RADDLE MARKINGS AND PRESENCE OF SPERMATOZOA IN VAGINAL SMEARS AS PREDICTORS OF EWES LAMBING TO MATINGS AT THE SYNCHRONIZED ESTRUS

1984 ◽  
Vol 64 (3) ◽  
pp. 563-568
Author(s):  
J. N. B. SHRESTHA ◽  
P. S. FISER ◽  
L. AINSWORTH
Keyword(s):  
Key Words ◽  
Substantial Improvement ◽  
Vaginal Smears ◽  
Type 1 Error ◽  
Ewe Lambs ◽  
Type 2 Error ◽  
Sexually Mature

A comparison was made of raddle markings and presence of spermatozoa in vaginal smears as a means of predicting which ewes will subsequently lamb. Data were obtained from 308 ewe lambs (6–7.5 mo old) and 464 sexually mature ewes (26–28 mo old) mated to experienced rams at the synchronized estrus induced by treatment with fluorogestone-acetate-impregnated intravaginal sponges and i.m. injection of pregnant mares' serum gonadotrophin. Ewes raddled during 12 h intervals from 0 to 72 h after sponge removal were recorded. Vaginal smears taken 48 and 72 h after sponge removal were examined microscopically for presence of spermatozoa. Of the ewes raddled, 41% of the ewe lambs and 86% of mature ewes, lambed. Corresponding results based on presence of spermatozoa in combined vaginal smears data were 51 and 89%. In ewe lambs, 96% of the ewes with no raddle markings and 100% of the ewes with no spermatozoa in vaginal smears were not pregnant. Corresponding results for mature ewes were 56 and 95%, respectively. This study shows that the presence of spermatozoa in vaginal smears 48 and 72 h after induction of a synchronized estrus improves the accuracy of predicting ewes that subsequently lamb to matings at the synchronized estrus over that based on ewes raddled. This improvement results from a reduction in the number of ewe lambs predicted as bred that failed to lamb (Type 1 error), and a substantial improvement in the percentage of mature ewes predicted as non-pregnant that subsequently lambed (Type 2 error). Key words: Raddle markings, spermatozoa, vaginal smears, synchronized estrus, lambing, ewes

Three essays on the commercialization of university discoveries

2020 ◽  
Author(s):  
◽  
Hao Cheng
Keyword(s):  
Selection Process ◽  
Empirical Studies ◽  
Project Selection ◽  
University Of Missouri ◽  
Type 1 Error ◽  
Ex Post ◽  
The University ◽  
Type 2 Error

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Universities commercialize their discoveries at an increasing pace in order to maximize their economic impact and generate additional funding for research. They form technology transfer offices (TTOs) to evaluate the commercial value of university inventions and choose the most promising ones to patent and commercialize. Uncertainties and asymmetric information in project selection make the TTO choices difficult and can cause both type 1 error (forgo valuable discoveries) and type 2 error (select low-value discoveries). In this dissertation, I examine the TTO's project selection process and the factors that influence the choice of academic inventions for patenting and commercialization, the type 1 error committed, and the final licensing outcome. The dissertation contains three essays. In the first essay, I analyze project selection under uncertainty when both the quality of the proposed project and the motives of the applicant are uncertain. Some inventors may have an incentive to disguise the true quality and commercial value of their discoveries in order to conform to organizational expectations of disclosure while retaining rights to potentially pursue commercialization of their discoveries outside the organization's boundaries for their own benefit. Inventors may equally, ex post, lose interest to the commercialization of their invention due to competing job demands. I develop a model to examine the decision process of a university TTO responsible for the commercialization of academic inventions under such circumstances. The model describes the conditions that prompt Type 1 and Type 2 errors and allows for inferences for minimizing each. Little is known about the factors that make project selection effective or the opposite and there has been limited empirical analysis in this area. The few empirical studies that are available, examine the sources of type 2 error but there is no empirical work that analyzes type 1 error and the contributing factors. Research on type 1 error encounters two main difficulties. First, it is difficult to ascertain the decision process and second, it is challenging to approximate the counterfactual. Using data from the TTO of the University of Missouri, in the second essay I study the factors that influence the project selection process of the TTO in and the ex post type 1 error realized. In most cases, universities pursue commercialization of their inventions through licensing. There have been a few empirical studies that have researched the factors that affect licensing and their relative importance. In the third essay, I examine the characteristics of university inventions that are licensed using almost 10 years of data on several hundred of inventions, their characteristics, and the licensing status.

scholarly journals Bias, precision and timeliness of historical (background) rate comparison methods for vaccine safety monitoring: an empirical multi-database analysis

