ArticleBMC medical research methodology2021
Bayesian Hodges-Lehmann tests for statistical equivalence in the two-sample setting: Power analysis, type I error rates and equivalence boundary selection in biomedical research.
Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05508009 (Phase 1b/2a Trial of Allogeneic Hematopoietic Stem Cell Transplantation), which is not on this map. Cited by 11 papers.
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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.
Phase 1b/2a Trial of Allogeneic Hematopoietic Stem Cell Transplantation (HSCT) From an HLA-partially Matched Related or Unrelated Donor After TCRαβ+ T-cell/CD19+ B-cell Depletion for Patients Who Will Receive a Kidney Transplant (KT) From the Same HSCT/KT Donor
Who cites it
11 citing papers in PubMed.
- Support for the coverage EMA: Participants can perceive and report discrete auditory event characteristics over 2 h in a simulated EMA scenario.Behavior research methods · 2026Article
- "Sentio ergo est": Unmasking the psychological realities of emotional misperception.Perception · 2025Article
- Statistical Testing for Protein Equivalence Identifies Core Functional Modules Conserved across 360 Cancer Cell Lines and Presents a General Approach to Investigating Biological Systems.Journal of proteome research · 2024Article
- Bayesian Mendelian randomization with an interval causal null hypothesis: ternary decision rules and loss function calibration.BMC medical research methodology · 2024Article
- A Bayesian approach for evaluating equivalence over multiple groups, and comparison with frequentist tost.Journal of applied statistics · 2024Article
- Learning emotional dialects: A British population study of cross-cultural communication.Perception · 2023Article
- Reducing the false discovery rate of preclinical animal research with Bayesian statistical decision criteria.Statistical methods in medical research · 2023Article
- A Case Study for Critical Reagent Qualification for Ligand Binding Assays Using Equivalence Test Methodology.The AAPS journal · 2023Article
- Understanding Randomness on a Molecular Level: A Diagnostic Tool.CBE life sciences education · 2023Article
- "The many faces of sorrow": An empirical exploration of the psychological plurality of sadness.Current psychology (New Brunswick, N.J.) · 2023Article
- Bayesian identification of structural coefficients in causal models and the causal false-positive risk of confounders and colliders in linear Markovian models.BMC medical research methodology · 2022Article
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Abstract
backgroundNull hypothesis significance testing (NHST) is among the most frequently employed methods in the biomedical sciences. However, the problems of NHST and p-values have been discussed widely and various Bayesian alternatives have been proposed. Some proposals focus on equivalence testing, which aims at testing an interval hypothesis instead of a precise hypothesis. An interval hypothesis includes a small range of parameter values instead of a single null value and the idea goes back to Hodges and Lehmann. As researchers can always expect to observe some (although often negligibly small) effect size, interval hypotheses are more realistic for biomedical research. However, the selection of an equivalence region (the interval boundaries) often seems arbitrary and several Bayesian approaches to equivalence testing coexist.
methodsA new proposal is made how to determine the equivalence region for Bayesian equivalence tests based on objective criteria like type I error rate and power. Existing approaches to Bayesian equivalence testing in the two-sample setting are discussed with a focus on the Bayes factor and the region of practical equivalence (ROPE). A simulation study derives the necessary results to make use of the new method in the two-sample setting, which is among the most frequently carried out procedures in biomedical research.
resultsBayesian Hodges-Lehmann tests for statistical equivalence differ in their sensitivity to the prior modeling, power, and the associated type I error rates. The relationship between type I error rates, power and sample sizes for existing Bayesian equivalence tests is identified in the two-sample setting. Results allow to determine the equivalence region based on the new method by incorporating such objective criteria. Importantly, results show that not only can prior selection influence the type I error rate and power, but the relationship is even reverse for the Bayes factor and ROPE based equivalence tests.
conclusionBased on the results, researchers can select between the existing Bayesian Hodges-Lehmann tests for statistical equivalence and determine the equivalence region based on objective criteria, thus improving the reproducibility of biomedical research.
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