You are here: Vanderbilt Biostatistics Wiki>Main Web>Education>HandoutsBioRes>JournalClubs>JournalClubPsychiatry (21 Oct 2015, FrankHarrell)EditAttach

- Review by Adam Pendleton and Madison Johnson
- The authors are to be commended for using a Bayesian approach for making inferences from logistic regression models
- The Bayesian approach allowed the authors to calculate direct probabilities of interesting things, unlike P-values which are probabilities of getting results more impressive than the observed results if nothing interesting is going on
- The paper computes posterior probabilities of being a risk factor
- Credible intervals are given for effects of regressors; these are the Bayesian analogs of confidence intervals but have a much simpler interpretation

- Bayes allows one to make exact inference, unlike traditional frequentist inference in which everything outside of ordinary regression uses approximations
- The authors used a wide distribution (Cauchy) as the prior distribution for each regression coefficient, after arbitrary scaling of the covariate
- They used the R
`arm`

package for the calculations. This package actually uses a rough approximation to full Bayes analysis by assuming that regression coefficients from an ordinary logistic model fit have a multivariate normal distribution. For logistic regression, such approximations are poor. The authors should have used exact Bayesian methods (e.g., the R package`MCMCpack`

). - They used model averaging which was not needed at all in this context
- The authors are confused about statistical terminology.
*Multivariate*was used to refer to multivariable (adjusted) analyses but should be reserved to refer only to the case where one is simulatneously analyzing multiple dependent variables

- Variation in glycohemoglobin explained by various risk factors. From
*Regression Modeling Strategies, second edition*by FE Harrell, 2015:

`size`

entry sums the influence of leg length, subscapular skinfold thickness, tricep skinfold thickness, and waist circumference. `re`

is a combined race + ethnicity multiple degree of freedom effect. Here the measure of explained variation is the Wald chi-square statistic minus the number of degrees of freedom required to achieve that chi-square. The subtraction is to level the playing field so that risk factors having many categories do not get more changes to explain variation in the outcome variable. I | Attachment | Action | Size | Date | Who | Comment |
---|---|---|---|---|---|---|

png | anova.png | manage | 18.0 K | 18 Oct 2015 - 10:12 | FrankHarrell | Variation in glycohemoglobin explained by various risk factors |

html | pendletonJohnsonReview.html | manage | 33.8 K | 21 Oct 2015 - 06:45 | FrankHarrell | Review of Am J Psychiatry March 2015 by Pendleton and Johnson |

Edit | Attach | Print version | History: r3 < r2 < r1 | Backlinks | View wiki text | Edit wiki text | More topic actions

Topic revision: r3 - 21 Oct 2015, FrankHarrell

Copyright © 2013-2022 by the contributing authors. All material on this collaboration platform is the property of the contributing authors.

Ideas, requests, problems regarding Vanderbilt Biostatistics Wiki? Send feedback

Ideas, requests, problems regarding Vanderbilt Biostatistics Wiki? Send feedback