Duplex Ultrasonography to Predict Internal Carotid Artery Stenoses Exceeding 50% and 70% as Defined by NASCET: The Need for Multiple Criteria

Carotid duplex scanning is being used more frequently as the sole preoperative diagnostic imaging modality for patients considered candidates for carotid endarterectomy. The North American Symptomatic Carotid Endarterectomy Trial (NASCET) has demonstrated the benefit of surgical treatment in patients with carotid stenoses exceeding 70%. The purpose of this study was to determine duplex criteria that accurately predict carotid stenoses exceeding 50% and 70% as defined by NASCET arteriographic criteria. One hundred forty-one patients (264 carotid arteries) considered surgical candidates were prospectively studied over a 2-year period by use of both duplex scanning and digital subtraction cerebral arteriography. Carotid artery stenosis was determined by a single radiologist using NASCET arteriographic criteria. Peak systolic velocity (PSV) and enddiastolic velocity (EDV) were measured in the internal carotid (ICA) and common carotid (CCA) arteries by use of duplex scanning. ICA/CCA velocity ratios were calculated for PSV and EDV. Sensitivity, specificity, positive and negative predictive values, and accuracy were calculated. PSVICA/CCA provided the highest sensitivity, and EDVICA the highest specificity in this study. Arteriographic stenoses exceeding 50% and 70% were reliably predicted with use of these duplex criteria. It is concluded that duplex criteria can predict carotid stenoses exceeding 50% and 70% as defined by NASCET arteriographic criteria. These criteria should be independently validated by other vascular laboratories.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68440/2/10.1177_153857449903300508.pd

Förbättrade skattningar av ATT från longitudinellt data

Our goal is to improve the estimation of the average treatment effect among treated (ATT) from longitudinal data. When the ATT is estimated at one time point (or separately at each), outcome-regression (OR), inverse probability weighting and doubly robust estimators can be used. These methods involve estimating the relationships that the covariates have with the outcome and/or propensity score, in different regression models. Assuming these relationships do not vary drastically between close-by time points, we can improve estimation by also using information from neighboring points. We use local regression to smooth the coefficient estimates in the outcome- and propensity score-model over time. Our simulation study shows that when the true coefficients are constant over time, the performance of all estimators is improved by smoothing. Especially in terms of precision, the improvement is greater the more the coefficient estimates are smoothed. We also evaluate the OR-estimator in more complex scenarios where the true regression coefficients vary linearly and non-linearly over time. Here we find that larger degrees of smoothing have a negative effect on the estimators’ accuracy, but continue to improve their precision. This is especially prominent in the non-linear scenario.

Förbättrade skattningar av ATT från longitudinellt data

Our goal is to improve the estimation of the average treatment effect among treated (ATT) from longitudinal data. When the ATT is estimated at one time point (or separately at each), outcome-regression (OR), inverse probability weighting and doubly robust estimators can be used. These methods involve estimating the relationships that the covariates have with the outcome and/or propensity score, in different regression models. Assuming these relationships do not vary drastically between close-by time points, we can improve estimation by also using information from neighboring points. We use local regression to smooth the coefficient estimates in the outcome- and propensity score-model over time. Our simulation study shows that when the true coefficients are constant over time, the performance of all estimators is improved by smoothing. Especially in terms of precision, the improvement is greater the more the coefficient estimates are smoothed. We also evaluate the OR-estimator in more complex scenarios where the true regression coefficients vary linearly and non-linearly over time. Here we find that larger degrees of smoothing have a negative effect on the estimators’ accuracy, but continue to improve their precision. This is especially prominent in the non-linear scenario.

Gender differences in job autonomy in Sweden and the United States

This paper examines gender differences in job autonomy in the United States and Sweden. It analyzes data from the 2005 Work Orientations III module of the International Social Survey Program, using multiple linear regression analysis. Women‟s concentration in the public sector, as a form of occupational segregation, as well as gender differences in unionization are assessed as possible explanations. Since these two factors vary greatly between the US and Sweden, these two cases are used to test the suitability of the explanatory approaches. While there are no gender differences in job autonomy in the US, Swedish women experience significantly lower job autonomy than Swedish men. These gender differences are primarily due to the fact that women in Sweden are concentrated in public sector employment, which offers lower autonomy. This supports occupational segregation as an explanation for gender differences in job autonomy. Meanwhile, the hypothesis that women‟s higher degree of unionization in Sweden would lead to a smaller gender gap in autonomy does not receive support from the data

