Stepwise multivariable regression model for predicting diseased survival time and application thereof
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A technique of multiple regression model and survival period, applied in the field of biomedicine
Inactive Publication Date: 2016-12-07
HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
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However, the massive molecular biological data generated are rarely systematically analyzed and utilized because they are not integrated with clinical data, especially the prediction of patient survival
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Embodiment 1
[0045] Embodiment 1 Data description and processing
[0046] The research data comes from The Cancer Genome Atlas / TCGA (https: / / tcga-data.nci.nih.gov / tcga / dataAccessMatrix.htm). The data level of miRNASeq and Experssion-Protein in patients with invasive breast cancer was selected as 3 downloads. Data processing is 1206×1046 miRNA expression level matrix, 937×285 Protein expression level matrix and clinical data matrix, among which 1046, 285 are the number of miRNA and Protein, 1206, 937, 1100 are the number of patients respectively. 112 are clinical attributes. A total of 78 patient samples with both miRNA and protein expression levels and survival time were selected.
[0047] In the miRNA expression data and protein expression data, there are 78 breast cancer patients with survival records, and these 78 individuals also have age, gender, cancer stage and tumor size and other data. The data of protein types shared by breast cancer patients were selected, and the final data ...
Embodiment 2
[0051] Embodiment 2 stepwise multiple regression model establishment method and result
[0052] 1. Establishment of stepwise multiple linear regression forecasting model
[0053] Let the patient's survival period y be related to miRNA, protein expression level and clinical data x 1 ,x 2 ,...,x p There may be a linear relationship whose regression model is:
[0054] y=Xβ+ε
[0055] where the lifetime vector is y=(y 1 ,y 2 ,...,y n )', the vector parameter is β=(β 0 ,β 1 ,β 2 ,...,β p ), the random error is ε=(ε 1 ,ε 2 ,…,ε p ), the matrix X is
[0056] X = 1 x 11 x 12 ... x 1 p ...
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Abstract
The invention provides a stepwise multivariable regression model for predicting diseased survival time and application thereof. Whether to introduce a new variable or delete an original variable is judged by constructing an F statistic in the stepwise multivariable regression model, and after introduction of each new variable, whether to delete one variable is judged, that is, whether the newly-introduced variable is associated with the original variable is verified; when the fitting coefficient is greater than 0.8, the fitting effect is relatively good. The stepwise multivariable regression model has relatively wide application to prediction of the survival time of patients suffering from cancers and serious diseases similar to the cancers.
Description
technical field [0001] The invention belongs to the field of biomedicine, and specifically relates to a stepwise multiple regression model and its application for predicting the survival period of a disease. Background technique [0002] Today in the 21st century, malignant tumors are still major diseases that seriously endanger human life and health. From a global perspective, the occurrence and development of tumors are not optimistic. With the gradual aging of the population, smoking, infection, environmental pollution, dietary structure and other problems, the situation of tumor diagnosis is extremely severe. According to the "2015 China Cancer Statistics" released by the National Cancer Registry Center, there are expected to be 4.292 million new cancer cases and 2.814 million deaths in China in 2015. Cancer has become the first cause of death in China, and the morbidity and mortality are still rising. Cancer has become a very important public health problem in my coun...
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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
CPCG16H50/20G16H50/70
Inventor 赵毅张阳
Owner HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
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