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A predictive risk probability approach for microarray data with survival as an endpoint.

Abstract：

:Gene expression profiling has played an important role in cancer risk classification and has shown promising results. Since gene expression profiling often involves determination of a set of top rank genes for analysis, it is important to evaluate how modeling performance varies with the number of selected top ranked genes incorporated in the model. We used a colon data set collected at Moffitt Cancer Center as an example of the study, and ranked genes based on the univariate Cox proportional hazards model. A set of top ranked genes was selected for evaluation. The selection was done by choosing the top k ranked genes for k = 1 to 12,500. An analysis indicated a considerable variation of classification outcomes when the number of top ranked genes was changed. We developed a predictive risk probability approach to accommodate this variation by identifying a range number of top ranked genes. For each number of top ranked genes, the procedure classifies each patient as having high risk (score = 1) or low risk (score = 0). The categorizations are then averaged, giving a risk score between 0 and 1, thus providing a ranking for the patient's need for further treatment. This approach was applied to the colon data set and demonstrated the strength of this approach by three criteria: First, a univariate Cox proportional hazards model showed a highly statistically significant level (log-rank chi(2) statistics = 110 with p-value <10(-16)) for the predictive risk probability classification. Second, the survival tree model used the risk probability to partition patients into five risk groups showing a good separation of survival curves (log-rank chi(2) statistics = 215). In addition, utilization of the risk group status identified a small set of risk genes that may be practical for biological validation. Third, analysis of resampling the risk probability suggested the variation pattern of the log-rank chi(2) in the colon cancer data set was unlikely caused by chance.

journal_name

J Biopharm Stat

journal_title

Journal of biopharmaceutical statistics

authors

Chen DT,Schell MJ,Chen JJ,Fulp WJ,Eschrich S,Yeatman T

doi

10.1080/10543400802277967

subject

Has Abstract

pub_date

2008-01-01 00:00:00

pages

841-52

issue

eissn

1054-3406

issn

1520-5711

pii

902368589

journal_volume

pub_type

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A predictive risk probability approach for microarray data with survival as an endpoint.