Gender differences in job autonomy in Sweden and the United States

This paper examines gender differences in job autonomy in the United States and Sweden. It analyzes data from the 2005 Work Orientations III module of the International Social Survey Program, using multiple linear regression analysis. Women‟s concentration in the public sector, as a form of occupational segregation, as well as gender differences in unionization are assessed as possible explanations. Since these two factors vary greatly between the US and Sweden, these two cases are used to test the suitability of the explanatory approaches. While there are no gender differences in job autonomy in the US, Swedish women experience significantly lower job autonomy than Swedish men. These gender differences are primarily due to the fact that women in Sweden are concentrated in public sector employment, which offers lower autonomy. This supports occupational segregation as an explanation for gender differences in job autonomy. Meanwhile, the hypothesis that women‟s higher degree of unionization in Sweden would lead to a smaller gender gap in autonomy does not receive support from the data

Förbättrade skattningar av ATT från longitudinellt data

Our goal is to improve the estimation of the average treatment effect among treated (ATT) from longitudinal data. When the ATT is estimated at one time point (or separately at each), outcome-regression (OR), inverse probability weighting and doubly robust estimators can be used. These methods involve estimating the relationships that the covariates have with the outcome and/or propensity score, in different regression models. Assuming these relationships do not vary drastically between close-by time points, we can improve estimation by also using information from neighboring points. We use local regression to smooth the coefficient estimates in the outcome- and propensity score-model over time. Our simulation study shows that when the true coefficients are constant over time, the performance of all estimators is improved by smoothing. Especially in terms of precision, the improvement is greater the more the coefficient estimates are smoothed. We also evaluate the OR-estimator in more complex scenarios where the true regression coefficients vary linearly and non-linearly over time. Here we find that larger degrees of smoothing have a negative effect on the estimators’ accuracy, but continue to improve their precision. This is especially prominent in the non-linear scenario.

Spelar det roll HUR vi gör fel? : Betydelsen av studiedesign och felspecificering av modeller när man utvärderar prestationen av dubbelt robusta estimatorer

This thesis concerns doubly robust (DR) estimation in missing data contexts. Previous research is not unanimous as to which estimators perform best and in which situations DR is to be preferred over other estimators. We observe that the conditions surrounding comparisons of DR- and other estimators vary between dierent previous studies. We therefore focus on the effects of three distinct aspects of study design on the performance of one DR-estimator in comparison to outcome regression (OR). These aspects are sample size, the way in which models are misspecified, and the degree of association between the covariates and propensities. We find that while there are no drastic eects of the type of model misspecication, all three aspects do affect how DR compares to OR. The results can be used to better understand the divergent conclusions of previous research

Security and Encryption Optical Systems Based on a Correlator with Significant Output Images

this paper we propose an optical security system that is based on existing optical correlators but has some additional benefits over those of the present generatio

Regional differences in initial labour market conditions and dynamics in lifetime income trajectories

We use longitudinal register data from Sweden to study patterns and dynamics in lifetime income trajectories. We examine divergences in these income trajectories by local economic conditions at labour market entry, in combination with other factors such as gender, education level and socio-economic background. We cannot assume that these relationships are constant over the course of individuals’ working lives. Therefore, we use methods from functional data analysis, allowing for a time-varying relationship between income and the explanatory variables. Our results show a large degree of heterogeneity in how lifetime income trajectories develop for different subgroups. We find that, for men, entering the labour market in an urban area is associated with higher cumulative lifetime income, especially later in life. The exception is men with only primary education, for whom those starting their working lives in a large city have lower incomes on average. This divergence increases in size over time. Women who enter into a large urban labour market receive higher lifetime income at all education levels. This relationship is strongest for women with primary education but decreases in strength over time for these women

Causal inference with a functional outcome

This article presents methods to study the causal effect of a binary treatment on a functional outcome with observational data. We define a Functional Average Treatment Effect (FATE) and develop an outcome regression estimator. We show how to obtain valid inference on the FATE using simultaneous confidence bands, which cover the FATE with a given probability over the entire domain. Simulation experiments illustrate how the simultaneous confidence bands take the multiple comparison problem into account. Finally, we use the methods to infer the effect of early adult location on subsequent income development for one Swedish birth cohort