Join Monika Wahi for an in-depth discussion in this video Reviewing existing literature for ideas, part of Healthcare Analytics: Regression in R.
- So far, you've made it to chapter one,…section three,…where I wanted to spend just a little time…talking conceptually about selecting a topic…for your BRFSS analysis,…mainly through reviewing what other topics…have already been studied and published…about the BRFSS.…So, I selected two example papers…that use BRFSS data sets from various years…to answer questions.…I have no connection with any of these papers.…I just found them in the literature,…and will use them as examples.…
These two papers are posted in your resources,…but in this lecture,…instead of focusing on the nuts and bolts…of operationalizing these components of this hypothesis,…as we have gone over,…I will instead focus on the big picture…of how these authors came up with the idea…to do this analysis and write this paper.…After all, the papers were so compelling…they even were able to get them published.…So let's think about what they did.…Let's turn our attention to the first one,…which is by Mukherjee and Segal in 2015…about discussions with healthcare providers…
Author
Monika Wahi
Released
1/19/2017- Dealing with scientific plausibility
- Selecting a hypothesis
- Interpreting diagnostic plots
- Working with indexes and model metadata
- Working with quartiles and ranking
- Making a working model
- Improving model fit
- Performing linear regression modeling
- Performing logistic regression modeling
- Performing forward stepwise regression
- Estimating parameters
- Interpreting an odds ratio
- Adding odds ratios to models
- Comparing nested models
- Presenting and interpreting the final model
Skill Level Advanced
Duration
Views
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Introduction
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Introduction to the course1m 40s
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1. Designing Your Research
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Scientific method review5m 13s
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2. Preparing for Linear Regression
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Indexes7m 20s
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Quartiles3m 41s
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Ranking4m 45s
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Regression review3m 53s
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Preparing to report results2m 39s
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3. Beginning Linear Regression Modeling
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Overview of modeling process4m 58s
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Linear regression output5m 29s
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Models 1 and 23m 21s
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Model metadata4m 20s
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4. Final Linear Regression Modeling
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Beginning Model 35m 7s
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Making a working Model 35m 23s
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Finalizing Model 33m 33s
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Looking at the final model5m 58s
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Fishing and interaction5m 5s
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Defending the final model4m 27s
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Presenting the final model6m 27s
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5. Preparing for Logistic Regression
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Odds ratio interpretation6m 26s
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Basic logistic code3m 53s
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6. Developing the Logistic Regression Model
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Running Model 15m 24s
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Adding odds ratios to models5m 35s
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Model metadata6m 36s
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Forward stepwise: Round 25m 53s
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Models 1 and 2 presentation6m 30s
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Model 3 presentation4m 14s
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Conclusion
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Review of metadata4m 41s
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Review of the process3m 21s
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Next steps2m 19s
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Video: Reviewing existing literature for ideas