Rapid Optical Clearing for High-Throughput Analysis of Tumour Spheroids

Tumour spheroids are fast becoming commonplace in basic cancer research and drug development. Obtaining high-quality data relating to the inner structure of spheroids is important for analysis, yet existing techniques often use equipment that is not commonly available, are expensive, laborious, cause significant size distortion, or are limited to relatively small spheroids. We present a high-throughput method of mounting, clearing, and imaging tumour spheroids that causes minimal size distortion. Spheroids are mounted in an agarose gel to prevent movement, cleared using a solution prepared from commonly available materials, and imaged using confocal microscopy. We find that our method yields high quality two- and three-dimensional images that provide information about the inner structure of spheroids.

Quantifying the data-dredging bias in structural break tests

Structural break tests are often applied as a pre-step to ensure the validity of subsequent statistical analyses. Without any a priori knowledge of the type of breaks to expect, eye-balling the data can indicate changes in some parameter, e.g., the mean. This, however, can distort the result of a structural break test for that parameter, because the data themselves suggested the hypothesis. In this paper, we formalize the eye-balling procedure and theoretically derive the implied size distortion of the structural break test. We also show that eye-balling a stretch of historical data for possible changes in a parameter does not invalidate the subsequent procedure that monitors for structural change in new incoming observations. An empirical application to Bitcoin returns shows that taking into account the data-dredging bias, which is incurred by looking at the data, can lead to different test decisions.

A-43 Factorial Relationships Among Copy and Memory Trials of the Benton Visual Retention Test

Objective We investigated the factorial relationships among categorical error groups on the Benton Visual Retention Test (BVRT) copy and memory trials. Methods A sample of 523 ambulatory American Veteran patients who presented for clinical evaluation with a wide variety of mixed neuropsychiatric diagnoses and general medical diagnoses were studied. There were no demographic or diagnostic exclusion criteria. Results Frequency summary scores for the six types of BVRT errors (omission, misplacement, size, distortion, perseveration, and rotation errors) were factored jointly by means of principal component analysis. Omission, misplacement, and size errors grouped factorially across copy and memory domains by error type. Results showed the factorial relationships are primarily defined by the type of error. Omission, size, and misplacement errors were grouped together regardless of whether they occurred on copy or memory trials. Rotation, distortion, and perseveration errors were factorially grouped on both the copy and memory trials, but the groupings of these similar error groups formed independent factors for the copy and memory trials. The copy error factor explained the most variance and the memory error factor o explained the least variance. Conclusions Omission, size, and misplacement errors on the BVRT copy and memory trials appear to be due to similar encoding process errors. Distortion, rotation, and perseveration errors on the BVRT copy and memory trials are related to each other within each trial type but different cognitive processes account for errors of this kind on the copy and memory trials.

On the Size Distortion of a Test for Equality between the ATE and FE Estimands

This note investigates the numerical performance of an existing asymptotic test for the null hypothesis of equality between the average treatment effect (ATE) and the group fixed-effect (FE) estimands based on the standardized difference between ATE and FE estimators. It shows that this test has a size distortion. This distortion has implications to empirical economic research. It can lead to erroneously confirm the relevance of heterogeneous responses to policy interventions.

Inference for Clustered Data

In this article, we introduce clusteff, a community-contributed command for checking the severity of cluster heterogeneity in cluster–robust analyses. Cluster heterogeneity can cause a size distortion leading to underrejection of the null hypothesis. Carter, Schnepel, and Steigerwald (2017, Review of Economics and Statistics 99: 698–709) develop the effective number of clusters to reflect a reduction in the degrees of freedom, thereby mirroring the distortion caused by assuming homogeneous clusters. clusteff generates the effective number of clusters. We provide a decision tree for cluster–robust analysis, demonstrate the use of clusteff, and recommend methods to minimize the size distortion.