A Framework for Evidence Evaluation and Methodological Issues in Implantable Device Studies

Datos Bibliográficos

ID9104917
AutoresArt Sedrakyan (0000-0003-3882-9765, Center for Devices and Radiological Health, autor de correspondencia), Danica Marinac-Dabic, Danica Marinac‐Dabic (0000-0002-1824-0104, Center for Devices and Radiological Health, autor de correspondencia), Sharon‐Lise T Normand (0000-0001-7027-4769, Harvard University), Sharon-Lise T Normand, Alvin I Mushlin (0000-0002-9298-9084, Cornell University), Alvin Mushlin, Tom Gro (0000-0001-8353-7388, Center for Devices and Radiological Health, autor de correspondencia), Tom Gross
Año2010
Volumen48
Número6
PáginasS121-S128
Fecha de publicación2010-06-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaMedical Care (JOURNAL)
Identificadores de la revistaISSN: 0025-7079 • E-ISSN: 1537-1948
EditorialOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0b013e3181d991c4
PMID20421824
OpenAlexW2063736901
IdiomaEN
Citas recibidas1
Referencias citadas30

Implantable medical devices (IMD) are frequently used in interventional medicine. There are a host of complex methodological issues to consider in conducting device studies. A general conceptual framework for evidence evaluation is needed to help investigators conduct comparative studies in this setting. It is known that clinical trials of implants require study design planning and creative execution that are quite different from those in pharmaceutical setting. Important study design issues such as randomization, masking and allocation concealment require unique approaches for each device. In addition, device comparative studies must cope with sources of variability different from pharmaceutical studies. These include operator learning curve effects, hospital-operator-patient interactions, and issues related to device technical characteristics. Observational studies of IMDs are particularly challenging. Selection of comparison groups, adjusting for confounding and addressing learning curve issues needs careful planning. We propose a general framework for IMD evaluation and provide an outline of the methodological issues that require further discussion. We hope this article will inspire and help to inform those interested in advancing comparative safety and effectiveness of IMDs and to plan and pursue future methodological work in this area

Alternative medicine · Comparative effectiveness research · Management science · Masking (illustration) · Observational study · Process management · Risk analysis (engineering) · Selection (genetic algorithm) · Advanced Causal Inference Techniques · Artificial Intelligence · Computer Science · Engineering · Health Systems, Economic Evaluations, Quality of Life · Medicine · Statistical Methods in Clinical Trials