2021 ◽  
Author(s):  
Xintong Li ◽  
Lana YH Lai ◽  
Anna Ostropolets ◽  
Faaizah Arshad ◽  
Eng Hooi Tan ◽  
...  
Keyword(s):  
Vaccine Safety ◽  
Negative Control ◽  
Historical Background ◽  
Background Rate ◽  
Type 1 Error ◽  
The Cost ◽  
Type 2 Error ◽  
Safety Signals

Using real-world data and past vaccination data, we conducted a large-scale experiment to quantify bias, precision and timeliness of different study designs to estimate historical background (expected) compared to post-vaccination (observed) rates of safety events for several vaccines. We used negative (not causally related) and positive control outcomes. The latter were synthetically generated true safety signals with incident rate ratios ranging from 1.5 to 4. Observed vs. expected analysis using within-database historical background rates is a sensitive but unspecific method for the identification of potential vaccine safety signals. Despite good discrimination, most analyses showed a tendency to overestimate risks, with 20%-100% type 1 error, but low (0% to 20%) type 2 error in the large databases included in our study. Efforts to improve the comparability of background and post-vaccine rates, including age-sex adjustment and anchoring background rates around a visit, reduced type 1 error and improved precision but residual systematic error persisted. Additionally, empirical calibration dramatically reduced type 1 to nominal but came at the cost of increasing type 2 error. Our study found that within-database background rate comparison is a sensitive but unspecific method to identify vaccine safety signals. The method is positively biased, with low (<=20%) type 2 error, and 20% to 100% of negative control outcomes were incorrectly identified as safety signals due to type 1 error. Age-sex adjustment and anchoring background rate estimates around a healthcare visit are useful strategies to reduce false positives, with little impact on type 2 error. Sufficient sensitivity was reached for the identification of safety signals by month 1-2 for vaccines with quick uptake (e.g., seasonal influenza), but much later (up to month 9) for vaccines with slower uptake (e.g., varicella-zoster or papillomavirus). Finally, we reported that empirical calibration using negative control outcomes reduces type 1 error to nominal at the cost of increasing type 2 error.

Profile Tolerances Modeling: A Unified Framework for Representing Geometric Variations for Line Profiles

2015 ◽  
Author(s):  
Jiao Chen ◽  
Yuan Li ◽  
Jianfeng Yu ◽  
Wenbin Tang
Keyword(s):  
Line Profile ◽  
Assessment Method ◽  
Maximum Deviation ◽  
Unified Framework ◽  
Tolerance Zone ◽  
Type 1 Error ◽  
Split Point ◽  
Type 2 Error

Tolerance modeling is the most basic issue in Computer Aided Tolerancing (CAT). It will negatively influence the performance of subsequent activities such as tolerance analysis to a great extent if the resultant model cannot accurately represent variations in tolerance zone. According to ASME Y14.5M Standard [1], there is a class of profile tolerances for lines and surfaces which should also be interpreted correctly. Aim at this class of tolerances, the paper proposes a unified framework called DOFAS for representing them which composed of three parts: a basic DOF (Degrees of Freedom) model for interpreting geometric variations for profiles, an assessment method for filtering out and rejecting those profiles cannot be accurately represented and a split algorithm for splitting rejected profiles into sub profiles to make their variations interpretable. The scope of discussion in this paper is restricted to the line profiles; we will focus on the surface profiles in forthcoming papers. From the DOF model, two types of errors result from the rotations of the features are identified and formulized. One type of the errors is the result of the misalignment between profile boundary and tolerance zone boundary (noted as type 1); and if the feature itself exceeds the range of tolerance zone the other type of errors will form (noted as type 2). Specifically, it is required that the boundary points of the line profile should align with the corresponding boundary lines of the tolerance zone and an arbitrary point of the line profile should lie within the tolerance zone when line profile rotates in the tolerance zone. To make DOF model as accurate as possible, an assessment method and a split algorithm are developed to evaluate and eliminate these two type errors. It is clear that not all the line features carry the two type errors; as such the assessment method is used as a filter for checking and reserving such features that are consistent with the error conditions. In general, feature with simple geometry is error-free and selected by the filter whereas feature with complex geometry is rejected. According to the two type errors, two sub-procedures of the assessment process are introduced. The first one mathematically is a scheme of solving the maximum deviation of rotation trajectories of profile boundary, so as to neglect the type 1 error if it approaches to zero. The other one is to solve the maximum deviation of trajectories of all points of the feature: type 2 error can be ignored when the retrieved maximum deviation is not greater than prescribed threshold, so that the feature will always stay within the tolerance zone. For such features rejected by the filter which are inconsistent with the error conditions, the split algorithm, which is spread into the three cases of occurrence of type 1 error, occurrence of type 2 error and concurrence of two type errors, is developed to ease their errors. By utilizing and analyzing the geometric and kinematic properties of the feature, the split point is recognized and obtained accordingly. Two sub-features are retrieved from the split point and then substituted into the DOFAS framework recursively until all split features can be represented in desired resolution. The split algorithm is efficient and self-adapting lies in the fact that the rules applied can ensure high convergence rate and expected results. Finally, the implementation with two examples indicates that the DOFAS framework is capable of representing profile tolerances with enhanced accuracy thus supports the feasibility of the proposed approach.

