Finalfit now includes bootstrap simulation for model prediction

This post was originally published here

If your new to modelling in R and don’t know what this title means, you definitely want to look into doing it.

I’ve always been a fan of converting model outputs to real-life quantities of interest. For example, I like to supplement a logistic regression model table with predicted probabilities for a given set of explanatory variable levels. This can be more intuitive than odds ratios, particularly for a lay audience.

For example, say I have run a logistic regression model for predicted 5 year survival after colon cancer. What is the actual probability of death for a patient under 40 with a small cancer that has not perforated? How does that probability differ for a patient over 40?

I’ve tried this various ways. I used Zelig for a while including here, but it started trying to do too much and was always broken (I updated it the other day in the hope that things were better, but was met with a string of errors again).

I also used rms, including here (checkout the nice plots!). I like it and respect the package. But I don’t use it as standard and so need to convert all the models first, e.g. to lrm. Again, for my needs it tries to do too much and I find datadist awkward.

Thirdly, I love Stan for this, e.g. used in this paper. The generated quantities block allows great flexibility to simulate whatever you wish from the posterior. I’m a Bayesian at heart will always come back to this. But for some applications it’s a bit much, and takes some time to get running as I want.

I often simply want to predict y-hat from lm and glm with bootstrapped intervals and ideally a comparison of explanatory levels sets. Just like sim does in Zelig. But I want it in a format I can immediately use in a publication.

Well now I can with finalfit.

You need to use the github version of the package until CRAN is updated

There’s two main functions with some new internals to help expand to other models in the future.

Create new dataframe of explanatory variable levels

finalfit_newdata is used to generate a new dataframe. I usually want to set 4 or 5 combinations of x levels and often find it difficult to get this formatted for predict. Pass the original dataset, the names of explanatory variables used in the model, and a list of levels for these. For the latter, they can be included as rows or columns. If the data type is incorrect or you try to pass factor levels that don’t exist, it will fail with a useful warning.

Run bootstrap simulations of model predictions

boot_predict takes standard lm and glm model objects, together with finalfit lmlist and glmlist objects from fitters, e.g. lmmulti and glmmulti. In addition, it requires a newdata object generated from finalfit_newdata. If you’re new to this, don’t be put off by all those model acronyms, it is straightforward.

Note that the number of simulations (R) here is low for demonstration purposes. You should expect to use 1000 to 10000 to ensure you have stable estimates.

Output to Word, PDF, and html via RMarkdown

Simulations are produced using bootstrapping and everything is tidily outputted in a table/dataframe, which can be passed to knitr::kable.

Make comparisons

Better still, by including boot_compare==TRUE, comparisons are made between the first row of newdata and each subsequent row. These can be first differences (e.g. absolute risk differences) or ratios (e.g. relative risk ratios). The comparisons are done on the individual bootstrap predictions and the distribution summarised as a mean with percentile confidence intervals (95% CI as default, e.g. 2.5 and 97.5 percentiles). A p-value is generated on the proportion of values on the other side of the null from the mean, e.g. for a ratio greater than 1.0, p is the number of bootstrapped predictions under 1.0. Multiplied by two so it is two-sided. (Sorry about including a p-value).

Scroll right here:

What is not included?

It doesn’t yet include our other common models, such as coxph which I may add in. It doesn’t do lmer or glmer either. bootMer works well mixed-effects models which take a bit more care and thought, e.g. how are random effects to be handled in the simulations. So I don’t have immediate plans to add that in, better to do directly.

Plotting

Finally, as with all finalfit functions, results can be produced as individual variables using condense == FALSE. This is particularly useful for plotting

So there you have it. Straightforward bootstrapped simulations of model predictions, together with comparisons and easy plotting.

Finalfit, knitr and R Markdown for quick results

This post was originally published here

Thank you for the many requests to provide some extra info on how best to get finalfit results out of RStudio, and particularly into Microsoft Word.

Here is how.

Make sure you are on the most up-to-date version of finalfit.

devtools::install_github("ewenharrison/finalfit")

What follows is for demonstration purposes and is not meant to illustrate model building.

