Package 'drMAIC' reference manual

Title:	Doubly Robust Matching-Adjusted Indirect Comparison for HTA
Description:	Implements Doubly Robust Matching-Adjusted Indirect Comparison (DR-MAIC) for population-adjusted indirect treatment comparisons in health technology appraisal (HTA). The package provides: (1) standard MAIC via entropy balancing / exponential tilting; (2) augmented/doubly robust MAIC combining inverse probability weighting with outcome regression; (3) comprehensive covariate balance diagnostics including standardised mean differences, Love plots, and effective sample size; (4) sensitivity analyses including E-values, weight trimming, and variable exclusion analyses; (5) bootstrap confidence intervals; and (6) submission-ready outputs aligned with NICE Decision Support Unit Technical Support Document 18, Cochrane Handbook guidance on indirect comparisons, and ISPOR best practice guidelines. Supports binary (risk difference, risk ratio, odds ratio) and time-to-event (hazard ratio) outcomes.
Authors:	Anupama Singh [aut, cre]
Maintainer:	Anupama Singh <[email protected]>
License:	GPL (>= 3)
Version:	0.1.0
Built:	2026-05-31 07:41:59 UTC
Source:	https://github.com/heorlytics/drmaic

Bootstrap Confidence Intervals for DR-MAIC

Description

Computes non-parametric bootstrap confidence intervals for the DR-MAIC indirect treatment comparison estimate. Three interval types are available: percentile, basic (Hall), and bias-corrected and accelerated (BCa).

Usage

bootstrap_ci(
  dr_maic_result,
  R = 1000L,
  ci_type = c("bca", "perc", "norm"),
  alpha = 0.05,
  seed = NULL,
  parallel = FALSE,
  verbose = TRUE
)
bootstrap_ci(
  dr_maic_result,
  R = 1000L,
  ci_type = c("bca", "perc", "norm"),
  alpha = 0.05,
  seed = NULL,
  parallel = FALSE,
  verbose = TRUE
)

Arguments

dr_maic_result

An object of class "dr_maic" from dr_maic().

R

Integer; number of bootstrap replicates. Default 1000. Use R >= 2000 for stable BCa intervals.

ci_type

Character; bootstrap CI type: "bca" (default), "perc", or "norm".

alpha

Numeric; significance level. Default 0.05.

seed

Integer; random seed for reproducibility.

parallel

Logical; use parallel computation if parallel package is available. Default FALSE.

verbose

Logical; show progress. Default TRUE.

Details

Bootstrap procedure: The IPD are resampled with replacement B times. For each bootstrap replicate:

MAIC weights are recomputed on the resampled IPD.
The DR-MAIC estimator is evaluated.
The ITC is computed.

Bootstrap resampling captures variance from both the weight estimation and outcome regression steps, providing honest uncertainty quantification for the doubly robust estimator.

Bootstrap CI methods (per Efron and Tibshirani, 1993):

Percentile: uses the 2.5th and 97.5th percentiles of the bootstrap distribution.
BCa (Bias-Corrected and Accelerated): adjusts for bias and skewness. Recommended method per ISPOR guidance.
Normal: uses bootstrap standard error with normal quantiles.

NICE guidance alignment: NICE DSU TSD 18 recommends bootstrap CIs for MAIC to capture parameter uncertainty from the weight estimation step.

Value

A list of class "maic_bootstrap" containing:

ci_maic: Bootstrap CI for standard MAIC ITC.
ci_stc: Bootstrap CI for STC ITC.
ci_dr: Bootstrap CI for DR-MAIC ITC.
se_boot_dr: Bootstrap SE for DR-MAIC.
boot_distribution: Data frame of bootstrap replicate estimates.
boot_plot: ggplot2 bootstrap distribution plot.
ci_type: CI type used.
R: Number of bootstrap replicates.

References

Efron B, Tibshirani RJ. (1993). An Introduction to the Bootstrap. Chapman and Hall/CRC.

Phillippo DM, et al. (2016). NICE DSU TSD 18.

