get_results_dataframe: Take predicted dataframe and calculate the outcome (risk...

In riskCommunicator: G-Computation to Estimate Interpretable Epidemiological Effects

Take predicted dataframe and calculate the outcome (risk difference/ratio, incidence rate difference/ratio, mean difference, and/or number needed to treat)

Description

Take predicted dataframe and calculate the outcome (risk difference/ratio, incidence rate difference/ratio, mean difference, and/or number needed to treat)

Usage

get_results_dataframe(predict.df, outcome.type)

Arguments

predict.df

(Required) A data.frame output from the make_predict_df function with predicted outcome for each observation at each level of treatment/exposure.

outcome.type

(Required) Character argument to describe the outcome type. Acceptable responses, and the corresponding error distribution and link function used in the glm, include: binary (Default) A binomial distribution with link = 'logit' is used. count A Poisson distribution with link = 'log' is used. count_nb A negative binomial model with link = 'log' is used, where the theta parameter is estimated internally; ideal for over-dispersed count data. rate A Poisson distribution with link = 'log' is used; ideal for events/person-time outcomes. rate_nb A negative binomial model with link = 'log' is used, where the theta parameter is estimated internally; ideal for over-dispersed events/person-time outcomes. continuous A gaussian distribution with link = 'identity' is used.

Value

A list containing the calculated results for the applicable measures (based on the outcome.type): Risk Difference, Risk Ratio, Odds Ratio, Incidence Risk Difference, Incidence Risk Ratio, Mean Difference, Number Needed to Treat, Average Tx (average predicted outcome of all observations with treatment/exposure), and Average noTx (average predicted outcome of all observations without treatment/exposure)

riskCommunicator documentation built on June 1, 2022, 1:07 a.m.