Does a tumour characteristic (differentiation) predict 5-year survival?

Demographics table

First explore variable of interest (exposure) by making it the dependent.

library(finalfit)
library(dplyr)

dependent = "differ.factor"

# Specify explanatory variables of interest
explanatory = c("age", "sex.factor", 
  "extent.factor", "obstruct.factor", 
  "nodes")

Note this useful alternative way of specifying explanatory variable lists:

colon_s %>% 
  select(age, sex.factor, 
  extent.factor, obstruct.factor, nodes) %>% 
  names() -> explanatory

Look at associations between our exposure and other explanatory variables. Include missing data.

colon_s %>% 
  summary_factorlist(dependent, explanatory, 
  p=TRUE, na_include=TRUE)

label              levels        Well    Moderate       Poor      p
       Age (years)           Mean (SD) 60.2 (12.8) 59.9 (11.7)  59 (12.8)  0.788
               Sex              Female   51 (11.6)  314 (71.7)  73 (16.7)  0.400
                                  Male    42 (9.0)  349 (74.6)  77 (16.5)       
  Extent of spread           Submucosa    5 (25.0)   12 (60.0)   3 (15.0)  0.081
                                Muscle   12 (11.8)   78 (76.5)  12 (11.8)       
                                Serosa   76 (10.2)  542 (72.8) 127 (17.0)       
                   Adjacent structures     0 (0.0)   31 (79.5)   8 (20.5)       
       Obstruction                  No    69 (9.7)  531 (74.4) 114 (16.0)  0.110
                                   Yes   19 (11.0)  122 (70.9)  31 (18.0)       
                               Missing    5 (25.0)   10 (50.0)   5 (25.0)       
             nodes           Mean (SD)   2.7 (2.2)   3.6 (3.4)  4.7 (4.4) 

Note missing data in obstruct.factor. We will drop this variable for now (again, this is for demonstration only). Also see that nodes has not been labelled.
There are small numbers in some variables generating chisq.test warnings (predicted less than 5 in any cell). Generate final table.

Hmisc::label(colon_s$nodes) = "Lymph nodes involved"
explanatory = c("age", "sex.factor", 
  "extent.factor", "nodes")

colon_s %>% 
  summary_factorlist(dependent, explanatory, 
  p=TRUE, na_include=TRUE, 
  add_dependent_label=TRUE) -> table1
table1

Dependent: Differentiation                            Well    Moderate       Poor      p
                Age (years)           Mean (SD) 60.2 (12.8) 59.9 (11.7)  59 (12.8)  0.788
                        Sex              Female   51 (11.6)  314 (71.7)  73 (16.7)  0.400
                                           Male    42 (9.0)  349 (74.6)  77 (16.5)       
           Extent of spread           Submucosa    5 (25.0)   12 (60.0)   3 (15.0)  0.081
                                         Muscle   12 (11.8)   78 (76.5)  12 (11.8)       
                                         Serosa   76 (10.2)  542 (72.8) 127 (17.0)       
                            Adjacent structures     0 (0.0)   31 (79.5)   8 (20.5)       
       Lymph nodes involved           Mean (SD)   2.7 (2.2)   3.6 (3.4)  4.7 (4.4) 

Logistic regression table

Now examine explanatory variables against outcome. Check plot runs ok.

explanatory = c("age", "sex.factor", 
  "extent.factor", "nodes", 
  "differ.factor")
dependent = "mort_5yr"
colon_s %>% 
  finalfit(dependent, explanatory, 
  dependent_label_prefix = "") -> table2

