Estimate the comparative effectiveness of new blood cancer treatment given complex evidence base: disconnected network of RCTs and multiple treatment combinations evaluated
Systematic review and evidence synthesis
Visible Analytics are experts in systematic review and evidence synthesis including both standard and novel methodologies.
These activities are essential foundations of healthcare decision-making and require rigorous design and execution.
Systematic literature reviews
Our systematic reviews are conducted in accordance with internationally recognised guidelines and meet the standards of HTA bodies such as NICE. We conduct reviews to identify clinical (both from trials and real-world evidence), economic and health-related quality of life data. To drive efficiency, we utilise AI solutions where appropriate. Our team have published numerous systematic reviews in a wide range of therapy areas.
Targeted and pragmatic reviews
Depending on the research objective, a full systematic review is not always necessary. We can advise on the most suitable approach for identifying evidence, bearing in mind time and budgetary constraints and acceptability to reviewers, if publication is required.
Meta-analysis and meta-regression
Following a systematic review, we can synthesise data from the studies identified by conducting a meta-analysis to obtain a pooled estimate of the results. We identify eligibility criteria for the inclusion of studies, ensuring they are “combinable”. Depending on the presence of heterogeneity and number of studies available we apply fixed- or random-effects models within a frequentist or Bayesian framework. Meta-regression can also be employed to investigate differences between subgroups of studies based on study characteristics.
Evidence synthesis
Visible Analytics has specialist expertise in the conduct of indirect treatment comparisons (ITCs). It is vital that ITCs are robust to ensure that relative efficacy estimates are credible, and we liaise closely with clients throughout the process to provide optimal solutions. As well as conventional network meta-analysis (NMA), we offer matching-adjusted indirect comparisons (MAIC), simulated trial comparisons (STC), and advanced methods such as multi-level network meta-regression (ML-NMR) which is able to combine aggregate and individual patient data from multiple studies.Whatever method is chosen, we provide clear and rigorous reporting of rationale, feasibility, assumptions, methods and interpretation to ensure transparency.
Case studies
We addressed the challenge of interval-censored time-to-event outcomes in indirect treatment comparisons (ITCs), where differing assessment schedules could introduce bias.
We addressed the challenge of estimating survival advantage for an innovative treatment with no European-standard comparator and high crossover in pivotal trial.
