Active Comparator, New-User Design

The active comparator, new-user (ACNU) design combines two restrictions that attack the two dominant sources of bias in observational drug studies. The new-user (incident-user) restriction requires that a patient have no dispensing of the study drug or its comparator during a defined washout, so that follow-up starts at first exposure (time zero) for everyone. The active-comparator restriction chooses the reference group as initiators of a different drug used for the same indication, rather than non-users. Together they emulate the eligibility and treatment-assignment structure of the head-to-head randomized trial you wish you could run.

Core conceptual distinction

Two design choices are doing the work, and they are separable. (1) New-user vs prevalent-user: starting follow-up at initiation removes immortal time, prevents adjustment for post-initiation variables on the causal pathway, and avoids depletion-of-susceptibles (the survivors who tolerate a drug look healthier than incident users). (2) Active comparator vs non-user: comparing two treatment decisions for the same indication removes most confounding by indication and healthy-user/healthy-adherer bias, because both arms cleared the same clinical threshold to be treated. The estimand is the comparative (drug A vs drug B) effect on initiation — an intention-to-treat-like contrast under a first-line strategy, or an as-treated/per-protocol contrast if you censor at switching/discontinuation and weight for informative censoring. ACNU does not estimate "drug vs no drug"; if that is the policy question, the active comparator is the wrong reference.

Pros, cons, and trade-offs

• vs new-user with a non-user / unexposed comparator: ACNU removes confounding by indication and healthy-user bias that cripple drug-vs-no-drug comparisons in claims, and yields better covariate overlap (both arms are treated). Cost: it answers a narrower question and loses power when the comparator is rarely used; if the comparator has its own effect on the outcome, the contrast is shifted, not unbiased for an absolute effect. Prefer ACNU for nearly all comparative safety/effectiveness questions among chronic-disease therapies.
• vs prevalent-user / ever-exposed designs: ACNU eliminates survivor bias, depletion of susceptibles, and time-zero misalignment. Cost: smaller cohorts and a population skewed toward initiators, who may differ from the prevalent users who dominate real-world practice. Prefer ACNU when early effects matter or when prevalent-user bias is plausible; consider a prevalent-new-user (Suissa) extension when initiation is too rare.
• vs target-trial emulation with clone-censor-weight: ACNU is the analytic core of most two-drug target-trial emulations and is far simpler to specify and defend. Cost: it is less flexible for sustained/dynamic strategies, grace periods that create eligibility-time ambiguity, or multi-option regimens, where g-methods or clone-censor-weight add value. Prefer plain ACNU unless the protocol genuinely requires a dynamic per-protocol estimand.

When NOT to use — and when it is actively misleading

• No clinically interchangeable comparator exists. Forcing a comparator that is prescribed to systematically different patients (e.g., a second-line agent vs a first-line agent) re-introduces confounding by indication and channeling — the bias you came to remove. Diagnose with baseline covariate balance and clinical review before trusting the cohort.
• The comparator affects the outcome of interest. Comparing two antihypertensives on stroke is fine; comparing them on a renal outcome that one drug class directly modifies makes the "null comparator" assumption false.
• The genuine question is drug vs no treatment (e.g., uptake, adherence's effect on cost). ACNU cannot answer it.
• Severe non-overlap / positivity violation. If one drug is reserved for sicker or renally-impaired patients, PS distributions separate, matching discards much of the cohort, and the surviving estimand no longer maps to a meaningful population.
• One drug is much older. Calendar-time imbalance (the comparator was first-line for a decade before the study drug launched) creates secular confounding; require both drugs to be co-available and consider restricting to overlapping calendar time.

Data-source operational depth

• Claims (FFS or commercial): Exposure is the pharmacy claim (NDC + `fill_date` + `days_supply`). Require continuous medical + pharmacy enrollment across the full washout (commonly 365 days) so the absence of prior dispensing is real, not unobserved. Confirm indication with diagnosis codes in the baseline window. Index date = first qualifying fill. Failure modes: Medicare Advantage and bundled/capitated arrangements drop fee-for-service claims, so "no prior fill" can be missingness, not a true washout — restrict to enrollees with both Parts A/B/D (or commercial pharmacy benefit) and exclude MA-only person-time. Sample fills, 90-day mail-order, and free samples distort `days_supply`.
• EHR: Initiation is the order or administration, not the dispensing; linkage to pharmacy fills is preferred to confirm the patient actually started. Problem lists, labs, and notes sharpen indication and baseline severity (an advantage over claims), but visit-driven capture means a patient who leaves the system is differentially lost — define observation windows explicitly and treat loss to follow-up as potentially informative.
• Registry: Strongest for indication, disease severity, and adjudicated outcomes (e.g., cancer stage); typically weak for complete pharmacy exposure. Link to claims for the full fill history and to a death index to firm up censoring.
• Linked claims–EHR–vital records: The ideal substrate — EHR severity + claims completeness + reliable mortality — but linkage introduces selection (only the linkable subset) and date-discrepancy issues between order, fill, and service dates that must be reconciled before time-zero assignment.

Worked claims example

Question: incident heart failure with second-generation sulfonylurea vs DPP-4 inhibitor among adults with type 2 diabetes in a commercial + Medicare FFS database.

1. Eligibility: age ≥18, ≥2 diabetes diagnoses, and 365 days of continuous A/B/D (or commercial medical+pharmacy) enrollment before the first study fill.
2. Washout: no fill of any sulfonylurea or DPP-4 inhibitor in the 365-day lookback — this is what makes both arms incident users.
3. Time zero: the date of that first qualifying fill; assign the arm from the NDC dispensed on that date.
4. Baseline covariates: measured only in the 365 days up to and including time zero (comorbidities, HbA1c proxies, prior insulin, healthcare utilization), feeding a high-dimensional propensity score.
5. Follow-up: from time zero to first validated HF event, censoring at disenrollment, death, end of data, and — for an as-treated analysis — treatment discontinuation (last `days_supply` end + a pre-specified grace period) or switch to the other arm.
6. Apply 1:1 PS matching (or overlap weighting), check standardized differences <0.1, and run sensitivity analyses on washout length, grace period, and a negative-control outcome to detect residual confounding.

Decision diagram

flowchart TD
  Pop[Source population with the indication] --> Wash[Continuous enrollment + drug-free washout<br/>no study drug or comparator fill]
  Wash --> Init[First fill of study drug<br/>OR active comparator]
  Init --> T0[Time zero = index fill date<br/>assign arm from dispensed NDC]
  T0 --> Base[Baseline covariates measured<br/>only up to time zero -> propensity score]
  Base --> Fup[Follow-up: identical outcome + censoring rules in both arms<br/>censor at disenroll / death / end of data / switch]
  Fup --> Sens[Sensitivity: washout length, grace period,<br/>negative-control outcome, alternative comparator]

gantt
  title ACNU timeline for one new initiator (claims)
  dateFormat YYYY-MM-DD
  axisFormat %b %Y
  section Baseline
  Continuous enrollment + washout (no study/comparator fill) :done, wash, 2023-01-01, 2023-12-31
  section Time zero
  First qualifying fill -> arm assignment :milestone, t0, 2024-01-01, 0d
  section Follow-up
  On-treatment exposure (days_supply + grace) :active, fu, 2024-01-01, 180d
  Censor at switch / disenroll / death / data end :crit, cen, 2024-06-29, 1d

Worked example

Scenario

We want to compare two diabetes drugs on the risk of being hospitalized for heart failure: a sulfonylurea (glipizide, our study drug) versus a DPP-4 inhibitor (sitagliptin, our active comparator). We pull pharmacy claims for two adults with type 2 diabetes. We require each to have a clean 365-day drug-free washout (no fill of either drug class) so both are true first-time starters, set each patient's first qualifying fill as their shared day zero, and follow both forward for 180 days under identical rules to see who has a heart failure hospitalization first.

Dataset

The raw rows an analyst would see in a claims pharmacy table, one row per fill. drug_class flags whether the fill is the study drug or the active comparator.

FIG. 1 — DESIGN TIMELINE

Steps

Result

Of 2 new initiators (1 study, 1 comparator), the study-drug patient had 1 heart failure hospitalization at day 135 of a 180-day follow-up; the comparator patient had 0 events over the full 180 days. Both had a clean 365-day washout and a shared index date of 2024-01-01, so the comparison is of two aligned first-time starters rather than of treated-vs-untreated patients.

Trade-offs

Runnable example

import pandas as pd
import numpy as np

WASHOUT_DAYS = 365  # drug-free + continuous-enrollment lookback that defines "new user"

def build_acnu_cohort(rx: pd.DataFrame, enroll: pd.DataFrame) -> pd.DataFrame:
    rx = rx.sort_values(["person_id", "fill_date"])

    # Candidate index = first fill of EITHER the study drug or the active comparator.
    study_fills = rx[rx["drug_class"].isin(["STUDY", "COMPARATOR"])]
    idx = (study_fills.groupby("person_id")
                      .first()
                      .reset_index()
                      .rename(columns={"fill_date": "index_date", "drug_class": "arm"}))

    # New-user check: no fill of study OR comparator in the WASHOUT_DAYS before the index date.
    prior = study_fills.merge(idx[["person_id", "index_date"]], on="person_id")
    prior_in_washout = prior[(prior["fill_date"] < prior["index_date"]) &
                             (prior["fill_date"] >= prior["index_date"] - pd.Timedelta(days=WASHOUT_DAYS))]
    idx = idx[~idx["person_id"].isin(prior_in_washout["person_id"])].copy()

