Using a Brier Score Analysis to Assess the Effectiveness of a Mathematics Placement Policy

2012 ◽  
Vol 14 (2) ◽  
pp. 209-225 ◽  
Author(s):  
Sally A. Lesik ◽  
Meg Leake

PRIMUS ◽  
2012 ◽  
Vol 22 (3) ◽  
pp. 177-185 ◽  
Author(s):  
Jeffrey K. Denny ◽  
David G. Nelson ◽  
Martin Q. Zhao




2018 ◽  
Vol 56 (01) ◽  
pp. E2-E89
Author(s):  
M Giesler ◽  
D Bettinger ◽  
M Rössle ◽  
R Thimme ◽  
M Schultheiss


2019 ◽  
Author(s):  
Matthew McBee ◽  
Rebecca Brand ◽  
Wallace E. Dixon

In 2004, Christakis and colleagues published an influential paper claiming that early childhood television exposure causes later attention problems (Christakis, Zimmerman, DiGiuseppe, & McCarty, 2004), which continues to be frequently promoted by the popular media. Using the same NLSY-79 dataset (n = 2,108), we conducted two multiverse analyses to examine whether the finding reported by Christakis et al. was robust to different analytic choices. We evaluated 848 models, including logistic regression as per the original paper, plus linear regression and two forms of propensity score analysis. Only 166 models (19.6%) yielded a statistically significant relationship between early TV exposure and later attention problems, with most of these employing problematic analytic choices. We conclude that these data do not provide compelling evidence of a harmful effect of TV on attention. All material necessary to reproduce our analysis is available online via Github (https://github.com/mcbeem/TVAttention) and as a Docker container (https://hub.docker.com/repository/docker/mmcbee/rstudio_tvattention)





2003 ◽  
Vol 42 (05) ◽  
pp. 564-571 ◽  
Author(s):  
M. Schumacher ◽  
E. Graf ◽  
T. Gerds

Summary Objectives: A lack of generally applicable tools for the assessment of predictions for survival data has to be recognized. Prediction error curves based on the Brier score that have been suggested as a sensible approach are illustrated by means of a case study. Methods: The concept of predictions made in terms of conditional survival probabilities given the patient’s covariates is introduced. Such predictions are derived from various statistical models for survival data including artificial neural networks. The idea of how the prediction error of a prognostic classification scheme can be followed over time is illustrated with the data of two studies on the prognosis of node positive breast cancer patients, one of them serving as an independent test data set. Results and Conclusions: The Brier score as a function of time is shown to be a valuable tool for assessing the predictive performance of prognostic classification schemes for survival data incorporating censored observations. Comparison with the prediction based on the pooled Kaplan Meier estimator yields a benchmark value for any classification scheme incorporating patient’s covariate measurements. The problem of an overoptimistic assessment of prediction error caused by data-driven modelling as it is, for example, done with artificial neural nets can be circumvented by an assessment in an independent test data set.



Author(s):  
David Martínez-Cecilia ◽  
Dennis A. Wicherts ◽  
Federica Cipriani ◽  
Giammauro Berardi ◽  
Leonid Barkhatov ◽  
...  


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