Statistical strategies to improve the efficiency of molecular studies of colorectal cancer prognosis

Abstract

The evaluation of tumour molecular markers may be beneficial in prognosis and predictive in therapy. We develop a stopping rule approach to assist in the efficient utilisation of resources and samples involved in such evaluations. This approach has application in determining whether a specific molecular marker has sufficient variability to yield meaningful results after the evaluation of molecular markers in the first n patients in a study of sample size N (n</=N). We evaluated colorectal tumours for mutations (microsatellite instability, K-ras, B-raf, PI3 kinase, and TGFbetaR-II) by PCR and protein markers (Bcl2, cyclin D1, E-cadherin, hMLH1, ki67, MDM2, and P53) by immunohistochemistry. Using this method, we identified and abandoned potentially uninformative molecular markers in favour of more promising candidates. This approach conserves tissue resources, time, and money, and may be applicable to other studies.

Figure 1

Lower and upper bounds calculated at 80% power, α=0.05, overall death rate d=0.6, and total sample size N=1000 for comparison with mutation rate estimated from n (n⩽N) patients. The shaded area represents the rejection region. A 95% confidence interval of mutation rate from n patients falling completely within this region suggests that the marker has little variability and likely insufficient power to predict survival, even if all data from N patients were collected.

Figure 2

Minimum variance calculated at 80% power, α=0.05, overall death rate d=0.6, and total sample size N=1000 for comparison with marker variance estimated from n (n⩽N) patients. The shaded area represents the rejection region. A 95% confidence interval of marker variance from n patients falling completely within this region suggests that the marker has little variability and likely insufficient power to predict survival, even if all data from N patients were collected.