Podcasts about therneau

Whenever Stetson Baseball is mentioned nationally, the first thought is normally about a pair of two-time Cy Young Award winners – Jacob deGrom and Corey Kluber. Pitching has been a hallmark for Hatters baseball for many years, and Associate Head Coach Dave Therneau has been in charge of Stetson's hurlers for the last six seasons. During that time, the accomplishments have been plentiful as the Hatters have sent 10 pitchers and three catchers into professional baseball. The Stetson baseball record book has also been re-written. The top four strikeout seasons in program history came in consecutive years from 2016-19 with a combined ERA during those four seasons of 3.77, which is better than any single-season ERA from the previous 20 years. Back in November, after the Hatters ended fall practice but before the players left at Thanksgiving for winter break, Therneau sat in for an episode of Hatter Chatter … The Podcast, presented by Insight Credit Union, to talk about the 2021 Stetson pitching staff.  

Now entering his fourth year as a member of the Stetson Baseball coaching staff, Joe Mercadante works with the Hatters catchers and hitters in addition to serving as recruiting coordinator. It is in that role that he, along with Head Coach Steve Trimper and Associate Head Coach Dave Therneau, along with the rest of the baseball staff, was able to bring in a crop of 12 new players during the November signing period this year.  In this edition of Hatter Chatter ... The Podcast, presented by Insight Credit Union, Mercadante provides a scouting report on each of the 12 newest Stetson players, players who will enroll in the fall of 2021 and be a part of the 2022 Stetson team. Mercadante also talks about fall practice, his catching unit as well as his path to Stetson, which was anything but direct from his hometown of Gainesville. This is the first of a five-part holiday look ahead to the spring and the return of Stetson baseball, with upcoming episodes to feature Trimper, Therneau, volunteer assistant coach Brandon Brewer and Director of Baseball Operations Mark Michaud.

Gracias por escuchar. Estas son los artículos a los que se hace referencia en este episodio del podcast:Ogdie, A. et al. Risk of major cardiovascular events in patients with psoriatic arthritis, psoriasis and rheumatoid arthritis: a population-based cohort study. Ann. Rheum. Dis. 74, 326–332 (2014).Avina-Zubieta, J. A., Thomas, J., Sadatsafavi, M., Lehman, A. J. & Lacaille, D. Risk of incident cardiovascular events in patients with rheumatoidarthritis: a meta-analysis of observational studies. Ann. Rheum. Dis. 71, 1524–1529 (2012).Schieir, O., Tosevski, C., Glazier, R. H., Hogg-Johnson, S.& Badley, E. M. Incident myocardial infarction associated with major types of arthritis in the general population: a systematic review and meta-analysis. Ann. Rheum. Dis. 76, 1396–1404 (2017). 21.D’Agostino, R. B. et al. General cardiovascular risk profile for use in primary care. Circulation 117, 743–753 (2008).Arts, E. E. A. et al. Performance of four current risk 23. algorithms in predicting cardiovascular events inpatients with early rheumatoid arthritis. Ann. Rheum.Dis. 74, 668–674 (2015). 24.Crowson, C. S., Matteson, E. L., Roger, V. L., Therneau, T. M. & Gabriel, S. E. Usefulness of risk scores to estimate the risk of cardiovascular disease in patients with rheumatoid arthritis. Am. J. Cardiol. 110, 420–424 (2012). 25.Agca, R. et al. EULAR recommendations for cardiovascular disease risk management in patients with rheumatoid arthritis and other forms ofinflammatory joint disorders: 2015/2016 update. 26. Ann. Rheum. Dis. 76, 17–28 (2017).Gómez-Vaquero, C. et al. SCORE and REGICOR function charts underestimate the cardiovascular risk in Spanish patients with rheumatoid arthritis. Arthritis 27. Res. Ther. 15, R91 (2013).Ahlehoff, O. et al. Psoriasis is associated with clinically significant cardiovascular risk: a Danish nationwide cohort study. J. Intern. Med. 270, 147–157 (2011).Polachek, A., Touma, Z., Anderson, M. & Eder, L. Risk of cardiovascular morbidity in patients with psoriatic arthritis: a meta-analysis of observational studies. Arthritis Care Res. 69, 67–74 (2017).Gulati, A. M. et al. On the HUNT for cardiovascular risk factors and disease in patients with psoriatic arthritis: population-based data from the Nord-Trøndelag Health Study. Ann. Rheum. Dis. 75, 819–824 (2016).Eder, L., Wu, Y., Chandran, V., Cook, R. & Gladman, D. D. Incidence and predictors for cardiovascular events in patients with psoriatic arthritis. Ann. Rheum. Dis. 75, 1680–1686 (2016).Eder, L. et al. Gaps in diagnosis and treatment of cardiovascular risk factors in patients with psoriatic disease: an international multicenter study.J. Rheumatol. 45, 378–384 (2018).Ridker, P. M., Cushman, M., Stampfer, M. J., Tracy, R. P. & Hennekens, C. H. Inflammation, aspirin, and the risk of cardiovascular disease in apparently healthy men. N. Engl. J. Med. 336, 973–979 (1997).Ridker, P. M. et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N. Engl. J. Med. 359, 2195–2207 (2008).Sever, P. S. et al. Evaluation of C-reactive protein before and on-treatment as a predictor of benefit of atorvastatin: a cohort analysis from the Anglo- Scandinavian Cardiac Outcomes Trial lipid-lowering arm. J. Am. Coll. Cardiol. 62, 717–729 (2013).Ridker, P. M. et al. Antiinflammatory therapy with canakinumab for atherosclerotic disease. N. Engl. J. Med. 377, 1119–1131 (2017).Ridker, P. M. et al. Low-dose methotrexate for the prevention of atherosclerotic events. N. Engl. J. Med. 380, 752–762 (2019).Szentpetery, A. et al. Higher coronary plaque burden in psoriatic arthritis is independent of metabolic syndrome and associated with underlying disease severity. Arthritis Rheumatol. 70, 396–407 (2018).Min, J. K. et al. Relationship of coronary artery plaque composition to coronary artery stenosis severity: results from the prospective multicenter ACCURACY trial. Atherosclerosis 219, 573–578 (2011).

