Bookmaker Consensus and Agreement for the UEFA Champions League 2008/09

Bookmakers odds are an easily available source of ``prospective" information that is thus often employed for forecasting the outcome of sports events. To investigate the statistical properties of bookmakers odds from a variety of bookmakers for a number of different potential outcomes of a sports event, a class of mixed-effects models is explored, providing information about both consensus and (dis)agreement across bookmakers. In an empirical study for the UEFA Champions League, the most prestigious football club competition in Europe, model selection yields a simple and intuitive model with team-specific means for capturing consensus and team-specific standard deviations reflecting agreement across bookmakers. The resulting consensus forecast performs well in practice, exhibiting high correlation with the actual tournament outcome. Furthermore, the teams' agreement can be shown to be strongly correlated with the predicted consensus and can thus be incorporated in a more parsimonious model for agreement while preserving the same consensus fit.Series: Research Report Series / Department of Statistics and Mathematic

History Repeating: Spain Beats Germany in the EURO 2012 Final

Four years after the last European football championship (EURO) in Austria and Switzerland, the two finalists of the EURO 2008 - Spain and Germany - are again the clear favorites for the EURO 2012 in Poland and the Ukraine. Using a bookmaker consensus rating - obtained by aggregating winning odds from 23 online bookmakers - the forecast winning probability for Spain is 25.8% followed by Germany with 22.2%, while all other competitors have much lower winning probabilities (The Netherlands are in third place with a predicted 11.3%). Furthermore, by complementing the bookmaker consensus results with simulations of the whole tournament, we can infer that the probability for a rematch between Spain and Germany in the final is 8.9% with the odds just slightly in favor of Spain for prevailing again in such a final (with a winning probability of 52.9%). Thus, one can conclude that - based on bookmakers' expectations - it seems most likely that history repeats itself and Spain defends its European championship title against Germany. However, this outcome is by no means certain and many other courses of the tournament are not unlikely as will be presented here. All forecasts are the result of an aggregation of quoted winning odds for each team in the EURO 2012: These are first adjusted for profit margins (overrounds), averaged on the log-odds scale, and then transformed back to winning probabilities. Moreover, team abilities (or strengths) are approximated by an inverse procedure of tournament simulations, yielding estimates of all pairwise probabilities (for matches between each pair of teams) as well as probabilities to proceed to the various stages of the tournament. This technique correctly predicted the EURO 2008 final (Leitner, Zeileis, Hornik 2008), with better results than other rating/forecast methods (Leitner, Zeileis, Hornik 2010a), and correctly predicted Spain as the 2010 FIFA World Champion (Leitner, Zeileis, Hornik 2010b). Compared to the EURO 2008 forecasts, there are many parallels but two notable differences: First, the gap between Spain/Germany and all remaining teams is much larger. Second, the odds for the predicted final were slightly in favor of Germany in 2008 whereas this year the situation is reversed

Predictive bookmaker consensus model for the UEFA Euro 2016

From 10 June to 10 July 2016 the best European football teams will meet in France to determine the European Champion in the UEFA European Championship 2016 tournament (Euro 2016 for short). For the first time 24 teams compete, expanding the format from 16 teams as in the previous five Euro tournaments. For forecasting the winning probability of each team a predictive model based on bookmaker odds from 19 online bookmakers is employed. The favorite is the host France with a forecasted winning probability of 21.5%, followed by the current World Champion Germany with a winning probability of 20.1%. The defending European Champion Spain follows after some gap with 13.7% and all remaining teams are predicted to have lower chances with England (9.2%) and Belgium (7.7%) being the "best of the rest". Furthermore, by complementing the bookmaker consensus results with simulations of the whole tournament, predicted pairwise probabilities for each possible game at the Euro 2016 are obtained along with "survival" probabilities for each team proceeding to the different stages of the tournament. For example, it can be determined that it is much more likely that top favorites France and Germany meet in the semifinal (7.8%) rather than in the final at the Stade de France (4.2%) - which would be a re-match of the friendly game that was played on 13 November 2015 during the terrorist attacks in Paris and that France won 2-0. Hence it is maybe better that the tournament draw favors a match in the semifinal at Marseille (with an almost even winning probability of 50.5% for France). The most likely final is then that either of the two teams plays against the defending champion Spain with a probability of 5.7% for France vs. Spain and 5.4% for Germany vs. Spain, respectively. All forecasts are the result of an aggregation of quoted winning odds for each team in the Euro 2016: These are first adjusted for profit margins ("overrounds"), averaged on the log-odds scale, and then transformed back to winning probabilities. Moreover, team abilities (or strengths) are approximated by an "inverse" procedure of tournament simulations, yielding estimates of probabilities for all possible pairwise matches at all stages of the tournament. This technique correctly predicted the winner of the FIFA 2010 and Euro 2012 tournaments while missing the winner but correctly predicting the final for the Euro 2008 and three out of four semifinalists at the FIFA 2014 World Cup (Leitner, Zeileis, and Hornik 2008, 2010a,b; Zeileis, Leitner, and Hornik 2012, 2014)

