Clinical Value Evidence
& Research Solutions

The product had been on the market for several years, and the company sought to strengthen clinical awareness and evidence impact for a specific indication in China while identifying the highest-priority evidence actions within limited resources

Clinical awareness of the existing evidence and value of the product in this indication remained limited. At the same time, with constrained resources, it was challenging to identify the most decision-relevant evidence questions and determine the most appropriate research and evidence-support pathway

Designed and implemented a structured evidence synthesis framework around the key evidence questions for the target indication, including systematic evidence retrieval, Evidence Mapping, identification of priority comparisons and evidence gaps, development of research hypotheses, and quantitative synthesis of key outcomes

Systematically identified existing evidence on the product and target indication, used Evidence Mapping to characterize the evidence landscape and identify key comparators and evidence gaps, developed priority research hypotheses, and conducted meta-analysis of key outcomes to generate a more robust and interpretable evidence body

As the product entered its growth phase, the company needed a clearer understanding of its relative clinical value versus key competing options and reliable comparative estimates to inform health-economic evaluation, medical communication and subsequent decisions

No direct head-to-head study was available between the target product and key competing options. Although several network meta-analyses had already been published on the same clinical question, differences in evidence scope, network structure, methodological quality and conclusions made it difficult to determine which estimates were sufficiently reliable for decision-making

Systematically identified previously published NMAs addressing the same clinical question and compared their PICO definitions, evidence networks, study inclusion, analytical approaches and methodological quality. Potential drivers of divergent conclusions were critically assessed, followed by a systematic search for newly available evidence. The decision-relevant scope was then refined and the NMA updated accordingly

Systematic identification and mapping of existing NMAs; comparison of PICO definitions and network structures; methodological quality and risk-of-bias assessment; exploration of drivers of discordant conclusions; systematic retrieval of newly available evidence; and updated network meta-analysis with evidence certainty assessment

As an innovative product approached a critical launch stage, the company needed to determine whether a credible quantitative comparison with a key competitor could be generated despite the absence of direct head-to-head evidence, to support clinical value communication and subsequent evidence strategy

No direct comparative study was available between the target product and a key competitor, and the existing evidence network did not provide an appropriate common comparator, limiting the feasibility of a conventional network meta-analysis. At the same time, medical and other stakeholders required quantitative evidence to contextualize the product’s relative value within a limited timeline and resource envelope

Assessed the existing evidence landscape and data availability to identify feasible indirect-comparison pathways. Once individual patient data for the target product and aggregate data for the comparator were confirmed to be available, a matching-adjusted indirect comparison (MAIC) was designed and conducted to adjust for cross-trial population differences and generate quantitative comparative estimates across key efficacy and safety outcomes

An unanchored MAIC was conducted using individual patient data from the target-product study. Propensity-score weighting aligned baseline characteristics with the aggregate comparator population. Adjusted comparative estimates were generated for binary outcomes, while reconstructed survival data were used for time-to-event outcomes such as PFS and OS; additional adjustment was applied to address differences in tumor-assessment schedules between studies