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

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

• Comparative Effectiveness Research Methods

  Kathleen N Lohr•Medical Care•2010

  Incluir item

• What is meant by intention to treat analysis? Survey of published randomised controlled trials

  Open Access•S Hollis, F Campbell•BMJ•1999

  Incluir item

• Is Volume Related to Outcome in Health Care? A Systematic Review and Methodologic Critique of the Literature

  Open Access•Ethan A Halm, Clara Lee et al.•Annals of Internal Medicine•2002

  Incluir item

• Estimating Causal Effects from Large Data Sets Using Propensity Scores

  Open Access•Donald B Rubin•Annals of Internal Medicine•1997

  Incluir item

• Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group

  Open Access•Ralph B D’agostino•Statistics in Medicine•1998

  Incluir item

• Identification of Causal Effects Using Instrumental Variables

  Joshua D Angrist, Guido W Imbens et al.•Journal of the American…•1996

  Incluir item

• Evidence based medicine

  Open Access•David L Sackett, William Rosenberg et al.•BMJ•1996

  Incluir item