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Two assumptions of the prior event rate ratio approach for controlling confounding can be evaluated by self-controlled case series and dynamic random intercept modeling.
Cheung, Yin Bun; Ma, Xiangmei; Mackenzie, Grant.
Afiliación
  • Cheung YB; Programme in Health Services & Systems Research, Duke-NUS Medical School, 8 College Road, Outram Park, Singapore 169857; Centre for Quantitative Medicine, Duke-NUS Medical School, 8 College Road, Outram Park, Singapore 169857; Tampere Center for Child, Adolescent and Maternal Health Research, Tampere University, Arvo Ylpön katu 34, Tampere 33520, Finland. Electronic address: yinbun.cheung@duke-nus.edu.sg.
  • Ma X; Centre for Quantitative Medicine, Duke-NUS Medical School, 8 College Road, Outram Park, Singapore 169857.
  • Mackenzie G; Medical Research Council Unit The Gambia at London School of Hygiene & Tropical Medicine, Fajara, P.O. Box 273, The Gambia; New Vaccines Group, Murdoch Children's Research Institute, Flemington Road, Melbourne, Victoria 3052, Australia; Department of Paediatrics, University of Melbourne, Parkville, Victoria 3010, Australia.
J Clin Epidemiol ; 175: 111511, 2024 Sep 02.
Article en En | MEDLINE | ID: mdl-39233134
ABSTRACT

OBJECTIVES:

The prior event rate ratio (PERR) is a recently developed approach for controlling confounding by measured and unmeasured covariates in real-world evidence research and observational studies. Despite its rising popularity in studies of safety and effectiveness of biopharmaceutical products, there is no guidance on how to empirically evaluate its model assumptions. We propose two methods to evaluate two of the assumptions required by the PERR, specifically, the assumptions that occurrence of outcome events does not alter the likelihood of receiving treatment, and that earlier event rate does not affect later event rate. STUDY DESIGN AND

SETTING:

We propose using self-controlled case series (SCCS) and dynamic random intercept modeling (DRIM), respectively, to evaluate the two aforementioned assumptions. A nonmathematical introduction of the methods and their application to evaluate the assumptions are provided. We illustrate the evaluation with secondary analysis of deidentified data on pneumococcal vaccination and clinical pneumonia in The Gambia, West Africa.

RESULTS:

SCCS analysis of data on 12,901 vaccinated Gambian infants did not reject the assumption of clinical pneumonia episodes had no influence on the likelihood of pneumococcal vaccination. DRIM analysis of 14,325 infants with a total of 1719 episodes of clinical pneumonia did not reject the assumption of earlier episodes of clinical pneumonia had no influence on later incidence of the disease.

CONCLUSION:

The SCCS and DRIM methods can facilitate appropriate use of the PERR approach to control confounding. PLAIN LANGUAGE

SUMMARY:

The prior event rate ratio is a promising approach for analysis of real-world data and observational studies. We propose two statistical methods to evaluate the validity of two assumptions it is based on. They can facilitate appropriate use of the prior even rate ratio.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Clin Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Clin Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos