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(25 Dec 2012,
FrankHarrell
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---+ BIOS 330 Concepts to Master 1 Assumptions of linear additive models 1 Methods for checking these assumptions. 1 Global vs. partial tests of association 1 Multiple ways of computing test statistics in multiple regression models that were fitted using ordinary least squares 1 Dummy variables and how their corresponding regression coefficients are interpreted 1 Interpretation of interaction effects 1 Assumptions of interaction tests 1 Writing null hypotheses precisely in terms of parameters being tested 1 Understanding tests for the overall association of a predictor with the response, and how to test sub-hypotheses such as linearity 1 Combined partial tests for multiple predictors 1 Combined tests for overall effects of a predictor when it interacts with other predictors 1 Regression splines (linear, cubic, and restricted cubic) and knots 1 How knots are chosen 1 How the number of knots relates to the flexibility allowed for the fit 1 Which if any terms of a predictor that is expanded into multiple constructed variables can be tested singly 1 What tests of effects, interactions, and nonlinearity are powered to detect 1 Nonparametric smoothers 1 Problems with naive approaches of handing missing data 1 The effect of changing how models are fitted based on looking at the data 1 Deciding on the number of degrees of freedom to "spend" in a model, and where to spend them 1 Understand regression to the mean 1 Have an initial understanding of data reduction 1 Elements of bootstrapping 1 Model validation approaches and which methods of validation are most stringent. 1 How to display a complex regression model to a non-statistician. 1 How to make a complex nonlinear relationship a non-issue to the reader. 1 A principle for estimating unknown parameters when least squares is not appropriate. 1 What is a Wald statistic and a likelihood ratio statistic in general terms, and which one works better. 1 When chi-square statistics are used instead of t or F statistics, and how to approximately relate a chi-square statistic to an F statistic. 1 Exact interpretation of logistic model coefficients in the linear regression case. 1 Assumptions of binary logistic regression. 1 The value and use of a nonparametric smoother in examining logistic model assumptions or in determining shapes of relationships when Y is binary. 1 How to convert between probabilities, odds, and log odds. 1 Measures of predictive accuracy and predictive ability for binary logistic models. 1 What is meant by an ordinal response variable and what is assumed about the data when you use a model or a rank test on an ordinal response. 1 How to interpret coefficients in proportional odds models. 1 What about odds ratios is assumed by the proportional odds model. 1 How are ordinary nonparametric rank tests relate to the proportional odds model. 1 What is the value of only using the ordering of Y. -- Main.FrankHarrell - 25 Dec 2012
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Topic revision: r1 - 25 Dec 2012,
FrankHarrell
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