ReviewBone marrow transplantation2012
Assessing causal relationships between treatments and clinical outcomes: always read the fine print.
Review in Bone marrow transplantation, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed.
- Augmenting randomized clinical trial data with historical control data: Precision medicine applications.Journal of the National Cancer Institute · 2023Trial
- Digital Intervention Components for Preventing and Reducing Substance Use in Adolescents and Emerging Adults: A Comprehensive Scoping Review.Journal of prevention (2022) · 2026Article
- Clarifying Causal Effects of Interest and Underlying Assumptions in Randomized and Nonrandomized Clinical Trials in Oncology Using Directed Acyclic Graphs and Single-World Intervention Graphs.JCO clinical cancer informatics · 2024Article
- First-line antibiotic treatment in patients with localized extragastric mucosa-associated lymphoid tissue lymphoma.EJHaem · 2023Article
- Weighing evidence: robustness vs quantity.Journal of the National Cancer Institute · 2023Article
- A brief guide to propensity score analysis.Medical journal of the Islamic Republic of Iran · 2018Article
- A systematic literature review on the efficacy-effectiveness gap: comparison of randomized controlled trials and observational studies of glucose-lowering drugs.Clinical epidemiology · 2017Review
- Targeted provision of oral iron: the evolution of a practical screening option.Advances in nutrition (Bethesda, Md.) · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Changes in clinical practice should be driven by relevant and reliable evidence. Hence, adoption of a new therapy requires demonstrating that it provides (causes) benefit. Such evidence is generally obtained from intent-to-treat analyses of randomized clinical trials (RCTs). In this paper, we review other approaches to assessing the causal relationship between treatments and outcomes: (1) inference from non-randomized (observational) studies, (2) analysis of randomized studies where patients received treatments other than those to which they were randomized and (3) analysis of studies where the outcome of interest is sometimes unobservable because of a competing event (competing risks). We conclude that for the practice-changing demonstration of a favorable benefit-to-risk ratio, the gold standard is the intent-to-treat analysis of RCTs. At the same time, we illustrate how careful application of special statistical methods for assessment of treatment-outcome causation can be instrumental in complementing existing randomized evidence and guiding design of future research.
Indexed as
Identifiers
21625225What 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.