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The textile industry is detrimental to social and environmental levels worldwide. As such information is increasingly coming to light, people are becoming more aware of the need to change both our consumption and production habits to be more sustainable. Thus the transition from a linear to a circular model is becoming increasingly popular for producers, however, for such an economy to succeed, it is vital for consumers to adopt this. There is a limited understanding of consumers’ perception of such products in the textile industry, especially when provided with information on the benefits of both social and environmental attributes of circular products. Thus, this paper investigates whether the provision of such information has a significant influence on consumers’ willingness to pay for circular products. This was assessed via an online experiment in the form of a between-subjects two-by-two factorial design. Subjects are randomly assigned to one of four treatment groups which determine what kind of information is presented to the subject prior to performing the tasks. Three different elicitation procedures are applied to measure willingness to pay (WTP) within each treatment group. This allows for additional insights into the main research question with regard to hypothetical bias and how these may differ across treatments. The results do not support a significant effect of information on consumer WTP. I find that providing pro-social and environmental information does not increase the willingness to pay for the respective circular product., submitted by: Carmen Campagnolo, Masterarbeit Universität Innsbruck 2023

For chronic lymphocytic leukaemia (CLL), targeted drugs have become the standard of care, in particular for second-line treatment. In this study, overall survival (OS), treatment-free survival (TFS) and adverse events (AE) were registered retrospectively in a Danish population-based cohort upon second-line treatment for CLL. Data were collected from medical records and the Danish National CLL register. For 286 patients receiving second-line treatment, three-year TFS was higher upon targeted treatment (ibrutinib/venetoclax/idelalisib) [63%, 95% confidence interval (CI) 50%-76%] compared with fludarabine, cyclophosphamide and rituximab or bendamustine and rituximab (FCR/BR) (37%, CI: 26%-48%) and chlorambucil+/-CD20-antibody (CD20Clb/Clb) (22%, CI: 10%-33%). Upon targeted treatment, three-year OS estimates were higher for targeted treatment (79%, CI: 68%-91%) compared with FCR/BR (70%, CI: 60%-81%) or CD20Clb/Clb (60%, CI: 47%-74%). The most common AEs were infections and haematological AEs; 92% of patients treated with targeted drugs had AEs, 53% of which were severe. Upon FCR/BR and CD20Clb/Clb, AEs were present for 75% and 53% respectively, of which 63% and 31% were severe. These real-world data demonstrate higher TFS and a tendency towards higher OS following targeted second-line treatment for CLL compared to chemoimmunotherapy, also for patients who may be frailer and more comorbid.

Background: Glioblastoma is the most common malignant brain tumor. Operation with maximal resection, if feasible, otherwise biopsy followed by radiotherapy and chemotherapy with temozolomide is standard therapy. The prognosis remains poor, with median overall survival being 15 months despite therapy. Improved monitoring and treatment response assessment will be important when seeking to improve treatment efficacy and patient quality of life.Aims: The present work sought to follow newly diagnosed glioblastoma patients by imaging and clinical monitoring. Specific aims were to study the impact of surgical resection degree on prognosis and the effects of currently used therapies, including arc-based rotation radiotherapy, longitudinally. Aims were also to study radiological parameters with advanced magnetic resonance imaging (MRI) as well as patient neurological and cognitive functions in order to early identify prognostic factors. Material and methods: In paper I, volumetric assessment by quantitative and subjective methods was retrospectively studied from pre- and postoperative MRI in glioblastoma patients undergoing tumor resection. Influence of extent of resection of contrast enhanced tumor on progression-free survival and overall survival was analyzed, measured as relative extent of resection (EOR) and absolute residual tumor volume (RTV). In the present MRI brain tumor study, patients newly diagnosed with glioblastoma undergoing treatment with arc-based radiotherapy were studied longitudinally over a one-year period and constituted the patient cohort of papers II-IV, using advanced MRI, including diffusion-weighted imaging sequences. Microstructural changes in non-tumorous brain structures, including white matter (corpus callosum, centrum semiovale) and the limbic system (hippocampus, amygdala), were assessed by diffusion tensor imaging (DTI) during and after irradiation. By parametric response mapping (PRM) changes of mean diffusivity (MD) in tumor region

Purpose: Soil nutrient management and fertilizer use by farmers are important for sustainable grain production. The authors examined the effect of an experimental agricultural extension program, the science and technology backyard, in promoting sustainable soil nutrient management in the North China Plain (NCP). The science and technology backyard integrates farmer field schools, field demonstrations, and case-to-case counselling to promote sustainable farming practices among rural smallholders. Design/methodology/approach: The authors conducted a large-scale household survey of more than 2,000 rural smallholders. The authors used a multivariate regression analysis as the benchmark to assess the effect of the science-and-technology backyard on smallholder soil nutrient management. Furthermore, the authors used coarse exact matching (CEM) methods to control for potential bias due to self-selection and the (endogenous) switching regression approach as the main empirical analysis. Findings: The results show that the science-and-technology backyard program increased smallholders' wheat yield by approximately 0.23 standard deviation; however, no significant increase in maize yield was observed. Regarding soil nutrient use efficiency, the authors found a significant improvement in smallholders' phosphorus and potassium use efficiencies for both wheat and maize production, and a significant improvement in nitrogen use efficiency for wheat production, but no significant improvement of nitrogen use efficiency for maize production. Originality/value: This study evaluated a novel participatory agricultural extension model to improve soil nutrient management practices among smallholders. The integration of agronomists' scientific knowledge and smallholders' local contextual experiences could be an effective way to improve farmers' soil nutrient management. This study provides the first quantitative estimates based on rigorous impact assessment methods of this novel extension ap

