Evidence map›Paper›PMID 36441486›Full record

ArticleDrugs - real world outcomes2023

Impact of "time zero" of Follow-Up Settings in a Comparative Effectiveness Study Using Real-World Data with a Non-user Comparator: Comparison of Six Different Settings.

Ryozo Wakabayashi, Takahiro Hirano, Thomas Laurent, Yoshiki Kuwatsuru, Ryohei Kuwatsuru

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Drugs - real world outcomes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Effectiveness of treatment initiation for secondary fracture prevention in the older population: Japanese Bone-fracture Osteoporosis in Late-stage population Database study (J-BOLD) - emulation of a randomized target trial.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025
    Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Gout risk in adults with pre-diabetes initiating metformin.Annals of the rheumatic diseases · 2024
    Article
  7. Observational
  8. Article
  9. Review
  10. External comparators and estimands.Frontiers in drug safety and regulation · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ryozo WakabayashiReal-World Evidence and Data Assessment (READS), Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan. ryozo_wakabayashi@jp-css.com.
Takahiro HiranoReal-World Evidence and Data Assessment (READS), Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
Thomas LaurentReal-World Evidence and Data Assessment (READS), Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
Yoshiki KuwatsuruDepartment of Radiology, School of Medicine, Juntendo University, Tokyo, Japan.
Ryohei KuwatsuruReal-World Evidence and Data Assessment (READS), Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTime-related bias can lead to misleading conclusions. Properly setting the "time zero" of follow-up is crucial for avoiding these biases. However, the time-zero setting is challenging when comparing users and non-users of a study drug because the latter do not have a time point for starting treatment.

objectiveThis methodological study aimed to illustrate the impact of different time-zero settings on effect estimates in a comparative effectiveness study using real-world data with a non-user comparator.

methodsData for type 2 diabetes patients were extracted from an administrative claims database, and the onset of diabetic retinopathy (study outcome) was compared between users (treatment group) and non-users (non-use group) of lipid-lowering agents. We applied six time-zero settings to the same dataset. The adjusted hazard ratio (HR) for the outcome was estimated using a Cox regression model in each time-zero setting, and the obtained results were compared among the settings.

resultsOf the six settings, three (study entry date [SED] vs SED [naïve approach], treatment initiation [TI] vs SED, TI vs Matched [random order]) showed that the treatment had a reduced risk of the outcome (HR [95% CI]: 0.65 [0.61-0.69], 0.92 [0.86-0.97], and 0.76 [0.71-0.82], respectively), one (TI vs Random) had an increased risk (HR [95% CI]: 1.52 [1.40-1.64]) , and two (SED vs SED [cloning method], and TI vs Matched [systematic order]) had neither increased nor decreased risk (HR [95% CI]: 0.95 [0.93-1.13], and 0.99 [0.93-1.07], respectively).

conclusionsThis study demonstrates that different time-zero settings can lead to different conclusions, even if the same dataset is analyzed for the same research question, probably because improper settings can introduce bias. To minimize such biases, researchers should carefully define time zero, particularly when designing a non-user comparator study using real-world data.

Identifiers

PMID36441486
PMCPMC9944480

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.