Mortality 5 year                           Alive        Died           OR (univariable)         OR (multivariable)
          Age (years)           Mean (SD) 59.8 (11.4) 59.9 (12.5)  1.00 (0.99-1.01, p=0.986)  1.01 (1.00-1.02, p=0.195)
                  Sex              Female  243 (47.6)  194 (48.0)                          -                          -
                                     Male  268 (52.4)  210 (52.0)  0.98 (0.76-1.27, p=0.889)  0.98 (0.74-1.30, p=0.885)
     Extent of spread           Submucosa    16 (3.1)     4 (1.0)                          -                          -
                                   Muscle   78 (15.3)    25 (6.2)  1.28 (0.42-4.79, p=0.681)  1.28 (0.37-5.92, p=0.722)
                                   Serosa  401 (78.5)  349 (86.4) 3.48 (1.26-12.24, p=0.027) 3.13 (1.01-13.76, p=0.076)
                      Adjacent structures    16 (3.1)    26 (6.4) 6.50 (1.98-25.93, p=0.004) 6.04 (1.58-30.41, p=0.015)
 Lymph nodes involved           Mean (SD)   2.7 (2.4)   4.9 (4.4)  1.24 (1.18-1.30, p

Odds ratio plot

colon_s %>% 
  or_plot(dependent, explanatory, 
  breaks = c(0.5, 1, 5, 10, 20, 30))

To MS Word via knitr/R Markdown

Important. In most R Markdown set-ups, environment objects require to be saved and loaded to R Markdown document.

# Save objects for knitr/markdown
save(table1, table2, dependent, explanatory, file = "out.rda")

We use RStudio Server Pro set-up on Ubuntu. But these instructions should work fine for most/all RStudio/Markdown default set-ups.

In RStudio, select File > New File > R Markdown.

A useful template file is produced by default. Try hitting knit to Word on the knitr button at the top of the .Rmd script window.

Now paste this into the file:

---
title: "Example knitr/R Markdown document"
author: "Ewen Harrison"
date: "22/5/2018"
output:
  word_document: default
---

```{r setup, include=FALSE}
# Load data into global environment. 
library(finalfit)
library(dplyr)
library(knitr)
load("out.rda")
```

## Table 1 - Demographics
```{r table1, echo = FALSE, results='asis'}
kable(table1, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))
```

## Table 2 - Association between tumour factors and 5 year mortality
```{r table2, echo = FALSE, results='asis'}
kable(table2, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))
```

## Figure 1 - Association between tumour factors and 5 year mortality
```{r figure1, echo = FALSE}
colon_s %>% 
  or_plot(dependent, explanatory)
```

It’s ok, but not great.

Create Word template file

Now, edit the Word template. Click on a table. The style should be compact. Right click > Modify... > font size = 9. Alter heading and text styles in the same way as desired. Save this as template.docx. Upload to your project folder. Add this reference to the .Rmd YAML heading, as below. Make sure you get the space correct.

The plot also doesn’t look quite right and it prints with warning messages. Experiment with fig.width to get it looking right.

Now paste this into your .Rmd file and run:

---
title: "Example knitr/R Markdown document"
author: "Ewen Harrison"
date: "21/5/2018"
output:
  word_document:
    reference_docx: template.docx  
---

```{r setup, include=FALSE}
# Load data into global environment. 
library(finalfit)
library(dplyr)
library(knitr)
load("out.rda")
```

## Table 1 - Demographics
```{r table1, echo = FALSE, results='asis'}
kable(table1, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))
```

## Table 2 - Association between tumour factors and 5 year mortality
```{r table2, echo = FALSE, results='asis'}
kable(table2, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))
```

## Figure 1 - Association between tumour factors and 5 year mortality
```{r figure1, echo = FALSE, warning=FALSE, message=FALSE, fig.width=10}
colon_s %>% 
  or_plot(dependent, explanatory)
```

This is now looking good for me, and further tweaks can be made.

To PDF via knitr/R Markdown

Default settings for PDF:

---
title: "Example knitr/R Markdown document"
author: "Ewen Harrison"
date: "21/5/2018"
output:
  pdf_document: default
---

```{r setup, include=FALSE}
# Load data into global environment. 
library(finalfit)
library(dplyr)
library(knitr)
load("out.rda")
```

## Table 1 - Demographics
```{r table1, echo = FALSE, results='asis'}
kable(table1, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))
```

## Table 2 - Association between tumour factors and 5 year mortality
```{r table2, echo = FALSE, results='asis'}
kable(table2, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))
```

## Figure 1 - Association between tumour factors and 5 year mortality
```{r figure1, echo = FALSE}
colon_s %>% 
  or_plot(dependent, explanatory)
```

Again, ok but not great.

