Statistics in the World of Medical Devices: The Contrast with Pharmaceuticals

Medical devices play a vital role in people's lives as these products are revolutionizing medicine with breathtaking advances in both the treatment and the detection of many diseases. While a similar, primarily therapeutic, revolution is ongoing in the pharmaceutical world; the focus here is the effect this device revolution is having on the statistical world. The similarities and differences between medical devices and pharmaceutical drugs are explored in terms of their natures, industries, and how they are regulated in the U.S. and globally. Statistical issues concerning the evaluation of devices versus those of drugs are compared and contrasted. These trends are creating new challenges for the statistical world in the development and evaluation of these new medical products.

[1]  Lilly Q Yue,et al.  Statistical and Regulatory Issues with the Application of Propensity Score Analysis to Nonrandomized Medical Device Clinical Studies , 2007, Journal of biopharmaceutical statistics.

[2]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[3]  Gregory Campbell,et al.  Some Statistical and Regulatory Issues in the Evaluation of Genetic and Genomic Tests , 2004, Journal of biopharmaceutical statistics.

[4]  Maqc Consortium The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements , 2006, Nature Biotechnology.

[5]  Peter Fayers,et al.  Statistical evaluation of learning curve effects in surgical trials , 2004, Clinical trials.

[6]  G. Campbell,et al.  Independence of the statistician who analyses unblinded data , 2004, Statistics in medicine.

[7]  J. Lewis,et al.  Statistical principles for clinical trials (ICH E9): an introductory note on an international guideline. , 1999, Statistics in medicine.

[8]  C B Begg,et al.  Biases in the assessment of diagnostic tests. , 1987, Statistics in medicine.

[9]  William DuMouchel,et al.  Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System , 1999 .

[10]  L. Katz,et al.  Medical devices. , 1985, Dimensions in health service.

[11]  G. Campbell The experience in the FDA's Center for Devices and Radiological Health with Bayesian strategies , 2005, Clinical trials.

[12]  L M Hinman,et al.  The drug diagnostic co-development concept paper Commentary from the 3rd FDA-DIA-PWG-PhRMA-BIO Pharmacogenomics Workshop , 2006, The Pharmacogenomics Journal.

[13]  G. Campbell,et al.  Interpretation of Subgroup Analyses in Medical Device Clinical Trials* , 1998 .

[14]  Daniel J Sargent,et al.  Clinical trial designs for predictive marker validation in cancer treatment trials. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[15]  Clinical trials of the effectiveness of devices: an analogy with drugs. , 2001, Surgery.

[16]  Chang S. Lao,et al.  Statistical Issues Involved in Medical Device Postmarketing Surveillance* , 2000 .

[17]  T. Kaptchuk,et al.  Do medical devices have enhanced placebo effects? , 2000, Journal of clinical epidemiology.

[18]  M. Zweig,et al.  Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. , 1993, Clinical chemistry.

[19]  D. DeMets The role of surrogate outcome measures in evaluating medical devices. , 2000, Surgery.

[20]  Xiao-Hua Zhou,et al.  Statistical Methods in Diagnostic Medicine , 2002 .

[21]  Innovation OR Stagnation Challenge and Opportunity on the Critical Path to New Medical Products , 2004 .

[22]  R. Simon,et al.  Development and evaluation of therapeutically relevant predictive classifiers using gene expression profiling. , 2006, Journal of the National Cancer Institute.

[23]  R. Kay Statistical Principles for Clinical Trials , 1998, The Journal of international medical research.

[24]  R. F. Wagner,et al.  Components-of-variance models and multiple-bootstrap experiments: an alternative method for random-effects, receiver operating characteristic analysis. , 2000, Academic radiology.

[25]  Kristian Linnet,et al.  Partly nonparametric approach for determining the limit of detection. , 2004, Clinical chemistry.

[26]  S. Normand,et al.  Application of models for multivariate mixed outcomes to medical device trials: coronary artery stenting , 2002, Statistics in medicine.

[27]  D. DeMets,et al.  Surrogate End Points in Clinical Trials: Are We Being Misled? , 1996, Annals of Internal Medicine.