Meta-Analysis / Network Meta-Analysis
Mapi’s in-house experts have had direct involvement with the ISPOR Task Force and many years of hands-on experience with evidence synthesis.Over 95 indirect treatment comparisons and network meta-analyses since 2006
Network meta-analyses developed by Mapi are used to inform internal strategic decision-making, promote the comparative efficacy or safety of an intervention, provide inputs for economic models, and support Health Technology Assessment submissions for external decision-makers.
Mapi also performs critical assessments of published network meta-analyses based on tools developed by ISPOR and NICE. These assessments help clients understand and/or promote the strengths and limitations of alternative approaches.
- Analyzing survival outcomes in NMA
- Summarizing survival treatment rankings for NMAs
- Reconstructing individual patient data from Kaplan-Meier curves
- Assessing the feasibility of performing NMAs
- Synthesizing repeated measures in NMA
- Incorporating individual patient data in NMA
- Integrating observational evidence in NMA
- Assessing methods to combine single-arm studies
- Performing critical reviews of NMAs
- Understanding bias in NMAs
Utilizing Evidence from Single-Arm Studies
In the absence of a suitable patient population typically required for large-scale RCTs (ie. for rare diseases), a single-arm study may be conducted to support accelerated regulatory approval for treatment effectiveness. Mapi has critically reviewed the strengths and limitations of a series of evidence synthesis methods that allow for comparisons of new treatments to best support care or existing therapies on the basis of single-arm studies. These reviews help identify methods that mitigate study factors and population-level differences associated with using single-arm studies to assess comparative effectiveness and incremental cost-effectiveness.
Identifying Important Subgroups
As interventions become more targeted, it is increasingly important to assess the validity of comparing targeted agents to alternative treatments. Mapi has developed a process to assess the feasibility of performing NMA, which allows transparent and reproducible results that visualize the differences across studies in terms of key study and patient differentiators. This process can also be applied to assess whether evidence from observational studies should be incorporated into the network of evidence, drawing or providing insight into the ‘real world’ effectiveness of alternative interventions. Finally, we have extended NMA models to incorporate individual patient-level data, which allows for exploring differences in patients within and between studies to better understand relevant subgroups.
Maximizing Survival and Longitudinal Data
Mapi is a leader in the analysis of survival data (time-to-event outcomes) for NMAs, which provide insight into the comparative effectiveness of new interventions for some of the most important outcomes, such as overall survival and progression-free survival. Flexible NMA models developed by Mapi for analysing survival data can provide improved fit to the data and can be used to directly model cost-effectiveness of alternative interventions. The methods used to extract survival data from published Kaplan-Meier studies offer for a more accurate and comprehensive assessment of the available evidence. Similarly, integrating data from repeated measures may provide more precise estimates and offer valuable insight into progression of outcomes over time, which can also inform the structure of the economic model.
Mapi’s team of experts offer a series of short courses and workshops at scientific conferences. In addition, several tailored training courses on the principles of network meta-analysis have been developed in-house, along with advanced topics related to our methods publications.
Overview of Recent Network Meta-Analysis
- Alzheimer’s disease
- Ankylosing spondylitis
- Atopic dermatitis
- Breast cancer
- Breakthrough cancer pain
- Cervical cancer
- Chronic lymphocytic leukemia
- Chronic obstructive pulmonary disease
- Crohn’s disease
- Colorectal cancer
- Cystic fibrosis
- Diabetes mellitus
- Elevated triglycerides
- Irritable bowel syndrome
- Heart failure
- High cholesterol
- Human immunodeficiency virus
- Multiple myeloma
- Multiple sclerosis
- Non-small cell lung cancer Osteoporosis Osteoarthritis
- Parkinson’s disease Psoriasis
- Prostate cancer Rheumatoid arthritis Stroke
- Venous thromboembolism