This episode is brought to you by baseballcloud and OnBaseU. iTunes  Stitcher Google Spotify   During this episode of Ahead of the Curve, I interview Dave Therneau, Pitching Coach at Stetson University in DeLand, Florida. Coach Therneau has been named Collegiate Baseball’s Pitching Coach of the Year in 2018, and shares the advice that he has found beneficial in recruiting great pitchers, training players to be their best, and enhancing his hard-working team culture through internal motivation.   Episode Highlights:   Why did Dave Therneau decide to get into coaching? What does day one look like during Dave Therneau’s program? How does a typical week come across in Dave’s pitching system? What are the most common problems that Coach Therneau notices? Which player elements stand out positively to Dave Therneau during recruitment? What exactly is the “hatter?” How does Coach Therneau go about developing the culture of the team? How does Dave motivate and keep his players competitive during training? How does Dave Therneau prioritize individual development in a team setting? What makes a good bullpen setting? How does he develop command of the pitch? What does a typical week look like during the season for a starting player? What is the latest thing that Coach Therneau is excited about using? Does he have fun traditions that his players enjoy engaging in? Which resources does Dave Therneau find the most useful? 3 Key Points: Video of Coach Therneau’s pitchers helps to improve their delivery. Pitchers are only as good as their strike zone. Self-motivation can be accomplished by getting players to compete against themselves. Tweetable Quotes: “I always talk to them about conditioning the arm. They don’t play catch. I don’t believe in that…I don’t like using that term hear.” – Dave Therneau (10:04:) “If you are trying to go ‘full go,’ whether it be on the mound, roaming short stop or center field…and you do that for a few games, and then you are not training in between, I think it puts kids at risk.” – Dave Therneau (13:38:) “If you want to be a hard-working, tough group, which is what we are trying to build here, we’ve had that, you have to bring those types of kids in.” – Dave Therneau (20:18:) “I try to get these guys to compete individually against themselves.” – Dave Therneau (23:02:) “You are pretty much using 25-27 guys, If you think about a major league roster, I think it’s around that, 25-27. All of those guys are contributors and important pieces to the team.” – Dave Therneau (27:06:) “Every pitch has a purpose.” – Dave Therneau (34:02:) “If something works for a guy, I like to study why.” – Dave Therneau (45:34:) “Teach the game and teaching routines, and I just hope that that is a focus, from all of us responsible for that in baseball, because as a college coach, sometimes we get kids that are unprepared.” – Dave Therneau (52:33:)   Resources Mentioned: Ahead of the Curve Podcast Twitter: @AOTC_podcast Dave Therneau’s Contact: gohatters.com/staff.aspx?staff=140 Website and Social Media sites for the show  www.aotcpodcast.com Twitter @aotc_podcast Facebook Ahead of the Curve Coaches Facebook group Instagram aotc_podcast