We can fix the plot in exactly the same way. But the table is off the side of the page. For this we use the kableExtra package. Install this in the normal manner. You may also want to alter the margins of your page using geometry in the preamble.

---
title: "Example knitr/R Markdown document"
author: "Ewen Harrison"
date: "21/5/2018"
output:
  pdf_document: default
geometry: margin=0.75in
---

```{r setup, include=FALSE}
# Load data into global environment. 
library(finalfit)
library(dplyr)
library(knitr)
library(kableExtra)
load("out.rda")
```

## Table 1 - Demographics
```{r table1, echo = FALSE, results='asis'}
kable(table1, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"),
						booktabs=TRUE)
```

## Table 2 - Association between tumour factors and 5 year mortality
```{r table2, echo = FALSE, results='asis'}
kable(table2, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"),
			booktabs=TRUE) %>% 
	kable_styling(font_size=8)
```

## Figure 1 - Association between tumour factors and 5 year mortality
```{r figure1, echo = FALSE, warning=FALSE, message=FALSE, fig.width=10}
colon_s %>% 
  or_plot(dependent, explanatory)
```

This is now looking pretty good for me as well.

There you have it. A pretty quick workflow to get final results into Word and a PDF.

Elegant regression results tables and plots in R: the finalfit package

This post was originally published here

The finafit package brings together the day-to-day functions we use to generate final results tables and plots when modelling. I spent many years repeatedly manually copying results from R analyses and built these functions to automate our standard healthcare data workflow. It is particularly useful when undertaking a large study involving multiple different regression analyses. When combined with RMarkdown, the reporting becomes entirely automated. Its design follows Hadley Wickham’s tidy tool manifesto.

Installation and Documentation

It lives on GitHub.

You can install finalfit from github with:

It is recommended that this package is used together with dplyr, which is a dependent.

Some of the functions require rstan and boot. These have been left as Suggests rather than Depends to avoid unnecessary installation. If needed, they can be installed in the normal way:

To install off-line (or in a Safe Haven), download the zip file and use devtools::install_local().

Main Features

1. Summarise variables/factors by a categorical variable

summary_factorlist() is a wrapper used to aggregate any number of explanatory variables by a single variable of interest. This is often “Table 1” of a published study. When categorical, the variable of interest can have a maximum of five levels. It uses Hmisc::summary.formula().

See other options relating to inclusion of missing data, mean vs. median for continuous variables, column vs. row proportions, include a total column etc.

summary_factorlist() is also commonly used to summarise any number of variables by an outcome variable (say dead yes/no).

Tables can be knitted to PDF, Word or html documents. We do this in RStudio from a .Rmd document. Example chunk:

2. Summarise regression model results in final table format

The second main feature is the ability to create final tables for linear (lm()), logistic (glm()), hierarchical logistic (lme4::glmer()) and
Cox proportional hazards (survival::coxph()) regression models.

The finalfit() “all-in-one” function takes a single dependent variable with a vector of explanatory variable names (continuous or categorical variables) to produce a final table for publication including summary statistics, univariable and multivariable regression analyses. The first columns are those produced by summary_factorist(). The appropriate regression model is chosen on the basis of the dependent variable type and other arguments passed.

Logistic regression: glm()

Of the form: glm(depdendent ~ explanatory, family="binomial")

Logistic regression with reduced model: glm()

Where a multivariable model contains a subset of the variables included specified in the full univariable set, this can be specified.

Mixed effects logistic regression: lme4::glmer()

Of the form: lme4::glmer(dependent ~ explanatory + (1 | random_effect), family="binomial")

Hierarchical/mixed effects/multilevel logistic regression models can be specified using the argument random_effect. At the moment it is just set up for random intercepts (i.e. (1 | random_effect), but in the future I’ll adjust this to accommodate random gradients if needed (i.e. (variable1 | variable2).

Cox proportional hazards: survival::coxph()

Of the form: survival::coxph(dependent ~ explanatory)

Add common model metrics to output

metrics=TRUE provides common model metrics. The output is a list of two dataframes. Note chunk specification for output below.