Examples


data(nsclc_ipd); data(nsclc_agd)
w   <- compute_weights(nsclc_ipd,
         c(age = nsclc_agd$mean_age, ecog = nsclc_agd$prop_ecog1,
           smoker = nsclc_agd$prop_smoker), c("age","ecog","smoker"))
res <- dr_maic(w, "response", "binary",
               comparator_estimate = nsclc_agd$response_rate,
               comparator_se       = nsclc_agd$response_se)
boot_res <- bootstrap_ci(res, R = 500, seed = 42)
print(boot_res)
boot_res$boot_plot


data(nsclc_ipd); data(nsclc_agd)
w   <- compute_weights(nsclc_ipd,
         c(age = nsclc_agd$mean_age, ecog = nsclc_agd$prop_ecog1,
           smoker = nsclc_agd$prop_smoker), c("age","ecog","smoker"))
res <- dr_maic(w, "response", "binary",
               comparator_estimate = nsclc_agd$response_rate,
               comparator_se       = nsclc_agd$response_se)
boot_res <- bootstrap_ci(res, R = 500, seed = 42)
print(boot_res)
boot_res$boot_plot

Check Assumptions for DR-MAIC

Description

Runs a structured check of the key assumptions required for valid DR-MAIC inference, aligned with NICE DSU TSD 18 and Cochrane guidance.

Usage

check_assumptions(
  maic_weights,
  dr_maic_result = NULL,
  ess_threshold = 30,
  smd_threshold = 0.1
)
check_assumptions(
  maic_weights,
  dr_maic_result = NULL,
  ess_threshold = 30,
  smd_threshold = 0.1
)

Arguments

maic_weights

A maic_weights object from compute_weights().

dr_maic_result

Optional dr_maic object for outcome model checks.

ess_threshold

Minimum acceptable ESS% threshold. Default 30.

smd_threshold

Maximum acceptable |SMD| after weighting. Default 0.1.

Value

A data frame summarising assumption check results (invisibly). Prints a structured checklist to the console.

Compute MAIC Weights via Entropy Balancing

Description

Estimates matching-adjusted indirect comparison (MAIC) weights by solving the entropy balancing / exponential tilting problem. Weights are chosen so that the weighted moments of the IPD covariates match the aggregate data (AgD) target moments. The method follows the approach described in Signorovitch et al. (2010) and formalised by Phillippo et al. (2016) and NICE DSU TSD 18.

Usage

compute_weights(
  ipd,
  target_moments,
  match_vars = NULL,
  match_var_types = NULL,
  optimizer = "BFGS",
  maxit = 10000L,
  verbose = TRUE
)
compute_weights(
  ipd,
  target_moments,
  match_vars = NULL,
  match_var_types = NULL,
  optimizer = "BFGS",
  maxit = 10000L,
  verbose = TRUE
)

Arguments

ipd

A data frame of individual patient data from the index trial. Must contain the covariates listed in match_vars.

target_moments

A named numeric vector or list of target aggregate statistics (means, and optionally variances/proportions) from the comparator study AgD. Names must match column names in ipd.

match_vars

Character vector of covariate names to match. If NULL, uses all names in target_moments.

match_var_types

Named character vector specifying the type of each matching variable: "mean" (continuous, match on mean), "mean_sd" (continuous, match on mean AND standard deviation via second moment), or "proportion" (binary, match on proportion). Defaults to "mean" for all variables.

optimizer

Optimisation algorithm passed to stats::optim(). Default "BFGS". Use "Nelder-Mead" if BFGS fails to converge.

maxit

Maximum iterations for optimiser. Default 10000.

verbose

Logical; print convergence information. Default TRUE.

Details

Statistical method: Given individual patient data (IPD) from study A (index trial) with covariates $X_i, i=1,\ldots,n$ and target aggregate statistics $\bar{X}_{target}$ from study B (comparator), MAIC weights are defined by exponential tilting:

$w_i = \exp(X_i^\top \hat{\lambda})$

where $\hat{\lambda}$ solves the moment-matching equations:

$\sum_{i=1}^n w_i X_i = n \bar{X}_{target}$

Equivalently, $\hat{\lambda}$ minimises the convex objective:

$Q(\lambda) = \sum_{i=1}^n \exp(X_i^\top \lambda) - n \lambda^\top \bar{X}_{target}$

Weights are returned in two forms: raw (unnormalized, sum to n_eff) and normalized (sum to 1). The normalized weights are used for estimation.