Statistical Power in Psychiatric Research

1986 ◽  
Vol 20 (2) ◽  
pp. 189-200 ◽  
Author(s):  
Kevin D. Bird ◽  
Wayne Hall
Keyword(s):  
Sample Size ◽  
Error Rate ◽  
Statistical Power ◽  
Error Rates ◽  
Psychiatric Research ◽  
Type 1 Error ◽  
Type 2 Error ◽  
Power Analyses

Statistical power is neglected in much psychiatric research, with the consequence that many studies do not provide a reasonable chance of detecting differences between groups if they exist in the population. This paper attempts to improve current practice by providing an introduction to the essential quantities required for performing a power analysis (sample size, effect size, type 1 and type 2 error rates). We provide simplified tables for estimating the sample size required to detect a specified size of effect with a type 1 error rate of α and a type 2 error rate of β, and for estimating the power provided by a given sample size for detecting a specified size of effect with a type 1 error rate of α. We show how to modify these tables to perform power analyses for multiple comparisons in univariate and some multivariate designs. Power analyses for each of these types of design are illustrated by examples.

scholarly journals Bias, Precision and Timeliness of Historical (Background) Rate Comparison Methods for Vaccine Safety Monitoring: An Empirical Multi-Database Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Xintong Li ◽  
Lana YH Lai ◽  
Anna Ostropolets ◽  
Faaizah Arshad ◽  
Eng Hooi Tan ◽  
...  
Keyword(s):  
Large Scale ◽  
Vaccine Safety ◽  
Historical Background ◽  
Real World Data ◽  
Background Rate ◽  
Type 1 Error ◽  
Type 2 Error ◽  
Safety Signals

Using real-world data and past vaccination data, we conducted a large-scale experiment to quantify bias, precision and timeliness of different study designs to estimate historical background (expected) compared to post-vaccination (observed) rates of safety events for several vaccines. We used negative (not causally related) and positive control outcomes. The latter were synthetically generated true safety signals with incident rate ratios ranging from 1.5 to 4. Observed vs. expected analysis using within-database historical background rates is a sensitive but unspecific method for the identification of potential vaccine safety signals. Despite good discrimination, most analyses showed a tendency to overestimate risks, with 20%-100% type 1 error, but low (0% to 20%) type 2 error in the large databases included in our study. Efforts to improve the comparability of background and post-vaccine rates, including age-sex adjustment and anchoring background rates around a visit, reduced type 1 error and improved precision but residual systematic error persisted. Additionally, empirical calibration dramatically reduced type 1 to nominal but came at the cost of increasing type 2 error.

scholarly journals Justify Your Alpha: A Primer on Two Practical Approaches

2021 ◽  
Author(s):  
Maximilian Maier ◽  
Daniel Lakens
Keyword(s):  
Statistical Power ◽  
Alternative Hypothesis ◽  
R Package ◽  
Error Rates ◽  
Shiny App ◽  
Alpha Level ◽  
Type 2 Error ◽  
Lindley’S Paradox

The default use of an alpha level of 0.05 is suboptimal for two reasons. First, decisions based on data can be made more efficiently by choosing an alpha level that minimizes the combined Type 1 and Type 2 error rate. Second, it is possible that in studies with very high statistical power p-values lower than the alpha level can be more likely when the null hypothesis is true, than when the alternative hypothesis is true (i.e., Lindley's paradox). This manuscript explains two approaches that can be used to justify a better choice of an alpha level than relying on the default threshold of 0.05. The first approach is based on the idea to either minimize or balance Type 1 and Type 2 error rates. The second approach lowers the alpha level as a function of the sample size to prevent Lindley's paradox. An R package and Shiny app are provided to perform the required calculations. Both approaches have their limitations (e.g., the challenge of specifying relative costs and priors), but can offer an improvement to current practices, especially when sample sizes are large. The use of alpha levels that have a better justification should improve statistical inferences and can increase the efficiency and informativeness of scientific research.

