Dataset Information

Statistical Analysis of Patient-Specific Pathway Activities via Mixed Models.

ABSTRACT: In the study of complex diseases, a major challenge is disease heterogeneity, where the dysregulation of different pathways often lead to similar disease phenotypes. As a result, a given pathway could be differentially expressed with respect to controls for some patients, but not for others. Therefore, to develop successful personalized treatment regime, in addition to identifying disease relevant pathways for the entire patient group, it's also important to test if a particular pathway is dysregulated for an individual patient. To this end, we compare pathway gene expression profile for a particular individual in the patient group to the "norm" (or standard) established by a group of control patients. We studied statistical analysis of patient-specific pathway activities under the mixed models framework. Using gene expression dataset with realistic correlation patterns, we showed the proposed hypothesis testing procedure had false positive rate (type I error) as expected. In addition, we illustrated the proposed methodology using a Type 2 Diabetes dataset. Our results showed a previously diabetes associated pathway was only differentially expressed (relative to the control group) in less than 30% of the diabetes patients, which provided an explanation for the moderate group level statistical significance seen in a previous study. This result also suggested targeting this particular pathway would likely be beneficial for only 30% of the patients. In addition to the case-control study we have illustrated, this model can be easily extended to handle more complex designs with additional covariates and multiple sources of variations. Moreover, the proposed model operates within a well-established statistical framework and can be implemented in common statistical packages.

SUBMITTER: Wang L

PROVIDER: S-EPMC3793893 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Statistical Analysis of Patient-Specific Pathway Activities via Mixed Models.

Wang Lily L Chen Xi X Zhang Bing B

Journal of biometrics & biostatistics 20120101 1

In the study of complex diseases, a major challenge is disease heterogeneity, where the dysregulation of different pathways often lead to similar disease phenotypes. As a result, a given pathway could be differentially expressed with respect to controls for some patients, but not for others. Therefore, to develop successful personalized treatment regime, in addition to identifying disease relevant pathways for the entire patient group, it's also important to test if a particular pathway is dysre ...[more]

PMID: 24124644

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Project description:ObjectiveThe aim of this study was to identify the care pathway and organisational factors that predict patient experience.DesignStatistical analysis of large National Health Service (NHS) datasets.Setting andparticipantsEngland; acute NHS organisational-level data.Primary and secondary outcome measuresThe relationship of care pathway and organisational variables to organisation-level patient experience.ResultsA framework of 18 care pathway and organisational variables were created based on the existing literature. 11 of these correlated to patient experience in univariate analyses. Multicollinearity tests resulted in 1 of the 11 variables holding a correlation to another variable larger than r=0.70. A significant multilinear regression equation, including the final 10 variables, was found (F(10,108)=6.214, p<0.00), with an [Formula: see text] of 0.365. Two variables were significant in predicting better in patient experience: Amount of support to clinical staff (beta=0.2, p=0.02) and the proportion of staff who would recommend the trust as a place to work or receive treatment (beta=0.26, p=0.01). Two variables were significant in predicting a negative impact on the patient's rating of their experience: Number of patients spending over 4 hours from decision to admit to admission (beta=-1.99, p=0.03) and the percentage of estates and hotel services contracted out (beta=-0.23, p=0.01).ConclusionsThese results indicate that augmenting clinical support and investing in the mechanisms that facilitate positive staff experience is essential to delivering appropriate, informative and patient-centric care. Reducing wait times and the extent of external contracting within hospitals is also likely to improve patient ratings of experience. Understanding the relationship between patient experience and objective, measurable organisational features promote a more patient-centric interpretation of quality and compel a better use of patient experience feedback to drive improvement.

Dataset Information

Statistical Analysis of Patient-Specific Pathway Activities via Mixed Models.

Publications

Statistical Analysis of Patient-Specific Pathway Activities via Mixed Models.

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