## gtsummary

The {gtsummary} package provides an elegant and flexible way to create publication-ready analytical and summary tables using the R programming language. The {gtsummary} package summarizes data sets, regression models, and more, using sensible defaults with highly customizable capabilities.

• Summarize data frames or tibbles easily in R. Perfect for presenting descriptive statistics, comparing group demographics (e.g creating a Table 1 for medical journals), and more. Automatically detects continuous, categorical, and dichotomous variables in your data set, calculates appropriate descriptive statistics, and also includes amount of missingness in each variable.

• Summarize regression models in R and include reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers (i.e. Odds Ratio and Hazard Ratio).

• Customize gtsummary tables using a growing list of formatting/styling functions. Bold labels, italicize levels, add p-value to summary tables, style the statistics however you choose, merge or stack tables to present results side by side… there are so many possibilities to create the table of your dreams!

• Report statistics inline from summary tables and regression summary tables in R markdown. Make your reports completely reproducible!

Installation Code: install.packages("gtsummary")

## Decision Curve Analysis

Decision-analytic techniques allow assessment of clinical outcomes but often require collection of additional information may be cumbersome to apply to models that yield a continuous result. Decision curve analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results.

• R Package
• Stata
• Installation Code: net install dca, from(https://raw.github.com/ddsjoberg/stata.dca/master/) replace

Vickers AJ, Elkin EB. “Decision curve analysis: a novel method for evaluating prediction models.” Medical Decision Making. 2006 Nov-Dec;26(6):565-74.

Steyerberg EW, Vickers AJ. “Decision curve analysis: a discussion.” Medical Decision Making. 2008 Jan-Feb;28(1):146-9.

Vickers AJ, Cronin AM, Elkin EB, Gonen M. “Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers.” BMC Medical Informatics and Decision Making. 2008 Nov 26;8(1):53.

## Kernel Smoothing

A kernel smoother is a technique to estimate a curve as the weighted average of neighboring observed data. The sjosmooth package (pronounced sō smüt͟h) was built primarily to perform kernel smoothing on censored time-to-event or survival data. The package provides kernel smoothed estimates of survival probabilities at specified times.

• R Package
• Installation Code: devtools::install_github("ddsjoberg/sjosmooth")
• Package documentation, examples, and explaination of statistical theory is on the package website.

Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. “The elements of statistical learning.” Vol. 1. New York: Springer series in statistics, 2001.