Home victory for Brazil in the 2014 FIFA World Cup

After 36 years the FIFA World Cup returns to South America with the 2014 event being hosted in Brazil (after 1978 in Argentina). And as in all previous South American FIFA World Cups, a South American team is expected to take the victory: Using a bookmaker consensus rating - obtained by aggregating winning odds from 22 online bookmakers - the clear favorite is the host Brazil with a forecasted winning probability of 22.5%, followed by three serious contenders. Neighbor country Argentina is the expected runner-up with a winning probability of 15.8% before Germany with 13.4% and Spain with 11.8%. All other competitors have much lower winning probabilities with the "best of the rest" being the "insider tip" Belgium with a predicted 4.8%. Furthermore, by complementing the bookmaker consensus results with simulations of the whole tournament, predicted pairwise probabilities for each possible game at the FIFA World Cup are obtained along with "survival" probabilities for each team proceeding to the different stages of the tournament. For example, it can be inferred that the most likely final is a match between neighbors Brazil and Argentina (6.5%) with the odds somewhat in favor of Brazil of winning such a final (with a winning probability of 57.8%). However, this outcome is by no means certain and many other courses of the tournament are not unlikely as will be presented here. All forecasts are the result of an aggregation of quoted winning odds for each team in the 2014 FIFA World Cup: These are first adjusted for profit margins ("overrounds"), averaged on the log-odds scale, and then transformed back to winning probabilities. Moreover, team abilities (or strengths) are approximated by an "inverse" procedure of tournament simulations, yielding estimates of probabilities for all possible pairwise matches at all stages of the tournament. This technique correctly predicted the EURO 2008 final (Leitner, Zeileis, and Hornik 2008), with better results than other rating/forecast methods (Leitner, Zeileis, and Hornik 2010a), and correctly predicted Spain as the 2010 FIFA World Champion (Leitner, Zeileis, and Hornik 2010b) and EURO 2012 Champion (Leitner, Zeileis, and Hornik 2012)

APPLICATION OF PHARMACOMETRIC METHODS TO OPTIMIZE TRIAL DESIGN AND DOSING IN CRITICALLY ILL INFANTS

Christoph P. Hornik: Application of Pharmacometric Methods to Optimize Trial Design and Dosing in Critically Ill Infants (under the direction of Daniel Gonzalez) Drug development in critically ill infants is challenging. Limited number of eligible trial participants, low consent rates, inability to perform or tolerate trial assessments, and ethical considerations all contribute to a low rate of successful clinical trials in this population. As a result, drugs administered to infants are often incompletely studied to ensure their efficacy and safety, and administered without a US Food and Drug Administration (FDA)-approved indication (off-label). Off-label drug use is associated with increased risk of unwanted drug toxicities or therapeutic failures, which can result in poor infant outcomes. To improve infant outcomes, innovative strategies in drug development are needed to generate the data necessary to identify safe and effective drug dosing regimens. The work performed in this dissertation provides 3 examples of innovative approaches to drug development in critically ill infants. Central to these innovations is leveraging pharmacometric methods to address 3 common obstacles: (1) sample size determination of infant pharmacokinetic (PK) trials; (2) characterization of the relationship between drug exposure and efficacy to identify efficacious doses; and (3) evaluation of the association between drug exposure and safety to identify safe doses. Each of these 3 obstacles is overcome with the help of a specific pharmacometric approach. In aim 1, populationPK (popPK) modeling and simulation is applied to determine optimal sample sizes for various infant PK trial designs. In aim 2, popPK/pharmacodynamic (PD) modeling is used to characterize the exposure response relationship between methylprednisolone and antiinflammatory changes in neonates undergoing cardiac surgery on cardiopulmonary bypass (CPB). In aim 3, popPK models are combined with electronic health record (EHR)-derived real-world data (RWD) sources to develop a novel platform to study the relationship between predicted drug exposures and safety events captured during routine clinical care.Doctor of Philosoph