For chronic lymphocytic leukaemia (CLL), targeted drugs have become the standard of care, in particular for second-line treatment. In this study, overall survival (OS), treatment-free survival (TFS) and adverse events (AE) were registered retrospectively in a Danish population-based cohort upon second-line treatment for CLL. Data were collected from medical records and the Danish National CLL register. For 286 patients receiving second-line treatment, three-year TFS was higher upon targeted treatment (ibrutinib/venetoclax/idelalisib) [63%, 95% confidence interval (CI) 50%-76%] compared with fludarabine, cyclophosphamide and rituximab or bendamustine and rituximab (FCR/BR) (37%, CI: 26%-48%) and chlorambucil+/-CD20-antibody (CD20Clb/Clb) (22%, CI: 10%-33%). Upon targeted treatment, three-year OS estimates were higher for targeted treatment (79%, CI: 68%-91%) compared with FCR/BR (70%, CI: 60%-81%) or CD20Clb/Clb (60%, CI: 47%-74%). The most common AEs were infections and haematological AEs; 92% of patients treated with targeted drugs had AEs, 53% of which were severe. Upon FCR/BR and CD20Clb/Clb, AEs were present for 75% and 53% respectively, of which 63% and 31% were severe. These real-world data demonstrate higher TFS and a tendency towards higher OS following targeted second-line treatment for CLL compared to chemoimmunotherapy, also for patients who may be frailer and more comorbid.

Background: Chemotherapy is ineffective in treating patients with Gastrointestinal Stromal Tumor (GIST). However, several types of tyrosine kinase inhibitors have been investigated since the approval of imatinib in 2001. The purpose of this report was to systematically review studies on the efficacy of neoadjuvant, adjuvant, and lifelong medical oncological treatment of GIST. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed throughout the review process. The protocol was submitted to the International prospective register of systematic reviews database (ID 251724). A systematic literature search was performed, including phase II- and III studies of biological treatment, reporting on treatment effect in patients with GIST. Results: Of 308 identified publications, 42 studies were included in this review. Conclusion: This review gives an overview of the existing evidence for approved lines of oncological treatments and potential alternatives for patients with GIST in the neoadjuvant-, adjuvant- and life-long setting.

Master in Economics. Empirical Applications and Policies. Academic Year 2021–2022., The Spanish antitrust authorities charged car makers with information sharing and collusion in the Spanish market in the period 2006 - 2013. Using data on car prices for models sold in a set of European countries, in the period 1993 - 2011, we measure how explicit collusion affected car prices in Spain in that period using the synthetic control method. By following a demand-driven approach, that is, based on the fact that the elasticity of demand is heterogeneous across segments, we focus the analysis on the most representative car segments within the Spanish and European market, selecting a repre- sentative car model for each of them. Our results indicate that sharing information caused higher prices in A and B segments, highlighting the great power of explicit collusion. In particular, we show how in oligopolies with a large number of initial members and in cartels that increase its size over time, such an agreement can be maintained, even in periods of economic crisis. Focusing on the segment with the largest market share, the C-segment, there is no evidence that sharing information had an effect of increasing final prices, suggesting that higher competition within the segment may succeed in nullifying the harmful effects of explicit collusion on social welfare.

Background: Chemotherapy is ineffective in treating patients with Gastrointestinal Stromal Tumor (GIST). However, several types of tyrosine kinase inhibitors have been investigated since the approval of imatinib in 2001. The purpose of this report was to systematically review studies on the efficacy of neoadjuvant, adjuvant, and lifelong medical oncological treatment of GIST. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed throughout the review process. The protocol was submitted to the International prospective register of systematic reviews database (ID 251724). A systematic literature search was performed, including phase II- and III studies of biological treatment, reporting on treatment effect in patients with GIST. Results: Of 308 identified publications, 42 studies were included in this review. Conclusion: This review gives an overview of the existing evidence for approved lines of oncological treatments and potential alternatives for patients with GIST in the neoadjuvant-, adjuvant- and life-long setting.

This dissertation develops modern statistical methods, targeted maximum likelihood estimation (TMLE) and ensemble machine learning, for two common problems in epidemiology and public health: 1) estimating the population size based on capture recapture designs, and 2) estimating the continuous and discrete treatment effect of multiple exposures. For the first problem, We proposed novel target parameters for each identification assumption and robust estimators based on TMLE, provided the efficient influence curves for each the parameter, proved the statistical properties of the estimators, and applied them to data collected from national-level infectious disease surveillance systems. We also used simulations with identification assumption violations to test the reliability of the estimation. For the second problem, we developed our target parameter and TMLE estimators and applied them to various types of empirical data collected by community-level follow-up surveys and state-level electronic health record data. We provided simulations and sensitivity analysis to show the performance of the TMLE estimators compared to existing ones. In chapter 1, we gave an introduction to TMLE, the road map of targeted learning, and a more detailed summary of the following chapters. In chapter 2, we developed a novel approach to estimate the population size based on capture recapture designs and evaluated the estimation reliability. In chapter 3, we utilized the targeted learning approach to assess the performance of a diabetes care program on glycemic control of type 2 diabetes patients, and identified patient subgroups with most successful treatment effects. In chapter 4, we proposed a robust variable importance measure based on TMLE and applied it in estimating the transmission effects of mother’s eating behavior on the next generation.

