Generalized Model Aggregation (GMA)
A five-year joint project between MIT Sloan School of Management and the School of Industrial & Systems Engineering at Georgia Tech
Rapid growth in scientific output requires flexible and robust methods for aggregating the findings from prior studies. In most cases, 'qualitative' reviews are conducted for taking stock of what is known, but they offer little 'quantitative' guidance. Also, the common quantitative aggregation methods (i.e., meta-analysis methods) usually combine one explanatory variable (e.g., a treatment) on one response variable (e.g., a health outcome) across multiple studies with similar designs.
Despite these limitations, the rapid growth of scientific literature has notably promoted increasing applications of meta-analysis. The number of articles in major databases with the term “meta-analysis” ONLY in the title shows over 25-fold growth over the last decade—reaching tens of thousands annually. Therefore, the value of a broader and more flexible method for synthesizing prior research can be immense across various disciplines.
Our recent paper in PLOS ONE outlines a new method (generalized model aggregation – GMA; see it on Wikipedia!) for aggregating into a meta-model the results of prior studies (of a phenomenon) when those studies vary in design and measures used. In the paper, we provide several numerical and empirical examples demonstrating the ability of GMA to aggregate evidence from methodologically diverse studies and obtain unbiased estimates from potentially mis-specified studies. By enabling more complex meta-analyses, GMA allows researchers to leverage previous findings to compare alternative theories and advance new models in diverse domains.
May 5, 2 pm to 3:30 pm, Harvard School of Public Health (building 2, Room 426), by Dr. Jalali
Seminar webpage (no registration required)
Potential applications of GMA
Here are some articles that seem to be potentially relevant applications for GMA. They often cunducted a systematic review of the literature and listed prior studies, but did not aggregate the findings quantitatively. Please note that these are found using a quick search on the Internet. There are many more problems that can benefit from GMA. If you are interested in applying GMA for any problem and need our assitance, please shoot us an email!
Health: Prediction models for colorectal cancer (link) | Prediction models for lung cancer (link) | Models for cardiovascular disease risk (link) | Risk models for diabetes complications (link) | Models for post disaster shelter needs (link)
Social sciences: Influence of climate on human conflict (link)
GMA on WIKIPEDIA
Recipe for GMA
Here we provide a quick recipe of the main steps to use GMA. Check out the supplementary document for the details of each step. Also, the Codes README file provides more information about the code files listed in each step--as well as all other code files, including their actions, inputs, and outputs.
Cite these materials as:
Rahmandad H, Jalali MS, Paynabar K (2017) A flexible method for aggregation of prior statistical findings. PLOS ONE 12(4): e0175111. doi: 10.1371/journal.pone.0175111