Clinical Pharmacology Studies in Critically Ill Children

Developmental and physiological changes in children contribute to variation in drug disposition with age. Additionally, critically ill children suffer from various life-threatening conditions that can lead to pathophysiological alterations that further affect pharmacokinetics (PK). Some factors that can alter PK in this patient population include variability in tissue distribution caused by protein binding changes and fluid shifts, altered drug elimination due to organ dysfunction, and use of medical interventions that can affect drug disposition (e.g., extracorporeal membrane oxygenation and continuous renal replacement therapy). Performing clinical studies in critically ill children is challenging because there is large inter-subject variability in the severity and time course of organ dysfunction; some critical illnesses are rare, which can affect subject enrollment; and critically ill children usually have multiple organ failure, necessitating careful selection of a study design. As a result, drug dosing in critically ill children is often based on extrapolations from adults or non-critically ill children. Dedicated clinical studies in critically ill children are urgently needed to identify optimal dosing of drugs in this population. This review will summarize the effect of critical illness on pediatric PK, the challenges associated with performing studies in this vulnerable subpopulation, and the clinical PK studies performed to date for commonly used drugs

Risk Factors and In-Hospital Outcomes following Tracheostomy in Infants

To describe the epidemiology, risk factors, and in-hospital outcomes of tracheostomy in infants in the neonatal intensive care unit (NICU)

Pharmacokinetics and Safety of Micafungin in Infants Supported With Extracorporeal Membrane Oxygenation

Candida is a leading cause of infection in infants on extracorporeal membrane oxygenation (ECMO). Optimal micafungin dosing is unknown in this population because ECMO can alter drug pharmacokinetics (PK)

Tracheostomy after Surgery for Congenital Heart Disease: An Analysis of the Society of Thoracic Surgeons Congenital Heart Surgery Database

Background Information concerning tracheostomy after operations for congenital heart disease has come primarily from single-center reports. We aimed to describe the epidemiology and outcomes associated with postoperative tracheostomy in a multi-institutional registry. Methods The Society of Thoracic Surgeons Congenital Heart Database (2000 to 2014) was queried for all index operations with the adverse event “postoperative tracheostomy” or “respiratory failure, requiring tracheostomy.” Patients with preoperative tracheostomy or weighing less than 2.5 kg undergoing isolated closure of patent ductus arteriosus were excluded. Trends in tracheostomy incidence over time from January 2000 to June 2014 were analyzed with a Cochran-Armitage test. The patient characteristics associated with operative mortality were analyzed for January 2010 to June 2014, including deaths occurring up to 6 months after transfer of patients to long-term care facilities. Results From 2000 to 2014, the incidence of tracheostomy after operations for congenital heart disease increased from 0.11% in 2000 to a high of 0.76% in 2012 (p < 0.0001). From 2010 to 2014, 648 patients underwent tracheostomy. The median age at operation was 2.5 months (25th, 75th percentile: 0.4, 7). Prematurity (n = 165, 26%), genetic abnormalities (n = 298, 46%), and preoperative mechanical ventilation (n = 275, 43%) were common. Postoperative adverse events were also common, including cardiac arrest (n = 131, 20%), extracorporeal support (n = 87, 13%), phrenic or laryngeal nerve injury (n = 114, 18%), and neurologic deficit (n = 51, 8%). The operative mortality was 25% (n = 153). Conclusions Tracheostomy as an adverse event of operations for congenital heart disease remains rare but has been increasingly used over the past 15 years. This trend and the considerable mortality risk among patients requiring postoperative tracheostomy support the need for further research in this complex population