Summary of Regression Modeling Concepts

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Configuring R

Functions from these packages will be used throughout this document:

library(conflicted) # check for conflicting function definitions
# library(printr) # inserts help-file output into markdown output
library(rmarkdown) # Convert R Markdown documents into a variety of formats.
library(pander) # format tables for markdown
library(ggplot2) # graphics
library(ggfortify) # help with graphics
library(dplyr) # manipulate data
library(tibble) # `tibble`s extend `data.frame`s
library(magrittr) # `%>%` and other additional piping tools
library(haven) # import Stata files
library(knitr) # format R output for markdown
library(tidyr) # Tools to help to create tidy data
library(plotly) # interactive graphics
library(dobson) # datasets from Dobson and Barnett 2018
library(parameters) # format model output tables for markdown
library(haven) # import Stata files
library(latex2exp) # use LaTeX in R code (for figures and tables)
library(fs) # filesystem path manipulations
library(survival) # survival analysis
library(survminer) # survival analysis graphics
library(KMsurv) # datasets from Klein and Moeschberger
library(parameters) # format model output tables for
library(webshot2) # convert interactive content to static for pdf
library(forcats) # functions for categorical variables ("factors")
library(stringr) # functions for dealing with strings
library(lubridate) # functions for dealing with dates and times
library(broom) # Summarizes key information about statistical objects in tidy tibbles
library(broom.helpers) # Provides suite of functions to work with regression model 'broom::tidy()' tibbles

Here are some R settings I use in this document:

rm(list = ls()) # delete any data that's already loaded into R

conflicts_prefer(dplyr::filter)
ggplot2::theme_set(
  ggplot2::theme_bw() + 
        # ggplot2::labs(col = "") +
    ggplot2::theme(
      legend.position = "bottom",
      text = ggplot2::element_text(size = 12, family = "serif")))

knitr::opts_chunk$set(message = FALSE)
options('digits' = 6)

panderOptions("big.mark", ",")
pander::panderOptions("table.emphasize.rownames", FALSE)
pander::panderOptions("table.split.table", Inf)
conflicts_prefer(dplyr::filter) # use the `filter()` function from dplyr() by default
legend_text_size = 9
run_graphs = TRUE

1 We use different probability models for different data types

• Binary outcomes: Bernoulli models
• Event rate outcomes: Poisson/Negative binomial models
• Time-to-event outcomes: Survival models
• Catch-all: Gaussian models

2 We use different link functions to connect these models with covariates

• Bernoulli models: logit link
• Count models: log link + offset
• Survival models: log link
• Gaussian models: identity link

Figure 1 shows how the various models we have studied have analogous structures: each row uses model-specific transformations to connect the modeled quantity to a linear predictor \(\eta(\tilde{x}) = \tilde{x} \cdot \tilde{\beta} + \eta_0\).

Figure 1: Parallel structures of GLMs and survival models. Each row shows the transformation chain from the modeled quantity to the linear predictor \(\eta(\tilde{x}) = \tilde{x} \cdot \tilde{\beta} + \eta_0\), where \(\eta_0\) denotes the model-specific intercept term, and \(\eta_0(t)\) denotes the Cox PH baseline log-hazard term.

Linear (Gaussian):

\[\underbrace{\mu}_{\text{mean}} \underset{\xleftarrow[]{=}}{\xrightarrow[]{=}} \underbrace{\eta(\tilde{x}) }_{\substack{\text{linear}\\\text{predictor}}} = \tilde{x} \cdot \tilde{\beta} + \mu(0) \]

Logistic (Binomial):

\[\underbrace{\mu}_{\text{mean}} \underset{ \xleftarrow[\pi \cdot n]{} }{ \xrightarrow{\frac{\mu}{n}} } \underbrace{\pi}_{\text{probability}} \underset{ \xleftarrow[\frac{\omega}{1+\omega}]{} }{ \xrightarrow{\frac{\pi}{1-\pi}} } \underbrace{\omega}_{\text{odds}} \underset{ \xleftarrow[\operatorname{exp}\mathopen{}\left\{\eta\right\}\mathclose{}]{} }{ \xrightarrow{\operatorname{log}\mathopen{}\left\{\omega\right\}\mathclose{}} } \underbrace{\eta}_{\text{log-odds}} = \tilde{x} \cdot \tilde{\beta} + \eta(0) \]

Count (Poisson):

\[\underbrace{\mu}_{\text{mean}} \underset{ \xleftarrow[{\lambda}\cdot t]{} }{ \xrightarrow{\mu / t} } \underbrace{{\lambda}}_{\text{rate}} \underset{ \xleftarrow[\operatorname{exp}\mathopen{}\left\{\eta\right\}\mathclose{}]{} }{ \xrightarrow{\operatorname{log}\mathopen{}\left\{{\lambda}\right\}\mathclose{}} } \underbrace{\eta}_{\text{log-rate}} = \tilde{x} \cdot \tilde{\beta} + \operatorname{log}\mathopen{}\left\{{\lambda}(0)\right\}\mathclose{} \]

Survival (Cox PH):

\[\underbrace{\mu}_{\text{mean}} \xleftarrow[\int_{t=0}^{\infty}{\operatorname{S}(t|\tilde{x})dt}]{} \underbrace{\operatorname{S}(t|\tilde{x})}_{\text{survival}} \underset{ \xleftarrow[\operatorname{exp}\mathopen{}\left\{-{\Lambda}(t|\tilde{x})\right\}\mathclose{}]{} }{ \xrightarrow{-\operatorname{log}\mathopen{}\left\{\operatorname{S}(t|\tilde{x})\right\}\mathclose{}} } \underbrace{{\Lambda}(t|\tilde{x})}_{\text{cumulative hazard}} \underset{ \xleftarrow[\int_0^t {\lambda}(u|\tilde{x})\,du]{} }{ \xrightarrow{{\Lambda}'(t|\tilde{x})} } \underbrace{{\lambda}(t|\tilde{x})}_{\text{hazard}} \underset{ \xleftarrow[\operatorname{exp}\mathopen{}\left\{\eta(t|\tilde{x})\right\}\mathclose{}]{} }{ \xrightarrow{\operatorname{log}\mathopen{}\left\{{\lambda}(t|\tilde{x})\right\}\mathclose{}} } \underbrace{\eta(t|\tilde{x})}_{\text{log-hazard}} = \tilde{x} \cdot \tilde{\beta} + \eta_0(t) \]

3 We use maximum likelihood estimation to fit models to data

• likelihood
• log-likelihood
• score function
• hessian

4 We use asymptotic normality of MLEs to quantify uncertainty about models

• observed information matrix
• expected information matrix
• standard error
• confidence intervals
• p-values

5 We use (log) likelihood ratios to compare models

Sometimes we adjust these comparisons for model size (AIC, BIC)

References