Detecting Anomalies Among Practice Sites Within Multicenter Trials.
Pubmed ID: 30857413
Pubmed Central ID: PMC6578590
Journal: Circulation. Cardiovascular quality and outcomes
Publication Date: 03/01/2019
Affiliation: Center for Primary Care, Harvard Medical School, Boston, MA (S.B.).
MeSH Terms: Humans, Male, Female, Aged, Aged, 80 and over, Multicenter Studies as Topic, Middle Aged, Data Interpretation, Statistical, Heart Failure, Treatment Outcome, Blood Pressure, Practice Guidelines as Topic, Diabetes Mellitus, Antihypertensive Agents, Guideline Adherence, Research Design, Mineralocorticoid Receptor Antagonists, Spironolactone, Data Accuracy
Grants: L30 DK103291, K23 DK109200
Authors: Basu S, Berkowitz SA, Rudolph KE
Cite As: Berkowitz SA, Rudolph KE, Basu S. Detecting Anomalies Among Practice Sites Within Multicenter Trials. Circ Cardiovasc Qual Outcomes 2019 Mar;12(3):e004907.
BACKGROUND: Recent multisite trials reveal striking heterogeneities in results between trial sites. These may be because of population differences indicating different treatment benefits among different types of participants or site anomalies, such as failures to adhere to study protocols that could negatively affect study validity. We sought to determine whether a new data analysis strategy-transportability methods-could suggest site anomalies not readily identified through standard methods. METHODS AND RESULTS: We applied transportability methods to 2 large, multicenter cardiovascular disease treatment trials: the TOPCAT trial (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist; n=3445) comparing spironolactone to placebo for heart failure (for which site anomalies were suspected) and the ACCORD BP trial (Action to Control Cardiovascular Risk in Diabetes-Blood Pressure; n=4733) comparing intensive-to-standard blood pressure treatment (for which site anomalies were not suspected). The transportability methods give expected results by standardizing from one site to another using data on participant covariates. The difference between the expected and observed results was assessed using calibration tests to identify whether treatment-effect differences between sites could be explained by participant population characteristics. Standard regression methods did not detect heterogeneities in TOPCAT between Russia/Georgia study sites suspected of study protocol violations and sites in the Americas ( P=0.12 for difference in primary cardiovascular outcome; P=0.20 for difference in total mortality). The transportability methods, however, detected the difference between Russia/Georgia sites and sites in the Americas ( P<0.001) and found that measured participant characteristics did not explain the between-site discrepancies. The transport methods found no such discrepancies between sites in ACCORD BP, suggesting participant characteristics explained between-site differences. CONCLUSIONS: Transportability methods may be superior to standard approaches for detecting anomalies within multicenter randomized trials and assist data monitoring boards to determine whether important treatment-effect heterogeneities can be attributed to participant differences or potentially to site performance differences requiring further investigation.