title: Sample size and power determination in joint modeling of longitudinal and survival data.
abstract: Owing to the rapid development of biomarkers in clinical trials, joint modeling of longitudinal and survival data has gained its popularity in the recent years because it reduces bias and provides improvements of efficiency in the assessment of treatment effects and other prognostic factors. Although much effort has been put into inferential methods in joint modeling, such as estimation and hypothesis testing, design aspects have not been formally considered. Statistical design, such as sample size and power calculations, is a crucial first step in clinical trials. In this paper, we derive a closed-form sample size formula for estimating the effect of the longitudinal process in joint modeling, and extend Schoenfeld's sample size formula to the joint modeling setting for estimating the overall treatment effect. The sample size formula we develop is quite general, allowing for p-degree polynomial trajectories. The robustness of our model is demonstrated in simulation studies with linear and quadratic trajectories. We discuss the impact of the within-subject variability on power and data collection strategies, such as spacing and frequency of repeated measurements, in order to maximize the power. When the within-subject variability is large, different data collection strategies can influence the power of the study in a significant way. Optimal frequency of repeated measurements also depends on the nature of the trajectory with higher polynomial trajectories and larger measurement error requiring more frequent measurements.
authors: Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, U.S.A.
authors: Liddy M
authors: LM
authors: Chen
authors: Joseph G
authors: JG
authors: Ibrahim
authors: Haitao
authors: H
authors: Chu
references: Control Clin Trials. 2002 Dec;23(6):626-34
references: 12505241
references: Biometrics. 1982 Dec;38(4):963-74
references: 7168798
references: Biometrics. 2003 Jun;59(2):221-8
references: 12926706
references: Biostatistics. 2000 Dec;1(4):465-80
references: 12933568
references: Stat Methods Med Res. 2002 Feb;11(1):25-48
references: 11923992
references: Biometrics. 2006 Jun;62(2):432-45
references: 16918907
references: Stat Med. 2007 Feb 20;26(4):800-11
references: 16612837
references: J Clin Oncol. 2010 Jun 1;28(16):2796-801
references: 20439643
references: Lifetime Data Anal. 2005 Jun;11(2):151-74
references: 15940822
references: J Clin Oncol. 2003 Feb 15;21(4):588-92
references: 12586793
references: Biometrics. 2006 Dec;62(4):1037-43
references: 17156277
references: Biometrics. 1983 Jun;39(2):499-503
references: 6354290
references: Biostatistics. 2002 Dec;3(4):511-28
references: 12933595
references: Biometrics. 1997 Mar;53(1):330-9
references: 9147598
references: Stat Med. 1997 Jan 15-Feb 15;16(1-3):259-72
references: 9004396
references: Biometrics. 2002 Dec;58(4):742-53
references: 12495128