NICE alignment: The weight computation follows NICE DSU TSD 18 (Phillippo et al., 2016, 2020) guidance on MAIC for population-adjusted ITC.

Value

A list of class "maic_weights" containing:

weights: Numeric vector of normalized weights (sum to 1), length n.
weights_raw: Numeric vector of unnormalized weights.
lambda: Estimated Lagrange multiplier vector $\hat{\lambda}$ .
ess: Effective sample size.
ess_pct: ESS as percentage of original n.
n_original: Original sample size.
convergence: Convergence code from stats::optim() (0 = success).
balance_before: SMD before weighting (named vector).
balance_after: SMD after weighting (named vector).
match_vars: Character vector of matched variables.
target_moments: Target moments used for matching.
ipd: Original IPD data frame (stored for downstream functions).

References

Signorovitch JE, Wu EQ, Yu AP, et al. (2010). Comparative effectiveness without head-to-head trials: a method for matching-adjusted indirect comparisons applied to psoriasis treatment with adalimumab or etanercept. PharmacoEconomics, 28(10), 935-945.

Phillippo DM, Ades AE, Dias S, et al. (2016). Methods for population-adjusted indirect comparisons in submissions to NICE. NICE Decision Support Unit TSD 18.

Phillippo DM, Ades AE, Dias S, et al. (2020). Population adjustment methods for indirect comparisons: A review of national institute for health and care excellence technology appraisals. International Journal of Technology Assessment in Health Care, 36(5), 454-461.

Examples


# Load example NSCLC dataset
data(nsclc_ipd)
data(nsclc_agd)

# Target moments from AgD comparator trial
target <- c(
  age     = nsclc_agd$mean_age,
  ecog    = nsclc_agd$prop_ecog1,
  smoker  = nsclc_agd$prop_smoker
)

# Compute MAIC weights
w <- compute_weights(
  ipd            = nsclc_ipd,
  target_moments = target,
  match_vars     = c("age", "ecog", "smoker")
)
print(w)
summary(w)

# Load example NSCLC dataset
data(nsclc_ipd)
data(nsclc_agd)

# Target moments from AgD comparator trial
target <- c(
  age     = nsclc_agd$mean_age,
  ecog    = nsclc_agd$prop_ecog1,
  smoker  = nsclc_agd$prop_smoker
)

# Compute MAIC weights
w <- compute_weights(
  ipd            = nsclc_ipd,
  target_moments = target,
  match_vars     = c("age", "ecog", "smoker")
)
print(w)
summary(w)

Doubly Robust MAIC Estimation

Description

Estimates a population-adjusted treatment effect using the doubly robust (augmented) MAIC estimator. This combines inverse probability weighting (MAIC) with outcome regression (parametric g-computation / STC) into a single estimator that is consistent if either the weight model or the outcome model is correctly specified.

Usage

dr_maic(
  maic_weights,
  outcome_var,
  outcome_type = c("binary", "continuous", "tte"),
  time_var = NULL,
  comparator_estimate,
  comparator_se = NULL,
  effect_measure = NULL,
  outcome_model_formula = NULL,
  outcome_model_family = NULL,
  additional_covariates = NULL,
  alpha = 0.05
)
dr_maic(
  maic_weights,
  outcome_var,
  outcome_type = c("binary", "continuous", "tte"),
  time_var = NULL,
  comparator_estimate,
  comparator_se = NULL,
  effect_measure = NULL,
  outcome_model_formula = NULL,
  outcome_model_family = NULL,
  additional_covariates = NULL,
  alpha = 0.05
)

Arguments

maic_weights

An object of class "maic_weights" from compute_weights().

outcome_var

Character string; name of the outcome column in the IPD.

outcome_type

Character string; type of outcome: "binary" (default), "continuous", or "tte" (time-to-event).

