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R/nomatch

DOI

A matching alternative for more efficiently evaluating effectiveness of interventions using observational data

Description

The nomatch package uses a G-computation style estimator to compute the effectiveness of a binary intervention in observational cohort studies that use the target trial emulation approach. The proposed estimator tends to produce similar point estimates as matching-based estimators but can be more efficient.

Installation

You can install the development version of nomatch with:

# install.packages("devtools")
devtools::install_github("ewu16/nomatch")

Example

This minimal example shows how to use nomatch to obtain cumulative incidences and their derived effect measures such as risk differences (RD), risk ratios (RR), and relative risk reductions (1 - RR).

We use a simple simulated dataset based on an observational vaccine study, although data from other disease settings can be used.


# Load package
library(nomatch)

# View example data
simdata <- as_tibble(simdata) #for prettier printing 
head(simdata)
#> # A tibble: 6 × 7
#>      ID    x1    x2     V D_obs     Y event
#>   <int> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1     1     1     7     1     2    92     0
#> 2     2     0     7     0    NA   210     0
#> 3     3     0    11     1    35   210     0
#> 4     4     0    10     1     6   210     0
#> 5     5     1    11     0    NA   210     0
#> 6     6     1     7     0    NA    90     0

The dataset contains the following:

  • one row per individual (ID)
  • a set of baseline covariates (x1, x2)
  • exposure status (V) with values 1 = vaccinated, 0 = unvaccinated.
  • time to vaccination (D_obs); for unvaccinated individuals this is left as NA.
  • right censored survival data (Y, event)
    • Y represents follow-up time for an outcome such as infection, hospitalization or death.
    • event indicates whether individual experienced the event or censoring with values 1 = event, 0 = censored.

# Use nomatch to compute cumulative incidence and effect estimates 
fit <- nomatch(data = simdata,
               outcome_time = "Y",
               outcome_status = "event",
               exposure = "V",
               exposure_time = "D_obs", 
               covariates = c("x1", "x2"),
               immune_lag = 14,
               timepoints = seq(30, 180, by = 30),
               boot_reps = 10)

# Examine object produced by nomatch - shows risk ratio estimates by default 
fit        
#> 
#>  Risk Ratio Estimates 
#> ================================================== 
#> Call: nomatch(data = simdata, outcome_time = "Y", outcome_status = "event", 
#>     exposure = "V", exposure_time = "D_obs", covariates = c("x1", 
#>         "x2"), immune_lag = 14, timepoints = seq(30, 180, by = 30), 
#>     boot_reps = 10) 
#> 
#> Result:
#>   Timepoint Estimate 95% Wald CI: Lower 95% Wald CI: Upper Wald p-value
#> 1        30    0.534              0.310              0.921     0.024016
#> 2        60    0.605              0.437              0.836     0.002347
#> 3        90    0.603              0.463              0.787     0.000195
#> 4       120    0.661              0.504              0.868     0.002896
#> 5       150    0.731              0.575              0.928     0.010058
#> 6       180    0.828              0.659              1.040     0.104331
#> 
#> Use summary() for more details
#> Use plot() to visualize results

# Plot cumulative incidence and effect estimates
plot(fit)

Issues

If you encounter any bugs or have any specific feature requests, please file an issue.

Citation

If you use the nomatch package in your work, please cite the following:

 @Manual{,
   title = {nomatch: Estimate Effectiveness of Interventions in Target Trial Emulation Observational Studies
             Without Using Matching},
   author = {Emily Wu},
   year = {2026},
   https://github.com/ewu16/nomatch},
   url = {https://ewu16.github.io/nomatch/},
 }