In chapter 1 we study explained variation under the additive hazards regression model for right-censored data. We consider different approaches for developing such a measure, and focus on one that estimates the proportion of variation in the failure time explained by the covariates. We study the properties of the measure both analytically, and through extensive simulations. We apply the measure to a well-known survival dataset as well as the linked surveillance, epidemiology, and end results-Medicare database for prediction of mortality in early stage prostate cancer patients using high-dimensional claims codes.In chapter 2 we propose a new flexible method for survival prediction: DeepHazard, a neural network for time-varying risks. Prognostic models in survival analysis are aimed at understanding the relationship between patients’ covariates and the distribution of survival time. Traditionally, semiparametric models, such as the Cox model, have been assumed. These often rely on strong proportionality assumptions of the hazard that might be violated in practice. Moreover, they do not often include covariates' information updated over time. Our approach is tailored for a wide range of continuous hazards forms, with the only restriction of being additive in time. A flexible implementation, allowing different optimization methods, along with any norm penalty, is developed. Numerical examples illustrate that our approach outperforms existing state-of-the-art methodology in terms of predictive capability evaluated through the C-index metric. The same is revealed on the popular real datasets as METABRIC, GBSG, ACTG and PBC.In chapter 3 we consider the conditional treatment effect for competing risks data in observational studies. While it is described as a constant difference between the hazard functions given the covariates, we do not assume the additive hazards model in order to adjust for the covariates. We derive the efficient score for the treatment effect using mode

In chapter 1 we study explained variation under the additive hazards regression model for right-censored data. We consider different approaches for developing such a measure, and focus on one that estimates the proportion of variation in the failure time explained by the covariates. We study the properties of the measure both analytically, and through extensive simulations. We apply the measure to a well-known survival dataset as well as the linked surveillance, epidemiology, and end results-Medicare database for prediction of mortality in early stage prostate cancer patients using high-dimensional claims codes.In chapter 2 we propose a new flexible method for survival prediction: DeepHazard, a neural network for time-varying risks. Prognostic models in survival analysis are aimed at understanding the relationship between patients’ covariates and the distribution of survival time. Traditionally, semiparametric models, such as the Cox model, have been assumed. These often rely on strong proportionality assumptions of the hazard that might be violated in practice. Moreover, they do not often include covariates' information updated over time. Our approach is tailored for a wide range of continuous hazards forms, with the only restriction of being additive in time. A flexible implementation, allowing different optimization methods, along with any norm penalty, is developed. Numerical examples illustrate that our approach outperforms existing state-of-the-art methodology in terms of predictive capability evaluated through the C-index metric. The same is revealed on the popular real datasets as METABRIC, GBSG, ACTG and PBC.In chapter 3 we consider the conditional treatment effect for competing risks data in observational studies. While it is described as a constant difference between the hazard functions given the covariates, we do not assume the additive hazards model in order to adjust for the covariates. We derive the efficient score for the treatment effect using mode

Purpose: Cotton is the backbone of Pakistan economy, as country is the 4th largest producer of cotton in the world. Despite this importance there is steep decline in cotton production over time due to climate change. The need to evaluate the potential of adaptation in improving cotton yield has necessitated this study. Design/Methodology/Approach: This study is based on the farm household survey of four cotton producing districts, two from each Punjab and Sindh that were purposively selected from heat stress regions of Pakistan. Data were analyzed through multinomial endogenous switching regression model and treatment effect framework. Findings: Farm management practices were evaluated for their significance in reducing adverse impacts of climatic extremes on cotton yield. Adaptation in the combination of first three strategies observed to be the most successful strategies in increasing yield. Implications/Originality/Value: For effective adaptation access to credit and extension, education, farming experience, and sources of information revealed to be important predictors

Background This retrospective study evaluates patient-reported outcomes in patients with multiple sclerosis (MS) spasticity who were treated with a cannabinoid oromucosal spray (Sativex®, USAN name: nabiximols) after not sufficiently responding to previous anti-spasticity medications. Methods Of 276 patients from eight centers in Belgium who began treatment prior to 31 December 2017, effectiveness assessment data were available for 238 patients during the test period of 4 to 8/12 weeks, and for smaller patient cohorts with continued treatment for 6/12 months. Results Mean 0–10 spasticity Numerical Rating Scale (NRS) scores improved from 8.1 at baseline to 5.2 (week 4), 4.6 (week 8) and 4.1 (week 12). Mean EuroQoL Visual Analogue Scale (EQ VAS) scores increased from 39 at baseline to 52 (week 4), 57 (week 8) and 59 (week 12). Mean NRS and EQ VAS scores remained in the same 12 weeks’ range in patients with longer-term data. The average dose of cannabinoid oromucosal spray was 6 sprays/day. Most of the 93 out of 276 patients, with initial prescription (33.7%), who discontinued treatment by week 12 did so within the first 8 weeks, mainly due to lack of effectiveness. By week 12, 171 (74%) of the 230 effectiveness evaluable patients reported a clinically meaningful response, corresponding to ≥30% NRS improvement. The tolerability of cannabinoid oromucosal spray was consistent with its known safety profile. Conclusions More than 60% of the patients with MS who started add-on treatment with cannabinoid oromucosal spray reported a clinically relevant symptomatic effect and continued treatment after 12 weeks.

This dissertation develops modern statistical methods, targeted maximum likelihood estimation (TMLE) and ensemble machine learning, for two common problems in epidemiology and public health: 1) estimating the population size based on capture recapture designs, and 2) estimating the continuous and discrete treatment effect of multiple exposures. For the first problem, We proposed novel target parameters for each identification assumption and robust estimators based on TMLE, provided the efficient influence curves for each the parameter, proved the statistical properties of the estimators, and applied them to data collected from national-level infectious disease surveillance systems. We also used simulations with identification assumption violations to test the reliability of the estimation. For the second problem, we developed our target parameter and TMLE estimators and applied them to various types of empirical data collected by community-level follow-up surveys and state-level electronic health record data. We provided simulations and sensitivity analysis to show the performance of the TMLE estimators compared to existing ones. In chapter 1, we gave an introduction to TMLE, the road map of targeted learning, and a more detailed summary of the following chapters. In chapter 2, we developed a novel approach to estimate the population size based on capture recapture designs and evaluated the estimation reliability. In chapter 3, we utilized the targeted learning approach to assess the performance of a diabetes care program on glycemic control of type 2 diabetes patients, and identified patient subgroups with most successful treatment effects. In chapter 4, we proposed a robust variable importance measure based on TMLE and applied it in estimating the transmission effects of mother’s eating behavior on the next generation.