time_var

Character string; for outcome_type = "tte", the name of the time variable in the IPD.

comparator_estimate

Numeric; the outcome estimate from the comparator (AgD) study B. For binary outcomes, a proportion; for TTE, a median survival or log-HR depending on effect_measure.

comparator_se

Numeric; standard error of comparator_estimate. Used for variance estimation of the ITC.

effect_measure

Character string; scale for the treatment effect: "RD" (risk difference), "RR" (risk ratio), "OR" (odds ratio), "HR" (hazard ratio). Default is "OR" for binary, "HR" for TTE, "MD" (mean difference) for continuous.

outcome_model_formula

A formula for the outcome regression model. If NULL (default), uses all matched covariates as predictors.

outcome_model_family

GLM family for outcome model. Default binomial() for binary outcomes, gaussian() for continuous.

additional_covariates

Character vector of additional prognostic covariates to include in the outcome model (beyond matched variables). These improve efficiency but are not required for the DR property.

alpha

Significance level for confidence intervals. Default 0.05.

Details

Statistical framework:

Let study A (index) provide IPD with outcome $Y_i$ and covariates $X_i$ , and study B (comparator) provide AgD with outcome summary statistic $\hat{\theta}_B$ . MAIC weights $\omega_i$ target the covariate distribution of population B.

Three estimators are implemented:

(1) Standard MAIC (IPW):

$\hat{\theta}_{MAIC} = \frac{\sum_i \omega_i Y_i}{\sum_i \omega_i}$

(2) Standardised Treatment Comparison / g-computation (STC):

$\hat{\theta}_{STC} = \frac{\sum_i \omega_i \hat{m}(X_i)}{\sum_i \omega_i}$

where $\hat{m}(X_i) = \hat{E}[Y \mid X_i]$ from an outcome regression model fitted on the IPD.

(3) Doubly Robust / Augmented estimator (DR-MAIC):

$\hat{\theta}_{DR} = \hat{\theta}_{STC} + \frac{\sum_i \omega_i (Y_i - \hat{m}(X_i))}{\sum_i \omega_i}$

The DR estimator equals the MAIC estimator plus a bias-correction term based on outcome model residuals, weighted towards population B. It is consistent if either (i) the weights $\omega_i$ correctly balance covariates (outcome model may be misspecified), or (ii) the outcome model $\hat{m}$ is correctly specified (weights may be misspecified) — the double robustness property (Lunceford and Davidian, 2004; Tan, 2010; Remiro-Azocar, 2022).

The indirect treatment comparison is then:

$\hat{\Delta} = \hat{\theta}_{DR,A} - \hat{\theta}_B$

For binary outcomes the comparison can be on the risk difference, log-risk ratio, or log-odds ratio scale. For time-to-event outcomes, the log-hazard ratio is estimated via a weighted Cox model (or Weibull regression).

Alignment with guidelines:

NICE DSU TSD 18 (Phillippo et al., 2016; 2020)
Cochrane Handbook Chapter 23 (Dias et al.)
ISPOR Task Force on Indirect Treatment Comparisons
Remiro-Azócar et al. (2022) Statistics in Medicine

Value

A list of class "dr_maic" containing:

theta_maic: MAIC (IPW) estimate in population B.
theta_stc: STC (g-computation) estimate in population B.
theta_dr: Doubly robust estimate in population B.
theta_comparator: Comparator estimate (from AgD).
itc_maic: Indirect treatment comparison (MAIC): A vs B effect.
itc_stc: Indirect treatment comparison (STC): A vs B effect.
itc_dr: Indirect treatment comparison (DR): A vs B effect.
effect_measure: Effect measure used.
outcome_model: Fitted outcome model object.
residuals: Outcome model residuals (Y_i - m_hat_i).
dr_correction: The augmentation term (STC correction).
maic_weights: The maic_weights object used.
n: Original sample size.
ess: Effective sample size.

References

Remiro-Azócar A, Heath A, Baio G. (2022). Methods for population-adjusted indirect comparisons in health technology appraisal. Medical Decision Making, 42(3), 386-401.

