ArticleStatistics in medicine2021
Conflating marginal and conditional treatment effects: Comments on "Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study".
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review and indirect treatment comparisons of ritlecitinib against baricitinib in alopecia areata.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Pooled it
- A Framework for Reliable, Transparent, and Reproducible Population-Adjusted Indirect Comparisons.PharmacoEconomics · 2025Article
- Interpreting the meaningfulness of treatment effects estimated in parallel groups designs: comment on Trigg et al.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
- Is the Use of Unanchored Matching-Adjusted Indirect Comparison Always Superior to Naïve Indirect Comparison on Survival Outcomes? A Simulation Study.Applied health economics and health policy · 2025Article
- A process to validate prognostic factors for unanchored matching-adjusted indirect comparison of single-arm trials in oncology: a proof-of-concept study.Journal of comparative effectiveness research · 2025Article
- Using natural experiments to evaluate population health and health system interventions: new framework for producers and users of evidence.BMJ (Clinical research ed.) · 2025Article
- Effect modification and non-collapsibility together may lead to conflicting treatment decisions: A review of marginal and conditional estimands and recommendations for decision-making.Research synthesis methods · 2025Review
- Bayes Factors for Mixed Models: Perspective on Responses.Computational brain & behavior · 2023Article
- Some considerations on target estimands for health technology assessment.Statistics in medicine · 2022Article
- Parametric G-computation for compatible indirect treatment comparisons with limited individual patient data.Research synthesis methods · 2022Article
- Two-stage matching-adjusted indirect comparison.BMC medical research methodology · 2022Article
- Target estimands for efficient decision making: Response to comments on "Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study".Statistics in medicine · 2021Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this commentary, we highlight the importance of: (1) carefully considering and clarifying whether a marginal or conditional treatment effect is of interest in a population-adjusted indirect treatment comparison; and (2) developing distinct methodologies for estimating the different measures of effect. The appropriateness of each methodology depends on the preferred target of inference.
Indexed as
Identifiers
33963582What OpenQuestion holds
Registered trials
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.