Rapid On-Site Cytologic Evaluation: A Feasibility Study Using Ancillary Interventional Pulmonary Personnel

<b><i>Background:</i></b> Ancillary health professionals helping in a procedural service is a common practice everywhere. <b><i>Objectives:</i></b> This was a proof-of-concept study to assess feasibility of using ancillary personnel for rapid on-site cytologic evaluation (ROSE) at interventional pulmonary procedures. <b><i>Methods:</i></b> After a training interval, a respiratory therapist (RT) performed ROSE on consecutive interventional pulmonary specimens. Sample sites included lymph nodes, lung, liver, and the left adrenal gland. RT findings were subsequently correlated with blinded cytopathology-performed ROSE and with final histopathology results, with primary foci of adequacy and the presence or absence of malignancy. <b><i>Results:</i></b> Seventy consecutive cases involved 163 separate sites for ROSE analysis. <b><i>Adequacy:</i></b> There was a high level of concordance between RT-performed ROSE (RT-ROSE) and cytopathology ROSE (CYTO-ROSE). They agreed upon the adequacy of 159 specimens. The Cohen’s κ coefficient ± asymptotic standard error (ASE) was 0.74 ± 0.175, with <i>p</i> &#x3c; 0.0001. <b><i>Malignancy:</i></b> RT-ROSE concurred highly with CYTO-ROSE, with agreement on 150 (92%) of the 163 specimens. Cohen’s κ coefficient ± ASE was 0.83 ± 0.045, with <i>p</i> &#x3c; 0.0001. When the comparison was for malignancy by case rather than individual site, Cohen’s κ coefficient ± ASE was 0.68 ± 0.08, with <i>p</i> &#x3c; 0.0001. <b><i>Conclusion:</i></b> This study demonstrates that ancillary personnel supporting an interventional pulmonary service can be trained to perform initial ROSE. Cytopathology can be called after sampling and staining have produced adequate samples. This setup streamlines ROSE evaluation with regard to time and cost.

Quantile Mixture and Probability Mixture Models in a Multi-Model Approach to Flood Frequency Analysis

The classical approach to flood frequency analysis (FFA) may result in significant jumps in the estimates of upper quantiles along with the lengthening series of measurements. Our proposal is a multi-model approach, also called the aggregation technique, which has turned out to be an effective method for the modeling of maximum flows, in large part eliminating the disadvantages of traditional methods. In this article, we present a probability mixture model relying on the aggregation the probabilities of non-exceedance of a constant flow value from the candidate distributions; and we compare it with the previously presented model of quantile mixture, which consists in aggregating the quantiles of the same order from individual models. Here, we defined an asymptotic standard error of design quantiles for both statistical models in two versions: without the bias of quantiles from candidate distributions with respect to aggregated quantiles and with taking it into account. The simulation experiment indicates that the latter version is more accurate and allows for reducing the quantile bias with respect to the unknown population quantile. For the case study, the 0.99 quantiles are determined for both variants of aggregation along with the assessment of its accuracy. The differences between the two proposed aggregation methods are discussed.

Efficient Standard Errors in Item Response Theory Models for Short Tests

In dichotomous item response theory (IRT) framework, the asymptotic standard error (ASE) is the most common statistic to evaluate the precision of various ability estimators. Easy-to-use ASE formulas are readily available; however, the accuracy of some of these formulas was recently questioned and new ASE formulas were derived from a general asymptotic theory framework. Furthermore, exact standard errors were suggested to better evaluate the precision of ability estimators, especially with short tests for which the asymptotic framework is invalid. Unfortunately, the accuracy of exact standard errors was assessed so far only in a very limiting setting. The purpose of this article is to perform a global comparison of exact versus (classical and new formulations of) asymptotic standard errors, for a wide range of usual IRT ability estimators, IRT models, and with short tests. Results indicate that exact standard errors globally outperform the ASE versions in terms of reduced bias and root mean square error, while the new ASE formulas are also globally less biased than their classical counterparts. Further discussion about the usefulness and practical computation of exact standard errors are outlined.