Lunceford JK, Davidian M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects. Statistics in Medicine, 23(19), 2937-2960.

Tan Z. (2010). Bounded, efficient and doubly robust estimation with inverse weighting. Biometrika, 97(3), 661-682.

Phillippo DM, Ades AE, Dias S, et al. (2016). NICE DSU Technical Support Document 18: Methods for population-adjusted indirect comparisons in submissions to NICE.

Examples


data(nsclc_ipd)
data(nsclc_agd)

# Step 1: Compute weights
w <- compute_weights(
  ipd            = nsclc_ipd,
  target_moments = c(age = nsclc_agd$mean_age,
                     ecog   = nsclc_agd$prop_ecog1,
                     smoker = nsclc_agd$prop_smoker),
  match_vars     = c("age", "ecog", "smoker")
)

# Step 2: DR-MAIC for binary outcome
res <- dr_maic(
  maic_weights         = w,
  outcome_var          = "response",
  outcome_type         = "binary",
  comparator_estimate  = nsclc_agd$response_rate,
  comparator_se        = nsclc_agd$response_se,
  effect_measure       = "OR"
)
print(res)
summary(res)


data(nsclc_ipd)
data(nsclc_agd)

# Step 1: Compute weights
w <- compute_weights(
  ipd            = nsclc_ipd,
  target_moments = c(age = nsclc_agd$mean_age,
                     ecog   = nsclc_agd$prop_ecog1,
                     smoker = nsclc_agd$prop_smoker),
  match_vars     = c("age", "ecog", "smoker")
)

# Step 2: DR-MAIC for binary outcome
res <- dr_maic(
  maic_weights         = w,
  outcome_var          = "response",
  outcome_type         = "binary",
  comparator_estimate  = nsclc_agd$response_rate,
  comparator_se        = nsclc_agd$response_se,
  effect_measure       = "OR"
)
print(res)
summary(res)

Covariate Balance Diagnostics for MAIC

Description

Produces covariate balance diagnostics following NICE DSU TSD 18 and Cochrane guidance. Includes: standardised mean differences (SMD) before and after weighting, Love plot, weight distribution visualisations, and effective sample size reporting.

Usage

maic_diagnostics(
  maic_weights,
  plot_type = "all",
  threshold = 0.1,
  title_suffix = NULL
)
maic_diagnostics(
  maic_weights,
  plot_type = "all",
  threshold = 0.1,
  title_suffix = NULL
)

Arguments

maic_weights

An object of class "maic_weights" from compute_weights().

plot_type

Character vector specifying which plots to produce: "love" (Love plot of SMDs), "weights" (weight distribution), "balance" (balance table), "all" (default — produces all).

threshold

Numeric; SMD threshold line on Love plot. Default 0.1 (standard threshold used by NICE and Austin and Stuart, 2015).

title_suffix

Optional character string appended to plot titles.

Details

Standardised Mean Difference (SMD):

$SMD_j = \frac{\bar{X}_{j,weighted} - \mu_{j,target}}{s_j}$

where $s_j$ is the unweighted standard deviation of covariate j in the IPD (per NICE DSU TSD 18 recommendation). |SMD| < 0.1 indicates good balance.

Effective Sample Size (ESS):

$ESS = \frac{(\sum_i w_i)^2}{\sum_i w_i^2}$

ESS should be interpreted relative to the original n. ESS < 30% of n is a warning threshold per NICE guidance.

Value

A list of class "maic_diagnostics" containing:

balance_table: Data frame with SMD before/after weighting.
ess: Effective sample size.
ess_pct: ESS as % of original n.
n_balanced: Number of variables with |SMD| < threshold after weighting.
love_plot: ggplot2 Love plot object.
weight_plot: ggplot2 weight distribution plot.
all_balanced: Logical; TRUE if all |SMD| < threshold after weighting.

References

Austin PC, Stuart EA. (2015). Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Statistics in Medicine, 34(28), 3661-3679.

Phillippo DM, Ades AE, Dias S, et al. (2016). NICE DSU TSD 18.

