Join Monika Wahi for an in-depth discussion in this video Defending the final model, part of Healthcare Analytics: Regression in R.
- Now that we have decided we can't improve…our model any further, I'll talk to you about ways…to defend your final model.…So, the model did not fit very well.…But we think that's the best we can do.…Not everyone on your research team…will agree with that necessarily.…In my experience, I'm used to getting pushback…from people on my research team.…Often people who don't know statistics,…so it's particularly annoying.…However, I have come up with some good defenses…for the choices I make for my final model,…and I'm going to share them with you in this movie.…
First, sometimes they don't want any…nonsignificant covariates in the model.…Or barely significant covariates, like the ones that were…on the line just above p = 0.05.…If that happens, just take them out.…No reason to fight.…But tell them that you will have less to talk about…in the paper, which is true.…And remember, you can't take out the exposure,…but you can take out any other ones.…Next, sometimes they want the opposite.…Some extra nonsignificant stuff in the model…
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: Defending the final model