Sample size and power determination when limited preliminary information is available

Christine E Mclaren,Wen-Pin Chen,Jeon H Chen,Bruce J Tromberg,Thomas D O’Sullivan,Daniel L Gillen,Min-Ying Su

doi:10.1186/s12874-017-0329-1

Abstract

BackgroundWe describe a novel strategy for power and sample size determination developed for studies utilizing investigational technologies with limited available preliminary data, specifically of imaging biomarkers. We evaluated diffuse optical spectroscopic imaging (DOSI), an experimental noninvasive imaging technique that may be capable of assessing changes in mammographic density. Because there is significant evidence that tamoxifen treatment is more effective at reducing breast cancer risk when accompanied by a reduction of breast density, we designed a study to assess the changes from baseline in DOSI imaging biomarkers that may reflect fluctuations in breast density in premenopausal women receiving tamoxifen.MethodWhile preliminary data demonstrate that DOSI is sensitive to mammographic density in women about to receive neoadjuvant chemotherapy for breast cancer, there is no information on DOSI in tamoxifen treatment. Since the relationship between magnetic resonance imaging (MRI) and DOSI has been established in previous studies, we developed a statistical simulation approach utilizing information from an investigation of MRI assessment of breast density in 16 women before and after treatment with tamoxifen to estimate the changes in DOSI biomarkers due to tamoxifen.ResultsThree sets of 10,000 pairs of MRI breast density data with correlation coefficients of 0.5, 0.8 and 0.9 were simulated and generated and were used to simulate and generate a corresponding 5,000,000 pairs of DOSI values representing water, ctHHB, and lipid. Minimum sample sizes needed per group for specified clinically-relevant effect sizes were obtained.ConclusionThe simulation techniques we describe can be applied in studies of other experimental technologies to obtain the important preliminary data to inform the power and sample size calculations.

Highlights

We describe a novel strategy for power and sample size determination developed for studies utilizing investigational technologies with limited available preliminary data, of imaging biomarkers
The simulation techniques we describe can be applied in studies of other experimental technologies to obtain the important preliminary data to inform the power and sample size calculations
Additional file 2: Tables S1, S2, and S3 show details of the minimum sample size needed per group for specified clinically-relevant sample sizes, assuming significance levels of 0.05 and 0.01, power of 80 and 90%, and correlation coefficients between pre- and post-treatment values of water, ctHHB, and lipid of 0.50, 0.80, and 0.90

Summary

Introduction

We describe a novel strategy for power and sample size determination developed for studies utilizing investigational technologies with limited available preliminary data, of imaging biomarkers. Because there is significant evidence that tamoxifen treatment is more effective at reducing breast cancer risk when accompanied by a reduction of breast density, we designed a study to assess the changes from baseline in DOSI imaging biomarkers that may reflect fluctuations in breast density in premenopausal women receiving tamoxifen. As an alternative to MRI and mammography, diffuse optical spectroscopic imaging (DOSI) is a non-invasive, relatively low-cost experimental imaging technique that provides quantitative metrics to measure and track changes in breast tissue composition and metabolism [7]. The strong significant linear relationship between the MRI-based breast density and these DOSI measures including water, deoxygenated hemoglobin (ctHHb) and lipid volume was reported [8]. This finding suggests that these DOSI imaging biomarkers are useful for monitoring changes in breast tissue composition and density. Further quanification of the association between within-subject changes in DOSI measures and changes in baseline is needed before DOSI measures can be considered as an alternative biomarker in treatment settings

Methods

Results

Discussion

Conclusion