Statistical and graphical evidence synthesis methods in health technology assessment

This thesis focusses on the challenges relating to clinical- and cost-effectiveness analysis in Health Technology Assessment (HTA). It includes methodological developments, both statistical and presentational, in evidence synthesis aiming to address those challenges. In HTA, analysts often face problems with limited availability of data required to inform economic model. This thesis proposes innovative evidence synthesis approaches to address this challenge, illustrated in two examples. Bivariate random-effects meta-analysis (BRMA) and network meta-analysis (NMA) were used to synthesise all available evidence to predict progression-free survival (PFS), in metastatic prostate cancer. This enabled the specification of a three-state Markov model previously limited to two states when PFS was not recorded. In the second example, a scenario in multiple sclerosis is considered where utility data for the trials included in a HTA were not available and external utility data from a single study was used instead. This thesis illustrates how BRMA can be applied to include all available evidence to inform utility estimates for use in a cost-effectiveness analysis. NMA, allowing for a simultaneous and coherent comparison of multiple interventions, is increasingly used in HTA. However, due to the inherent complexity of presenting NMA results, it is important to ease their interpretability. A review of existing methods of presenting NMA results in HTA reports revealed that there is no standardised presentational tool for their reporting. Novel presentational approaches were developed which are presented in this thesis. The original contributions of this thesis are the innovative approaches to incorporate historical data to predict and increase the precision of parameter estimates for cost-effectiveness analysis to better inform health policy decision-making; and three novel graphical tools to aid clear presentation and facilitate interpretation of NMA results. Ultimately, the hope is that the graphical tools developed will be recommended in updated guidance setting the standards for future HTAs

Bayesian multi-parameter evidence synthesis to inform decision-making: a case study in metastatic hormone-refractory prostate cancer

In health technology assessment, decisions are based on complex cost-effectiveness models which require numerous input parameters. When not all relevant estimates are available the model may have to be simplified. Multi-parameter evidence synthesis combines data from diverse sources of evidence which results in obtaining estimates required in clinical decision-making that otherwise may not be available. We demonstrate how bivariate meta-analysis can be used to predict an unreported estimate of a treatment effect enabling implementation of a multi-state Markov model, which otherwise needs to be simplified. To illustrate this, we used an example of cost-effectiveness analysis for docetaxel in combination with prednisolone in metastatic hormone-refractory prostate cancer. Bivariate meta-analysis was used to model jointly available data on treatment effects on overall survival and progression-free survival (PFS) to predict the unreported effect on PFS in a study evaluating docetaxel with prednisolone. The predicted treatment effect on PFS enabled implementation of a three-state Markov model comprising of stable disease, progressive disease and dead states, whilst lack of the estimate restricted the model to a two-state model (with alive and dead states). The two-state and three-state models were compared by calculating the incremental cost-effectiveness ratio (which was much lower in the three-state model: £22,148 per QALY gained compared to £30,026 obtained from the two-state model) and the expected value of perfect information (which increased with the three-state model). The three-state model has the advantage of distinguishing surviving patients who progressed from those who did not progress. Hence, the use of advanced meta-analytic techniques allowed obtaining relevant parameter estimates to populate a model describing disease pathway more appropriately, whilst helping to prevent valuable clinical data from being discarded

Novel presentational approaches were developed for reporting network meta-analysis

OBJECTIVES: To present graphical tools for reporting network meta-analysis (NMA) results aiming to increase the accessibility, transparency, interpretability, and acceptability of NMA analyses. STUDY DESIGN AND SETTINGS: The key components of NMA results were identified based on recommendations by agencies such as the National Institute for Health and Care Excellence (United Kingdom). Three novel graphs were designed to amalgamate the identified components using familiar graphical tools such as the bar, line, or pie charts and adhering to good graphical design principles. RESULTS: Three key components for presentation of NMA results were identified, namely relative effects and their uncertainty, probability of an intervention being best, and between-study heterogeneity. Two of the three graphs developed present results (for each pairwise comparison of interventions in the network) obtained from both NMA and standard pairwise meta-analysis for easy comparison. They also include options to display the probability best, ranking statistics, heterogeneity, and prediction intervals. The third graph presents rankings of interventions in terms of their effectiveness to enable clinicians to easily identify "top-ranking" interventions. CONCLUSIONS: The graphical tools presented can display results tailored to the research question of interest, and targeted at a whole spectrum of users from the technical analyst to the nontechnical clinician