Rather than going all-in-one, any number of subset models can be manually added on to a summary_factorlist() table using finalfit_merge(). This is particularly useful when models take a long-time to run or are complicated.

Note the requirement for fit_id=TRUE in summary_factorlist(). fit2df extracts, condenses, and add metrics to supported models.

Bayesian logistic regression: with stan

Our own particular rstan models are supported and will be documented in the future. Broadly, if you are running (hierarchical) logistic regression models in [Stan](http://mc-stan.org/users/interfaces/rstan) with coefficients specified as a vector labelled beta, then fit2df() will work directly on the stanfit object in a similar manner to if it was a glm or glmerMod object.

3. Summarise regression model results in plot

Models can be summarized with odds ratio/hazard ratio plots using or_plot, hr_plot and surv_plot.

OR plot

HR plot

Kaplan-Meier survival plots

KM plots can be produced using the library(survminer)

Notes

Use Hmisc::label() to assign labels to variables for tables and plots.

Export dataframe tables directly or to R Markdown knitr::kable().

Note wrapper summary_missing() is also useful. Wraps mice::md.pattern.

Development will be on-going, but any input appreciated.

Elegant regression results tables and plots in R: the finalfit package

This post was originally published here

The finafit package brings together the day-to-day functions we use to generate final results tables and plots when modelling. I spent many years repeatedly manually copying results from R analyses and built these functions to automate our standard healthcare data workflow. It is particularly useful when undertaking a large study involving multiple different regression analyses. When combined with RMarkdown, the reporting becomes entirely automated. Its design follows Hadley Wickham’s tidy tool manifesto.

Installation and Documentation

It lives on GitHub.

You can install finalfit from github with:

# install.packages("devtools")
devtools::install_github("ewenharrison/finalfit")

It is recommended that this package is used together with dplyr, which is a dependent.

Some of the functions require rstan and boot. These have been left as Suggests rather than Depends to avoid unnecessary installation. If needed, they can be installed in the normal way:

install.packages("rstan")
install.packages("boot")

To install off-line (or in a Safe Haven), download the zip file and use devtools::install_local().

Main Features

1. Summarise variables/factors by a categorical variable

summary_factorlist() is a wrapper used to aggregate any number of explanatory variables by a single variable of interest. This is often “Table 1” of a published study. When categorical, the variable of interest can have a maximum of five levels. It uses Hmisc::summary.formula().

library(finalfit)
library(dplyr)

# Load example dataset, modified version of survival::colon
data(colon_s)

# Table 1 - Patient demographics by variable of interest ----
explanatory = c("age", "age.factor", 
  "sex.factor", "obstruct.factor")
dependent = "perfor.factor" # Bowel perforation
colon_s %>%
  summary_factorlist(dependent, explanatory,
  p=TRUE, add_dependent_label=TRUE)

See other options relating to inclusion of missing data, mean vs. median for continuous variables, column vs. row proportions, include a total column etc.

summary_factorlist() is also commonly used to summarise any number of variables by an outcome variable (say dead yes/no).

# Table 2 - 5 yr mortality ----
explanatory = c("age.factor", 
  "sex.factor",
  "obstruct.factor")
dependent = 'mort_5yr'
colon_s %>%
  summary_factorlist(dependent, explanatory, 
  p=TRUE, add_dependent_label=TRUE)

Tables can be knitted to PDF, Word or html documents. We do this in RStudio from a .Rmd document. Example chunk:

```{r, echo = FALSE, results='asis'}
knitr::kable(example_table, row.names=FALSE, 
    align=c("l", "l", "r", "r", "r", "r"))
```

2. Summarise regression model results in final table format

The second main feature is the ability to create final tables for linear (lm()), logistic (glm()), hierarchical logistic (lme4::glmer()) and
Cox proportional hazards (survival::coxph()) regression models.

The finalfit() “all-in-one” function takes a single dependent variable with a vector of explanatory variable names (continuous or categorical variables) to produce a final table for publication including summary statistics, univariable and multivariable regression analyses. The first columns are those produced by summary_factorist(). The appropriate regression model is chosen on the basis of the dependent variable type and other arguments passed.

