Your browser doesn't support javascript.
loading
A more powerful test for three-arm non-inferiority via risk difference: Frequentist and Bayesian approaches.
Paul, Erina; Tiwari, Ram C; Chowdhury, Shrabanti; Ghosh, Samiran.
Afiliación
  • Paul E; Center of Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA.
  • Tiwari RC; Division of Biostatistics, Center for Devices and Radiological Health, Office Surveillance and Biometrics, FDA, Silver Spring, MD, USA.
  • Chowdhury S; Center of Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA.
  • Ghosh S; Family Medicine & Public Health Sciences, Wayne State University, Detroit, MI, USA.
J Appl Stat ; 50(4): 848-870, 2023.
Article en En | MEDLINE | ID: mdl-36925904
Necessity for finding improved intervention in many legacy therapeutic areas are of high priority. This has the potential to decrease the expense of medical care and poor outcomes for many patients. Typically, clinical efficacy is the primary evaluating criteria to measure any beneficial effect of a treatment. Albeit, there could be situations when several other factors (e.g. side-effects, cost-burden, less debilitating, less intensive, etc.) which can permit some slightly less efficacious treatment options favorable to a subgroup of patients. This often leads to non-inferiority (NI) testing. NI trials may or may not include a placebo arm due to ethical reasons. However, when included, the resulting three-arm trial is more prudent since it requires less stringent assumptions compared to a two-arm placebo-free trial. In this article, we consider both Frequentist and Bayesian procedures for testing NI in the three-arm trial with binary outcomes when the functional of interest is risk difference. An improved Frequentist approach is proposed first, which is then followed by a Bayesian counterpart. Bayesian methods have a natural advantage in many active-control trials, including NI trial, as it can seamlessly integrate substantial prior information. In addition, we discuss sample size calculation and draw an interesting connection between the two paradigms.
Palabras clave

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Etiology_studies / Risk_factors_studies Idioma: En Revista: J Appl Stat Año: 2023 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Etiology_studies / Risk_factors_studies Idioma: En Revista: J Appl Stat Año: 2023 Tipo del documento: Article