In chapter 1 we study explained variation under the additive hazards regression model for right-censored data. We consider different approaches for developing such a measure, and focus on one that estimates the proportion of variation in the failure time explained by the covariates. We study the properties of the measure both analytically, and through extensive simulations. We apply the measure to a well-known survival dataset as well as the linked surveillance, epidemiology, and end results-Medicare database for prediction of mortality in early stage prostate cancer patients using high-dimensional claims codes.In chapter 2 we propose a new flexible method for survival prediction: DeepHazard, a neural network for time-varying risks. Prognostic models in survival analysis are aimed at understanding the relationship between patients’ covariates and the distribution of survival time. Traditionally, semiparametric models, such as the Cox model, have been assumed. These often rely on strong proportionality assumptions of the hazard that might be violated in practice. Moreover, they do not often include covariates' information updated over time. Our approach is tailored for a wide range of continuous hazards forms, with the only restriction of being additive in time. A flexible implementation, allowing different optimization methods, along with any norm penalty, is developed. Numerical examples illustrate that our approach outperforms existing state-of-the-art methodology in terms of predictive capability evaluated through the C-index metric. The same is revealed on the popular real datasets as METABRIC, GBSG, ACTG and PBC.In chapter 3 we consider the conditional treatment effect for competing risks data in observational studies. While it is described as a constant difference between the hazard functions given the covariates, we do not assume the additive hazards model in order to adjust for the covariates. We derive the efficient score for the treatment effect using mode

Background: Sample size calculation is a key point in the design of a randomized controlled trial. With time-to-event outcomes, it’s often based on the logrank test. We provide a sample size calculation method for a composite endpoint (CE) based on the geometric average hazard ratio (gAHR) in case the proportional hazards assumption can be assumed to hold for the components, but not for the CE. Methods: The required number of events, sample size and power formulae are based on the non-centrality parameter of the logrank test under the alternative hypothesis which is a function of the gAHR. We use the web platform, CompARE, for the sample size computations. A simulation study evaluates the empirical power of the logrank test for the CE based on the sample size in terms of the gAHR. We consider different values of the component hazard ratios, the probabilities of observing the events in the control group and the degrees of association between the components. We illustrate the sample size computations using two published randomized controlled trials. Their primary CEs are, respectively, progression-free survival (time to progression of disease or death) and the composite of bacteriologically confirmed treatment failure or Staphylococcus aureus related death by 12 weeks. Results: For a target power of 0.80, the simulation study provided mean (± SE) empirical powers equal to 0.799 (±0.004) and 0.798 (±0.004) in the exponential and non-exponential settings, respectively. The power was attained in more than 95% of the simulated scenarios and was always above 0.78, regardless of compliance with the proportional-hazard assumption. Conclusions: The geometric average hazard ratio as an effect measure for a composite endpoint has a meaningful interpretation in the case of non-proportional hazards. Furthermore it is the natural effect measure when using the logrank test to compare the hazard rates of two groups and should be used instead of the standard hazard ratio., G. G´omez and J. Cort´es were partially supported by the Ministerio de Econom´ıa y Competitividad (Spain) [MTM2015-64465-C2-1-R (MINECO/FEDER)], the Ministerio de Ciencia, innovaci´on y Universidades [PID2019-104830RB-I00] and the Departament d’Economia i Coneixement de la Generalitat de Catalunya (Spain)[2017 SGR 622 (GRBIO)]. Ronald B. Geskus was supported by the Wellcome Trust (grant number 106680/Z/14/Z)., Peer Reviewed, Postprint (author's final draft)

Purpose: Cotton is the backbone of Pakistan economy, as country is the 4th largest producer of cotton in the world. Despite this importance there is steep decline in cotton production over time due to climate change. The need to evaluate the potential of adaptation in improving cotton yield has necessitated this study. Design/Methodology/Approach: This study is based on the farm household survey of four cotton producing districts, two from each Punjab and Sindh that were purposively selected from heat stress regions of Pakistan. Data were analyzed through multinomial endogenous switching regression model and treatment effect framework. Findings: Farm management practices were evaluated for their significance in reducing adverse impacts of climatic extremes on cotton yield. Adaptation in the combination of first three strategies observed to be the most successful strategies in increasing yield. Implications/Originality/Value: For effective adaptation access to credit and extension, education, farming experience, and sources of information revealed to be important predictors

Purpose: Cotton is the backbone of Pakistan economy, as country is the 4th largest producer of cotton in the world. Despite this importance there is steep decline in cotton production over time due to climate change. The need to evaluate the potential of adaptation in improving cotton yield has necessitated this study. Design/Methodology/Approach: This study is based on the farm household survey of four cotton producing districts, two from each Punjab and Sindh that were purposively selected from heat stress regions of Pakistan. Data were analyzed through multinomial endogenous switching regression model and treatment effect framework. Findings: Farm management practices were evaluated for their significance in reducing adverse impacts of climatic extremes on cotton yield. Adaptation in the combination of first three strategies observed to be the most successful strategies in increasing yield. Implications/Originality/Value: For effective adaptation access to credit and extension, education, farming experience, and sources of information revealed to be important predictors

