Generalized Additive Mixed Modeling of Longitudinal Tumor Growth Reduces Bias and Improves Decision Making in Translational Oncology.
Anilides
/ administration & dosage
Animals
Antineoplastic Agents, Alkylating
/ administration & dosage
Bias
Decision Making
Disease Models, Animal
Female
Genes, Tumor Suppressor
Glioblastoma
/ drug therapy
Heterografts
Humans
Medical Oncology
/ statistics & numerical data
Mice
Mice, Nude
Models, Statistical
Neoplasm Transplantation
Neoplasms
/ pathology
Normal Distribution
Patched-1 Receptor
/ genetics
Piperazines
/ administration & dosage
Pyridines
/ administration & dosage
Random Allocation
Statistics, Nonparametric
Temozolomide
/ administration & dosage
Translational Research, Biomedical
/ statistics & numerical data
Tumor Burden
Journal
Cancer research
ISSN: 1538-7445
Titre abrégé: Cancer Res
Pays: United States
ID NLM: 2984705R
Informations de publication
Date de publication:
15 11 2020
15 11 2020
Historique:
received:
26
02
2020
revised:
01
07
2020
accepted:
22
09
2020
pubmed:
27
9
2020
medline:
13
2
2021
entrez:
26
9
2020
Statut:
ppublish
Résumé
Scientists working in translational oncology regularly conduct multigroup studies of mice with serially measured tumors. Longitudinal data collected can feature mid-study dropouts and complex nonlinear temporal response patterns. Parametric statistical models such as ones assuming exponential growth are useful for summarizing tumor volume over ranges for which the growth model holds, with the advantage that the model's parameter estimates can be used to summarize between-group differences in tumor volume growth with statistical measures of uncertainty. However, these same assumed growth models are too rigid to recapitulate patterns observed in many experiments, which in turn diminishes the effectiveness of their parameter estimates as summary statistics. To address this problem, we generalized such models by adopting a nonparametric approach in which group-level response trends for logarithmically scaled tumor volume are estimated as regression splines in a generalized additive mixed model. We also describe a novel summary statistic for group level splines over user-defined, experimentally relevant time ranges. This statistic reduces to the log-linear growth rate for data well described by exponential growth and also has a sampling distribution across groups that is well approximated by a multivariate Gaussian, thus facilitating downstream analysis. Real-data examples show that this nonparametric approach not only enhances fidelity in describing nonlinear growth scenarios but also improves statistical power to detect interregimen differences when compared with the simple exponential model so that it generalizes the linear mixed effects paradigm for analysis of log-linear growth to nonlinear scenarios in a useful way. SIGNIFICANCE: This work generalizes the statistical linear mixed modeling paradigm for summarizing longitudinally measured preclinical tumor volume studies to encompass studies with nonlinear and nonmonotonic group response patterns in a statistically rigorous manner.
Identifiants
pubmed: 32978171
pii: 0008-5472.CAN-20-0342
doi: 10.1158/0008-5472.CAN-20-0342
doi:
Substances chimiques
Anilides
0
Antineoplastic Agents, Alkylating
0
HhAntag691
0
PTCH1 protein, human
0
Patched-1 Receptor
0
Piperazines
0
Pyridines
0
palbociclib
G9ZF61LE7G
Temozolomide
YF1K15M17Y
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5089-5097Informations de copyright
©2020 American Association for Cancer Research.
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