Logistic regression: glm()

Of the form: glm(depdendent ~ explanatory, family="binomial")

explanatory = c("age.factor", "sex.factor", 
  "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  finalfit(dependent, explanatory)

Logistic regression with reduced model: glm()

Where a multivariable model contains a subset of the variables included specified in the full univariable set, this can be specified.

explanatory = c("age.factor", "sex.factor", 
  "obstruct.factor", "perfor.factor")
explanatory_multi = c("age.factor", 
  "obstruct.factor")
dependent = 'mort_5yr'
colon_s %>%
  finalfit(dependent, explanatory, 
  explanatory_multi)

Mixed effects logistic regression: lme4::glmer()

Of the form: lme4::glmer(dependent ~ explanatory + (1 | random_effect), family="binomial")

Hierarchical/mixed effects/multilevel logistic regression models can be specified using the argument random_effect. At the moment it is just set up for random intercepts (i.e. (1 | random_effect), but in the future I’ll adjust this to accommodate random gradients if needed (i.e. (variable1 | variable2).

explanatory = c("age.factor", "sex.factor", 
  "obstruct.factor", "perfor.factor")
explanatory_multi = c("age.factor", "obstruct.factor")
random_effect = "hospital"
dependent = 'mort_5yr'
colon_s %>%
  finalfit(dependent, explanatory, 
  explanatory_multi, random_effect)

Cox proportional hazards: survival::coxph()

Of the form: survival::coxph(dependent ~ explanatory)

explanatory = c("age.factor", "sex.factor", 
"obstruct.factor", "perfor.factor")
dependent = "Surv(time, status)"
colon_s %>%
  finalfit(dependent, explanatory)

Add common model metrics to output

metrics=TRUE provides common model metrics. The output is a list of two dataframes. Note chunk specification for output below.

explanatory = c("age.factor", "sex.factor", 
  "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  finalfit(dependent, explanatory, 
  metrics=TRUE)

```{r, echo=FALSE, results="asis"}
knitr::kable(table7[[1]], row.names=FALSE, align=c("l", "l", "r", "r", "r"))
knitr::kable(table7[[2]], row.names=FALSE)
```

Rather than going all-in-one, any number of subset models can be manually added on to a summary_factorlist() table using finalfit_merge(). This is particularly useful when models take a long-time to run or are complicated.

Note the requirement for fit_id=TRUE in summary_factorlist(). fit2df extracts, condenses, and add metrics to supported models.

explanatory = c("age.factor", "sex.factor", 
  "obstruct.factor", "perfor.factor")
explanatory_multi = c("age.factor", "obstruct.factor")
random_effect = "hospital"
dependent = 'mort_5yr'

# Separate tables
colon_s %>%
  summary_factorlist(dependent, 
  explanatory, fit_id=TRUE) -> example.summary

colon_s %>%
  glmuni(dependent, explanatory) %>%
  fit2df(estimate_suffix=" (univariable)") -> example.univariable

colon_s %>%
  glmmulti(dependent, explanatory) %>%
  fit2df(estimate_suffix=" (multivariable)") -> example.multivariable

colon_s %>%
  glmmixed(dependent, explanatory, random_effect) %>%
  fit2df(estimate_suffix=" (multilevel)") -> example.multilevel

# Pipe together
example.summary %>%
  finalfit_merge(example.univariable) %>%
  finalfit_merge(example.multivariable) %>%
  finalfit_merge(example.multilevel) %>%
  select(-c(fit_id, index)) %>% # remove unnecessary columns
  dependent_label(colon_s, dependent, prefix="") # place dependent variable label

Bayesian logistic regression: with stan

Our own particular rstan models are supported and will be documented in the future. Broadly, if you are running (hierarchical) logistic regression models in [Stan](http://mc-stan.org/users/interfaces/rstan) with coefficients specified as a vector labelled beta, then fit2df() will work directly on the stanfit object in a similar manner to if it was a glm or glmerMod object.

