Effectiveness of triple therapy with direct-acting antivirals for hepatitis C genotype 1 infection: application of propensity score matching in a national HCV treatment registry

Abstract

BackgroundObservational studies are used to measure the effectiveness of an intervention in non-experimental, real world scenarios at the population level and are recognised as an important component of the evidence pyramid. Such data can be accrued through prospective cohort studies and a patient registry is a proven method for this type of study. The national hepatitis C (HCV) registry was established in Ireland in 2012 with the aim of monitoring the clinical and economic outcomes from new, high cost regimens for the treatment of HCV infection. A sustained virological response (SVR) 24 weeks following completion of therapy with interferon-containing regimens is considered a cure. Non-randomisation in these studies can result in confounding or selection bias. Propensity score (PS) matching is one of a number of statistical tools that can be used to mitigate the effects of confounding in observational studies.MethodsWe analysed the data of 309 patients who underwent triple therapy treatment with telaprevir (TPV) in combination with pegylated-interferon and ribavirin (PR) or boceprevir (BOC)/PR between June 2012 and December 2014. The decision to initiate treatment and the selection of the treatment regimen was at the discretion of the physician. To adjust for confounding, three approaches to propensity score matching were assessed Adjusted sustained-virological response rates (SVR), odds ratios, p-values and 95% confidence intervals were calculated from the three PS matched dataset.ResultsPrior to matching, the unadjusted sustained virological response rates 24 weeks after treatment complete (SVR24) were 74% (n = 158/215) and 61% (n = 57/94) for telaprevir/PR and boceprevir/PR, respectively. After matching, adjusted SVR24 rates were between 73–74% and 60–61% for telaprevir/PR and boceprevir/PR, respectively.ConclusionEfficacy rates were comparable with those reported in pivotal clinical trials and real world studies. After adjusting for confounding, we conclude that there was no difference in treatment effect after PS matching. The small sample size limits the conclusions that can be made about the effect of PS matching. Propensity score adjustment remains a tool that can be applied to future analysis, however, we suggest, where possible, using a larger sample size in order to reduce the uncertainty around the outcomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-017-2188-1) contains supplementary material, which is available to authorized users.

A Meta-Analysis of Instructional Management Model for Students’ Creative Thinking Development: An Application of Propensity Score Matching

“…As a result, the causal arguments and study results are completer and more precise. This method is often applied to reduce publication bias with many quasi-experimental and observational bias studies (Gray et al, 2017;Justus, et al, 2014;Yu et al, 2017). It is in line with research by Arikan et al (2018), which studied "The use of propensity score matching helps to understand sources of DIF and mathematics performance differences of Indonesian, Turkish, Australian, and Dutch students in PISA."…”

“…A propensity score is thereupon an alternative to assessing the likelihood of obtaining treatment according to the fundamental nature of the research. It is determined by the balance of the probabilistic score calculated from several common variables (Gray et al, 2017;Yu et al, 2017).…”

“…It is a statistical method used to match experimental and control subjects using propensity scores of equal or similar value. Mostly, the researchers will apply a 1:1 matching method, which is the traditional method with the limited cases where the sample size is small or where the sample is difficult to find; therefore, 1:2, 1:3, 1:4, 1:many, or many: many matching methods can be substituted (Gray et al, 2017;Justus et al, 2014;Yu et al , 2017) This pairing can help simplify cause-and-effect reasoning for the quasi experiment and observational bias in reducing publication bias and comparing propensity scores with independent t-tests or paired t-test. It can be concluded that propensity score matching is advantageous for controlling extraneous variables influencing dependent variables well (Thoemmes & West, 2011).…”