Wellesley College 1875-1975: A Century of Women

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Cezalandırılmış lojistik regresyon

Lojistik regresyon kategorik verilerin modellemesinde sıklıkla kullanılan bir istatistiksel tekniktir. Kategorik verilerin en yaygın formu ?başarılı? veya ?başarısız?, ?evet? veya ?hayır? gibi ikili kategorilerin olduğu durumlardır. Regresyon modelini oluşturan değişkenler arasında çoklu doğrusal bağlantı olması durumunda, regresyon modelinin başarı oranı önemli ölçüde düşmektedir. Bu çalışmada, çoklu doğrusal bağlantı sorununu gidermek ve modelin başarı oranını arttırmak için karesel cezalandırılmış lojistik regreson modeli kullanılmıştır. En uygun cezalandırma miktarını belirlemek için çeşitli ölçüler kullanılmıştır. Bu iki yöntem gerçek veri setine (koroner kalp krizi verileri) uygulanmış ve performansları bakımından karşılaştırmaları yapılmıştır. Logistic regression (LR) is frequently used modeling technique for categorical response variables in statistical researches. Binary data are the most common form of categorical response for which the binary outcomes ?success? or ?failure?, ?yes? or ?no?. The estimation of regression parameters and classification rate is not accurate when there is multicollinearity among the predictors. In this thesis, we study the penalized logistic regression (PLR) model with quadratic penalization to eliminate the multicollinearity problem and improve the classification rate. We concentrate on several measures for determining the optimum amount of penalization on logistic regression model. We model the real data, coronary heart attack disease data, by both the PLR and LR model and compare their performances

Comparing the diagnostic performance of methods used in a full-factorial design multi-reader multi-case studies

© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.In radiology, patients are frequently diagnosed according to the subjective interpretations of radiologists based on an image. Such diagnosis results may be biased and significantly differ among evaluators (i.e., readers) due to different education levels and experiences. One solution to overcome this problem is to use a multi-reader multi-case study design in which there are multiple readers, and the same images are evaluated multiple times. Several methods, including model-based and bootstrap-based, are available for analyzing the multi-reader multi-case studies. In this study, we aimed to compare the performance of available methods on a mammogram dataset. We also conducted a comprehensive simulation study to generalize the results to more general scenarios. We considered the effect of the number of samples and readers, data structures (i.e., correlation structures and variance components), and overall accuracy of diagnostic tests (AUC) in the simulation set-up. Results showed that the model-based methods had type-I error rates close to the nominal level as the number of samples and readers increased. Bootstrap-based methods, on the other hand, were generally conservative. However, they performed the best when the sample size was small, and the AUC level was high. In conclusion, the performance of the proposed methods was not the same under all conditions and was affected by the factors we considered in the simulation study. Therefore, it is not a perfect strategy to use one method under all scenarios because it may lead to biased conclusions

The joint modeling approach with a simulation study for evaluating the association between the trajectory of serum albumin levels and mortality in peritoneal dialysis patients

We aimed to study the association between mortality and trajectory of serum albumin levels (g/dL) in peritoneal dialysis patients via a joint modeling approach. Joint modeling is a statistical method used to evaluate the relationship between longitudinal and time-to event processes by fitting both sub-models simultaneously. A comprehensive simulation study was conducted to evaluate model performances and generalize the findings to more general scenarios. Model performances and prediction accuracies were evaluated using the time-dependent ROC area under the curve (AUC) and Brier score (BS). According to the real-life dataset results, the trajectory of serum albumin levels was inversely associated with mortality increasing the risk of death 2.21 times (p=0.003). The simulation results showed that the model performances increased with sample size. However, the model complexity had increased as more repeated measurements were taken from patients and resulted in lower prediction accuracy unless the sample size was increased. In conclusion, using the trajectory of risk predictors rather than baseline (or averaged) values provided better predictive accuracy and prevented biased results. Finally, the study design (e.g., number of samples and repeated measurements) should be carefully defined since it played an important role in model performances