Cobert's Manual Of Drug Safety And Pharmacovigilance (Third Edition). William Gregory

      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.

      CHAPTER

      6

      Epidemiology and Pharmacoepidemiology: What Are They? What Are Their Limitations and Advantages?

       This chapter is not meant to be an introduction to epidemiology or pharmacoepidemiology. There are many excellent textbooks and references in those fields. Rather, this chapter attempts, briefly, to place epidemiology and pharmacoepidemiology in the context of their use in the practical world of drug safety.

Introduction

      What is epidemiology? There are several similar definitions as follows:

      

The study of the distribution and determinants of diseases in populations. Epidemiological studies can be divided into two main types:

      

Descriptive epidemiology describes disease and/or exposure and may consist of calculating, for example, disease incidence rates and prevalence. Such descriptive studies do not use control groups and often are used to generate hypotheses. However, if descriptive studies are used to test a hypothesis on disease incidence rate, comparisons are not appropriate because control groups are not included in the study design. Studies of drug utilization and background incidence rates of AEs would generally fall under descriptive studies. The design usually does not include an analytical statistical analysis component.

      

Analytic epidemiology includes two types of studies: (1) observational studies, such as case-control and cohort studies, and (2) experimental studies, which would include clinical trials, such as randomized controlled trials (RCTs). The analytic studies compare an exposed group with a comparator group and are usually designed as hypothesis-testing studies. (From the International Society of Pharmacoepidemiology.)

      

The study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems. (From the Centers for Disease Control and Prevention.)

      

The study of the frequency, distribution, and behavior of a disease within a population.

      

The study of the incidence, distribution, and control of disease in a population.