3. Summarise regression model results in plot

Models can be summarized with odds ratio/hazard ratio plots using or_plot, hr_plot and surv_plot.

OR plot

# OR plot
explanatory = c("age.factor", "sex.factor", 
  "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
  or_plot(dependent, explanatory)
# Previously fitted models (`glmmulti()` or 
# `glmmixed()`) can be provided directly to `glmfit`

HR plot

# HR plot
explanatory = c("age.factor", "sex.factor", 
  "obstruct.factor", "perfor.factor")
dependent = "Surv(time, status)"
colon_s %>%
  hr_plot(dependent, explanatory, dependent_label = "Survival")
# Previously fitted models (`coxphmulti`) can be provided directly using `coxfit`

Kaplan-Meier survival plots

KM plots can be produced using the library(survminer)

# KM plot
explanatory = c("perfor.factor")
dependent = "Surv(time, status)"
colon_s %>%
  surv_plot(dependent, explanatory, 
  xlab="Time (days)", pval=TRUE, legend="none")

Notes

Use Hmisc::label() to assign labels to variables for tables and plots.

label(colon_s$age.factor) = "Age (years)"

Export dataframe tables directly or to R Markdown knitr::kable().

Note wrapper summary_missing() is also useful. Wraps mice::md.pattern.

colon_s %>%
  summary_missing(dependent, explanatory)

Development will be on-going, but any input appreciated.

Install github package on safe haven server

This post was originally published here

I’ve had few enquires about how to install the summarizer package on a server without internet access, such as the NHS Safe Havens.

  1. Uploadsummarizer-master.zip from here to server.
  2. Unzip.
  3. Run this:


library(devtools)
source = devtools:::source_pkg("summarizer-master")
install(source)

Edit

As per comments, devtools::install_local() has previously failed, but may now also work directly.

Install github package on safe haven server

This post was originally published here

I’ve had few enquires about how to install the summarizer package on a server without internet access, such as the NHS Safe Havens.

  1. Uploadsummarizer-master.zip from here to server.
  2. Unzip.
  3. Run this:


library(devtools)
source = devtools:::source_pkg("summarizer-master")
install(source)

Edit

As per comments, devtools::install_local() has previously failed, but may now also work directly.

P-values from random effects linear regression models

This post was originally published here

 is a useful frequentist approach to hierarchical/multilevel linear regression modelling. For good reason, the model output only includes t-values and doesn’t include p-values (partly due to the difficulty in estimating the degrees of freedom, as discussed here).

Yes, p-values are evil and we should continue to try and expunge them from our analyses. But I keep getting asked about this. So here is a simple bootstrap method to generate two-sided parametric p-values on the fixed effects coefficients. Interpret with caution.

 

P-values from random effects linear regression models

This post was originally published here

lme4::lmer

 is a useful frequentist approach to hierarchical/multilevel linear regression modelling. For good reason, the model output only includes t-values and doesn’t include p-values (partly due to the difficulty in estimating the degrees of freedom, as discussed here).

Yes, p-values are evil and we should continue to try and expunge them from our analyses. But I keep getting asked about this. So here is a simple bootstrap method to generate two-sided parametric p-values on the fixed effects coefficients. Interpret with caution.

library(lme4)

# Run model with lme4 example data
fit = lmer(angle ~ recipe + temp + (1|recipe:replicate), cake)

# Model summary
summary(fit)

# lme4 profile method confidence intervals
confint(fit)

# Bootstrapped parametric p-values
boot.out = bootMer(fit, fixef, nsim=1000) #nsim determines p-value decimal places 
p = rbind(
  (1-apply(boot.out$t<0, 2, mean))*2,
  (1-apply(boot.out$t>0, 2, mean))*2)
apply(p, 2, min)

# Alternative "pipe" syntax
library(magrittr)

lmer(angle ~ recipe + temp + (1|recipe:replicate), cake) %>% 
  bootMer(fixef, nsim=100) %$% 
  rbind(
  (1-apply(t<0, 2, mean))*2,
  (1-apply(t>0, 2, mean))*2) %>% 
  apply(2, min)