PubMed · 16368381
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Joe B Putnam. 2006. Invited commentary.. https://doi.org/10.1016/j.athoracsur.2005.09.069
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Post-marketing drug safety data sets are often massive, and entail problems with heterogeneity and selection bias. Nevertheless, quantitative methods have proven a very useful aid to help clinical experts in screening for previously unknown associations in these data sets. The WHO international drug safety database is the world's largest data set of its kind with over three million reports on suspected adverse drug reaction incidents. Since 1998, an exploratory data analysis method has been in routine use to screen for quantitative associations in this data set. This method was originally based on large sample approximations and limited to pairwise associations, but in this article we propose more accurate credibility interval estimates and extend the method to allow for the analysis of more complex quantitative associations. The accuracy of the proposed credibility intervals is evaluated through comparison to precise Monte Carlo simulations. In addition, we propose a Mantel-Haenszel-type adjustment to control for suspected confounders.
OBJECTIVE: To review Public Law (PL) 109-41-the Patient Safety and Quality Improvement Act of 2005 (PSQIA)-and summarize key medication error research that contributed to congressional recognition of the need for this legislation. DATA SOURCES: Relevant publications related to medication error research, patient safety programs, and the legislative history of and commentary on PL 109-41, published in English, were identified by MEDLINE, PREMEDLINE, Thomas (Library of Congress), and Internet search engine-assisted searches using the terms healthcare quality, medication error, patient safety, PL 109-41, and quality improvement. Additional citations were identified from references cited in related publications. STUDY SELECTION AND DATA EXTRACTION: All relevant publications were reviewed. Summarization of the PSQIA was carried out by legal textual analysis. DATA SYNTHESIS: PL 109-41 provides privilege and confidentiality for patient safety work product (PSWP) developed for reporting to patient safety organizations (PSOs). It does not establish federal mandatory reporting of significant errors; rather, it relies on existing state reporting systems. The Act does not preempt stronger state protections for PSWP. The Agency for Healthcare Research and Quality is directed to certify PSOs and promote the establishment of a national network of patient safety databases. Whistleblower protection and penalties for unauthorized disclosure of PSWP are among its enforcement mechanisms. CONCLUSIONS: The Act protects clinicians who report minor errors to PSOs and protects the information from disclosure, but providers must increasingly embrace a culture of interdisciplinary concern for patient safety if this protection is to have real impact on patient care.
The purpose of this study was to examine the accuracy of the Runs Test, Reverse Arrangements Test, and modified Reverse Arrangements Test for assessing stationarity of surface electromyographic (EMG) signals. Five stationary signals were generated by custom programs written with LabVIEW programming software. These signals consisted of sine waves, sums of sine waves, and sums of sine waves and random noise. The sixth signal was a stationary computer generated surface EMG signal downloaded from the surface EMG for the non-invasive assessment of muscles (SENIAM) project database. There were no changes in the amplitude or frequency contents of the stationary signals over time. Several nonstationary signals were also created, including a nonstationary chirp signal generated with LabVIEW programming software, a nonstationary computer generated surface EMG signal downloaded from the SENIAM project database, and a real surface EMG signal recorded from the biceps brachii during a concentric isokinetic muscle action of the forearm flexors at a velocity of 30 degrees s(-1). Both the stationary and nonstationary signals were tested for stationarity using the Runs Test, Reverse Arrangements Test, and modified Reverse Arrangements Test. The results indicated that each of the three stationarity tests demonstrated at least one form of inaccuracy (i.e. false positive and/or false negative results) in examining the stationarity of the test signals. These findings may reflect the fact that these tests were designed to determine whether or not a signal is random, rather than examine signal stationarity exclusively. Thus, the Runs Test, Reverse Arrangements Test, and modified Reverse Arrangements Test may not be appropriate for assessing stationarity in surface EMG signals.