Concept: Observational error
Adaptive radiation therapy (ART) aims at compensating for anatomic and pathological changes to improve delivery along a treatment fraction sequence. Current ART protocols require time-consuming manual updating of all volumes of interest on the images acquired during treatment. Deformable image registration (DIR) and contour propagation stand as a state of the ART method to automate the process, but the lack of DIR quality control methods hinder an introduction into clinical practice. We investigated the scale invariant feature transform (SIFT) method as a quantitative automated tool (1) for DIR evaluation and (2) for re-planning decision-making in the framework of ART treatments. As a preliminary test, SIFT invariance properties at shape-preserving and deformable transformations were studied on a computational phantom, granting residual matching errors below the voxel dimension. Then a clinical dataset composed of 19 head and neck ART patients was used to quantify the performance in ART treatments. For the goal (1) results demonstrated SIFT potential as an operator-independent DIR quality assessment metric. We measured DIR group systematic residual errors up to 0.66 mm against 1.35 mm provided by rigid registration. The group systematic errors of both bony and all other structures were also analyzed, attesting the presence of anatomical deformations. The correct automated identification of 18 patients who might benefit from ART out of the total 22 cases using SIFT demonstrated its capabilities toward goal (2) achievement.
Common problems to many longitudinal HIV/AIDS, cancer, vaccine, and environmental exposure studies are the presence of a lower limit of quantification of an outcome with skewness and time-varying covariates with measurement errors. There has been relatively little work published simultaneously dealing with these features of longitudinal data. In particular, left-censored data falling below a limit of detection may sometimes have a proportion larger than expected under a usually assumed log-normal distribution. In such cases, alternative models, which can account for a high proportion of censored data, should be considered. In this article, we present an extension of the Tobit model that incorporates a mixture of true undetectable observations and those values from a skew-normal distribution for an outcome with possible left censoring and skewness, and covariates with substantial measurement error. To quantify the covariate process, we offer a flexible nonparametric mixed-effects model within the Tobit framework. A Bayesian modeling approach is used to assess the simultaneous impact of left censoring, skewness, and measurement error in covariates on inference. The proposed methods are illustrated using real data from an AIDS clinical study. Copyright © 2013 John Wiley & Sons, Ltd.
The objective of this study was to assess the robustness of a novel test bolus (TB)-based computed tomographic angiography (CTA) contrast-enhancement-prediction (CEP) algorithm by retrospectively quantifying the systematic and random errors between the predicted and true enhancements.
Electromagnetic (EM) tracking systems are highly susceptible to field distortion. The interference can cause measurement errors up to a few centimeters in clinical environments, which limits the reliability of these systems. Unless corrected for, this measurement error imperils the success of clinical procedures. It is therefore fundamental to dynamically calibrate EM tracking systems and compensate for measurement error caused by field distorting objects commonly present in clinical environments. We propose to combine a motion model with observations of redundant EM sensors and compensate for field distortions in real-time. We employ a simultaneous localization and mapping (SLAM) technique to accurately estimate the pose of the tracked instrument while creating the field distortion map. We conducted experiments with 6 degrees-of-freedom motions in the presence of field distorting objects in research and clinical environments. We applied our approach to improve the EM tracking accuracy and compared our results to a conventional sensor fusion technique. Using our approach, the maximum tracking error was reduced by 67% for position measurements and by 64% for orientation measurements. Currently, clinical applications of EM trackers are hampered by the adverse distortion effects. Our approach introduces a novel method for dynamic field distortion compensation, independent from pre-operative calibrations or external tracking devices, and enables reliable EM navigation for potential applications.
Ultrasound estimation of fetal weight is a highly influential factor in antenatal management, guiding both the timing and mode of delivery of a pregnancy. Although substantial research has investigated the most accurate ultrasound formula for calculating estimated fetal weight, current evidence indicates significant error levels. The aim of this systematic review was to identify the most accurate method, whilst identifying sources of inaccuracy in order to facilitate recommendations for future practice. Seven studies met the inclusion criteria and 11 different formulae were assessed; ultrasound calculation of fetal weight was most commonly overestimated. The Hadlock A formula produced the most accurate results, with the lowest levels of random error. Methods incorporating just two measurement parameters were inconsistent, producing large random errors across multiple studies. Key sources of inaccuracy included difficulties obtaining accurate fetal measurements in late gestation; the remainder were operator dependent, including lack of experience and insufficient training and audit. The accuracy of ultrasound estimated fetal weight has improved in the last decade, though a lack of consistency remains evident. National implementation of a rigorous audit programme would likely improve accuracy further, and increase the confidence and clinical value of the method.