Purpose: Cotton is the backbone of Pakistan economy, as country is the 4th largest producer of cotton in the world. Despite this importance there is steep decline in cotton production over time due to climate change. The need to evaluate the potential of adaptation in improving cotton yield has necessitated this study. Design/Methodology/Approach: This study is based on the farm household survey of four cotton producing districts, two from each Punjab and Sindh that were purposively selected from heat stress regions of Pakistan. Data were analyzed through multinomial endogenous switching regression model and treatment effect framework. Findings: Farm management practices were evaluated for their significance in reducing adverse impacts of climatic extremes on cotton yield. Adaptation in the combination of first three strategies observed to be the most successful strategies in increasing yield. Implications/Originality/Value: For effective adaptation access to credit and extension, education, farming experience, and sources of information revealed to be important predictors

Foreign direct investment (FDI) facilitates modernization of domestic agri-food systems in emerging economies through increased use of vertical coordination. This paper sheds lights on how international brewer investments in African food systems affect smallholder market participation and value chain development. In particular, we analyze the impact of contracts among malt barley producers in Ethiopia. Using cross-sectional survey data, we employ inverse probability-weighted regression adjustment (IPWRA) and propensity score matching (PSM) techniques to analyze the economic impact of contracting. We find that contrary to popular belief, contracting has positive and significant impact on malt barley production, intensification, commercialization, quality improvement, and farm gate prices, ultimately resulting in increased net income and spillover into the productivity of other food crops.

We evaluate the impact of duration on the treatment effect of multifocal spectacle lenses used to inhibit myopia progression in children. A systematic literature search identified randomized controlled trials where multifocal lenses were prescribed as the intervention, with single-vision lenses as the control. Nine randomized control trials involving 1,701 children aged 8–13 years were included in the meta-analysis. Treatment effects, that is, differences in spherical equivalent refraction between intervention and nonintervention groups, were analyzed over both 6- and 12-month intervals. As treatment duration increased, effectiveness reduced. In 6-month intervals, treatment effects were 0.07 D (95 % CI 0.02, 0.13), 0.03 D (95% CI −0.02, 0.08), and 0.02 D (95% CI −0.05, 0.11) for baseline to 6, 6–12, and 12–18 months, respectively. For 12-month intervals, treatment effects were 0.21 D (95% CI 0.12, 0.29), 0.11 D (95% CI 0.03, 0.19), and 0.12 D (95% CI −0.01, 0.25) for baseline to 12, 12–24, and 24–36 months, respectively. Even during the second 6 months of wear, the ability of multifocal spectacle lenses to inhibit myopia progression was reduced. It is not appropriate to extrapolate the treatment effect observed in the first 6 months or 12 months to estimate the likely future benefit of treatment.

[EN] This paper analyzes the effects that the Arab Spring and the subsequent revolution had on per capita real Gross Domestic Product in Egypt. The estimation procedure that we follow is the synthetic control method. After comparing the observed evolution of Egyptian real output in the period 2011–2017 with that of synthetic Egypt, our estimates show (i) an accumulated loss in the growth rate of per capita real Gross Domestic Product of 12.04% (a yearly average of 1.56%); (ii) an accumulated loss in the per capita real Gross Domestic Product of 6279.7 dollars (a yearly average of 897.1 dollars); and (iii) an accumulated loss in the aggregate real Gross Domestic Product of 582.5 billion dollars (a yearly average of 83.2 billion dollars).

A new causal inference method called the Stability-Controlled Quasi-Experiment (SCQE) was recently proposed for identifying the effect of a treatment on units in a population that are treated. It allows for non-randomized or otherwise unknown means of selection into treatment, relying only on an assumption about the stability of the non-treatment outcomes of the population. We apply SCQE to the case of a national public health intervention in Tanzania to prevent active tuberculosis in HIV-positive patients by prescribing Isoniazid (IPT). Our preliminary results suggest much of the treatment is given to patients at low risk for developing active tuberculosis. In the process, we offer best practices and discuss challenges faced during its implementation. We share steps currently underway to generate confidence in these results, extend the method further, and derive valid standard errors.

Introducing new financial legislation to financial markets require caution to achieve the intended outcome. This thesis aims to investigate whether or not the newly installed revised Markets in Financial Instruments Directive- the MIFID II regulation - temporally influenced the trading stock volume levels of Nasdaq Stockholm during its introduction to the Swedish stock market. A first approach of a generalized Negative Binomial model is carried out on aggregated data, followed by an individual Fixed Effects model in an attempt to eliminate omitted variable bias caused by missing unobserved variables for the individual stocks. The aggregated data is attained by taking the equally weighted average of the trading volume and adjusting for seasonality through Seasonal and Trend decomposition using Loess in combination with a regression model with ARIMA errors to mitigate calendar effects. Due to robustness of the aggregated data, the Negative Binomial model manage to capture significant effects of the regulation on the Small Cap. segment, even though clusters of the data show signs of divergent reactions to MIFID II. Since the Fixed Effects model operate on non-aggregated TSCS data and because of the varying effects on each stock the Fixed Effect model fails in its attempt to do the same., Implementation av nya finansiella regelverk på finansmarknaden kräver aktsamhet för att uppnå de tilltänka målen. Det här arbetet undersöker huruvida MIFID II regleringen orsakade en temporär medelvärdesskiftning av de handlade aktievolymerna på Nasdaq Stockholm under regelverkets introduktion på den svenska marknaden. Först testas en generaliserad Negative Binomial regression applicerat på aggregerad data, därefter en individuell Fixed Effects modell för att försöka eliminera fel på grund av saknade, okända variabler. Det aggrigerade datasettet erhålls genom att ta genomsnittet av handelsvolymerna och justera dessa för sässongsmässiga mönster med metoden STL i kombination med regression med ARIMA residualer för att även ta hänsyn till kalender relaterade effekter. Eftersom den aggrigerade datan är robust lyckas the Negative Binomial regressionen fånga signifikanta effekter av regleringen för Small Cap. segmentet trots att datat uppvisar tecken på att subgrupper inom segmentet reagerat väldigt olika på den nya regleringen. Eftersom Fixed Effects modellen är applicerad på icke-aggrigerad TSCS data och pågrund av den varierande effekten på de individuella aktierna lyckas inte denna modell med detta.