Examples


data(nsclc_ipd)
data(nsclc_agd)

w <- compute_weights(
  ipd            = nsclc_ipd,
  target_moments = c(age = nsclc_agd$mean_age,
                     ecog = nsclc_agd$prop_ecog1,
                     smoker = nsclc_agd$prop_smoker),
  match_vars     = c("age", "ecog", "smoker")
)

diag <- maic_diagnostics(w)
print(diag)
diag$love_plot
diag$weight_plot


data(nsclc_ipd)
data(nsclc_agd)

w <- compute_weights(
  ipd            = nsclc_ipd,
  target_moments = c(age = nsclc_agd$mean_age,
                     ecog = nsclc_agd$prop_ecog1,
                     smoker = nsclc_agd$prop_smoker),
  match_vars     = c("age", "ecog", "smoker")
)

diag <- maic_diagnostics(w)
print(diag)
diag$love_plot
diag$weight_plot

Generate NICE/HTA Submission-Ready Report

Description

Produces a structured, submission-ready report of the DR-MAIC analysis aligned with NICE DSU TSD 18, Cochrane Handbook Chapter 23, and ISPOR best practice guidance for population-adjusted indirect treatment comparisons.

Usage

nice_report(
  dr_maic_result,
  bootstrap_result = NULL,
  sensitivity_result = NULL,
  study_a_name = "Study A",
  study_b_name = "Study B",
  indication = "",
  treatment_a = "Treatment A",
  treatment_b = "Treatment B",
  submission_date = format(Sys.Date(), "%d %B %Y"),
  output_format = "console"
)
nice_report(
  dr_maic_result,
  bootstrap_result = NULL,
  sensitivity_result = NULL,
  study_a_name = "Study A",
  study_b_name = "Study B",
  indication = "",
  treatment_a = "Treatment A",
  treatment_b = "Treatment B",
  submission_date = format(Sys.Date(), "%d %B %Y"),
  output_format = "console"
)

Arguments

dr_maic_result

An object of class "dr_maic".

bootstrap_result

Optional object of class "maic_bootstrap" from bootstrap_ci().

sensitivity_result

Optional object of class "maic_sensitivity" from sensitivity_analysis().

study_a_name

Character; name of the index trial (Study A). Default "Study A".

study_b_name

Character; name of the comparator trial (Study B). Default "Study B".

indication

Character; disease indication. Default "".

treatment_a

Character; treatment in index trial.

treatment_b

Character; treatment in comparator trial.

submission_date

Character; date of submission. Default: today's date.

output_format

Character; "console" (default) or "list".

Details

The report includes the following sections:

Study and population characteristics - IPD summary and AgD target moments
Weight estimation - ESS, convergence, moment-matching results
Covariate balance - SMD table (NICE TSD 18 format)
Treatment effect estimates - MAIC, STC, and DR-MAIC ITCs
Uncertainty - Bootstrap CIs (if provided)
Sensitivity analysis - E-values and trimming (if provided)
Assumptions and limitations - per NICE and Cochrane guidance
Methods description - citable methods paragraph

Value

A list of class "nice_report" (invisibly) containing all formatted sections. Also prints to console.

References

Phillippo DM, Ades AE, Dias S, et al. (2016). NICE DSU TSD 18.

Dias S, Sutton AJ, Ades AE, Welton NJ. (2013). A generalised linear modelling framework for pairwise and network meta-analysis of randomised controlled trials. Statistics in Medicine, 32(13), 2312-2330.

Higgins JPT, et al. (2023). Cochrane Handbook for Systematic Reviews of Interventions, Version 6.4. Chapter 23.

ISPOR Task Force. (2014). Indirect treatment comparison/network meta-analysis study questionnaire to assess relevance and credibility. Value in Health.

