Matching Platform of Case Control Study on Big Data

The objective of this research is to make a platform, helping users to obtain the study population of observational studies by a four stages procedure including data querying, variables creating, control group matching and significance testing. Data querying platform was used to find out the study population. It provides a simple interface for NoSQL database query. It also automatically makes a flow chart of query results, helping users manage the process of query. Variables creating platform let users extract detail attribute information of selected patients from previous stage such as disease diagnoses or drugs taken records. It was done by a Mongo Map-Reduce process and export to a csv file for next stage. Control group matching platform read the patients and variables from the previous stage and do propensity score matching. Users choose treatment (or exposure), outcome or other covariates from the variables, then fit the generalized linear model and match the control group by fitted value. Significant testing platform did t-test on each variable and chi-square goodness of fit test on each age group between case group and control group to see if there is any significant difference. The first two stages of working process can be separated from others into two independent parts. The first part prepares data for observational study, the second part implements statistic analyzing. Users may have their own analyses on the data we prepared or loading their own data into our matching platform.