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Key Persons | Name | Contact | Office Hours |
---|---|---|---|

Instructors: | Frank E. Harrell, Jr. | f.harrell@vanderbilt.edu | By appointment |

Thomas G. Stewart | thomas.stewart@vanderbilt.edu | By appointment |

- Detailed Course and Reading Schedule
- Grades & Assignments
- Whitlock & Schluter Datasets (also known as ABD Datasets or Analysis of Biological Data Datasets)
- Department collection of Datasets

- Syllabus
- RMS Handouts (AKA Lecture Notes)
- BBR Handouts
- Supplemental Handouts
- Key Course Concepts
- Glossary
- Formulas for OLS
- Supplemental Material on Biostatistical Modeling

- Read assigned sections of books
- Read assigned sections of course notes, listening to audio narrative and watching short movies demonstrating statistical methods that are linked from the notes
- Read assigned supplemental articles

- Review key elements of the assigned material
- Ample time for students' questions about the material and the concepts
- Interactive demonstrations of the methods using datasets from ABD
- In-class assignments using Stata

- Write interpretations of selected analyses done during class
- Take self-quizzes to gauge understanding of key concepts

- (Required) The Analysis of Biological Data, 2nd Edition by MC Whitlock and Dolph Schluter | Supplemental Material, Data, and R Code from ABD
- (Required) Harrell FE:
*Regression Modeling Strategies*, 2nd edition, 2015 (available at the VU bookstore at 2525 West End Ave. and at Amazon) - (Recommended, excellent STATA resources) Dupont WD:
*Statistical Modeling for Biomedical Researchers*, 2nd edition, 2009

- Class announcements and homework assignments will appear on the course slack site. It is an excellent way to keep in touch with the class and even more to ask and answer questions. We hope that all students will use it to:
- ask or answer any question whatsoever related to group assignments
- ask or answer any logistical or purely technical questions related to individual work assignments
- ask or answer any questions about modeling or statistical computing concepts that are not directly related to a pending individual work assignment

- Please also take advantage of the general regression modeling strategies discussion board: stats.stackexchange

- Prognostic estimates can be used to inform the patient about likely outcomes of her disease.
- A physician can use estimates of diagnosis or prognosis as a guide for ordering additional tests and selecting appropriate therapies.
- Outcome assessments are useful in the evaluation of technologies; for example, diagnostic estimates derived both with and without using the results of a given test can be compared to measure the incremental diagnostic information provided by that test over what is provided by prior information.
- A researcher may want to estimate the effect of a single factor (e.g., treatment given) on outcomes in an observational study in which many uncontrolled confounding factors are also measured. Here the simultaneous effects of the uncontrolled variables must be controlled (held constant mathematically if using a regression model) so that the effect of the factor of interest can be more purely estimated. An analysis of how variables (especially continuous ones) affect the patient outcomes of interest is necessary to ascertain how to control their effects.
- Predictive modeling is useful in designing randomized clinical trials. Both the decision concerning which patients to randomize and the design of the randomization process (e.g., stratified randomization using prognostic factors) are aided by the availability of accurate prognostic estimates before randomization. It is also important to adjust for prognostic factors in randomized studies to achieve optimum power and precision. Lastly, accurate prognostic models can be used to test for differential therapeutic benefit or to estimate the clinical benefit for an individual patient in a clinical trial, taking into account the fact that low-risk patients must have less absolute benefit (e.g., lower change in survival probability). To accomplish these objectives, researchers must create multivariable models that accurately reflect the patterns existing in the underlying data and that are valid when applied to comparable data in other settings or institutions. Models may be inaccurate due to violation of assumptions, omission of important predictors, high frequency of missing data and/or improper imputation methods, and especially with small datasets, overfitting.

- Steyerberg EW.
*Clinical Prediction Models*. New York: Springer; 2009. - Cosma Shalizi's Undergraduate Advanced Data Analysis course
- TRIPOD: Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD):

Topic revision: r81 - 05 Jan 2018, ThomasStewart

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