Categorizing continuous variables arises as an important task in statistical analysis, especially in analyzing dose-response relationships. Creating meaningful groups of the predictor variables regarding the outcome variable is desirable in many settings, especially if the form of the relationship is unknown. However it is not always obvious how many groups should be build and where the cutpoints should be placed. Usually more than one explanatory variable has to be included in the analysis, and therefore one has to apply an appropriate statistical model. For this purpose we need a simple approach to model the data without many requirements. Another important issue in statistical analysis and especially in toxicology studies is proving a dose response relationship: increasing response probability with increasing predictor variable. This theses deals with cases where categorization of numerical or categorical predictor variables results as an effect of the dose-response relationship. Isotonic regression is an alternative proposal when one wishes to establish a dose-response relationship, categorize continuous variables and estimate threshold values. The only assumption for this approach is the monotonicity in the response variable. The isotonic regression summarizes the description of n observations to l categories (level sets or solution blocks) by automatically splitting the predictor in constant risk groups. The result is always a step function, and therefore the isotonic regression can be used to fit a changepoint model. The Pooled Adjacent Violators Algorithm (PAVA) is used to fit the data. In relation to model fitting and testing, some problems arise when the response is binary, and in the present work the difficulties are highlighted and some proposals to solve them are given. Regarding isotonic regression and binary response, the isotonic test for trend, the reduced isotonic model, multidimensional isotonic models and methods to assess threshold limit values are discussed. The isotonic framework provides a reliable test for trend which unlike other widely used tests (the Cochran-Armitage test for example) is independent of any monotonic transformation of the dose variable and does not assume a linear shape. However the proposed large sample approximation (a weighted chi-square distribution) does not hold when the overall response probability is less than 5% and thus exact methods are proposed in order to assess the correct p-value. In a simulation study it has been shown that the isotonic likelihood ratio test is more powerful than the Cochran-Armitage test, the Wilcoxon test and the Iso-chi-squared test. The model resulting from PAVA can become more parsimonious if the level sets which correspond to a non significant change for the response variable are eliminated. This model is called reduced isotonic regression. That can be accomplished by two means: a sequence of Fisher tests for the adjacent 2x2 tables or the application of a variation of a "closed testing" procedure. The correction for multiple comparisons is made for the first method by an a-priori estimation of the overall significance level in a permutation procedure. In the second method the control for the expense of the type I error is effected by the closure principal. To select between full isotonic and reduced model, a procedure based on parametric bootstrap is proposed. A simulation study proved that when the maximal coefficient of determination for the analyzed data set is at least 50% and the data can be represented by a step function, the reduced monotonic regression controls successfully the trade off between model complexity and goodness of fit. When more than one predictor is to be taken into account an additive isotonic model can be applied. Alternatively, an isotonic-surfaces model is proposed. This can be estimated by an iterative version of the Pooled Adjacent Violators Algorithm. The result is a sequence of surfaces which is monotonic in every dimension. This approach models interaction and categorizes the predictors in "multivariate" groups by combining them regarding restrictions to the outcome variable. This approach is very useful since, unlike the additive model, it can be easily combined with the reducing procedures to give a simple and interpretable model. However, for practical reasons a maximum of three predictors can be taken into account. A special aspect in analyzing dose-response relationships for a compound known to have harmful health effects, is to estimate a threshold limit value (TVL). On this regard a "hockey stick" threshold model is usually used. As alternative the use of a step function model by fitting the data using isotonic regression is proposed. A set of candidate threshold values is returned, and some threshold value estimation procedures are studied here. One of them starts from the isotonic model and applies the likelihood ratio test to detect the threshold value (method 1). Method 2 is based on the reduced isotonic regression. The performance of these two approaches is outlined in a simulation study under different scenarios and their properties are explored with categorical predictors. It is concluded that these methods possess a satisfactory power to reject the constant risk assumption, when a dose-response relationship exists as well as to estimate the actual threshold. Some limitations regarding the sample size and the force of trend are also discussed. A third method has also been presented. This modifies the closed testing procedure for the special case of thresholds, by setting one end of the regression line conditional to the other. All three threshold value estimation methods can be combined with the isotonic-surfaces model to provide thresholds, taking into account interactions between the predictor variables. The use of isotonic regression and its reduced version can also be extended to other settings. The capability of isotonic regression to be implemented in several models is outlined by describing how isotonic regression can model and test time-varying effects in Cox regression. The monotonic variation in the impact of a predictor included in the model during an observational period can be represented by a step function. An estimation of the time-dependent effect in the extended Cox model is presented based on isotonic regression framework. Smoothing the Schoenfeld residuals plotted against time applying PAVA, can reveal the changepoints without any a priori information about their location. The corresponding step function is then introduced in the model. The power of the Grambsch and Therneau test (which tests for time-variation in the effect of the predictors) can be improved if the isotonic transformation for the Schoenfeld residuals is used. Although this test appears to increase the type I error, its power is higher compared to conventional Grambsch and Therneau test and tests based on fractional polynomials. In summary it arises that isotonic framework is characterized by simplicity and stability. The main drawback underlying its application is the lack of asymptotic support in testing. This can make the use of isotonic models cumbersome since exact or bootstrap methods need to be used.

