PDXpower: A Power Analysis Tool for Experimental Design in Pre-clinical Xenograft Studies for Uncensored and Censored Outcomes

Li, Shanpeng; Telesca, Donatello; Kornblum, Harley I.; Nathanson, David; Pajonk, Frank; Cui, Elvis Han; Palmer, Joycelynne; Li, Gang

doi:10.51387/25-NEJSDS76

The New England Journal of Statistics in Data Science

PDXpower: A Power Analysis Tool for Experimental Design in Pre-clinical Xenograft Studies for Uncensored and Censored Outcomes

Volume 3, Issue 1 (2025), pp. 42–54

Shanpeng Li Donatello Telesca Harley I. Kornblum All authors (8)

https://doi.org/10.51387/25-NEJSDS76

Pub. online: 5 February 2025 Type: Software Tutorial And/or Review

Open Access

Area: Cancer Research

Accepted
24 January 2025

Published
5 February 2025

Abstract

In cancer research, leveraging patient-derived xenografts (PDXs) in pre-clinical experiments is a crucial approach for assessing innovative therapeutic strategies. Addressing the inherent variability in treatment response among and within individual PDX lines is essential. However, the current literature lacks a user-friendly statistical power analysis tool capable of concurrently determining the required number of PDX lines and animals per line per treatment group in this context. In this paper, we present a simulation-based R package for sample size determination, named ‘PDXpower’, which is publicly available at The Comprehensive R Archive Network (https://CRAN.R-project.org/package=PDXpower). The package is designed to estimate the necessary number of both PDX lines and animals per line per treatment group for the design of a PDX experiment, whether for an uncensored outcome, or a censored time-to-event outcome. Our sample size considerations rely on two widely used analytical frameworks: the mixed effects ANOVA model for uncensored outcomes and Cox’s frailty model for censored data outcomes, which effectively account for both inter-PDX variability and intra-PDX correlation in treatment response. Step-by-step illustrations for utilizing the developed package are provided, catering to scenarios with or without preliminary data.

References

[1]

Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y., Allen, J., McPherson, J., Dipert, A. and Borges, B. shiny: Web Application Framework for R. R package version 1.8.1.9000 (2024). https://github.com/rstudio/shiny

[2]

Chen, L. M., Ibrahim, J. G. and Chu, H. Sample size determination in shared frailty models for multivariate time-to-event data. Journal of Biopharmaceutical Statistics 24(4) 908–923 (2014). https://doi.org/10.1080/10543406.2014.901346. MR3210438

[3]

Dinart, D., Bellera, C. and Rondeau, V. Sample size estimation for recurrent event data using multifrailty and multilevel survival models. Journal of Biopharmaceutical Statistics, 1–16 (2024)

[4]

Donohue, M. C. longpower: Power and sample size calculations for linear mixed models. R package version 1.0.23 (2021)

[5]

Duchateau, L. and Janssen, P. The Frailty Model. Springer (2008). MR2723929

[6]

Eckel-Passow, J. E., Kitange, G. J., Decker, P. A., Kosel, M. L., Burgenske, D. M., Oberg, A. L. and Sarkaria, J. N. Experimental design of preclinical experiments: number of PDX lines vs subsampling within PDX lines. Neuro-Oncology 23(12) 2066–2075 (2021)

[7]

Green, P. and MacLeod, C. J. simr: an R package for power analysis of generalised linear mixed models by simulation. Methods in Ecology and Evolution 7(4) 493–498 (2016)

[8]

Kleinman, K., Sakrejda, A., Moyer, J., Nugent, J. and Reich, N. clusterPower: Power calculations for cluster-randomized and cluster-randomized crossover trials. R package version 0.7.0 (2021)

[9]

Kumle, L., Võ, M.L.-H. and Draschkow, D. Estimating power in (generalized) linear mixed models: an open introduction and tutorial in R. Behavior Research Methods 53(6) 2528–2543 (2021)

[10]

Liu, G. and Liang, K. Y. Sample size calculations for studies with correlated observations. Biometrics 53(3) 937–47 (1997)

[11]

Lu, K., Luo, X. and Chen, P.-Y. Sample size estimation for repeated measures analysis in randomized clinical trials with missing data. The International Journal of Biostatistics 4(1) 9 (2008). https://doi.org/10.2202/1557-4679.1098. MR2426114

[12]

Martin, J. G., Nussey, D. H., Wilson, A. J. and Reale, D. Measuring individual differences in reaction norms in field and experimental studies: a power analysis of random regression models. Methods in Ecology and Evolution 2(4) 362–374 (2011)

[13]

PASS. PASS 2022 Power Analysis and Sample Size Software. NCSS, LLC. Kaysville, Utah, USA, ncss.com/software/pass (2022)

[14]

R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2022)

[15]

Rondeau, V., Marzroui, Y. and Gonzalez, J. R. frailtypack: an R package for the analysis of correlated survival data with frailty models using penalized likelihood estimation or parametrical estimation. Journal of Statistical Software 47 1–28 (2012)

[16]

Rosner, B. Fundamentals of Biostatistics. Cengage learning (2015)

[17]

SAS Institute Inc. The SAS Software, Version 9.4. Cary. http://www.sas.com/ (2013)

Full article

Open access article under the CC BY license.

Keywords

Frailty model Mixed effects ANOVA model Power analysis Sample size determination Xenograft study

Funding

This research was partially supported by National Institutes of Health (P50CA211015, P30 CA-16042, R01NS121319, P50 CA092131, and P01CA236585, GL, P30 CA-033572, JP), and the National Center for Advancing Translational Sciences (UL1-TR-001420, GL).

Metrics

since December 2021

120

Article info
views

111

Full article
views

PDF
downloads

XML
downloads

RSS

Authors

Abstract

References

Export citation

Copy and paste formatted citation

Download citation in file