In this study, I examine the employment effects on average earnings and duration to work during a 45 month period, after receiving social assistance (SA) in October 2019, compared to receiving unemployment insurance (UI) the same month. A distinction is made between two treatment groups; receiving SA in addition to UI (treatment I) and receiving SA (treatment II). Using propensity score matching (PSM), I estimate the average treatment effects on the treated on earnings, as well as duration to work by using the Kaplan-Meier survival estimator with the matched observations. I use newly released Swedish administrative micro level data of individuals’ monthly labour market status (BAS) between 2020-2023, from Statistics Sweden. During this thesis process, where Statistics Sweden allowed me data access, I was allowed an additional year of data, for 2019. Results showed that the inflow of SA recipients in October 2019, on average had 25.5 percent lower earnings between November 2019-July 2023, compared to the inflow of UI recipients the same month. In addition, the inflow of SA recipients in October 2019, on average spent 4 months longer in unemployment, compared to those receiving UI the same month. However, results were insignificant when comparing effects between the inflow of those receiving SA in addition to UI in October 2019 with the inflow of UI recipients the same month. Results for this group were insignificant for both employment outcomes; average earnings and duration to work.

When categorical data are misplaced into the wrong category, we say the data is affected by misclassification. This is common for data collection. It is well-known that naive estimators of category probabilities and coefficients for regression that ignore misclassification can be biased. In this dissertation, we develop methods to provide improved estimators and confidence intervals for a proportion when only a misclassified proxy is observed, and provide improved estimators and confidence intervals for regression coefficients when only misclassified covariates are observed. Following the introduction and literature review, we develop two estimators for a proportion , one which reduces the bias, and one with smaller mean square error. Then we will give two methods to find a confidence interval for a proportion, one using optimization techniques, and the other one using Fieller's method. After that, we will focus on developing methods to find corrected estimators for coefficients of regression with misclassified covariates, with or without perfectly measured covariates, and with a known estimated misclassification/reclassification model. These correction methods use the score function approach, regression calibration and a mixture model. We also use Fieller's method to find a confidence interval for the slope of simple regression with misclassified binary covariates. Finally, we use simulation to demonstrate the performance of our proposed methods.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24288号 / 医博第4904号 / 新制||医||1061(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 山本 洋介, 教授 中本 裕士, 教授 森田 智視 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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The Systemic Clinical Outcome and Routine Evaluation version 15 (SCORE-15) is an assessment used to assess for clinical change in family functioning. The SCORE-15 has been demonstrated in the past to be a reliable and valid measure for assessing for clinical change and is largely used throughout the UK. However, the SCORE-15 lacks the ability to determine whether an individual's change in family functioning is clinically significant. This study aims to establish a reliable change index and clinical cutoff score based on a US sample so that researchers and clinicians can determine clinically significant change. A sample of 63 clinical participants and 244 community participants completed the SCORE-15, including 165 community participants who completed the SCORE-15 a second time. Results established a cutoff of 51.92 and a reliable change index of 17.51 for the SCORE-15. This indicates that therapy clients who improve their SCORE-15 score by at least 17.5 points and who cross the threshold of 52 during the course of therapy are considered to have experienced clinical significant improvement.

Intracranial aneurysms represent a potentially life-threatening condition and occur in 3–5% of the population. They are increasingly diagnosed due to the broad application of cranial magnetic resonance imaging and computed tomography in the context of headaches, vertigo, and other unspecific symptoms. For each affected individual, it is utterly important to estimate the rupture risk of the respective aneurysm. However, clinically applied decision tools, such as the PHASES score, remain insufficient. Therefore, a machine learning approach assessing the rupture risk of intracranial aneurysms is proposed in our study. For training and evaluation of the algorithm, data from a single neurovascular center was used, comprising 446 aneurysms (221 ruptured, 225 unruptured). The machine learning model was then compared with the PHASES score and proved superior in accuracy (0.7825), F1-score (0.7975), sensitivity (0.8643), specificity (0.7022), positive predictive value (0.7403), negative predictive value (0.8404), and area under the curve (0.8639). The frequency distributions of the predicted rupture probabilities and the PHASES score were analyzed. A symmetry can be observed between the rupture probabilities, with a symmetry axis at 0.5. A feature importance analysis reveals that the body mass index, consumption of anticoagulants, and harboring vessel are regarded as the most important features when assessing the rupture risk. On the other hand, the size of the aneurysm, which is weighted most in the PHASES score, is regarded as less important. Based on our findings we discuss the potential role of the model for clinical practice in geographically confined aneurysm patients.

Budgets for transportation improvements are limited so it is important for governments to focus on improving locations most in need of safety funding. The objective of the Two-Output Model for Safety (TOMS) is to provide the Utah Department of Transportation (UDOT) a reliable method to prioritize safety improvements on state-owned roadways among the different regions. This research will improve the existing Crash Analysis Methodology for Segments (CAMS) and Intersection Safety Analysis Methodology (ISAM) being used to analyze crashes on Utah roadways. The scope of this project is improving on the existing CAMS and ISAM to work together within R, to incorporate segment and intersection severity in safety hot spot analysis, to develop overall severity distributions, and to develop limited recommendations and conclusions related to the research. TOMS uses UDOT data to create a statistical input. Each segment is homogenous with respect to five variables: average annual daily traffic, functional class, number of through lanes, speed limit, and urban code. Intersections are provided as a separate dataset. In the statistical analyses performed on the data, five years of crash data (2016-2020) are used to determine a weighted risk score for segments and intersections of similar characteristics. Those segments or intersections with excess weighted risk scores are designated as crash hot spots. Two-page technical reports with road characteristics and crash data are created for the top 10 hot spots for segments and intersections in Utah. The reports are sent to UDOT where region engineers may review and determine which locations might be addressed.

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The aim of the present study was to analyze possible relationships between pulmonary vessel obstruction and clinically relevant parameters and scores in patients with pulmonary embolism (PE). Overall, 246 patients (48.8% women and 51.2% men) with a mean age of 64.0 17.1 years were involved in the retrospective study. The following clinical scores were calculated in the patients: Wells score, Geneva score, and pulmonary embolism severity index (PESI) score. Levels of D-dimer (g/mL), lactate, pH, troponin, and N-terminal natriuretic peptide (BNP, pg/mL) were acquired. Thrombotic obstruction of the pulmonary arteries was quantified according to Mastora score. The data collected were evaluated by means of descriptive statistics. Spearman’s correlation coeffcient was used to analyze associations between the investigated parameters. P values < 0.05 were taken to indicate statistical significance. Mastora score correlated weakly with lactate level and tended to correlate with D-dimer and BNP levels. No other clinical or serological parameters correlated significantly with clot burden. Thrombotic obstruction of pulmonary vessels did not correlate with clinical severity of PE.