Type 1 and Type 2 error, power and sample size

2012 ◽  
pp. 105-114
Author(s):  
Steve McKillup ◽  
Melinda Darby Dyar
Keyword(s):  
Sample Size ◽  
Type 2 Error

scholarly journals Risk of infection in diabetes before and during COVID-19: a review

2021 ◽  
Vol 24 (2) ◽  
pp. 116
Author(s):  
Adinolfi, V.
Keyword(s):  
High Risk ◽  
Glycemic Control ◽  
Key Words ◽  
Diabetic Patients ◽  
Risk Of Infection ◽  
Type 2 Diabetic Patients ◽  
Almost All ◽  
Type 2 Diabetic

The risk of almost all types of infection is increased both in type 1 and type 2 diabetic patients. Notably, infections are one of the most important cause of hospitalization and death in diabetes and the risk is increased in patients with poor glycemic control. SARS-CoV2 pandemic stressed this association, since diabetic patients showed a particularly high risk of worse outcomes. This review analyzes the association of diabetes and different infections, underlining how the risk of worse outcomes was present both before and during SARS-CoV2 pandemic. KEY WORDS COVID-19; infection; diabetes; immunology; susceptibility.

scholarly journals Simplifying Surveillance Sampling: Can Environmental Surveillance Replace Perianal Screening?

2020 ◽  
Vol 41 (S1) ◽  
pp. s15-s16
Author(s):  
Kyle Gontjes ◽  
Kristen Gibson ◽  
Bonnie Lansing ◽  
Marco Cassone ◽  
Lona Mody
Keyword(s):  
Selection Bias ◽  
Active Surveillance ◽  
Patient Characteristics ◽  
Multidrug Resistant ◽  
Resistant Organism ◽  
Environmental Surveillance ◽  
Study Visit ◽  
Type 2 Error

Background: Although active surveillance for multidrug-resistant organism (MDRO) colonization permits timely intervention, obtaining cultures can be time-consuming, costly, and uncomfortable for patients. We evaluated clinical differences between patients with and without attainable perianal cultures, and we sought to determine whether environmental surveillance could replace perianal screening. Methods: We collected active surveillance cultures from patient hands, nares, groin, and perianal area upon enrollment, at day 14, and monthly thereafter in 6 Michigan nursing homes. Methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), and resistant gram-negative bacilli (RGNB) were identified using standard methods. Patient characteristics were collected by trained research professionals. This substudy focused on visits during which all body sites were sampled. To determine the contribution of perianal screening to MDRO detection, site of colonization was categorized into 2 groups: perianal and non-perianal. We evaluated the utility of multisite surveillance (eg, type 1 and type 2 error) using nonperianal sites and environment surveillance. To evaluate characteristics associated with the acquisition of perianal cultures (eg, selection bias), we compared clinical characteristics, overall patient colonization, and room environment contamination of patients in whom all body sites were sampled during a study visit (533 patients; 1,026 visits) to patients with all body sites except the perianal culture sampled during a study visit (108 patients; 168 visits). Results: Of 651 patients, 533 met the inclusion criteria; average age was 74.5 years, 42.6% were male, and 60.8% were white. Of 1,026 eligible visits, 620 visits detected MDRO colonized patients; 155 MRSA, 363 VRE, and 386 RGNB (Table 1). If perianal cultures were not collected, nonperianal surveillance misses 7.7%, 41.3%, and 45.1% of MRSA, VRE, and RGNB colonized visits, respectively. The addition of environmental surveillance to non-perianal screening detected 95.5%, 82.9%, and 67.9% of MRSA, VRE, and RGNB colonized visits, respectively. The specificity of environmental screening was 85.3%, 72.7%, and 73.4% for MRSA, VRE, and RGNB, respectively. Patients without attainable perianal cultures had significantly more comorbidities, worse functional status, shorter length of stay, and higher baseline presence of wounds than patients with attainable perianal cultures; introducing potential selection bias to surveillance efforts (Table 2). No significant differences in overall patient colonization and room contamination were noted between patients with and without attainable perianal cultures. Conclusion: Perianal screening is important for the detection of VRE and RGNB colonization. Infection prevention must be cognizant of the tradeoff between reducing type 2 error and the selection bias that occurs with required attainment of perianal cultures. In the absence of perianal cultures, environmental surveillance improves MDRO detection while introducing type 1 error.Funding: NoneDisclosures: None

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