Examples


data(nsclc_ipd); data(nsclc_agd)
w   <- compute_weights(nsclc_ipd,
         c(age = nsclc_agd$mean_age, ecog = nsclc_agd$prop_ecog1,
           smoker = nsclc_agd$prop_smoker), c("age","ecog","smoker"))
res <- dr_maic(w, "response", "binary",
               comparator_estimate = nsclc_agd$response_rate,
               comparator_se       = nsclc_agd$response_se)
nice_report(res,
            study_a_name  = "KEYNOTE-024",
            study_b_name  = "IMpower150",
            indication    = "Advanced NSCLC",
            treatment_a   = "Pembrolizumab",
            treatment_b   = "Atezolizumab + Bevacizumab + Chemo")


data(nsclc_ipd); data(nsclc_agd)
w   <- compute_weights(nsclc_ipd,
         c(age = nsclc_agd$mean_age, ecog = nsclc_agd$prop_ecog1,
           smoker = nsclc_agd$prop_smoker), c("age","ecog","smoker"))
res <- dr_maic(w, "response", "binary",
               comparator_estimate = nsclc_agd$response_rate,
               comparator_se       = nsclc_agd$response_se)
nice_report(res,
            study_a_name  = "KEYNOTE-024",
            study_b_name  = "IMpower150",
            indication    = "Advanced NSCLC",
            treatment_a   = "Pembrolizumab",
            treatment_b   = "Atezolizumab + Bevacizumab + Chemo")

Simulated NSCLC Aggregate Data (Study B — Comparator)

Description

Simulated aggregate data (AgD) representing published summary statistics from a hypothetical comparator trial in advanced NSCLC.

Usage

nsclc_agd
nsclc_agd

Format

A list with the following elements:

mean_age: Mean age of trial population.
prop_ecog1: Proportion with ECOG 1/2.
prop_smoker: Proportion with smoking history.
prop_pdl1_high: Proportion with PD-L1 >=50%.
prop_prior_lines: Proportion with >= 1 prior line.
response_rate: Observed response rate (proportion).
response_se: Standard error of response rate.
n_agd: Sample size of comparator trial.
os_median: Median OS in months.
os_hr_ref: Log-HR (vs common reference arm, if available).

Source

Simulated data. Not based on any real trial. For illustration only.

Examples

data(nsclc_agd)
str(nsclc_agd)
data(nsclc_agd)
str(nsclc_agd)

Simulated NSCLC Individual Patient Data (Study A)

Description

A simulated individual patient dataset (IPD) representing a hypothetical single-arm trial of an immunotherapy agent in advanced non-small cell lung cancer (NSCLC). Created for demonstration of the drMAIC package.

Usage

nsclc_ipd
nsclc_ipd

Format

A data frame with 200 rows and 8 variables:

patient_id: Patient identifier.
age: Age in years (continuous).
ecog: ECOG performance status (binary: 0 = ECOG 0, 1 = ECOG 1/2).
smoker: Smoking history (binary: 1 = ever-smoker, 0 = never-smoker).
pdl1_high: PD-L1 expression >=50% (binary: 1 = high, 0 = low/negative).
prior_lines: Number of prior lines of therapy (0 or 1).
response: Objective response (binary: 1 = responder, 0 = non-responder).
os_time: Overall survival time in months (censored).
os_event: Overall survival event indicator (1 = death, 0 = censored).

Source

Simulated data. Not based on any real trial. For illustration only.

Examples

data(nsclc_ipd)
head(nsclc_ipd)
summary(nsclc_ipd)
data(nsclc_ipd)
head(nsclc_ipd)
summary(nsclc_ipd)

Sensitivity Analysis for DR-MAIC

Description

Conducts pre-specified sensitivity analyses for the DR-MAIC indirect treatment comparison, including: (1) E-value analysis for unmeasured confounding; (2) weight trimming sensitivity; (3) variable exclusion (leave-one-out) analysis; and (4) outcome model specification sensitivity.

Usage

sensitivity_analysis(
  dr_maic_result,
  trim_percentiles = c(0.9, 0.95, 0.99),
  lovo = TRUE,
  outcome_model_specs = NULL,
  alpha = 0.05
)
sensitivity_analysis(
  dr_maic_result,
  trim_percentiles = c(0.9, 0.95, 0.99),
  lovo = TRUE,
  outcome_model_specs = NULL,
  alpha = 0.05
)

Arguments

dr_maic_result

An object of class "dr_maic" from dr_maic().

trim_percentiles

Numeric vector of weight trimming percentiles (0-1). Default c(0.90, 0.95, 0.99).

lovo

Logical; run leave-one-variable-out analysis. Default TRUE.

outcome_model_specs

Optional list of alternative outcome model formulas for specification sensitivity.

alpha

Significance level. Default 0.05.

