Article Text
Abstract
Objective Multichotomous tests have three or more outcome or risk categories, and can provide richer information and a better fit with clinical decision-making than dichotomous tests. Our objective is to present a fully developed approach to the meta-analysis of multichotomous clinical prediction rules (CPRs) and tests, including meta-analysis of stratum specific likelihood ratios.
Study design We have developed a novel approach to the meta-analysis of likelihood ratios for multichotomous tests that avoids the need to dichotomise outcome categories, and demonstrate its application to a sample CPR. We also review previously reported approaches to the meta-analysis of the area under the receiver operating characteristic curve (AUROCC) and meta-analysis of a measure of calibration (observed:expected) for multichotomous tests or CPRs.
Results Using data from 10 studies of the Cancer of the Prostate Risk Assessment (CAPRA) risk score for prostate cancer recurrence, we calculated summary estimates of the likelihood ratios for low, moderate and high risk groups of 0.40 (95% CI 0.32 to 0.49), 1.24 (95% CI 0.99 to 1.55) and 4.47 (95% CI 3.21 to 6.23), respectively. Applying the summary estimates of the likelihood ratios for each risk group to the overall prevalence of cancer recurrence in a population allows one to estimate the likelihood of recurrence for each risk group in that population.
Conclusion An approach to meta-analysis of multichotomous tests or CPRs is presented. A spreadsheet for data preparation and code for R and Stata are provided for other researchers to download and use. Combined with summary estimates of the AUROCC and calibration, this is a comprehensive strategy for meta-analysis of multichotomous tests and CPRs.
- primary care
- statistics & research methods
- urological tumours
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Footnotes
Contributors The project was conceptualised and led by Mark Ebell. Brian McKay wrote and tested the R code. Tom Fahey provided input on the conceptualisation, assisted with writing and reviewed the final manuscript. Mary Walsh and Fiona Boland collaborated on the meta-analysis of receiver operating characteristic curves and meta-analysis of observed:expected ratios, and Fiona Boland also helped create Table 2. All co-authors reviewed and approved the final manuscript.
Funding This work was supported in part by a 2019 Fulbright Teaching/Research Scholar award for Dr Ebell (grant number N/A), and funding from the Health Research Board of Ireland to support the HRB Primary Care Research Centre at the Royal College of Surgeons in Ireland, Research Centre Grant no. HRC/2014/1.
Competing interests None declared.
Patient consent for publication Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data sharing not applicable as no data sets generated and/or analysed for this study. There was no original data collection for this study. R code and an excel spreadsheet for data preparation have been made available to the public under 'Supplemental Files'. The data preparation spreadsheet (Excel) and the R code for stratum specific likelihood ratios can be found at the Zenodo archive: https://doi.org/10.5281/zenodo.3936001.
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