scholarly journals Statistical analysis of comparative tumor growth repeated measures experiments in the ovarian cancer patient derived xenograft (PDX) setting

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ann L. Oberg ◽  
Ethan P. Heinzen ◽  
Xiaonan Hou ◽  
Mariam M. Al Hilli ◽  
Rachel M. Hurley ◽  
...  
Keyword(s):  
Tumor Growth ◽  
Repeated Measures ◽  
Regression Models ◽  
Statistical Power ◽  
Mixed Effects ◽  
Information Criteria ◽  
Tumor Doubling Time ◽  
Linear Mixed Effects ◽  
Patient Derived Xenograft ◽  
Pdx Models

AbstractRepeated measures studies are frequently performed in patient-derived xenograft (PDX) models to evaluate drug activity or compare effectiveness of cancer treatment regimens. Linear mixed effects regression models were used to perform statistical modeling of tumor growth data. Biologically plausible structures for the covariation between repeated tumor burden measurements are explained. Graphical, tabular, and information criteria tools useful for choosing the mean model functional form and covariation structure are demonstrated in a Case Study of five PDX models comparing cancer treatments. Power calculations were performed via simulation. Linear mixed effects regression models applied to the natural log scale were shown to describe the observed data well. A straight growth function fit well for two PDX models. Three PDX models required quadratic or cubic polynomial (time squared or cubed) terms to describe delayed tumor regression or initial tumor growth followed by regression. Spatial(power), spatial(power) + RE, and RE covariance structures were found to be reasonable. Statistical power is shown as a function of sample size for different levels of variation. Linear mixed effects regression models provide a unified and flexible framework for analysis of PDX repeated measures data, use all available data, and allow estimation of tumor doubling time.

scholarly journals The power of modelling pulsatile profiles

2021 ◽  
Author(s):  
Michiel J. van Esdonk ◽  
Jasper Stevens
Keyword(s):  
Growth Hormone ◽  
Statistical Power ◽  
Quantitative Description ◽  
Mixed Effects ◽  
Post Dose ◽  
Deconvolution Analysis ◽  
Linear Mixed Effects ◽  
Non Linear ◽  
Over Time ◽  
Modelling Approach

AbstractThe quantitative description of individual observations in non-linear mixed effects models over time is complicated when the studied biomarker has a pulsatile release (e.g. insulin, growth hormone, luteinizing hormone). Unfortunately, standard non-linear mixed effects population pharmacodynamic models such as turnover and precursor response models (with or without a cosinor component) are unable to quantify these complex secretion profiles over time. In this study, the statistical power of standard statistical methodology such as 6 post-dose measurements or the area under the curve from 0 to 12 h post-dose on simulated dense concentration–time profiles of growth hormone was compared to a deconvolution-analysis-informed modelling approach in different simulated scenarios. The statistical power of the deconvolution-analysis-informed approach was determined with a Monte-Carlo Mapped Power analysis. Due to the high level of intra- and inter-individual variability in growth hormone concentrations over time, regardless of the simulated effect size, only the deconvolution-analysis informed approach reached a statistical power of more than 80% with a sample size of less than 200 subjects per cohort. Furthermore, the use of this deconvolution-analysis-informed modelling approach improved the description of the observations on an individual level and enabled the quantification of a drug effect to be used for subsequent clinical trial simulations.

scholarly journals Estimating functional linear mixed-effects regression models

2017 ◽  
Vol 106 ◽  
pp. 153-164 ◽  
Author(s):  
Baisen Liu ◽  
Liangliang Wang ◽  
Jiguo Cao
Keyword(s):  
Regression Models ◽  
Mixed Effects ◽  
Linear Mixed Effects

scholarly journals Secukinumab provides sustained low rates of radiographic progression in psoriatic arthritis: 52-week results from a phase 3 study, FUTURE 5

Rheumatology ◽  
2019 ◽  
Vol 59 (6) ◽  
pp. 1325-1334 ◽  
Author(s):  
Désirée van der Heijde ◽  
Philip J Mease ◽  
Robert B M Landewé ◽  
Proton Rahman ◽  
Hasan Tahir ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Radiographic Progression ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Inadequate Response ◽  
Phase 3 ◽  
Linear Mixed Effects Model ◽  
Linear Mixed Effects ◽  
Random Slope ◽  
Modified Total Sharp Score

Abstract Objective To evaluate the effect of secukinumab on radiographic progression through 52 weeks in patients with PsA from the FUTURE 5 study. Methods Patients with active PsA, stratified by prior anti-TNF use (naïve or inadequate response), were randomized to s.c. secukinumab 300 mg load (300 mg), 150 mg load (150 mg), 150 mg no load regimens or placebo at baseline, at weeks 1, 2 and 3 and every 4 weeks starting at week 4. Radiographic progression was assessed by change in van der Heijde-modified total Sharp score (vdH-mTSS; mean of two readers). Statistical analysis used a linear mixed-effects model (random slope) at weeks 24 and 52, and observed data at week 52. Assessments at week 52 included additional efficacy endpoints (non-responders imputation and mixed-effects models for repeated measures) and safety. Results The majority (86.6%) of patients completed 52 weeks of treatment. The proportion of patients with no radiographic progression (change from baseline in vdH-mTSS ⩽0.5) was 91.8, 85.2 and 87.2% in 300, 150 and 150 mg no load groups, respectively, at week 52. The change in vdH-mTSS from baseline to week 52 using random slope [mean change (s.e.)] was –0.18 (0.17), 0.11 (0.18) and –0.20 (0.18) in 300, 150 and 150 mg no load groups, respectively; the corresponding observed data [mean change (s.d.)] was –0.09 (1.02), 0.13 (1.39) and 0.21 (1.15). Clinical efficacy endpoints were sustained, and no new or unexpected safety signals were reported through 52 weeks. Conclusion Secukinumab 300 and 150 mg with or without s.c. loading regimen provided sustained low rates of radiographic progression through 52 weeks of treatment. Trial registration ClinicalTrials.gov, http://clinicaltrials.gov, NCT02404350.

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