A new causal inference method called the Stability-Controlled Quasi-Experiment (SCQE) was recently proposed for identifying the effect of a treatment on units in a population that are treated. It allows for non-randomized or otherwise unknown means of selection into treatment, relying only on an assumption about the stability of the non-treatment outcomes of the population. We apply SCQE to the case of a national public health intervention in Tanzania to prevent active tuberculosis in HIV-positive patients by prescribing Isoniazid (IPT). Our preliminary results suggest much of the treatment is given to patients at low risk for developing active tuberculosis. In the process, we offer best practices and discuss challenges faced during its implementation. We share steps currently underway to generate confidence in these results, extend the method further, and derive valid standard errors.

BackgroundResection may be appropriate for select patients with recurrent glioblastoma. The incidence of histopathological findings related to prior treatment and their prognostic implications are incompletely characterized.ObjectiveTo quantify the incidence and survival outcomes associated with treatment effect at resection of recurrent glioblastoma (GBM).MethodsPatients who underwent resection for recurrent GBM were retrospectively reviewed, and pathology, treatment history, and survival data were collected. Treatment effect was defined as any component of treatment-related changes on pathology.ResultsIn total, 110 patients underwent 146 reoperations. Median age at first reoperation was 57.2 yr and overall survival from reoperation was 10.8 mo. Treatment effect of any kind was noted in 81 of 146 reoperations (55%). Increased treatment effect was observed closer to radiotherapy; by quartile of time from radiotherapy, the rates of treatment effect were 77.8%, 55.6%, 40.7%, and 44.4% (P = .028). Treatment effect was associated with earlier reoperation (8.9 vs 13.8 mo after radiotherapy, P = .003), and the presence of treatment effect did not impact survival from primary surgery (25.4 vs 24.3 mo, P = .084). Patients treated with bevacizumab prior to reoperation were less likely to have treatment effect (20% vs 65%, P < .001).ConclusionHistopathological treatment-related changes are evident in a majority of patients undergoing resection for recurrent glioblastoma. There was no association of treatment effect with overall survival from primary surgery.

Introducing new financial legislation to financial markets require caution to achieve the intended outcome. This thesis aims to investigate whether or not the newly installed revised Markets in Financial Instruments Directive- the MIFID II regulation - temporally influenced the trading stock volume levels of Nasdaq Stockholm during its introduction to the Swedish stock market. A first approach of a generalized Negative Binomial model is carried out on aggregated data, followed by an individual Fixed Effects model in an attempt to eliminate omitted variable bias caused by missing unobserved variables for the individual stocks. The aggregated data is attained by taking the equally weighted average of the trading volume and adjusting for seasonality through Seasonal and Trend decomposition using Loess in combination with a regression model with ARIMA errors to mitigate calendar effects. Due to robustness of the aggregated data, the Negative Binomial model manage to capture significant effects of the regulation on the Small Cap. segment, even though clusters of the data show signs of divergent reactions to MIFID II. Since the Fixed Effects model operate on non-aggregated TSCS data and because of the varying effects on each stock the Fixed Effect model fails in its attempt to do the same., Implementation av nya finansiella regelverk på finansmarknaden kräver aktsamhet för att uppnå de tilltänka målen. Det här arbetet undersöker huruvida MIFID II regleringen orsakade en temporär medelvärdesskiftning av de handlade aktievolymerna på Nasdaq Stockholm under regelverkets introduktion på den svenska marknaden. Först testas en generaliserad Negative Binomial regression applicerat på aggregerad data, därefter en individuell Fixed Effects modell för att försöka eliminera fel på grund av saknade, okända variabler. Det aggrigerade datasettet erhålls genom att ta genomsnittet av handelsvolymerna och justera dessa för sässongsmässiga mönster med metoden STL i kombination med regression med ARIMA residualer för att även ta hänsyn till kalender relaterade effekter. Eftersom den aggrigerade datan är robust lyckas the Negative Binomial regressionen fånga signifikanta effekter av regleringen för Small Cap. segmentet trots att datat uppvisar tecken på att subgrupper inom segmentet reagerat väldigt olika på den nya regleringen. Eftersom Fixed Effects modellen är applicerad på icke-aggrigerad TSCS data och pågrund av den varierande effekten på de individuella aktierna lyckas inte denna modell med detta.

