Use of statistical analysis, data mining, decision analysis and cost effectiveness analysis to analyze medical data: Application to comparative effectiveness of lumpectomy and mastectomy for breast cancer

In this study, data used were from the Nationwide Inpatient Sample (NIS) 2005, the Thomson Reuter's MarketScan 2000--2001, the medical literature on clinical trials and online individuals' posts in discussion boards on breastcancer.org. The NIS was used to compare lumpectomy to mastectomy in terms of hospital length of stay, total charges and in-hospital death at the time of surgery.;A simple comparison of the two procedures using the NIS 2005, a discharge-level data, showed that in general, a lumpectomy surgery is associated with a significantly longer stay and more charges on average. From the MarketScan data, a person-level data where a patient can be followed longitudinally, it was found that for the initial hospitalization, patients who underwent mastectomy had a non-significant longer hospital stay and significantly lower charges. The post-operative number of outpatient services, prescribed medications as well as length of stay and charges for post-operative hospital admissions were not statistically significant. Using the MarketScan data, it was also found that the best model to predict 90-day post-operative hospital admission was logistic regression. A logistic regression revealed that the risk of a hospital re-admission within 90 days after surgery was 65% for a patient who underwent lumpectomy and 48% for a patient who underwent mastectomy. A cost effectiveness analysis using Markov models for up to 100 days after surgery showed that having lumpectomy saved hospital related costs every day with a minimum saving of ;In conclusion, the current project showed how to use data mining, decision analysis and cost effectiveness methods to supplement statistical analysis when using real world non-clinical trial data for a more complete analysis. The application of this combination of methods on the comparative effectiveness of lumpectomy and mastectomy showed that in terms of cost and patients' quality of life measured as satisfaction, lumpectomy was found to be the better choice. (Abstract shortened by UMI.);Data were pre-processed and prepared for analysis using data mining techniques such as clustering, sampling and text mining. To clean the data for statistical models, some continuous variables were normalized using methods such as logarithmic transformation. Statistical models such as linear regression, generalized linear models, logistic and proportional hazard (Cox) regressions were used to compare post-operative outcomes of lumpectomy versus mastectomy. Neural networks, decision tree and logistic regression predictive modeling techniques were compared to create a simple predictive model predicting 90-day post-operative hospital re-admission. Cost and effectiveness were compared with the Incremental Cost Effectiveness Ratio (ICER). A simple method to process and analyze online postings was created and used for patients' input in the comparison of lumpectomy to mastectomy. All statistical analyses were performed in SAS 9.2. Data Mining was performed in SAS Enterprise Miner (EM) 6.1 and SAS Text Miner. Decision analysis and Cost Effectiveness Analysis were performed in TreeAge Pro 2011.