Reconsideration of a simple approach to quantile regression for panel data

Summary This note discusses two errors in the approach proposed in Canay (2011) for constructing a computationally simple two-step estimator in a quantile regression model with quantile-independent fixed effects. Firstly, we show that Canay’s assumption about n/Ts → 0 for some s > 1 is not strong enough and can entail severe bias or even the non-existence of the limiting distribution for the estimator of the vector of coefficients. The condition n/T → 0 appears to be closer to the required set of restrictions. These problems are likely to cause incorrect inference in applied papers with large n/T, but the impact is less in applications with small n/T. In an attempt to improve Canay’s estimator, we propose a simple correction that may reduce the bias. The second error concerns the incorrect asymptotic standard error of the estimator of the constant term. We show that, contrary to Canay’s assumption, the within estimator has an influence function that is not i.i.d. and this affects inference. Moreover, the constant term is unlikely to be estimable at rate $\sqrt{nT}$, so a different estimator may not be available. However, the issue concerning the constant term does not have an effect on slope coefficients. Finally, we give recommendations to practitioners and conduct a meta-review of applied papers that use Canay’s estimator.

In situ evaluation of residual breast tumor and tumor grade using medical hyperspectral imaging (MHSI)

10677 Background: MHSI is a camera-based technique providing spectral data regarding tissue chemistry for each pixel in an image. Over 30% of women suffer local recurrence after resection. Intraoperative assessment of residual tumor & tumor grade would optimize care. Methods: We studied 42 S-D rats w/ breast tumors induced by gavage of DMBA & 15 controls. Tumors were exposed & resected, intentionally leaving ∼1mm residual tumor pieces. Gross examination, histo-pathology & MHSI (total 335) were performed for tumors, tumor beds after partial and total resection & control sites. A visible light MHSI system (HyperMed,Waltham, MA) w/ 40μm resolution & algorithms based on spectral features of the surgical field were developed and implemented for this study. Gross observation at surgery represents truth, as small tumor pieces were left intentionally by the surgeon and recorded. Samples from tumor beds were collected and histopathologically analyzed. When seen, gross tumor was removed from tumor bed by the pathologist. Results: MHSI performed well at identifying tumor. The kappa statistic(κ) for gross vs MHSI (84%) is significantly higher than κ for gross vs histopathology (76%) where for the κ the estimated asymptotic standard error is 3%. MHSI associates more strongly with gross than histopathology does. 81 tissue samples were separated into histologic grade: 0 = normal, 1 = benign tumor, 2 = intraductal Ca, 3 = papillary & cribiform Ca, 4 = papillary & cribiform Ca with invasion &/or comedo Ca. The imaging team (blinded) assigned tumor grade to each MHSI image. Statistical analysis defined 3 histologic groups: 9 normal (grade 0) tissue, 18 benign & intraductal tumors (grades 1–2), 54 advanced tumors (papillary, cribiform with invasion/comedo Ca, grades 3–4). Both histopathology & MHSI identified all 9 normal samples. Of 18 samples in group 2 (benign/intraductal by histopathology), 17 were qualified as benign/intraductal by MHSI (94% sens) & 1 as advanced. Of 54 samples with adv tumors by histopathology, MHSI identified 48 (89% sens) as advanced & 6 as intraductal. Conclusions: MHSI may provide convenient intraoperative, near real-time images with useful data about residual tumor & tumor grade. Human trials are planned. [Table: see text]

Determining Gene­Environment Interaction Using Projected Score Method

The present article proposes a new method for estimating multiplicative interactions between genetic and environmental factor on the risk of a dbease using projected score approach (Waterman and Lindsay {1996a, b)). First we review the existing methods for determining gene­environment interactions through case­control study and case­only study design. Then we discuss the proposed method based on projection theory. We derive an unbiased estimating equation for estimating the parameter of interest and then derive its asymptotic standard error. In addition, we derive a score statistic based on the derived estimating equation. The advantage of using the proposed method are : 1. Gain in efficiency of the parameter estimate and 2. Robustness to the other model parameters and gene­environment independence assumption. We apply the proposed method to a real case­control data on lung cancer. Finally, we compare the existing methods with the proposed method through a small simulation study which shows that the proposed method performs better than the existing methods in terms of efficiency in all situations irrespective of whether the genetic and environmental factor are independent in the population.

Asymptotic Standard Error of Equipercentile Equating

We propose simplified formulas for computing the standard errors of equiper-centile equating for continuous and discrete test scores. The suggested formulas are conceptually simple and easily extended to more complicated equating designs such as chained equipercentile equating, smoothed equipercentile equating, and equating using the frequency estimation method. Results from an empirical study indicate that the derived formulas work reasonably well for samples with moderate sizes (e.g., 1,000 examinees).