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M-28
Analyzing Patient-Reported Outcomes in Breast Cancer through Item-Response Theory Pharmacometric
Modeling
Emilie Schindler1*, Bei Wang2, Bert Lum2, Sandhya Girish2, Jin Y. Jin2, Lena E. Friberg1, Mats O. Karlsson1
1
Department of Pharmaceutical Biosciences, Uppsala University, Sweden; 2Genentech Inc.
Objectives: Patient-reported outcomes (PROs) provide valuable information on the subjective impact of a disease
and treatment on patients. However, methodological challenges exist for interpreting PROs due to their multi-scale
nature and frequent missing data. This analysis aims to describe Functional Assessment of Cancer Therapy-Breast
(FACT-B) questionnaire data and investigate relationships to drug exposure in T-DM1 (ado-trastuzumab emtansine,
Kadcyla) treated patients with HER2+ locally-advanced or metastatic breast cancer using item-response theory
(IRT) based pharmacometric approaches [1].
Methods: Item-level FACT-B data (n=2655) were available from 484 T-DM1-treated patients included in a phase 3
trial [2]. FACT-B contains 36 ordered categorical items divided into 5 subscales (physical, social, emotional and
functional well-being, and breast-cancer specific). Each item was modeled using a proportional-odds model, where
the probability of a score is described as a function of item-specific fixed effect parameters and a subject-specific
random variable (WBi) describing the underlying well-being. For each patient, one WB i variable was estimated for
each subscale. Correlations between the 5 subscales were estimated. WB i are assumed to be standard normally
distributed at baseline. The effect of time after first dose and TDM-1 AUCcycle1 [3] on WBi parameters were
investigated.
Results: A total of 180 item-specific parameters were estimated. Correlations between subscales exceeded 33%.
Well-being improved over time for all subscales except social well-being. However, well-being increased with
increased AUCcycle1 for all subscales. Item-characteristic curves showed that items within the breast-cancer subscale
may not relate to the same underlying variable. These items would benefit from reassignment to another subscale.
Conclusions: This work is the first attempt to our knowledge to use IRT-based modeling approach as a framework
for analyzing PROs in oncology. The exploratory analysis showed positive relationships between time, drug
exposure and FACT-B data. The developed model will be used to further evaluate time-varying exposure-response
relationships for PROs.
References:
[1] Ueckert et al. Pharm Res. 2014;31(8):2152-65.
[2] Welslau et al. Cancer. 2014;120(5):642-51
[3] Lu et al. Cancer Chemother Pharmacol. 2014;74(2):399–410.