Cost-Effectiveness Study Comparing Cefoperazone-Sulbactam to a Three-Drug Combination for Treating Intraabdominal Infections in an Indian Health-Care Setting

AbstractObjectiveThis article presents the methodology and results of the pharmacoeconomic analysis of the Magnex Against Standard COmbination Therapy study comparing cefoperazone-sulbactam (Magnex) versus ceftazidime+ amikacin+metronidazole, in the treatment of intra-abdominal infections.MethodsThis prospective, open label, phase IV study was conducted at 17 study sites in India and randomized subjects to receive either cefoperazone-sulbactam or the combination. Pharmacoeconomic analysis was included as a secondary objective and conducted in the clinical efficacy-evaluable (CEE) and the successfully treated patients. All comparisons between treatment groups were conducted using analysis of variance (ANOVA) or Wilcoxon Two-Sample tests. All costs were reported as Indian Rupee (INR) and actual unit costs collected in 2006 were used for the analyses [1 USD ∼ 40 INR; 1 Euro ∼ 56 INR].ResultsIn the CEE and the successfully treated subset of patients, the average cost of treatment was numerically lower in the cefoperazone-sulbactam arm (not statistically significant). The analyses found that the cost-effectiveness ratio (CER) for cefoperazone-sulbactam was INR 17,640.53 and that for the comparator group was INR 22,075.16. Additionally, the incremental CER results showed that the cost of treatment was INR 21,505.59 lower per additional successfully treated patient in the cefoperazone-sulbactam group.ConclusionsThe present study was the first of its kind to be conducted in the “price sensitive” Indian health-care setting. Though study was not powered for the difference in average cost of treatments, there was a trend favoring cefoperazone sulbactam. The findings from this study should encourage further conduct of similar analyses and increase the knowledge regarding pharmacoeconomics in India

Phase IV of Drug Development

Not all Phase IV studies are post-marketing surveillance (PMS) studies but every PMS study is a phase IV study. Phase IV is also an important phase of drug development. In particular, the real world effectiveness of a drug as evaluated in an observational, non-interventional trial in a naturalistic setting which complements the efficacy data that emanates from a pre-marketing randomized controlled trial (RCT). No matter how many patients are studied pre-marketing in a controlled environment, the true safety profile of a drug is characterized only by continuing safety surveillance through a spontaneous adverse event monitoring system and a post-marketing surveillance/non-interventional study. Prevalent practice patterns can generate leads that could result in further evaluation of a new indication via the RCT route or even a signal that may necessitate regulatory action (change in labeling, risk management/minimization action plan). Disease registries are another option as are the large simple hybrid trials. Surveillance of spontaneously reported adverse events continues as long as a product is marketed. And so Phase IV in that sense never ends

Real world evidence (RWE) - Are we (RWE) ready?

Real world evidence is important as it complements data from randomised controlled trials (RCTs). Both have limitations in design, interpretation, and extrapolatability. It is imperative one designs real world studies in the right way, else it can be misleading. An RCT is always considered higher in the evidence ladder and when there is discordance between a real world study and an RCT, it is the latter which is always considered pristine because of the way it is conducted, e.g., randomization, prospective, double-blind, etc. A real world study can also be done prospectively, and propensity score matching can be used to construct comparable cohorts but may not be able to account for certain biases or confounding factors the way an RCT can do. Nevertheless, comparative effectiveness research in the real world is being resorted to, especially for efficiency studies or pharmacoeconomic analyses, and with the advent of machine learning, the electronic healthcare database mining can result in algorithms that help doctors identify clinical characteristics that correlate with optimal response of a patient to a drug/regimen, thus helping him/her select the right patient for the right drug