Details

E-value analysis: The E-value quantifies the minimum strength of unmeasured confounding required to explain away the observed treatment effect (VanderWeele & Ding, 2017). For an observed risk ratio RR:

$E\text{-value} = RR + \sqrt{RR(RR - 1)}$

For OR, the converted RR = $OR^{0.5}$ is used (when outcome is not rare). A large E-value indicates the result is robust to unmeasured confounding.

Weight trimming sensitivity: Extreme weights are trimmed at specified percentiles, and the effect estimate is recomputed. This assesses the influence of patients with extreme weights on the ITC.

Leave-one-variable-out (LOVO): Each matched variable is excluded in turn, and the full DR-MAIC analysis is re-run. This assesses which variables most influence the estimate.

Assumptions tested (per NICE DSU TSD 18 and Cochrane guidance):

Transportability: the matched population is exchangeable with target B
No unmeasured effect modifiers: E-value addresses this
Positivity: overlap between IPD and target assessed via weight distribution
SUTVA: assumed throughout

Value

A list of class "maic_sensitivity" containing:

evalue: E-value for the main DR-MAIC estimate.
evalue_ci: E-value for the confidence interval boundary.
trim_results: Data frame of trimming sensitivity results.
lovo_results: Data frame of leave-one-variable-out results.
evalue_plot: ggplot2 E-value plot.
trim_plot: ggplot2 trimming sensitivity plot.
lovo_plot: ggplot2 LOVO plot.

References

VanderWeele TJ, Ding P. (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine, 167(4), 268-274.

Phillippo DM, et al. (2016). NICE DSU TSD 18.

Cochrane Handbook, Chapter 23: Including variants on randomized trials.

Examples


data(nsclc_ipd)
data(nsclc_agd)

w   <- compute_weights(nsclc_ipd,
         c(age = nsclc_agd$mean_age, ecog = nsclc_agd$prop_ecog1,
           smoker = nsclc_agd$prop_smoker),
         c("age","ecog","smoker"))
res <- dr_maic(w, "response", "binary",
               comparator_estimate = nsclc_agd$response_rate,
               comparator_se       = nsclc_agd$response_se)
sa  <- sensitivity_analysis(res)
sa$evalue
sa$trim_plot


data(nsclc_ipd)
data(nsclc_agd)

w   <- compute_weights(nsclc_ipd,
         c(age = nsclc_agd$mean_age, ecog = nsclc_agd$prop_ecog1,
           smoker = nsclc_agd$prop_smoker),
         c("age","ecog","smoker"))
res <- dr_maic(w, "response", "binary",
               comparator_estimate = nsclc_agd$response_rate,
               comparator_se       = nsclc_agd$response_se)
sa  <- sensitivity_analysis(res)
sa$evalue
sa$trim_plot

Package 'drMAIC'

Help Index

Bootstrap Confidence Intervals for DR-MAIC

Description

Usage

Arguments

Details

Value

References

Examples

Check Assumptions for DR-MAIC

Description

Usage

Arguments

Value

Compute MAIC Weights via Entropy Balancing

Description

Usage

Arguments

Details

Value

References

Examples

Doubly Robust MAIC Estimation

Description

Usage

Arguments

Details

Value

References

Examples

Covariate Balance Diagnostics for MAIC

Description

Usage

Arguments

Details

Value

References

Examples

Generate NICE/HTA Submission-Ready Report

Description

Usage

Arguments

Details

Value

References

Examples

Simulated NSCLC Aggregate Data (Study B — Comparator)

Description

Usage

Format

Source

Examples

Simulated NSCLC Individual Patient Data (Study A)

Description

Usage

Format

Source

Examples

Sensitivity Analysis for DR-MAIC

Description

Usage

Arguments

Details

Value

References

Examples