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首页> 外文学位 >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.
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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.

机译：使用统计分析，数据挖掘，决策分析和成本效益分析来分析医学数据：在乳腺癌的乳房切除术和乳房切除术的比较有效性中的应用。

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摘要

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.
机译：在这项研究中，使用的数据来自2005年全国住院患者样本（NIS），Thomson Reuter的MarketScan 2000--2001，有关临床试验的医学文献以及在breastcancer.org讨论板上的在线个人帖子。 NIS用于比较乳房切除术和乳房切除术的住院时间，总费用和手术时的医院内死亡。;使用NIS 2005对两种手术的简单比较，出院水平数据显示总的来说，乳房切除术与平均更长的住院时间和更多的费用相关。从MarketScan数据（可以纵向追踪患者的个人水平数据）可以发现，对于初次住院的患者，接受乳房切除术的患者住院时间无显着延长，费用明显降低。术后门诊服务数量，处方药以及住院时间和术后住院费用均无统计学意义。使用MarketScan数据，还发现预测90天术后住院的最佳模型是逻辑回归。 Logistic回归显示，手术后90天内接受乳房切除术的患者再次入院的风险为65％，接受乳房切除术的患者为48％。手术后长达100天使用Markov模型进行的成本效益分析表明，进行肿块切除术每天可节省医院相关成本，最低节省为；总而言之，当前项目展示了如何使用数据挖掘，决策分析和成本效益方法来进行手术。使用真实的非临床试验数据进行更全面的分析时，可以补充统计分析。这两种方法的组合在乳房切除术和乳房切除术的比较效果中的应用表明，就成本和患者生活质量（以满意度衡量）而言，发现乳房切除术是更好的选择。 （摘要由UMI缩短。）；使用聚类，采样和文本挖掘等数据挖掘技术对数据进行预处理和准备以进行分析。为了清理统计模型的数据，使用对数转换等方法对一些连续变量进行了归一化。统计模型，例如线性回归，广义线性模型，逻辑和比例风险（Cox）回归，用于比较乳房切除术与乳房切除术的术后结果。将神经网络，决策树和逻辑回归预测建模技术进行比较，以创建一个简单的预测模型，以预测术后90天的医院再入院率。将成本和效果与增量成本效率比（ICER）进行了比较。创建了一种用于处理和分析在线帖子的简单方法，并将其用于患者在乳房切除术与乳房切除术比较中的输入。所有统计分析均在SAS 9.2中进行。数据挖掘是在SAS Enterprise Miner（EM）6.1和SAS Text Miner中执行的。决策分析和成本效益分析是在TreeAge Pro 2011中进行的。

著录项

  • 作者

    Ugiliweneza, Beatrice.;

  • 作者单位

    University of Louisville.;

  • 授予单位 University of Louisville.;
  • 学科 Biology Biostatistics.;Statistics.;Applied Mathematics.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 217 p.
  • 总页数 217
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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