Matching Adjusted Indirect Comparison
Basic Kaplan Meier (KM) plot function
Basic Kaplan Meier (KM) plot function using ggplot
Bucher method for combining treatment effects
Calculate Statistics for Weight Plot Legend
Center individual patient data (IPD) variables using aggregate data av...
Check to see if weights are optimized correctly
Create dummy variables from categorical variables in an individual pat...
Note on Expected Sample Size Reduction
Derive individual weights in the matching step of MAIC
helper function: transform TTE ADaM data to suitable input for surviva...
Calculate standard error from the reported confidence interval.
Create pseudo IPD given aggregated binary data
Convert Time Values Using Scaling Factors
Helper function to summarize outputs from glm fit
Kaplan Meier (KM) plot function for anchored and unanchored cases
Kaplan-Meier (KM) plot function for anchored and unanchored cases usin...
Anchored MAIC for binary and time-to-event endpoint
Unanchored MAIC for binary and time-to-event endpoint
maicplus: Matching Adjusted Indirect Comparison
Helper function to retrieve median survival time from a `survival::sur...
PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-tim...
PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit
Diagnosis plot of proportional hazard assumption for anchored and unan...
Plot MAIC weights in a histogram with key statistics in legend
Plot MAIC weights in a histogram with key statistics in legend using `...
Pre-process aggregate data
Reformat maicplus_bucher alike object
Helper function to select set of variables used for Kaplan-Meier plot
Get and Set Time Conversion Factors
Facilitates performing matching adjusted indirect comparison (MAIC) analysis where the endpoint of interest is either time-to-event (e.g. overall survival) or binary (e.g. objective tumor response). The method is described by Signorovitch et al (2012) <doi:10.1016/j.jval.2012.05.004>.
Useful links