The review, led by Baoqi Zeng and Feng Sun of Peking University together with colleagues at Sichuan University, searched PubMed and Embase from their inceptions through November 6, 2025, and supplemented the search with backward and forward citation tracking. The team applied a deliberately strict operational definition: a study qualified only if it involved a target evidence base, drew on a related evidence source, specified a statistical mechanism for sharing information between them, and included a conceptual no-sharing comparator against which the borrowing could be judged. That four-part test matters because it separates genuine information-sharing methodology from the vast surrounding literature of ordinary meta-analysis, where pooling happens but no explicit borrowing mechanism is articulated.

The final map contained 140 reports, of which 131 were original methodological articles and 9 were methodology reviews. The numbers reveal where the field’s center of gravity lies. Bayesian frameworks dominated, appearing in 89 of the 131 original articles, or 67.9 percent, while frequentist approaches accounted for 38 articles, or 29.0 percent. Informative priors and power priors, techniques that let external evidence flow into an analysis while discounting it according to how well it agrees with the data at hand, were among the most frequently employed tools. The dominance of Bayesian machinery is no accident: expressing prior beliefs and updating them with observed data is a natural mathematical language for describing exactly how much information one dataset should lend to another.

Clinically, oncology loomed largest. Twenty-eight of the 140 reports, one in five, applied or developed information-sharing methods in cancer research, a field where rare molecular subtypes, early-phase trials, and surrogate endpoints routinely produce evidence bases too thin for conventional synthesis. But the review’s real contribution is not the headcount; it is the architecture. The authors classified every method into four core analytical relationships that describe the mathematical form of the sharing, and they stressed that these categories are not mutually exclusive, so a single model can draw on several at once.

The largest category, multivariate correlations, appeared in 65 articles, or 49.6 percent. These methods exploit the fact that outcomes measured on the same patients or related endpoints across trials tend to move together, and multivariate models use that correlation structure to borrow strength across dimensions of the data. Functional links came next, in 46 articles, or 35.1 percent. Here the connection between evidence sources is a deterministic mathematical relationship, such as a dose-response curve or a known mapping between one outcome scale and another, which allows data collected on one metric to inform conclusions on a different one. Exchangeability assumptions, present in 38 articles or 29.0 percent, are the classical engine of hierarchical and network meta-analysis: the assumption that effects across studies, treatments, or subgroups can be treated as draws from a common distribution, so that extreme results are shrunk toward a shared center.

The fourth category, prior distributions, appeared in 21 articles, or 16.0 percent. This is the most direct form of borrowing, in which external evidence is encoded as a prior belief that the target analysis then updates. Power priors deserve special mention because they occupy a middle ground, down-weighting external data by a parameter that can be fixed in advance or estimated from how consistent the external and current evidence turn out to be. In regulatory and reimbursement settings, where the credibility of borrowed evidence is often the crux of the argument, such discounting mechanisms are frequently what makes an analysis defensible at all.

To make this landscape navigable, the review mapped the methods onto the specific evidence-scarcity problems they were built to solve, organizing the challenges into four domains: Population and Indication, Intervention and Structure, Outcomes, and Evidence Integration. The authors coded challenges, domains, and sharing mechanisms as non-mutually exclusive labels and then constructed a multi-stage Sankey diagram, a flow visualization that traces the path from a clinical problem through the mathematical relationship that links the evidence sources to the statistical solution that exploits it. The result reads almost like a routing table for desperate analysts. A disconnected treatment network, in which two drugs of interest have never been compared directly or through common comparators, points toward methods that bridge networks through related populations or functional relationships. A missing long-term endpoint, where trials report early outcomes but decision-makers need survival or durability estimates, points toward multivariate and correlation-based models that borrow across time points or linked outcomes.

For health technology assessment, the implications are concrete. HTA agencies must decide, often under deadline and with limited data, whether a treatment works, for whom, and at what cost, and sparse evidence is the rule rather than the exception in rare diseases, pediatric indications, and rapidly evolving technologies. By organizing the methodological literature into a structured framework, the review gives practitioners a way to move from a named problem to a shortlist of candidate analytical strategies, rather than navigating 131 papers of dense statistical theory unaided. The authors are careful about the limits of the exercise: this is an evidence map, not a recommendation engine, and the choice among candidate methods still depends on the plausibility of the underlying assumptions in a given application.

One striking gap the review surfaces is the relative scarcity of validation. Only 49 of the 131 original articles, or 37.4 percent, included a simulation study to test how their methods behave when the truth is known. In a field whose entire purpose is to make weak data more informative, that means most proposed techniques have not been stress-tested in controlled settings, and the review implicitly flags an agenda for methodologists: demonstrate, through simulation or empirical comparison against the no-sharing benchmark, when borrowing helps and when it imports bias. The study was supported by the National Natural Science Foundation of China, and the authors report no competing interests.

What makes the work resonate beyond specialist circles is the broader lesson about evidence itself. Modern medicine increasingly asks questions that no single trial can answer, and the honest response is neither to pretend the data are sufficient nor to abandon synthesis, but to make the act of borrowing information explicit, quantified, and auditable. This review does for information-sharing methods what a good cartographer does for unmapped terrain: it does not tell travelers where to go, but it shows them the routes others have taken, the bridges that exist, and the places where the map still ends. For every analyst facing a nearly empty evidence table, that is a genuinely useful gift.

Subject of Research: Information-sharing statistical methods for meta-analysis of sparse evidence in health technology assessment

Article Title: Mapping information-sharing methods for sparse evidence meta-analysis: a scoping review and implications for health technology assessment

Article References: Zeng, B., Zheng, J., Yang, Q., & Sun, F. (2026). Mapping information-sharing methods for sparse evidence meta-analysis: a scoping review and implications for health technology assessment. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15761-y

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15761-y

Keywords: meta-analysis, sparse evidence, information sharing, health technology assessment, Bayesian methods, power priors, network meta-analysis, scoping review, evidence synthesis, oncology, multivariate models, exchangeability