Training Opportunities

Knowledge Synthesis: Systematic Reviews and Meta-analysis – February 24-26^th, 2025

APPLICATION DEADLINE: December 20^th, 2024

The University of Texas MD Anderson Cancer Center, Houston, Texas

Knowledge synthesis is a systematic approach to identify, appraise, and combine the available evidence on specific questions using qualitative methods, and/or quantitative probabilistic and statistical techniques. This approach can be used to directly or indirectly compare evidence for alternative interventions, playing a key role in the development of evidence-based recommendations prior to knowledge translation.

Systematic reviews are syntheses of evidence that incorporate strategies to minimize bias and to maximize precision. The methodology used is explicitly stated to provide transparency and ensure the validity of the results. Meta-analysis is a statistical technique in which the results of two or more studies are combined to increase the precision of the results.

This 3-day workshop will include the following topics:

• Finding the evidence
• Study selection and data collection
• Assessing risk of bias
• Critical appraisal of quantitative and qualitative data
• Qualitative and quantitative synthesis of results
• Advanced meta-analytic approaches
• Reporting and disseminating the findings of systematic reviews

Observational Studies and Data Registries – April 7-9^th, 2025

APPLICATION DEADLINE: January 17^th, 2025

The University of Texas MD Anderson Cancer Center, Houston, Texas

Observational studies, especially those conducted in real-world settings are essential in comparative effectiveness research. Well-conducted observational studies can provide comparative estimates of harms and benefits of interventions once deployed in the community of interest. Explanatory clinical trials usually address efficacy rather than effectiveness as they often exclude large segments of the population, particularly those who are older or have comorbid conditions. Furthermore, many highly relevant questions related to the comparative effectiveness of multiple interventions will never be addressed by clinical trials, as they would require very large samples in community settings, at a very high cost. With burgeoning amounts of data available, including secondary analysis of data from registries, healthcare claims, and electronic health records, robust methods are needed to avoid bias and confounding, and obtain valid, usable, results.

This 3-day workshop will include the following topics:

• Study design, participants, and sources of bias in observational studies
• Selection of controls
• Outcome measures for comparative effectiveness research
• Exposures of interest and covariates
• Data sources and real-world evidence for cancer research
• Confounding and risk adjustment
• Basic analytic methods including survival analysis
• Advanced analytic methods
• Fundamentals of economic evaluations in cancer research
• Basic concepts of decision analysis

Pragmatic Clinical Trials – April 2^nd-4^th, 2025

APPLICATION DEADLINE: January 17^th, 2025

The University of Texas MD Anderson Cancer Center, Houston, Texas

Pragmatic clinical trials, in contrast to explanatory trials, evaluate the effectiveness of healthcare interventions as delivered in routine ‘real-world’ practice. They can provide information on benefits and harms as they are expected to occur in community settings, unlike explanatory trials which are conducted in selected populations under restricted conditions that often do not reflect usual care. Novel strategies applied to pragmatic trials include adaptive methods, where features in the design or allocation are changed on the basis of ongoing results,  mimicking what would happen in ‘real-world’ practice. In addition, to demonstrate value, pragmatic clinical trials must include patient-centered outcomes that are relevant to stakeholders.

This 3-day workshop will include the following topics:

• Pragmatic versus explanatory clinical trials
• Study design, settings, and participants
• Outcomes and independent variables
• Design issues
• Basic data analysis
• Advanced data analysis
• Sample size and power
• Overview of intervention and implementation mapping
• Implementation strategies
• Hybrid effectiveness-implementation research studies