The hazard ratio is routinely used as a summary measure to assess the treatment effect in clinical trials with time-to-event endpoints. It is frequently assumed as constant over time although this assumption often does not hold. When the hazard ratio deviates considerably from being constant, the average of its plausible values is not a valid measure of the treatment effect, can be clinically misleading and common sample size formulas are not appropriate. In this paper, we study the hazard ratio along time of a two-component composite endpoint under the assumption that the hazard ratio for each component is constant. This work considers two measures for quantifying the non-proportionality of the hazard ratio: the difference D between the maximum and minimum values of hazard ratio over time and the relative measure R representing the ratio between the sample sizes for the minimum detectable and the average effects. We illustrate D and R by means of the ZODIAC trial where the primary endpoint was progression-free survival. We have run a simulation study deriving scenarios for different values of the hazard ratios, different event rates and different degrees of association between the components. We illustrate situations that yield non-constant hazard ratios for the composite endpoints and consider the likely impact on sample size. Results show that the distance between the two component hazard ratios plays an important role, especially when they are close to 1. Furthermore, even when the treatment effects for each component are similar, if the two-component hazards are markedly different, hazard ratio of the composite is often non-constant., Peer Reviewed, Preprint

[EN] In 2011 a wave of revolutionary movements, the so-called Arab Spring, spread in the Middle East and North Africa. Libya was one of the most affected countries, ending Gaddafi’s dictatorship after an international intervention and a civil war. This paper assesses the effects that this revolution had on Libyan economy. The analysis is made by means of the synthetic control method. Our estimates for the 2011–2014 period show (i) a cumulative loss in the growth rate of per capita real GDP of 64.15%; (ii) a cumulative loss in per capita real GDP of 56,548 dollars; and (iii) a cumulative loss in the aggregate real GDP of 350.5 billion dollars.

This paper considers the identification of treatment effects on conditional transition probabilities. We show that even under random assignment only the instantaneous average treatment effect is point identified. Since treated and control units drop out at different rates, randomization only ensures the comparability of treatment and controls at the time of randomization, so that long-run average treatment effects are not point identified. Instead, we derive bounds on these average effects. Our bounds do not impose (semi)parametric restrictions, for example, proportional hazards. We also explore assumptions such as monotone treatment response, common shocks and positively correlated outcomes that tighten the bounds.

This paper considers the identification of treatment effects on conditional transition probabilities. We show that even under random assignment only the instantaneous average treatment effect is point identified. Since treated and control units drop out at different rates, randomization only ensures the comparability of treatment and controls at the time of randomization, so that long-run average treatment effects are not point identified. Instead, we derive bounds on these average effects. Our bounds do not impose (semi)parametric restrictions, for example, proportional hazards. We also explore assumptions such as monotone treatment response, common shocks and positively correlated outcomes that tighten the bounds.

This paper considers the identification of treatment effects on conditional transition probabilities. We show that even under random assignment only the instantaneous average treatment effect is point identified. Since treated and control units drop out at different rates, randomization only ensures the comparability of treatment and controls at the time of randomization, so that long-run average treatment effects are not point identified. Instead, we derive bounds on these average effects. Our bounds do not impose (semi)parametric restrictions, for example, proportional hazards. We also explore assumptions such as monotone treatment response, common shocks and positively correlated outcomes that tighten the bounds.

This paper estimates the effect of job loss on mortality for older male workers with a strong labor force attachment. Using Dutch administrative data, we find that job loss due to firm closure increased the probability of death within five years by a sizable 0.60 percentage points. Importantly, this effect is estimated using a model that controls for firm-level worker characteristics, such as lagged firm-level annual average mortality rates. On the mechanism driving the effect of job loss on mortality, we provide evidence for an effect running through stress and changes in life style.

This paper considers the identification of treatment effects on conditional transition probabilities. We show that even under random assignment only the instantaneous average treatment effect is point identified. Since treated and control units drop out at different rates, randomization only ensures the comparability of treatment and controls at the time of randomization, so that long-run average treatment effects are not point identified. Instead, we derive bounds on these average effects. Our bounds do not impose (semi)parametric restrictions, for example, proportional hazards. We also explore assumptions such as monotone treatment response, common shocks and positively correlated outcomes that tighten the bounds.

To what extent does United Nations peacekeeping assist in laying the foundations for economic development? We conduct the first exploratory analysis of the effect of peace operations on the economic development of the host countries. We highlight the need for new inferential methods to reveal the extent to which robust conclusions about the success of missions can be drawn. We then apply synthetic control methods to 11 peace operations deployed since the end of the Cold War. Our results suggest that, in seven cases, peacekeeping does not seem to significantly affect economic rehabilitation. In two of the remaining four cases, the impact is negative rather than positive, pointing to persistent hurdles to identification.

This paper estimates the effect of job loss on mortality for older male workers with a strong labor force attachment. Using Dutch administrative data, we find that job loss due to firm closure increased the probability of death within five years by a sizable 0.60 percentage points. Importantly, this effect is estimated using a model that controls for firm-level worker characteristics, such as lagged firm-level annual average mortality rates. On the mechanism driving the effect of job loss on mortality, we provide evidence for an effect running through stress and changes in life style.

This paper considers the identification of treatment effects on conditional transition probabilities. We show that even under random assignment only the instantaneous average treatment effect is point identified. Since treated and control units drop out at different rates, randomization only ensures the comparability of treatment and controls at the time of randomization, so that long-run average treatment effects are not point identified. Instead, we derive bounds on these average effects. Our bounds do not impose (semi)parametric restrictions, for example, proportional hazards. We also explore assumptions such as monotone treatment response, common shocks and positively correlated outcomes that tighten the bounds.

Group sequential designs play an important role in monitoring clinical trials. In this dissertation, we consider the construction of one-sided group sequential designs where the stopping rule includes boundaries for early stopping to accept for futility and to reject for efficacy. The traditional assumption that all patients have the same likelihood of benefiting from the treatment is sometimes unrealistic and it can underestimate the required sample size. This motivates us to power the design for an alternative where the treatment group observations come from a mixture of normal distributions. For the proposed setting, we use standardized test statistics based on sample means. Stopping boundaries and arm size for the design are determined by Type I and Type II error spending equations. The unknown variance case is discussed, and we introduce the group sequential t-test for the mixture setting. With the mixture model, we discuss a more general definition of treatment effect. The maximum likelihood estimator and method of moment estimator for the treatment effect are discussed.