The violation of the proportional hazards assumption in Cox model occurs quite often in studies concerning solid tumours or leukaemia. Then the time varying coefficients model is its most popular extension used. The function f(t) that measures the time variation of a covariate, can be assessed through several smoothing techniques, such as cubic splines. However, for practical propose, it is more convenient to assess f(t) by a step function. The main drawback of this approach is the lack of stability since there is no standard method of defining the cutpoints of the underlined step function. The variation in the effect of a predictor can be assumed to be monotonic during the observational period. In these cases, we propose a method to estimate f(t) based on the isotonic regression framework. Applying the idea of Grambsch and Therneau, where smoothing the Schoenfeld residuals plotted against time reveal the shape of the underlined f(t) function, we use the Pooled Adjacent Violators Algorithm as smoother. As a result a set of cutpoints is returned without any a priori information about their location. Subsequently, the corresponding step function is introduced in the model and the standard likelihood-based method is applied to estimate it while adjusting for other covariates. This approach presents the advantage that additional decisions that can effect the result, as the number of knots in cubic splines, do not need to be taken. The performance of the provided PH test and the stability of the method are explored in a simulation study.

In this paper a semiparametric hazard model introduced by Cox (1972) is used to model transitions intensities for a long term care (LTC) data set. The main focus is the inclusion of the diagnoses which led to LTC as explanatory variables. Modern model diagnostic techniques are applied to check the model assumptions. Fractional Polynomials proposed by Royston and Altman (1994) are used to model the functional form of continuous covariates. Time dependency is examined graphically by using scaled Schoenfeld residuals (see Grambsch and Therneau(1994)). It is shown that the inclusion of diagnoses significantly improves the estimated transition probabilities on which premiums are based. As an alternative approach a piecewise exponential model is fitted and compared to the semiparametric hazard model.

Despite a sophisticated research on modelling of survival data in the last years, the most popular model used in practice is still the proportional hazards regression model proposed by Cox (1972). This is mainly due to its exceptional simplicity. Nevertheless the fundamental assumption of the Cox model is the proportionality of the hazards, which particularly implies that the covariate effects are constant over time. For many applications this assumption is, however, doubtful. Other, more flexible approaches, which are able to cope with non-proportional hazards usually require non-standard estimation techniques, which are often rather complex and thus not favoured in application. Moreover, the selection of an appropriate test-statistic, to examine the improvement of the fit, is not obvious. In this paper we propose a flexible, yet simple method for modelling dynamic effects in survival data within the Cox framework. The method is based on Fractional Polynomials as introduced by Royston and Altman (1994). This allows for a transformation of the dynamic predictor which leads back to the conventional Cox model and hence fitting is straightforward using standard estimation techniques. In addition, it offers the possibility to easily verify the existence of time-variation. We describe a model selection algorithm which enables to include time-varying effects only when evidence is given in the data, in order to construct a model, which is just as complex as needed. We illustrate the properties of the approach in a simulation study and an application to gastric carcinoma data and compare it with other methods (e.g. the residual score test and smoothed Schoenfeld residuals of Grambsch and Therneau, 1994; natural smoothing splines of Hastie and Tibshirani, 1993).