Novel Proteomic Biomarker Panel for Prediction of Aggressive Metastatic Hepatocellular Carcinoma Relapse in Surgically Resectable Patients

The natural course of early HCC is unknown, and its progression to intermediate and advanced HCC can be diverse. Some early stage HCC patients enjoy prolonged disease-free survival, whereas others suffer aggressive relapse to stage IV metastatic cancer within a year. Comparative proteomics of HCC tumor tissues was carried out using 2D-DIGE and MALDI-TOF/TOF MS to identify proteins that can distinguish these two groups of stage I HCC patients. Twelve out of 148 differentially regulated protein spots were found to differ by approximately 2-fold for the relapse versus nonrelapse patient tissues. Four proteins, namely, heat shock 70 kDa protein 1, argininosuccinate synthase, isoform 2 of UTP-glucose-1-phosphate uridylyltransferase, and transketolase, were shown to have the potential to differentiate metastatic relapse (MR) from nonrelapse (NR) HCC patients after validation by western blotting and immunohistochemical assays. Subsequent TMA analysis revealed a three marker panel of HSP70, ASS1, and UGP2 to be statistically significant in stratifying the two groups of HCC patients. This combination panel achieved high levels of sensitivity and specificity, which has potential for clinical use in identifying HCC tumors prone to MR. This stratification will allow development of clinical management, including close follow-up and possibly treatment options, in the near future

Novel Proteomic Biomarker Panel for Prediction of Aggressive Metastatic Hepatocellular Carcinoma Relapse in Surgically Resectable Patients

The natural course of early HCC is unknown, and its progression to intermediate and advanced HCC can be diverse. Some early stage HCC patients enjoy prolonged disease-free survival, whereas others suffer aggressive relapse to stage IV metastatic cancer within a year. Comparative proteomics of HCC tumor tissues was carried out using 2D-DIGE and MALDI-TOF/TOF MS to identify proteins that can distinguish these two groups of stage I HCC patients. Twelve out of 148 differentially regulated protein spots were found to differ by approximately 2-fold for the relapse versus nonrelapse patient tissues. Four proteins, namely, heat shock 70 kDa protein 1, argininosuccinate synthase, isoform 2 of UTP-glucose-1-phosphate uridylyltransferase, and transketolase, were shown to have the potential to differentiate metastatic relapse (MR) from nonrelapse (NR) HCC patients after validation by western blotting and immunohistochemical assays. Subsequent TMA analysis revealed a three marker panel of HSP70, ASS1, and UGP2 to be statistically significant in stratifying the two groups of HCC patients. This combination panel achieved high levels of sensitivity and specificity, which has potential for clinical use in identifying HCC tumors prone to MR. This stratification will allow development of clinical management, including close follow-up and possibly treatment options, in the near future

Comparison of the Adequacy Index and Log Likelihood ratios of diagnostic models.

<p>Comparison of the Adequacy Index and Log Likelihood ratios of diagnostic models.</p

Demographics of patients (SGH cohort).

<p>Demographics of patients (SGH cohort).</p

Comparison of serum MCP-1, prolactin and AFP levels in HCC and non-HCC patients.

<p>Serum concentrations of (A) MCP-1, (B) prolactin and (C) AFP in non-HCC chronic hepatitis B carriers (NC group, n = 115) and HCC patients (HCC group, n = 126) in the SGH cohort of patients were analyzed by multiplex sandwich ELISA (Quantibody Array). Serum MCP-1 concentrations in asymptomatic HBV/HCV carriers (AC group, n = 100), chronic hepatitis patients with evidence of transaminitis (CH group, n = 101) and HCC patients (HCC group, n = 98) in the MRIN cohort (A) were analyzed by sandwich ELISA. The boxes represent the central 50% of the data, spanning between the 25^th and 75^th percentiles and the horizontal line within each box indicates the median. The cut-off points were: 1.5×IQR above 75^th percentile (upper limit) and 1.5×IQR below 25^th percentile (lower limit). Values beyond the cut-off points were considered as outliers and are represented by the dots. Comparison of biomarker values between groups was performed using the Mann-Whitney <i>U</i> test.</p

Correlation between serum markers estimated by Spearman’s rank correlation coefficient, rho (n = 241).

*<p>The upper part are the Spearman’s rank correlation coefficients, rho; the lower part are the p-values of the Spearman’s rank correlation test.</p