Real World Evidence

Real-World Evidence Analysis in R

Overview

Methods for analyzing real-world data (RWD) to generate real-world evidence (RWE). Covers target trial emulation, comparative effectiveness research, propensity score methods for observational data, external control arms, bias quantification, and sensitivity analysis for unmeasured confounding.

Target Trial Emulation

Conceptual Framework

# Target trial emulation framework
# Specify the target trial protocol, then emulate using observational data

# Key elements to specify:
# 1. Eligibility criteria
# 2. Treatment strategies
# 3. Assignment procedures
# 4. Follow-up period
# 5. Outcome
# 6. Causal contrast (ITT, per-protocol, etc.)
# 7. Analysis plan

Using TrialEmulation Package

library(TrialEmulation)

# Prepare data for target trial emulation
# Data should be in long format with time-varying covariates

# Example: Clone-censor-weight approach
trial_data 
  group_by(patient_id) |>
  mutate(
    # Check eligibility at each visit
    eligible = check_eligibility(age, lab_values, prior_treatment),
    # Time zero for each potential trial
    trial_start = if_else(eligible & treatment_initiated, visit_date, NA)
  ) |>
  ungroup()

# Step 2: Clone patients for each trial they're eligible for
# Step 3: Apply artificial censoring per assigned strategy
# Step 4: Weight for selection and artificial censoring
# Step 5: Pool and analyze

Propensity Score Methods for RWE

Propensity Score Estimation

library(MatchIt)
library(WeightIt)
library(cobalt)

# Estimate propensity scores
ps_model  select(age, sex, bmi, smoking, diabetes, baseline_egfr),
  Q.SL.library = c("SL.glm", "SL.ranger", "SL.xgboost"),
  g.SL.library = c("SL.glm", "SL.ranger"),
  k_split = 5,
  verbose = FALSE
)

aipw_result$fit()$summary()

# Risk difference and risk ratio
aipw_result$summary()

External Control Arms

Historical Controls Integration

# Combine trial data with external controls

# Step 1: Identify comparable external controls
external_controls 
  filter(
    # Match eligibility criteria
    age >= 18 & age = 30,
    no_prior_treatment == TRUE
  )

# Step 2: Propensity score matching/weighting
library(MatchIt)

combined  mutate(source = "trial", treatment = treatment),
  external_controls |> mutate(source = "external", treatment = 0)
)

# Match external controls to trial control arm characteristics
match_ext  filter(treatment == 0 | source == "external"),
  method = "nearest",
  caliper = 0.2
)

# Step 3: Analysis with matched external controls
matched_combined 
  group_by(patient_id) |>
  mutate(
    p_uncensored = 1 - predict(cens_model, type = "response"),
    ipcw = cumprod(p_uncensored)
  ) |>
  ungroup()

# Stabilized weights
rwd_long 
  mutate(
    sw = ps_weight * ipcw / mean(ps_weight * ipcw, na.rm = TRUE)
  )

# Weighted analysis with stabilized weights
library(survey)
design_ipcw 
  filter(adjusted_effect 
  summarise(across(everything(), ~mean(!is.na(.)) * 100)) |>
  pivot_longer(everything(), names_to = "variable", values_to = "pct_complete")

# 2. Plausibility
plausibility_checks 
  summarise(
    age_range_ok = all(age >= 0 & age = admission_date, na.rm = TRUE),
    lab_in_range = all(egfr >= 0 & egfr 
  count(year = year(index_date)) |>
  ggplot(aes(year, n)) +
  geom_col() +
  labs(title = "Patient Enrollment Over Time")

# 4. Treatment patterns
rwd |>
  group_by(treatment) |>
  summarise(
    n = n(),
    mean_age = mean(age),
    pct_male = mean(sex == "male") * 100,
    mean_followup = mean(followup_time)
  )

Reporting RWE Studies

# Create Table 1 for RWE study
library(tableone)

# Unweighted baseline characteristics
vars <- c("age", "sex", "bmi", "smoking", "diabetes", "egfr", "stage")
cat_vars <- c("sex", "smoking", "diabetes", "stage")

tab1_unweighted <- CreateTableOne(
  vars = vars,
  strata = "treatment",
  data = rwd,
  factorVars = cat_vars,
  test = FALSE
)

# Weighted baseline characteristics (after PS weighting)
tab1_weighted <- svyCreateTableOne(
  vars = vars,
  strata = "treatment",
  data = design_weighted,
  factorVars = cat_vars,
  test = FALSE
)

# Print with SMD
print(tab1_unweighted, smd = TRUE)
print(tab1_weighted, smd = TRUE)

Key Packages Summary

| Package | Purpose | |---------|---------| | TrialEmulation | Target trial emulation | | WeightIt | Propensity score weighting | | MatchIt | Propensity score matching | | cobalt | Balance diagnostics | | AIPW | Doubly robust estimation | | adjustedCurves | Adjusted survival curves | | tipr | Tipping point analysis | | EValue | E-values for unmeasured confounding | | ipw | Inverse probability weighting | | episensr | Quantitative bias analysis |

Best Practices

1. Protocol first: Define target trial before touching data
2. Eligibility: Apply strict, transparent eligibility criteria
3. Time zero: Align treatment initiation with follow-up start
4. Immortal time: Avoid immortal time bias in study design
5. Balance: Check and report covariate balance (SMD < 0.1)
6. Positivity: Assess overlap and address violations
7. Sensitivity: Quantify impact of unmeasured confounding
8. Reporting: Follow RECORD/STROBE-RWE guidelines

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: choxos
• Source: choxos/BiostatAgent
• License: MIT

Install and usage instructions live in the source repository linked above.