Real-time quantification of head impacts using wearable sensors is an appealing approach to assess concussion risk. Traditionally, sensors were evaluated for accurately measuring peak resultant skull accelerations and velocities. With growing interest in utilizing model-estimated tissue responses for injury prediction, it is important to evaluate sensor accuracy in estimating tissue response as well. Here, we quantify how sensor kinematic measurement errors can propagate into tissue response errors. Using previous instrumented mouthguard validation datasets, we found that skull kinematic measurement errors in both magnitude and direction lead to errors in tissue response magnitude and distribution. For molar design instrumented mouthguards susceptible to mandible disturbances, 150-400% error in skull kinematic measurements resulted in 100% error in regional peak tissue response. With an improved incisor design mitigating mandible disturbances, errors in skull kinematics were reduced to <50%, and several tissue response errors were reduced to <10%. Applying 30[Formula: see text] rotations to reference kinematic signals to emulate sensor transformation errors yielded below 10% error in regional peak tissue response; however, up to 20% error was observed in peak tissue response for individual finite elements. These findings demonstrate that kinematic resultant errors result in regional peak tissue response errors, while kinematic directionality errors result in tissue response distribution errors. This highlights the need to account for both kinematic magnitude and direction errors and accurately determine transformations between sensors and the skull.
Exposure measurement error in PM2.5 health effects studies: A pooled analysis of eight personal exposure validation studies
- Environmental health : a global access science source
- Published over 5 years ago
Exposure measurement error is a concern in long-term PM2.5 health studies using ambient concentrations as exposures. We assessed error magnitude by estimating calibration coefficients as the association between personal PM2.5 exposures from validation studies and typically available surrogate exposures.
With the increased use of data not originally recorded for research, such as routine care data (or ‘big data’), measurement error is bound to become an increasingly relevant problem in medical research. A common view among medical researchers on the influence of random measurement error (i.e. classical measurement error) is that its presence leads to some degree of systematic underestimation of studied exposure-outcome relations (i.e. attenuation of the effect estimate). For the common situation where the analysis involves at least one exposure and one confounder, we demonstrate that the direction of effect of random measurement error on the estimated exposure-outcome relations can be difficult to anticipate. Using three example studies on cardiovascular risk factors, we illustrate that random measurement error in the exposure and/or confounder can lead to underestimation as well as overestimation of exposure-outcome relations. We therefore advise medical researchers to refrain from making claims about the direction of effect of measurement error in their manuscripts, unless the appropriate inferential tools are used to study or alleviate the impact of measurement error from the analysis.
Interviewer-administered surveys are an important method of collecting population-level epidemiological data, but suffer from declining response rates and increasing costs. Web surveys offer more rapid data collection and lower costs. There are concerns, however, about data quality from web surveys. Previous research has largely focused on selection biases, and few have explored measurement differences. This paper aims to assess the extent to which mode affects the responses given by the same respondents at two points in time, providing information on potential measurement error if web surveys are used in the future.
Precise MEG estimates of neuronal current flow are undermined by uncertain knowledge of the head location with respect to the MEG sensors. This is either due to head movements within the scanning session or systematic errors in co-registration to anatomy. Here we show how such errors can be minimised using subject-specific head-casts produced using 3D printing technology. The casts fit the scalp of the subject internally and the inside of the MEG dewar externally, reducing within session and between session head movement. Systematic errors in matching to MRI coordinate system are also reduced through the use of MRI-visible fiducial markers placed on the same cast. Bootstrap estimates of absolute co-registration error were of the order of 1mm. Estimates of relative co-registration error were <1.5mm between sessions. We corroborated these scalp based estimates by looking at the MEG data recorded over a 6month period. We found the between session sensor variability of the subject's evoked response was of the order of the within session noise, showing no appreciable noise due to between-session movement. Simulations suggest that the between-session sensor level amplitude SNR improved by a factor of 5 over conventional strategies. We show that at this level of coregistration accuracy there is strong evidence for anatomical models based on the individual rather than canonical anatomy; but that this advantage disappears for errors of greater than 5mm. This work paves the way for source reconstruction methods which can exploit very high SNR signals and accurate anatomical models; and also significantly increases the sensitivity of longitudinal studies with MEG.