Currently available drug value frameworks integrate data about a medication's benefits and risks. However, the strength of the research data supporting the use of one medication versus another is highly variable. The greater the number and quality of well-conducted trials underlying a certain intervention, the stronger its potential favorable contribution to public health. Drug value should therefore also be a function of the magnitude of clinical evidence gleaned from high-quality clinical trials.
We propose an investigation-driven value framework that determines the merit of a drug based on the overall available evidence, including the rigour of relevant trials. The NCCN Hodgkin Lymphoma guidelines were reviewed and data collected about commonly used regimens for Classic HL. Value scores were determined for each drug combination.
A model was designed incorporating information about the number of RCTs conducted, the total number of participants enrolled, and the generalizability to real-world populations, in addition to the drug's efficacy, significant toxicity and cost (Figure 1). Practice-changing trials supporting each of the currently-endorsed regimens were collected from the HL NCCN guidelines (ABVD, 4; Stanford V 4; Esc BEACOPP 2). The average number of participants per trial was higher for ABVD (1,126) and Esc BEACOPP (1,890) than for Stanford V (267). The generalizability score for the three compounds was 0.5 (ABVD), 0.25 (Stanford V) and -1 (Esc BEACOPP). The mean 5-year overall survival rate was higher in the case of ABVD (96.2%) and Esc BEACOPP (96.1%) compared with Stanford V (92.0%). The toxicity score was 1.24 (ABVD), 3 (Stanford V) and 2.5 (Esc BEACOPP). Using a multi-factorial value scoring system, ABVD, Esc BEACOPP and Stanford V obtained 11.5, 9.5 and 8.5 points, respectively. A scoring system that focused on traditional factors alone (efficacy, toxicity, cost) yielded different scores (ABVD, 7.7; Stanford V 6.7; Esc BEACOPP, 6.5).
The robustness of clinical trials investigating anti-cancer regimens deserves special consideration when defining drug value. Three high-quality trial characteristics help stratify common regimens in HL into higher/lower value strata using a simple, easy to use algorithm.
No relevant conflicts of interest to declare.
Asterisk with author names denotes non-ASH members.