For this study, data for 205 patients treated with the IFN-free regimens from April 2015 to July 2016 were analysed. Patients were eligible for this study if they were treated for HCV infection with an IFN-free regimen, were 18 years or older, and had evidence of compensated cirrhosis (CTP A). HIV co-infected patients were also eligible for inclusion. Based on the 2015 AASLD and EASL guidelines 7, 122, patients were initiated
on treatment with one of the following three regimens:
Sofosbuvir/ledipasvir (400mg/90mg) taken once daily by mouth with or without
weight-based RBV administered orally in two divided doses for 12-weeks (SOF/LDVRBV).
A combination of 12.5mg ombitasvir, 75mg paritaprevir and 50mg of ritonavir
once daily with 250mg dasabuvir with or without weight-based RBV administered orally in two divided doses for 12 weeks, with the exception of those with GT1a, who required 24 weeks of therapy (3DRBV).
Sofosbuvir/daclatasvir (400mg/60mg) taken once daily by mouth with or without
weight-based RBV administered orally in two divided doses for 24 weeks
(SOF/DCVRBV) 122.
and percentages. Baseline continuous data were expressed as medians (with the interquartile range (IQR)). Univariate analyses were performed using the Chi-square, Fisher’s Exact or Student’s t-test as appropriate. A one-way ANOVA was used to test
for significant differences for those elements with greater than two categories. A p- value <0.05 was considered significant. Odds ratios were reported with their 95% confidence interval.
Where required, multiple imputation was used to impute missing values on baseline variables that were identified as covariates in the confounding analysis. Despite manually collecting data from the majority of treatment sites, there was still missing data on simple variables such as gender. This arose from the sites, which submitted their data to us, whereby data collection forms were received with incomplete data for these simple variables.
A confounder is a variable that can influence both the treatment assignment and the treatment outcome. The confounding variables were identified through careful analysis of published literature and following consultation with clinical experts. The goal of the multiple imputation was to ensure that there was no missing data for the identified confounding variables. This ensured that we could include all subjects in the PS analysis. Missing data relating to the confounders would have resulted in the exclusion of these subjects before commencing the PS analysis, compromising the outcomes in a dataset already limited by a small sample size. The standard SPSS procedure for multiple imputation using the fully conditional specification statement and the Markov Chain Monte Carlo (MCMC) methodology was used. Imputation was based on a linear regression model.
Propensity scoring was used in both sub-study 1 and sub-study 3†. In sub-study 1, the PS, the probability of being treated with TPV/PR as opposed to BOC/PR, given other known baseline demographics and HCV characteristics, was computed using multivariate logistic regression. Following a review of the literature and consultation with clinicians, nine covariates were entered into the model: previous treatment experience, presence of cirrhosis, age, BMI, GT1, GT1b, IL28B CT, IL28B TT and baseline HCV > 800,000 IU/ml 218-222. For genotype and IL28B allele, both of which had
more than two distinct categories (i.e. GT1 (no subtype), GT1a, GT1b), dummy variables had to be created to represent these subgroups in the regression analysis (Appendix 2 Table A 5 and Table A 6). The resultant PS (range = 0.0 – 1.0) was a single score per patient, with a high score representing a high probability that the patient would be treated with TPV/PR, based on the given information. Prior to applying PS matching, a comparison of confounders between the TPV/PR and BOC/PR groups was completed. The standardised difference was used to compare the mean of continuous and binary variables between treatment groups and was not influenced by the sample size. Three approaches to matching without replacement were employed to match patients who received TPV/PR with patients who received BOC/PR. A treated subject (TPV/PR) was first selected at random. The untreated subject (BOC/PR) whose propensity score was closest to that of this randomly selected treated subject was chosen for matching. This process was then repeated until all untreated subjects had been matched to all treated subjects. In our first approach, naïve matching was used whereby all ninety-four BOC/PR subjects were matched to a TPV/PR subject. A second matching approach was completed where patients were matched on the logit of the PS using a caliper width limit equal to 0.1 of the logit of the propensity score (i.e. the difference of the logit of the PS between the TPV/PR and BOC/PR matched
extended to 0.2 of the logit of the PS. In all three approaches, each TPV/PR treated subject was matched to the BOC/PR subject with the closest PS. To assess the balance of covariates after PS matching, the balance in measured confounders between treated and untreated subjects within the PS matched sample were compared for all three approaches using standardised differences. Adjusted SVR rates, odds ratios, p-values and 95% confidence intervals were calculated.
In sub-study 3, PS stratification was employed as it allowed all the data to be used in a dataset that already contained limited numbers. This tool was applied in the GT1 cohort of patients to determine the adjusted odds ratio of achieving a SVR in those treated
with SOF/LDV±RBV compared with 3DRBV. PS could not be used in other genotypes or treatment regimens due to insufficient patient numbers and a lack of a suitable comparator. The PS, the probability of being treated with SOF/LDVRBV, as opposed
to 3DRBV, given other known baseline demographics and HCV characteristics, was
computed for each individual using a multivariable logistic regression model. Seven covariates were identified after reviewing the literature and consulting with clinicians 121, 360. Those covariates identified and entered into the model were previous protease-
inhibitor treatment failure, previous PR treatment failure, age, gender, GT1a, GT1b, and baseline HCV > 800,000 IU/ml. In this sub-study, we stratified on the propensity score. Subjects were ranked according to the calculated PS and divided into five strata, which were created based on the quintiles of the PS distribution in the cohort. To assess the balance in measured confounders after stratifying on the PS, within-strata standardised differences were computed to compare the distribution of baseline covariates between treated and untreated subjects within the same PS stratum. The mean standardised difference was then determined across the strata. The OR was calculated to express the chances of a SVR12 with 3DRBV compared with SOF/LDV±RBV.
Demographic and outcome analyses and multiple imputation were conducted using IBM SPSS Version 21 (IBM Corp, Armonk, NY, USA). Propensity scoring was conducted with STATA Version 14 (STATACorp, College Station, Texas, USA).
For all three sub-studies, the effectiveness rates reported in this chapter were compared with efficacy rates reported in pivotal clinical trials and effectiveness rates reported in international real world studies.