    # Continuous, FFS-observable enrollment spanning the full washout through index (no MA-only gaps).
    e = enroll.merge(idx[["person_id", "index_date"]], on="person_id")
    e["covers"] = ((e["enroll_start"] <= e["index_date"] - pd.Timedelta(days=WASHOUT_DAYS)) &
                   (e["enroll_end"]   >= e["index_date"]) &
                   (~e["ma_only"]))
    eligible = e.loc[e["covers"], "person_id"].unique()

    cohort = idx[idx["person_id"].isin(eligible)].copy()
    cohort["baseline_start"] = cohort["index_date"] - pd.Timedelta(days=WASHOUT_DAYS)  # covariate window
    return cohort[["person_id", "arm", "index_date", "baseline_start"]]

library(data.table)
WASHOUT_DAYS <- 365L

build_acnu_cohort <- function(rx, enroll) {
  setDT(rx); setDT(enroll)
  setorder(rx, person_id, fill_date)

  study <- rx[drug_class %chin% c("STUDY", "COMPARATOR")]
  idx <- study[, .(index_date = fill_date[1L], arm = drug_class[1L]), by = person_id]

  # New-user: drop anyone with a study/comparator fill in the washout window before index.
  study <- merge(study, idx[, .(person_id, index_date)], by = "person_id")
  prior_ids <- unique(study[fill_date < index_date &
                            fill_date >= index_date - WASHOUT_DAYS, person_id])
  idx <- idx[!person_id %chin% prior_ids]

  # Continuous FFS-observable enrollment across the full washout through index.
  e <- merge(enroll, idx[, .(person_id, index_date)], by = "person_id")
  ok <- e[enroll_start <= index_date - WASHOUT_DAYS &
          enroll_end   >= index_date & !ma_only, unique(person_id)]

  cohort <- idx[person_id %chin% ok]
  cohort[, baseline_start := index_date - WASHOUT_DAYS]
  cohort[, .(person_id, arm, index_date, baseline_start)]
}

%let washout = 365;

/* First fill of study drug or comparator = candidate time zero, with the arm dispensed that day. */
/* Keep only qualifying fills, sort by person and date, then take the earliest row per person. */
proc sort data=work.rx(where=(drug_class in ('STUDY','COMPARATOR'))) out=rx_q;
  by person_id fill_date;
run;

data idx;
  set rx_q;
  by person_id;
  if first.person_id;
  index_date = fill_date;
  format index_date date9.;
  length arm $12;
  arm = drug_class;             /* arm = drug_class on the earliest qualifying fill */
  keep person_id index_date arm;
run;

/* New-user restriction: exclude any prior study/comparator fill inside the washout window. */
proc sql;
  create table newuser as
  select i.*
  from idx i
  where not exists (
    select 1 from work.rx p
    where p.person_id = i.person_id
      and p.drug_class in ('STUDY','COMPARATOR')
      and p.fill_date <  i.index_date
      and p.fill_date >= i.index_date - &washout
  );
quit;

/* Continuous, FFS-observable enrollment across the full washout through index (no MA-only spans). */
proc sql;
  create table cohort as
  select n.person_id, n.arm, n.index_date,
         n.index_date - &washout as baseline_start format=date9.
  from newuser n
  where exists (
    select 1 from work.enroll e
    where e.person_id = n.person_id
      and e.ma_only = 0
      and e.enroll_start <= n.index_date - &washout
      and e.enroll_end   >= n.index_date
  );
quit;

/* 1:1 nearest-neighbor PS matching on baseline covariates (the standard ACNU analytic step). */
proc psmatch data=work.analytic region=allobs;            /* analytic = cohort joined to work.cov */
  class arm <categorical baseline covariates>;
  psmodel arm(treated='STUDY') = <baseline covariates>;    /* covariates from the baseline window only */
  match method=greedy(k=1) distance=lps caliper=0.2;       /* 0.2 SD of the logit-PS caliper */
  assess lps var=(<key covariates>) / plots=(boxplot);     /* standardized differences pre/post match */
  output out(obs=match)=matched matchid=mid;
run;

Citations

1. [1]Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. American Journal of Epidemiology. 2003;158(9):915-920.
2. [2]Lund JL, Richardson DB, Stürmer T. The active comparator, new user study design in pharmacoepidemiology: historical foundations and contemporary application. Current Epidemiology Reports. 2015;2(4):221-228.
3. [3]Yoshida K, Solomon DH, Kim SC. Active-comparator design and new-user design in observational studies. Nature Reviews Rheumatology. 2015;11(7):437-441.