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Although the disproportionality method is commonly used by companies and health agencies to screen and detect potential signals, other methods have been developed to compensate for some of these problems. Some of the other approaches are found in the broad category of “Bayesian approaches”. These methods account for the number of cases (cell sizes) and decrease the sensitivity of the PRR score if the cell sizes are small. One method, the Bayesian confidence propagation neural network (BCPNN), was developed by the Uppsala Monitoring Centre and is used for signal detection in their database. Other methods, such as the gamma poisson shrinker (GPS) and the multi-item gamma poisson shrinker (MGPS), are also used to attempt to make the PRR more useful. These methods have been used by various health agencies, including the Food and Drug Administration (FDA), EMA, and Medicines and Healthcare Products Regulatory Agency (MHRA). Treatment of these methodologies in detail is beyond the scope of this book, and the reader is referred to the standard textbooks of pharmacoepidemiology. A good, approachable summary of the field is available in Module IX Addendum I — Methodological aspects of signal detection from spontaneous reports of suspected adverse reactions (EMA Good PV Practices Web page).¹⁴

A brief note on other terms you may run across:

Event rate — The number of subjects experiencing an AE as a proportion of the number of people in the population at risk over a specific period of time. For example, 5 per 1,000 person-days.

Absolute risk — The probability of occurrence of an AE in patients exposed to a drug. For example, one may say the absolute risk of a myocardial infarction with drug X is 5%. Obviously, this value is often hard or impossible to obtain and is one reason that pharmacovigilance exists.

Absolute risk reduction — The arithmetic difference between two absolute risk rates. For example, the absolute risk of a myocardial infarction with drug X is 5% and with drug Y 2%. The risk reduction is 5% − 2% = 3%.

Relative risk (or risk ratio) — In a trial or in an observational cohort, the ratio between the rate of an adverse outcome (e.g., an AE) in a group exposed to a treatment and the rate in a control group. It is a measure of the strength of a cause–effect relationship. For example, the rate of myocardial infarction in the drug X group is 5% and in the control group 2.5%. The ratio (relative risk) is 5%/2% = 2.5%.

Relative risk reduction — The difference in event rates between two groups, expressed as a proportion of the event rate in the untreated group.

Odds ratio — Also known as estimated (or approximate) relative risk. In case-control studies concerning drug safety, the ratio between the rate of exposure to a suspect drug in a group of cases (with the AE) and the rate of exposure in a group of non-cases (i.e., controls without the AE). Like relative risk from cohort studies, the odds ratio is a useful estimation of the strength of cause–effect relationships. This parameter is often used in systematic reviews and meta-analyses.

Risk difference or attributable risk — The difference between the rate of an adverse outcome (e.g., an AE) in a group exposed to an experimental drug and the rate in a control group.

Number needed to harm (NNH) — Also called number needed to harm one. The NNH is the number of patients that must be exposed to a drug to produce an AE/ADR in one patient. Exposure may, for example, be one course or 1 year of treatment. For example, one could calculate based on a study that the number needed to produce one case of rhabdomyolysis with a particular statin is 3,500 patients.

Number needed to benefit (NNB) — A similar concept to the NNH, but reflects the number needed to receive a positive effect from the drug. For example, one might need to treat three patients with a particular statin to get a positive effect (e.g., cholesterol reduction).

Benefit–risk ratio — Various techniques have been developed using such data as NNH and NNB to calculate an actual number for the benefit–risk ratio. Although a number of quantitative approaches have been investigated, it is not possible to calculate an accurate “ratio” using estimates. However, if one can calculate the NNH and the NNB for the particular drug one can calculate a benefit–risk ratio. This is rarely done however as the data are incomplete and inaccurate.

Confidence intervals — Most studies are based on samples, not entire populations, which adds an element of uncertainty and unreliability to the results because the whole population was not studied. Thus, we cannot be totally sure that the 15% of the study population that had serious AEs represents the true value for the whole population rather than just for the smaller sample. The confidence interval represents the range of the correct or true value for the whole population and gives an idea of the reliability of the data and of the estimate. One can calculate various levels of “assurance”, 90%, 95%, 99%, 99.9%, and so on, for the confidence interval. Usually, the 95% level is used. The narrower or smaller the distance between the upper and lower values of the confidence interval (called the confidence limits), is generally better. The more patients in the study usually produce a narrower (better) confidence interval.

¹Hill, Austin Bradford, The environment and disease: Association or causation?, Proc Roy Soc Med 1965; 58(5): 295–300.

²Weber, Advances in Inflammation Research, Raven Press, New York, 1984, pp. 1–7.

³Hartnell NR and Wilson JP, Replication of the Weber effect using post-marketing adverse event reports voluntarily submitted to the United States Food and Drug Administration 2004, 24(6):743–749.

⁴Keith B. Hoffman, Mo Dimbil, Colin B. Erdman, Nicholas P. Tatonetti, and Brian M Overstreet, The Weber effect and the United States Food and Drug Administration’s Adverse Event Reporting System (FAERS): Analysis of sixty-two drugs approved from 2006 to 2010, Drug Safety 2014; 37(5): 381. See the abstract at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3975089/.

⁵Ankur Arora, Rajinder K Jalali, and Divya Vohora, Relevance of the Weber effect in contemporary pharmacovigilance of oncology drugs, Ther Clin Risk Manag. 2017; 13: 1195–1203. See the abstract at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5602442/.

⁶Sachs and Bortnichak, Am J Med. 1986; 81(suppl 5B): 49.

⁷Murch SH, Anthony A, Casson DH, Malik M, Berelowitz M, Dhillon AP et al., Retraction of an interpretation, Lancet. 2004; 363: 750.

⁸Rawlins, J R Coll Phys Lond. 1995; 29: 41–49.

⁹Scott, Rosenbaum, Waters et al., R I Med J. 1987; 70: 311–316.

¹⁰Bégaud B, Martin K, Haramburu F et al., Rates of spontaneous reporting of adverse drug reactions in France, J Am Med Assoc. 2002; 288: 1588.

¹¹See Drug Safety 2006; 29(5): 385–396. Abstract: http://europepmc.org/abstract/med/16689555.

¹²Bäckström, Mjörndal, and Dahlqvist, Pharmacoepidemiol Drug Safety 2004; 13(7): 483–487.

¹³On these methods, the following references can be looked up: (a) Evans SJ, Waller PC, Davis S, Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports, Pharmacoepidemiol Drug Safety 2001; 10(6): 483–486. (b) DuMouchel W, Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system (with discussion), Am Stat. 1999; 53(3): 177–222. (c) van Puijenbroek E, Diemont W, van Grootheest K, Application of quantitative signal detection in the Dutch spontaneous reporting system for adverse drug reactions, Drug Safety 2003; 26(5): 293–301. (d) Bate A, Lindquist M, Edwards IR, Olsson S, Orre R, Lansner A et al., A Bayesian neural network method for adverse drug reaction signal generation, Eur J Clin Pharmacol. 1998; 54(4): 315–321.

¹⁴See also “Practical Aspects of Signal Detection in Pharmacovigilance,” Report of the CIOMS Working Group VIII. Counsel for the International Organizations of Medical Sciences, Geneva, 2010. See also http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2017/10/WC500236405.pdf.