The analysis of longitudinal quality of life measures with informative drop-out: a pattern mixture approach

Abstract

Purpose: The analysis of longitudinal health-related quality of life measures (HRQOL) can be seriously hampered due to informative drop-out. Random effects models assume Missing At Random and do not take into account informative drop-out. We therefore aim to correct the bias due to informative drop-out.

Methods: Analyses of data from a trial comparing standard-dose and high-dose chemotherapy for patients with breast cancer with respect to long-term impact on HRQOL will serve as illustration. The subscale Physical Function (PF) of the SF36 will be used. A pattern mixture approach is proposed to account for informative drop-out. Patterns are defined based on events related to HRQOL, such as death and relapse. The results of this pattern mixture approach are compared to the results of the commonly used random effects model.

Results: The findings of the pattern mixture approach are well interpretable, and different courses over time in different patterns are distinguished. In terms of estimated differences between standard dose and high dose, the results of both approaches are slightly different, but have no consequences for the clinical evaluation of both doses.

Conclusion: Under the assumption that drop-out is at random within the patterns, the pattern mixture approach adjusts the estimates to a certain degree. This approach accounts in a relatively simple way for informative drop-out.

Fig. 1

Observed mean and standard error per treatment and per time point for all available PF measures

Fig. 2

a Observed means and standard errors per time point per pattern. b Observed means and standard errors per arm and per time point for deceased patients. c Observed means and standard errors per arm and per time point for patients alive with relapse. d Observed means and standard errors per arm and per time point for relapse-free patients

Fig. 3

The estimated course of PF over time per treatment based on the final random effects model assuming MAR

Fig. 4

a The estimated course of PF over time per treatment for pattern ‘deceased’. b The estimated course of PF over time per treatment for pattern ‘alive with relapse’. c The estimated course of PF over time per treatment for pattern ‘disease free’

Fig. 5

The estimated course of PF over time per treatment based on the pattern mixture model