Group sequential designs play an important role in monitoring clinical trials. In this dissertation, we consider the construction of one-sided group sequential designs where the stopping rule includes boundaries for early stopping to accept for futility and to reject for efficacy. The traditional assumption that all patients have the same likelihood of benefiting from the treatment is sometimes unrealistic and it can underestimate the required sample size. This motivates us to power the design for an alternative where the treatment group observations come from a mixture of normal distributions. For the proposed setting, we use standardized test statistics based on sample means. Stopping boundaries and arm size for the design are determined by Type I and Type II error spending equations. The unknown variance case is discussed, and we introduce the group sequential t-test for the mixture setting. With the mixture model, we discuss a more general definition of treatment effect. The maximum likelihood estimator and method of moment estimator for the treatment effect are discussed.

The paper discusses the renewed short selling regulation (Regulation (EU) No 236/2012) in the European Union. The focus is on the provisions that deal with prohibiting short selling in exceptional market circumstances. The Regulation further enforces certain obligations to report and disclose short positions. It is concluded that banning short selling is not an effective tool to contain extreme price volatility. The difference-in-differences regression and repeated measures GLM were used to test whether short selling bans were successful in containing volatility of those Spanish and Italian stocks that were subject to two back-to-back prohibitions during the years 2011-2013. The results are consistent with the majority of previous research, suggesting that the effectiveness of short sale constraints in reducing volatility is limited at best. Furthermore, there are evidence of counterproductive effects: constraints on short selling may actually increase volatility as well as deteriorate liquidity. However, based on theory and previous studies, reporting and disclosure requirements shall be favored provided they improve market efficiency as well as supervisory work of regulatory bodies.This paper discusses the renewed short selling regulation (Regulation (EU) No 236/2012) in the European Union. The focus is on the provisions that deal with prohibiting short selling in exceptional market circumstances. The Regulation further enforces certain obligations to report and disclose short positions. It is concluded that banning short selling is not an effective tool to contain extreme price volatility. The difference-in-differences regression and repeated measures GLM were used to test whether short selling bans were successful in containing volatility of those Spanish and Italian stocks that were subject to two back-to-back prohibitions during the years 2011-2013. The results are consistent with the majority of previous research, suggesting that the effectiveness of short sa

The poultry red mite, Dermanyssus gallinae, is the most significant pest of egg laying hens in many parts of the world. Control of D. gallinae could be greatly improved with advanced Integrated Pest Management (IPM) for D. gallinae in laying hen facilities. The development of a model forecasting the pests’ population dynamics in laying hen facilities without and post-treatment will contribute to this advanced IPM and could consequently improve implementation of IPM by farmers. The current work describes the development and demonstration of a model which can follow and forecast the population dynamics of D. gallinae in laying hen facilities given the variation of the population growth of D. gallinae within and between flocks. This high variation could partly be explained by house temperature, flock age, treatment, and hen house. The total population growth variation within and between flocks, however, was in part explained by temporal variation. For a substantial part this variation was unexplained. A dynamic adaptive model (DAP) was consequently developed, as models of this type are able to handle such temporal variations. The developed DAP model can forecast the population dynamics of D. gallinae, requiring only current flock population monitoring data, temperature data and information of the dates of any D. gallinae treatment. Importantly, the DAP model forecasted treatment effects, while compensating for location and time specific interactions, handling the variability of these parameters. The characteristics of this DAP model, and its compatibility with different mite monitoring methods, represent progression from existing approaches for forecasting D. gallinae that could contribute to advancing improved Integrated Pest Management (IPM) for D. gallinae in laying hen facilities.

This paper proposes nonparametric tests for the null hypothesis that a treatment has a zero effect on conditional variance for all subpopulations defined by covariates. Rather than the mean of outcome, which measures to what extent treatment changes the level of outcome, researchers are also interested in how the treatment affects the dispersion of outcome. We use variance to measure the dispersion and estimate the conditional variances by series method. We give a test rule comparing a Wald-type test statistic with the critical value from chi-squared distribution. We also construct a normalized test statistic that is asymptotically standard normal under the null hypothesis. We illustrate the usefulness of the proposed test by Monte Carlo simulations and an empirical example that investigates the effect of unionism on wage dispersion.

Evaluation studies aim to provide answers to important questions like: How does this program or policy intervention affect the outcome variables of interest? In order to answer such questions, using the traditional statistical evaluation (or causal inference) methods, some conditions must be satised. One requirement is that the outcomes of individuals are not affected by the treatment given to other individuals, i.e., that the no-interference assumption is satisfied. This assumption might, in many situations, not be plausible. However, recent progress in the research field has provided us with statistical methods for causal inference even under interference. In this paper, we review some of themost important contributions made. We also discuss how we think these methods can or cannot be used within the field of policy evaluation and if there are some measures to be taken when planning an evaluation study in order to be able to use a particular method. In addition, we give examples on how interference has been dealt within some evaluation applications including, but not limited to, labor market evaluations, in the recent past.

Evaluation studies aim to provide answers to important questions like: How does this program or policy intervention affect the outcome variables of interest? In order to answer such questions, using the traditional statistical evaluation (or causal inference) methods, some conditions must be satised. One requirement is that the outcomes of individuals are not affected by the treatment given to other individuals, i.e., that the no-interference assumption is satisfied. This assumption might, in many situations, not be plausible. However, recent progress in the research field has provided us with statistical methods for causal inference even under interference. In this paper, we review some of themost important contributions made. We also discuss how we think these methods can or cannot be used within the field of policy evaluation and if there are some measures to be taken when planning an evaluation study in order to be able to use a particular method. In addition, we give examples on how interference has been dealt within some evaluation applications including, but not limited to, labor market evaluations, in the recent past.