{"doi":"10.1016/j.eclinm.2022.101686","title":"Re-analysis of data from a cluster RCT entitled “health literacy and exercise-focused interventions on clinical measurements in Chinese diabetes patients”","abstract":"Wang et al.1Wang L Fang H Xia Q et al.Health literacy and exercise-focused interventions on clinical measurements in Chinese diabetes patients: a cluster randomized controlled trial.EClin Med. 2019; 17100211https://doi.org/10.1016/j.eclinm.2019.11.004Summary Full Text Full Text PDF Scopus (10) Google Scholar examined the effects of three health literacy and exercise interventions on HbA1c (the primary outcome) in a four-arm cluster randomized trial (cRCT), but did not account for clustering and nesting. Eight Healthcare Centers (CHCs) were randomly assigned to four conditions (two per condition). Repeated measurements occurred at four timepoints after enrollment. Nominally significant p-values were reported for intervention effects on HbA1c. We reanalyzed the data accounting for clustering and nesting, and present the results herein. An assumption underlying the validity of typical inferential statistical methods is independence of observations: the outcome of each respondent is not related to the outcomes of other respondents. In cRCTs, clusters are randomized, but inferences about the intervention effects are often intended to individuals. In the study, individuals from the same CHC (cluster) are expected to be more similar than those from different clusters, resulting in a pattern of correlated data so ‘errors’ (model residuals) are not independent across individual participants.2Murray DM Varnell SP Blitstein JL. Design and analysis of group-randomized trials: a review of recent methodological developments.Am J Public Health. 2004; 94: 423-432Crossref PubMed Scopus (478) Google Scholar,3Sainani K. The importance of accounting for correlated observations.PM&R. 2010; 2: 858-861Crossref PubMed Scopus (71) Google Scholar The inherent correlation among observations from the same cluster typically inflates type I error rates. Not accounting for non-independence within clusters can lead to incorrect estimation of the variance; p-values that are smaller than what a valid analysis will produce for intervention effects; and thus invalid inferences about intervention effects.4Brown AW Li P Bohan Brown MM et al.Best (but oft-forgotten) practices: designing, analyzing, and reporting cluster randomized controlled trials.Am J Clin Nutr. 2015; 102: 241-248Crossref PubMed Scopus (41) Google Scholar By valid analysis we mean an analysis which under the null hypothesis, with continuously distributed data and a continuously distributed test-statistic, produces a sampling distribution of p-values that is uniform on the interval [0,1]. An invalid analysis refers to a testing procedure that produces any other sampling distribution of p-values. In the study, generalized estimating equations (GEE) models with random effect for individual subjects were used. This accounts for repeated measurements nested within participants, but not for participants nested within CHCs. The authors of the study collegially shared their data with us. We were able to reproduce published results per the original methods1Wang L Fang H Xia Q et al.Health literacy and exercise-focused interventions on clinical measurements in Chinese diabetes patients: a cluster randomized controlled trial.EClin Med. 2019; 17100211https://doi.org/10.1016/j.eclinm.2019.11.004Summary Full Text Full Text PDF Scopus (10) Google Scholar with negligible differences: the p-value for change of HDL within the control group at 24-month follow-up (our p-value = 0.042, p-value reported in the study ≥0.05), and differences that may reasonably arise due to rounding. We then reanalyzed data for the intervention effects (each intervention compared to control) on HbA1c and the secondary outcomes from baseline to each follow-up point. Herein, we present a model that accounts for clustering and nesting effects of the design within the context of other methodologic choices of the original authors, which involved null hypothesis significance testing based on p-values and a 0.05 alpha level. Thus, debate about","journal":"EClinicalMedicine","year":2022,"id":295913,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.5458,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":501096,"name":"Lilian Golzarri‐Arroyo","orcid":"0000-0002-1221-6701","position":1,"is_corresponding":false},{"id":528482,"name":"Colby J. Vorland","orcid":"0000-0003-4225-372X","position":2,"is_corresponding":false},{"id":85471,"name":"Andrew W. Brown","orcid":"0000-0002-1758-8205","position":3,"is_corresponding":false},{"id":71621,"name":"David B. Allison","orcid":"0000-0003-3566-9399","position":4,"is_corresponding":false},{"id":797347,"name":"Yasaman Jamshidi‐Naeini","orcid":"0000-0003-4769-2764","position":0,"is_corresponding":true}],"reference_count":13,"raw_metadata":null,"created_at":"2026-07-19T00:31:12.531553Z","pmid":"36238693","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}