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Logistic Regression and Prediction for Health Data · LearnSpace
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Logistic Regression and Prediction for Health Data

Курс от University of Michigan
Средний≈ 11.3 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course introduces learners to the analysis of binary/dichotomous outcomes. Learners will become familiar with fundamental tests for two-group comparisons and statistical inference plus prediction more broadly using logistic regression. They will understand the connection between prevalence, risk ratios, and odds ratios. By the end of this course, learners will be able to understand how binary outcomes arise, how to use R to compare proportions between two groups, how to fit logistic regressions in R, how to make predictions using logistic regression, and how to assess the quality of these predictions. All concepts taught in this course will be covered with multiple modalities: slide-based lectures, guided coding practice with the instructor, and independent but structured exercises.

Навыки, которые вы освоите

Logistic RegressionStatistical InferenceStatistical Hypothesis TestingModel EvaluationPredictive ModelingDescriptive StatisticsEpidemiologyRegression AnalysisStatistical MethodsStatistical ModelingProbability & StatisticsPredictive AnalyticsBiostatisticsData AnalysisR ProgrammingStatistical Analysis

Программа курса

3 модулей · 62 учебных материалов

01Simple Comparisons of Binary Outcomes24 материалов

1.0 Welcome to the Course

Data Science for Health Research: Specialization IntroductionВидеоMeet Your InstructorsЧтениеWelcome & Course SyllabusЧтениеMeet Your Fellow Global ClassmatesОбсуждение

Учитесь у экспертов

Philip S. Boonstra

Associate Professor, Biostatistics

Bhramar Mukherjee

Former John D Kalbfleisch Distinguished University Professor of Biostatistics

Logistic Regression and Prediction for Health Data
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Обучение на Coursera

≈ 11.3 ч

3 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Испанский, Японский

Часть программы вашего университета
Pre-Course SurveyЧтение
Introduction To and How To Use Independent Guides Чтение
Introduction to the BPUrban DataЧтение

1.1 How Binary Outcomes Arise

How and When Binary Outcomes Can AriseВидеоA Need for Models Beyond Linear RegressionВидео

1.2 Comparing Risk Between Two Groups

Binary Outcomes, Comparing Between Two Groups (Part 1)ВидеоBinary Outcomes, Comparing Between Two groups (part 2)ВидеоBinary Outcomes, Comparing Between Two groups (part 3)ВидеоGuided Practice: Z-TestВидеоGuided Practice: Fisher's Exact TestВидео1.2 Independent GuideЧтениеAnalyzing a Binary Outcome and Binary Exposure with the Odds RatioВидеоInterpreting the Odds RatioВидео2x2 Example: The WCGS Cardiovascular StudyВидео1.2 Practice QuizЗадание1.2 Discussion PromptОбсуждение1.2 Discussion Prompt Suggested AnswerЧтение

End of Module 1 Activities

Module 1 QuizЗаданиеEnd of Module 1 Discussion PromptОбсуждениеEnd of Module 1 Discussion Prompt Suggested AnswerЧтение
02Introducing Logistic Regression16 материалов

2.1 Modeling Binary Outcomes With Logistic Regression

Limitations of the 2x2 Table AnalysisВидеоLogistic Regression: A First LookВидеоVisualizing and Interpreting a Logistic RegressionВидеоRevising the 2x2 Example: WCGS Cardiovascular StudyВидеоGuided practice: Fitting a Simple Logistic Regression Against One VariableВидео2.1 Independent GuideЧтение2.1 Practice QuizЗадание

2.2 Multivariable Logistic Regression

Extending the WCGS Cardiovascular Model with Multivariable Logistic RegressionВидеоPrediction with Multivariable Logistic RegressionВидеоLogistic Regression: A Recap and ReviewВидеоGuided Practice: Fitting a Logistic Regression Against More Than One VariableВидеоGuided Practice: Calculating Predicted ProbabilitiesВидеоGuided Practice: Visualizing a Fitted Logistic Regression ModelВидео

End of Module 2 Activities

Module 2 QuizЗадание
03Assessing the Predictive Accuracy of Logistic Regression Models22 материалов

3.1 Assessing the Performance of Risk Prediction Models

Why Do We Need to Assess Predictions? ВидеоExtracting Probabilities from a Logistic RegressionВидео

3.2 Calibration Versus Discrimination

How Do We Determine if Predicted Probabilities are "Good"?ВидеоModel CalibrationВидеоHosmer-Lemeshow TestВидеоModel DiscriminationВидеоChanging the Cutpoint Changes Sensitivity and SpecificityВидео

3.3 Receiver Operating Characteristic (ROC) Curves and Area Under the ROC Curve (AUC)

Receiver Operating Characteristic (ROC) CurveВидеоArea Under the ROC Curve (AUC)ВидеоAUC Example: Risk of Coronary Heart DiseaseВидеоBrier ScoreВидеоCross ValidationВидеоGuided Practice: Assessing the Predictive Ability of Logistic Regression ModelsВидео

End of Module 3 Activities

Module 3 QuizЗаданиеEnd of Module 3 Discussion PromptОбсуждениеEnd of Module 3 Discussion Prompt Suggested AnswerЧтение

Post-Course Survey

Post-Course SurveyЧтение
2.2 Independent GuideЧтение
2.2 Practice QuizЗадание
Guided Practice: ROC and AUCВидео
Guided Practice: Brier ScoreВидео
3.3 Independent GuideЧтение
Case Study: Treatment of Testicular CancerВидео
3.3 Practice QuizЗадание