Beating the Odds - A State Space Model for predicting match results in the Australian Football League

This thesis investigates whether state space models have the potential to pre- dict the outcome of Australian Rules Football matches and can produce significant positive return over the bookmaker’s odds. The point of departure is a sample of 18 Australian football teams over the period 2012 to 2016. Modeling and predicting a football match is a challenging task, since the model should incorporate two di↵er- ent random processes. Firstly, the evolution of parameters, i.e. the team strengths change over time as it incorporates changes in team composition, coaching, train- ing ground, injuries etc. Secondly, the distribution of ranking data with these time-varying propensities changes stochastically over time. Given that we cannot observe all team-specific and location-specific factors, a dynamic state space model for team strengths is introduced. The team strengths are assumed to follow an order-one autoregressive process and are estimated using a recursive Kalman filter algorithm. Smoothed state estimates are applicable for ranking teams and predict- ing future outcomes of the matches. We show that beating the bookmaker’s odds is a challenging task indicating that the betting markets are ecient. Keywords: Sport Analytics; Kalman Filter Algorithm; State Space models; Predictive inference; Ranking; Australian Rules Football.

Impact of the male factor on the prediction of natural conception

Veen, F. [Promotor]van der Mol, B.W.J. [Promotor]Hompes, P.G.A. [Copromotor]Steeg, J.W. van der [Copromotor

Predicting live birth, preterm and low birth weight infant after in-vitro fertilisation: a prospective study of 144018 treatment cycles

Background The extent to which baseline couple characteristics affect the probability of live birth and adverse perinatal outcomes after assisted conception is unknown. Methods and Findings We utilised the Human Fertilisation and Embryology Authority database to examine the predictors of live birth in all in vitro fertilisation (IVF) cycles undertaken in the UK between 2003 and 2007 (n = 144,018). We examined the potential clinical utility of a validated model that pre-dated the introduction of intracytoplasmic sperm injection (ICSI) as compared to a novel model. For those treatment cycles that resulted in a live singleton birth (n = 24,226), we determined the associates of potential risk factors with preterm birth, low birth weight, and macrosomia. The overall rate of at least one live birth was 23.4 per 100 cycles (95% confidence interval [CI] 23.2–23.7). In multivariable models the odds of at least one live birth decreased with increasing maternal age, increasing duration of infertility, a greater number of previously unsuccessful IVF treatments, use of own oocytes, necessity for a second or third treatment cycle, or if it was not unexplained infertility. The association of own versus donor oocyte with reduced odds of live birth strengthened with increasing age of the mother. A previous IVF live birth increased the odds of future success (OR 1.58, 95% CI 1.46–1.71) more than that of a previous spontaneous live birth (OR 1.19, 95% CI 0.99–1.24); p-value for difference in estimate <0.001. Use of ICSI increased the odds of live birth, and male causes of infertility were associated with reduced odds of live birth only in couples who had not received ICSI. Prediction of live birth was feasible with moderate discrimination and excellent calibration; calibration was markedly improved in the novel compared to the established model. Preterm birth and low birth weight were increased if oocyte donation was required and ICSI was not used. Risk of macrosomia increased with advancing maternal age and a history of previous live births. Infertility due to cervical problems was associated with increased odds of all three outcomes—preterm birth, low birth weight, and macrosomia. Conclusions Pending external validation, our results show that couple- and treatment-specific factors can be used to provide infertile couples with an accurate assessment of whether they have low or high risk of a successful outcome following IVF

Predicting the development of stress urinary incontinence 3 years after hysterectomy

We aimed to develop a prediction rule to predict the individual risk to develop stress urinary incontinence (SUI) after hysterectomy. Prospective observational study with 3-year follow-up among women who underwent abdominal or vaginal hysterectomy for benign conditions, excluding vaginal prolapse, and who did not report SUI before surgery (n = 183). The presence of SUI was assessed using a validated questionnaire. Significant prognostic factors for de novo SUI were BMI (OR 1.1 per kg/m(2), 95% CI 1.0-1.2), younger age at time of hysterectomy (OR 0.9 per year, 95% CI 0.8-1.0) and vaginal hysterectomy (OR 2.3, 95% CI 1.0-5.2). Using these variables, we developed the following rule to predict the risk of developing SUI: 32 + BMI-age + (7.5 × route of surgery). We defined a prediction rule that can be used to counsel patients about their individual risk on developing SUI following hysterectom

External validation and calibration of IVFpredict:A national prospective cohort study of 130,960 in vitro fertilisation Cycles

© 2015 Smith et al. Background Accurately predicting the probability of a live birth after in vitro fertilisation (IVF) is important for patients, healthcare providers and policy makers. Two prediction models (Templeton and IVFpredict) have been previously developed from UK data and are widely used internationally. The more recent of these, IVFpredict, was shown to have greater predictive power in the development dataset. The aim of this study was external validation of the two models and comparison of their predictive ability. Methods and Findings 130,960 IVF cycles undertaken in the UK in 2008-2010 were used to validate and compare the Templeton and IVFpredict models. Discriminatory power was calculated using the area under the receiver-operator curve and calibration assessed using a calibration plot and Hosmer-Lemeshow statistic. The scaled modified Brier score, with measures of reliability and resolution, were calculated to assess overall accuracy. Both models were compared after updating for current live birth rates to ensure that the average observed and predicted live birth rates were equal. The discriminative power of both methods was comparable: the area under the receiver-operator curve was 0.628 (95% confidence interval (CI): 0.625-0.631) for IVFpredict and 0.616 (95% CI: 0.613-0.620) for the Templeton model. IVFpredict had markedly better calibration and higher diagnostic accuracy, with calibration plot intercept of 0.040 (95% CI: 0.017-0.063) and slope of 0.932 (95% CI: 0.839 - 1.025) compared with 0.080 (95% CI: 0.044-0.117) and 1.419 (95% CI: 1.149-1.690) for the Templeton model. Both models underestimated the live birth rate, but this was particularly marked in the Templeton model. Updating the models to reflect improvements in live birth rates since the models were developed enhanced their performance, but IVFpredict remained superior. Conclusion External validation in a large population cohort confirms IVFpredict has superior discrimination and calibration for informing patients, clinicians and healthcare policy makers of the probability of live birth following IVF

Integrative DNA Methylation and Gene Expression Analyses Identify DNA Packaging and Epigenetic Regulatory Genes Associated with Low Motility Sperm

In previous studies using candidate gene approaches, low sperm count (oligospermia) has been associated with altered sperm mRNA content and DNA methylation in both imprinted and non-imprinted genes. We performed a genome-wide analysis of sperm DNA methylation and mRNA content to test for associations with sperm function. (NCBI 1788). There was a trend among altered expression of these epigenetic regulatory genes and RPMM DNA methylation class.Using integrative genome-wide approaches we identified CpG methylation profiles and mRNA alterations associated with low sperm motility

Reporting and Methods in Clinical Prediction Research: A Systematic Review

Walter Bouwmeester and colleagues investigated the reporting and methods of prediction studies in 2008, in six high-impact general medical journals, and found that the majority of prediction studies do not follow current methodological recommendations

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

<p>Abstract</p> <p>Background</p> <p>The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.</p> <p>Methods</p> <p>We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.</p> <p>Results</p> <p>Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).</p> <p>Conclusions</p> <p>We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.</p