulations, misspecified forcing functions). The comparison between observations and posterior predictive monthly distributions indicates that the plankton models calibrated under the Bayesian hierarchical scheme provided accurate system representations for all the scenarios examined. Our results also suggest that the Bayesian hierarchical approach allows overcoming problems of insufficient local data by “borrowing strength” from well-studied sites and this feature will be highly relevant to conservation practices of regions with a high number of freshwater resources for which complete data could never be practically collected. Finally, we discuss the prospect of extending this framework to spatially explicit biogeochemical models (e.g., more effectively connect inshore with offshore areas) along with the benefits for environmental management, such as the optimization of the sampling design of monitoring programs and the alignment with the policy practice of adaptive management.

Characterization of the measurement error structure in 1D 1H NMR data for metabolomics studies

NMR-based metabolomics is characterized by high throughput measurements of the signal intensities of complex mixtures of metabolites in biological samples by assaying, typically, bio-fluids or tissue homogenates. The ultimate goal is to obtain relevant biological information regarding the dissimilarity in patho-physiological conditions that the samples experience. For a long time now, this information has been obtained through the analysis of measured NMR signals via multivariate statistics. NMR data are quite complex and the use of such multivariate statistical methods as principal components analysis (PCA) for their analysis assumes that the data are multivariate normal with errors that are identical, independent and normally distributed ( i.e. iid normal). There is a consensus that these assumptions are not always true for these data and, thus, several methods have been devised to transform the data or weight them prior to analysis by PCA. The structure of NMR measurement noise, or the extent to which violations of error homoscedasticity affect PCA results have neither been characterized nor investigated. A comprehensive characterization of measurement uncertainties in NMR based metabolomics was achieved in this work using an experiment designed to capture contributions of several sources of error to the total variance in the measurements. The noise structure was found to be heteroscedastic and highly correlated with spectral characteristics that are similar to the mean of the spectra and their standard deviation. A model was subsequently developed that potentially allows errors in NMR measurements to be accurately estimated without the need for extensive replication.

Guidelines for Evaluation of Error Structure for Impedance Response of Polymer Electrolyte Membrane (PEM) Fuel Cells

Impedance spectroscopy provides the opportunity for in-situ identification and quantification of physical processes in any electrochemical system. However, the impedance response is influenced by instrumental artifacts and non-stationary behavior. General guidelines, based on use of a measurement model, are outlined for estimation of an error structure of impedance measurements. The methodology is applied to estimate error in the impedance response of a Polymer Electrolyte Membrane (PEM) fuel cell. The analysis was used to show that low-frequency inductive loops in impedance response of the fuel cell were, in some cases, fully self consistent, and could therefore be attributed to processes occurring in the fuel cell. The error analysis was also used to improve the sensitivity of the impedance to onset of flooding in the fuel cell.

A simulation‐based comparison of techniques to correct for measurement error in matched case–control studies

The presence of measurement errors affecting the covariates in regression models is a relevant topic in many scientific areas, as, for example, in epidemiology. An example is given by an epidemiological population-based matched case-control study on the aetiology of childhood malignancies, which is currently under completion in Italy. This study was aimed at evaluating the effects of childhood exposure to extremely low electromagnetic fields on the risk of disease occurrence by taking into account the possibility of erroneous measures of the exposure. Within this framework, we focus on the application of likelihood methods to correct for measurement error. This approach, which has received less attention in literature with respect to alternatives, is compared with commonly used methods such as regression calibration and SIMEX. The comparison is performed by simulation, under a broad range of measurement error structures.

Statistical Characterization of Protein Ensembles

When accounting for structural fluctuations or measurement errors, a single rigid structure may not be sufficient to represent a protein. One approach to solve this problem is to represent the possible conformations as a discrete set of observed conformations, an ensemble. In this work, we follow a different richer approach, and introduce a framework for estimating probability density functions in very high dimensions, and then apply it to represent ensembles of folded proteins. This proposed approach combines techniques such as kernel density estimation, maximum likelihood, cross-validation, and bootstrapping. We present the underlying theoretical and computational framework and apply it to artificial data and protein ensembles obtained from molecular dynamics simulations. We compare the results with those obtained experimentally, illustrating the potential and advantages of this representation.

Efficient Estimation of Population-Level Summaries in General Semiparametric Regression Models

This article considers a wide class of semiparametric regression models in which interest focuses on population-level quantities that combine both the parametric and the nonparametric parts of the model. Special cases in this approach include generalized partially linear models, generalized partially linear single-index models, structural measurement error models, and many others. For estimating the parametric part of the model efficiently, profile likelihood kernel estimation methods are well established in the literature. Here our focus is on estimating general population-level quantities that combine the parametric and nonparametric parts of the model (e.g., population mean, probabilities, etc.). We place this problem in a general context, provide a general kernel-based methodology, and derive the asymptotic distributions of estimates of these population-level quantities, showing that in many cases the estimates are semiparametric efficient. For estimating the population mean with no missing data, we show that the sample mean is semiparametric efficient for canonical exponential families, but not in general. We apply the methods to a problem in nutritional epidemiology, where estimating the distribution of usual intake is of primary interest and semiparametric methods are not available. Extensions to the case of missing response data are also discussed.

THE HANFORD THYROID DISEASE STUDY: AN ALTERNATIVE VIEW OF THE FINDINGS

The Hanford Thyroid Disease Study (HTDS) is one of the largest and most complex epidemiologic studies of the relation between environmental exposures to I and thyroid disease. The study detected no dose-response relation using a 0.05 level for statistical significance. The results for thyroid cancer appear inconsistent with those from other studies of populations with similar exposures, and either reflect inadequate statistical power, bias, or unique relations between exposure and disease risk. In this paper, we explore these possibilities, and present evidence that the HTDS statistical power was inadequate due to complex uncertainties associated with the mathematical models and assumptions used to reconstruct individual doses. We conclude that, at the very least, the confidence intervals reported by the HTDS for thyroid cancer and other thyroid diseases are too narrow because they fail to reflect key uncertainties in the measurement-error structure. We recommend that the HTDS results be interpreted as inconclusive rather than as evidence for little or no disease risk from Hanford exposures.

Skew binary regression with measurement errors

In this paper, we extend the structural probit measurement error model by considering that the unobserved covariate follows a skew-normal distribution. The new model is termed the structural skew-normal probit model. As in the normal case, the likelihood function is obtained analytically which can be maximized by using existing statistical software. A Bayesian approach using Markov chain Monte Carlo techniques to generate from the posterior distributions is also developed. A simulation study demonstrates the usefulness of the approach in avoiding attenuation which is the case with the naive procedure and it seems to be more efficient than using the structural probit model when the distribution of the covariate (predictor) is skew.