Found a Predictive Probabilities Analysis software for download at the Dept of Biostatistics and Applied Mathematics (BAM Software Download Site), maintained by the MD Anderson Cancer Center of the University of Texas.
This is primarily a software to support clinical trials in which the endpoint is binary or time-to-event (TTE).
After considerable search and thinking session, (and of course, conversation with Mike Young always help), it become increasingly clear to me that survival analysis may be even more appropriate than data mining. In some circle, data mining may be construde as fishing for data, and is always suspect. A more direct data analysis method would of course be desirable, but which one?
I have already come across various time-stamping methods, and the most closely “sounding” one may be time-series analysis. However, after considerable search into the method, it turned out to be closer to financial analysis (as in moving averages) than what I am hoping for. No go…
Then, I came across some literature on time-to-event analysis… (as in this BMJ article). I think I have found the right thing. (Mike seemed to think so, too.)
WebMonkey has a short write-up about Twitter’s StreamGraph. This is a new text-mining product by Twitter (after it acquired Summize. It visually maps the latest 200 tweets containing a particular given word/phrase. One can also mine a user’s Twitter contribution using the “@user” search function call.
StreamGraph for"Serious Ganes"
“The StreamGraph shows the usage over time for the words most highly associated with the search word. One of these series together with a time period are in a selected state and coloured red. The tweets that contain this word in the given time period are shown below the graph. You can click on another word series or time period to see different matches. In the match list you click on any word to create a different graph with tweets containing that word. You can also click on the user or comment icons and any URL to see the appropriate content in another window. If you see a large spike in one time period that hides the detail in all the other periods it will be useful to click in the area to the left of the y-axis in order to change the vertical scale.”