At the Academy of Managed Care Pharmacy Annual Meeting in Denver, Colorado, Kevin C. Maki, PhD, CLS, FNLA, FTOS, FACN, of Midwest Biomedical Research/Center for Metabolic and Cardiovascular Health in Chicago, Illinois, discussed methods of quantifying treatment effects in cardiovascular outcomes trials.
At the Academy of Managed Care Pharmacy Annual Meeting in Denver, Colorado, Kevin C. Maki, PhD, CLS, FNLA, FTOS, FACN, of Midwest Biomedical Research/Center for Metabolic and Cardiovascular Health in Chicago, Illinois, discussed methods of quantifying treatment effects in cardiovascular outcomes trials. Maki also contextualized the advantages of these methods and described some of the potential pitfalls of comparing treatment effects across trials using these statistics.1
Maki discussed 3 common ways of representing cardiovascular outcome trial results. These include: assessing time to occurrence of cardiovascular outcomes, performing a survival analysis, and summarizing treatment effects through various statistical methods. Cardiovascular outcome trials assessing the time to occurrence of an event include studies that assess the percentage of patients over time with any major adverse cardiovascular event. Time-to-occurrence analyses differ from survival analyses in that patients enrolled in survival analyses may have varying length of follow-up. Finally, treatment effect summaries include the Kaplan-Meier curve, hazard ratio, relative risk reduction, absolute risk reduction, and number needed to treat.1,2
Although clinical trials are intended to measure the efficacy of a treatment intervention, many factors beyond treatment efficacy may affect outcomes. These factors relate to the study design, how the study is conducted, how data are analyzed, and the characteristics of the patient population (eg, demographics, disease severity). An important factor in determining treatment effect is the baseline risk of patients receiving therapy. Patients with a high underlying risk of experiencing an event may appear to experience a small absolute risk reduction in event rates. However, in a patient group with a low baseline risk, the same treatment might result in a similar absolute risk reduction but a greater relative risk reduction.1,3-5
In cardiovascular outcome trials, multiple studies have evaluated patient factors that contribute to cardiovascular risk. Factors such as history of cardiovascular events, preexisting lipid disorders, comorbid type 2 diabetes, and comorbid hypertension are among the many risk factors that are predictive of cardiovascular risk. Treatment guidelines for the management of cardiovascular risk from various professional organizations recommend assessing patient baseline cardiovascular risk and initiating treatment to reduce low-density lipoprotein cholesterol (LDL-C) level based on the patient’s individual risk category. The recommended treatment target varies by the organization issuing the guidelines, and may be an absolute value for an LDL-C goal level or a percentage reduction in LDL-C level.1,6-11
Differences in baseline cardiovascular risk levels of populations between studies may lead to misleading comparisons of product efficacy when comparing the results of cardiovascular outcome trials. For example, if one medication (drug A) is tested in patients with relatively low underlying cardiovascular risk, and 10% of patients in the control group experience cardiovascular events versus 8% of patients in the treatment group, the absolute risk reduction is 2 percentage points, the number needed to treat is 50, and the relative risk reduction is 20%. In contrast, a second medication (drug B) studied in patients with relatively high baseline cardiovascular risk might yield a 25% event rate in a control group versus a 22% event rate in a treatment group, for a 3 percentage point absolute risk reduction, a number needed to treat of 33, and a relative risk reduction of 12%. Although it is tempting to conclude that drug B is superior to drug A based on these results, it is actually impossible to compare these 2 hypothetical medications based on these results alone, as the underlying baseline risks of the study populations in these 2 hypothetical trials differ.1,5
When comparing the results of studies, it is crucial to take into account differences in study design, the length of follow-up, and the underlying population characteristics of patients enrolled. Clinicians must be aware of the limitations of summary statistics when evaluating and comparing the results of cardiovascular outcome trials conducted in heterogenous patient populations with differing levels of baseline cardiovascular risk.
1. Maki KC. Calculations in Cardiovascular Outcome Trials. Presented at the Academy of Managed Care Pharmacy Managed Care & Specialty Annual Meeting 2017; March 27-30, 2017; Denver, CO.
2. Pocock SJ, McMurray JJ, Collier TJ. Making sense of statistics in clinical trial reports: part 1 of a 4-part series on statistics for clinical trials. J Am Coll Cardiol. 2015;66(22):2536-2549. doi: 10.1016/j.jacc.2015.10.014.
3. Schechtman E. Odds ratio, relative risk, absolute risk reduction, and the number needed to treat--which of these should we use? Value Health. 2002;5(5):431-436.’
4. Kip KE, Hollabaugh K, Marroquin OC, Williams DO. The problem with composite end points in cardiovascular studies: the story of major adverse cardiac events and percutaneous coronary intervention. J Am Coll Cardiol. 2008;51(7):701-707. doi: 10.1016/j.jacc.2007.10.034.
5. Ranganathan P, Pramesh CS, Aggarwal R. Common pitfalls in statistical analysis: absolute risk reduction, relative risk reduction, and number needed to treat. Perspect Clin Res. 2016;7(1):51-53. doi: 10.4103/2229-3485.173773.
6. Stone NJ, Robinson JG, Lichtenstein AH, et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63(25 pt B):2889-2934. doi: 10.1016/j.jacc.2013.11.002.
7. Keenan TE, Rader DJ. Genetics of lipid traits and relationship to coronary artery disease. Curr Cardiol Rep. 2013;15(9):396. doi: 10.1007/s11886-013-0396-9.
8. Mendis S, Lindholm LH, Anderson SG, et al. Total cardiovascular risk approach to improve efficiency of cardiovascular prevention in resource constrain settings. J Clin Epidemiol. 2011;64(12):1451-1462. doi: 10.1016/j.jclinepi.2011.02.001.
9. Catapano AL, Graham I, De Backer G, et al. 2016 ESC/EAS guidelines for the management of dyslipidaemias: The Task Force for the Management of Dyslipidaemias of the European Society of Cardiology (ESC) and European Atherosclerosis Society (EAS) Developed with the special contribution of the European Assocciation for Cardiovascular Prevention & Rehabilitation (EACPR). Atherosclerosis. 2016;253:281-344. doi: 10.1016/j.atherosclerosis.2016.08.018.
10. Jellinger PS, Handelsman Y, Rosenblit PD, et al. American Association of Clinical Endocrinologists and American College of Endocrinology guidelines for management of dyslipidemia and prevention of atherosclerosis. Endocr Pract. 2017. doi: 10.4158/EP171764.GL.
11. Jacobson TA, Ito MK, Maki KC, et al. National Lipid Association recommendations for patient-centered management of dyslipidemia: part 1--full report. J Clin Lipidol. 2015;9(2):129-169. doi: 10.1016/j.jacl.2015.02.003.