A wild bootstrap algorithm for propensity score matching estimators

We introduce a wild bootstrap algorithm for the approximation of the sampling distribution of pair or one-to-many propensity score matching estimators. Unlike the conventional iid bootstrap, the proposed wild bootstrap approach does not construct bootstrap samples by randomly resampling from the observations with uniform weights. Instead, it fixes the covariates and constructs the bootstrap approximation by perturbing the martingale representation for matching estimators. We also conduct a simulation study in which the suggested wild bootstrap performs well even when the sample size is relatively small. Finally, we provide an empirical illustration by analyzing an information intervention in rural development programs

The finite sample performance of inference methods for propensity score matching and weighting estimators

This paper investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyse both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation design, which is based on large scale labor market data from Germany and varies w.r.t. treatment selectivity, effect heterogeneity, the share of treated, and the sample size. The results suggest that in general, the bootstrap procedures dominate the asymptotic ones in terms of size and power for both matching and weighting estimators. Furthermore, the results are qualitatively quite robust across the various simulation features

Assessing Uncertainty in Volunteered Geographic Information for Emergency Response

This research project examines data produced by volunteers through the Ushahidi web platform in response to the earthquake that struck Haiti in January 2010. Volunteers translated messages submitted by victims in Haiti, categorized each message based on its content, and georeferenced each message on a dynamic web based map. When categorizing the data, volunteers were able to assign up to 8 main and 42 subcategories to each message. Initial inspection of the attribute data produced by the volunteers indicated a strong discrepancy between the contents of the messages submitted by the victims and the corresponding attributes assigned to those messages by the volunteers. By comparing the attributes of the data originally produced by the volunteers to data that I re-categorized, I was able to examine the degree of inconsistency among the attribute data produced by the volunteers. I found that only 26.59% of the messages submitted by the victims were consistently categorized compared to the data set that I re-categorized. However, when aggregating the subcategories up to their appropriate main category, I found 49.88% of messages were consistently categorized indicating that approximately half of the messages were conveying the main idea or ideas of the victims’ messages. These numbers are significantly lower than the estimate of 64% correct categorization produced by an independent review of the Ushahidi platform. Despite these low indicators of consistent categorization, the volunteer response to the Haitian earthquake represents a paradigm shift in emergency response and victim empowerment that has been repeated in numerous natural and man-made disasters around the world.GeographyMastersUniversity of New Mexico. Dept. of GeographyFreundschuh, ScottBenedict, KarlLippitt, Christophe

The finite sample performance of inference methods for propensity score matching and weighting estimators

This paper investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyse both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation designs, which are based on German register data and U.S. survey data. We vary the design w.r.t. treatment selectivity, effect heterogeneity, share of treated, and sample size. The results suggest that in general, theoretically justified bootstrap procedures (i.e. wild bootstrapping for pair matching and standard bootstrapping for ‘smoother’ treatment effect estimators) dominate the asymptotic approximations in terms of coverage rates for both matching and weighting estimators. Most findings are robust across simulation designs and estimators

The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators

This article investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyze both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation designs, which are based on German register data and U.S. survey data. We vary the design w.r.t. treatment selectivity, effect heterogeneity, share of treated, and sample size. The results suggest that in general, theoretically justified bootstrap procedures (i.e., wild bootstrapping for pair matching and standard bootstrapping for “smoother” treatment effect estimators) dominate the asymptotic approximations in terms of coverage rates for both matching and weighting estimators. Most findings are robust across simulation designs and estimators

Sonographic and Magnetic Resonance Characteristics of Gynecological Sarcoma

Introduction: Gynecological sarcomas are rare malignant tumors with an incidence of 1.5–3/100,000 and are 3–9% of all malignant uterine tumors. The preoperative differentiation between sarcoma and myoma becomes increasingly important with the development of minimally invasive treatments for myomas, as this means undertreatment for sarcoma. There are currently no reliable laboratory tests or imaging-characteristics to detect sarcomas. The objective of this article is to gain an overview of sarcoma US/MRI characteristics and assess their accuracy for preoperative diagnosis. Methods: A systematic literature review was performed and 12 studies on ultrasound and 21 studies on MRI were included. Results: For the ultrasound, these key features were gathered: solid tumor > 8 cm, unsharp borders, heterogeneous echogenicity, no acoustic shadowing, rich vascularization, and cystic changes within. For the MRI, these key features were gathered: irregular borders; heterogeneous; high signal on T2WI intensity; and hemorrhagic and necrotic changes, with central non-enhancement, hyperintensity on DWI, and low values for ADC. Conclusions: These features are supported by the current literature. In retrospective analyses, the ultrasound did not show a sufficient accuracy for diagnosing sarcoma preoperatively and could also not differentiate between the different subtypes. The MRI showed mixed results: various studies achieved high sensitivities in their analysis, when combining multiple characteristics. Overall, these findings need further verification in prospective studies with larger study populations

The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators

This paper investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyse both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation designs, which are based on German register data and U.S. survey data. We vary the design w.r.t. treatment selectivity, effect heterogeneity, share of treated, and sample size. The results suggest that in general, theoretically justified bootstrap procedures (i.e. wild bootstrapping for pair matching and standard bootstrapping for ‘smoother’ treatment effect estimators) dominate the asymptotic approximations in terms of coverage rates for both matching and weighting estimators. Most findings are robust across simulation designs and estimators

Broad Scope Aminocyclization of Enynes with Cationic JohnPhos–Gold(I) Complex as the Catalyst

A practical aminocyclization of 1,6-enynes with a wide variety of substituted anilines, including <i>N</i>-alkyl anilines, has been achived by using cationic [JohnPhosAu­(MeCN)]­SbF₆ as a general purpose catalyst. The resulting adducts can be easily converted into polycyclic compounds by palladium- and gold-catalyzed reactions

Development of a Community-Driven Mosquito Surveillance Program for Vectors of La Crosse Virus to Educate, Inform, and Empower a Community

The fields of entomology, geospatial science, and science communication are understaffed in many areas, resulting in poor community awareness and heightened risks of vector-borne diseases. This is especially true in East Tennessee, where La Crosse encephalitis (LACE) causes pediatric illness each year. In response to these problems, we created a community engagement program that includes a yearlong academy for secondary STEM educators in the 6–12 grade classroom. The objectives of this program were to support inquiry-driven classroom learning to foster student interest in STEM fields, produce community-driven mosquito surveillance, and enhance community awareness of LACE. We trained educators in medical entomology, geospatial science, and science communication, and they incorporated those skills into lesson plans for a mosquito oviposition experiment that tested hypotheses developed in the classroom. Here, we share results from the first two years of the MEGA:BITESS academy, tailored for our community by having students ask questions directly related to Aedes mosquito oviposition biology and La Crosse encephalitis. In year one, we recruited 17 educators to participate in the project, and 15 of those educators returned in year two. All participating educators completed the academy, conducted the oviposition experiment, and informed over 400 students about a variety of careers and disciplines for their students. Here, we present a community-based program that helps to address the problems associated with long-term mosquito surveillance, health and science education and communication, career opportunities, and the community needs of Appalachia, as well as the initial data on the effectiveness of two years of an educator-targeted professional-development program