Health services research, diagnosis, and prognosis
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Current Notes (2023)
2023 January 30
Benjamin Collins (Ellen Clayton), Biomedical Informatics, Biomedical Ethics and Society
Development of a measure for patient literacy of artificial intelligence in healthcare. Planning sample size for testing and validation of scale and statistical analysis of results. Mentor confirmed.
2023 January 23
Matt Christensen (Michael Ward), Pulmonary and Critical Care Medicine
We aim to develop a clinical prediction score to estimate the risk of a MRSA infection among patients diagnosed with Sepsis in the ED.
1) feedback on appropriate population? We can analyze data from all ED encounters, or use data from an existing clinical trial (ACORN) which enrolled patients at VUMC with an order for an anti-pseudomonal antibiotic.
2) How to estimate sample size for building a clinical prediction tool?
3) Which method to use for building clinical prediction score (ehr based that can be augmented by provider input)? - principle component analysis? - sequential component selection? - Others? Mentor confirmed.
Clinic Notes:
- Looking to do risk predictions score for MRSA. Aims: 1. Compare performance of existing strategies (risk factors, syndrome-specific scores, MRSA PCR) for predicting MRSA risk to current practice (provided order for anti-MRSA). 2. Derive an automated EHR based MRS risk score in sepsis and validate internally. Population: adults with suspected sepsis. Effective sample size: number of MRSA*3.
- Recommendations:
- Try to collapse variables that act similarly (variable reduction).
- Broader population (from a non-study) vs. narrower population (from a regulated prior study): use the clinical trial data to get a model, and not assume the model is calibrated correctly for low-severity patients. Weakness: when real data has many noise predictors, obtaining intercept needs to be carefully done.
- Wouldn't recommend accessing sensitivity/specificity.
- Can use C index or AUC (area under the curve) to assess the discriminative power of the prediction score.
Carson Moore (Thomas F. Scherr), Chemistry
I work in the Mobile Health for Global Health group. We are currently working on a project that maps human schistosomiasis infections and environmental factors related to the spread of this disease. We are interested in consulting with the Biostats Core to work on calculating the number of sites needed to sample and number of intermediate host snails needed to collect to provide statistically valid results, and assistance on determining the best methods to randomly sample mapped areas. Mentor confirmed.
Clinic Notes:
- Interested in schistosomiasis infections. Would like to know the best way to sample to validate the risk map (existence of snails).
- Recommendations:
- Do not recommend classification method (presence/absence of snails). Consider semiparametric model that uses ranks (more abundance means higher in rank).
- Use lift curve (as in marketing) for areas.
- Consider geospatial models that account for correlation between areas. Use random effects in the model.
- Stratifying regions valid for resource savings, whereas complete randomization assures unbiasness.