We conduct literature reviews and meta-analyses using the most rigorous methodologies, so you get reliable and reproducible answers to your research questions.
High-quality literature reviews can help you understand:
- Definitions, etiology, and diagnosis of a disease
- Epidemiologic, humanistic, and economic burden of a disease
- Treatment guidelines and patterns in clinical practice
- Clinical and cost effectiveness of comparator treatments
- Best practices in economic modeling methods in a disease area
- Economic model inputs including resource use, cost, and utility estimates
Literature reviews often serve as the foundation for follow-on research, so it is imperative that they are done well. From preparing the search protocol to conducting the search and review and extracting results, our quality-controlled processes consistently ensure reliable and reproducible deliverables.
When the information from a literature review is combined with meta-analyses to synthesize the evidence quantitatively, new information can be generated that was not available from the individual studies. These results may be used as supplemental evidence, for input to economic models, or in some cases, to support HTA submissions.
We are trained in state-of-the art techniques to design and perform meta-analyses using a variety of software. We perform direct and indirect comparisons using standard techniques and have experience with more complex methods recommended by NICE and the ISPOR Task Force on Indirect Comparisons, including:
- Network meta-analysis and mixed treatment comparisons (MTC)
- Bayesian and frequentist methodologies
- Fixed and random effect models
- Meta-regression techniques to assess the impact of covariates
- Methods to assess heterogeneity
It is important that the best use of the available data in a network is achieved. This means that a number of different techniques may be required within a single study. We are able to fit MTCs to wide variety of different types of data including:
- Binomial responses including adjustment for different trial lengths
- Continuous change from baseline
- Ordinal responses including a mixture of binomial and ordinal data
- Survival outcomes
- Poisson outcomes including epidemiological data