Outcome
| Type |
Measure |
Description |
Time frame |
Safety issue |
| Primary |
Objective Response Rate (ORR) as of Week 24 (ORR24wk) |
ORR as of Week 24 was defined as the percentage of participants with best overall response (BOR) of complete response (CR) or partial response (PR) as of the Week 24 time point or earlier, as measured by Response Evaluation Criteria in Solid Tumors Version 1.1 (RECIST 1.1). CR: was disappearance of all target lesions. Any pathological lymph nodes (target or non-target) had to be reduced in short axis to less than (<) 10 millimeter (mm). PR: was at least a 30 percent (%) decrease in sum of diameter (SOD) of target lesions, taking as reference the baseline SOD. |
From the date of randomization up to Week 24 |
|
| Primary |
Percentage of Participants With Grade 3 or Higher Treatment-emergent Adverse Events (TEAEs) in the First 24 Weeks |
This outcome measure reports TEAEs in the first 24 weeks only. A TEAE was defined as any adverse event (AE) that had an onset date on or after the first dose of study drug up to 28 days following the last dose of study drug, or a worsening in severity from Baseline (pretreatment). In addition, if an AE reemerged during treatment, having been present at pretreatment (Baseline) but stopped before treatment, it was also counted as a TEAE. A severity grade was defined by the Common Terminology Criteria for Adverse Events (CTCAE) Version 4.03. As per CTCAE, Grade 1 scales as Mild; Grade 2 scales as Moderate; Grade 3 scales as severe or medically significant but not immediately life threatening; Grade 4 scales as life-threatening consequences; and Grade 5 scales as death related to AE. |
Baseline up to Week 24 |
|
| Secondary |
Progression-free Survival (PFS) |
PFS, defined as the time from the date of randomization to the date of first documentation of PD, or date of death, whichever occurs first, as measured by RECIST V1.1. PD: 20% increase in the sum of the pertinent diameters (SOD) of target lesions, taking as reference the smallest sum SOD recorded since the treatment started or the appearance of one or more new lesions. PFS was analyzed using the Kaplan-Meier method. As planned, data for this endpoint was analyzed and collected till Primary completion date. |
Time from the date of randomization to the date of first documentation of PD, or date of death, whichever occurs first up to approximately 2 years 6 months |
|
| Secondary |
PFS After Next Line of Treatment (PFS2) |
PFS2, defined as the time from randomization to PD on next-line treatment, or death from any cause, whichever occurred first, as measured by RECIST V1.1. PD: 20% increase in the SOD of target lesions, taking as reference the smallest sum SOD recorded since the treatment started or the appearance of one or more new lesions. PFS was analyzed using the Kaplan-Meier method. As planned, data for this endpoint was analyzed and collected till Primary completion date. |
Time from randomization to PD on next-line treatment or death from any cause, whichever occurs first up to approximately 2 years 6 months |
|
| Secondary |
Number of Participants With TEAE and Serious Adverse Events (SAEs) |
TEAEs were defined as those AEs that occurred (or worsened, if present at Baseline) after the first dose of study drug through 28 days after the last dose of study drug. An AE was defined as any untoward medical occurrence in a participants or clinical investigation participant administered an investigational product. An AE does not necessarily have a causal relationship with medicinal product. SAE was defined as any AE if it resulted in death or life-threatening AE or required inpatient hospitalization or prolongation of existing hospitalization or resulted in persistent or significant incapacity or substantial disruption of the ability to conduct normal life functions or was a congenital anomaly/birth defect. |
From date of first administration of study drug up to 28 days after last dose of study drug up to approximately 3 years 3 months |
|
| Secondary |
Time to Treatment Discontinuation Due to an Adverse Event (AE) |
Time to Treatment Discontinuation due to an AE (such as abdominal distention, appendicitis perforated, arthralgia, anemia, etc) was analyzed using the Kaplan-Meier method. As planned, data for this endpoint was analyzed and collected till Primary completion date. |
From date of first administration of study drug up to approximately 2 years 6 months |
|
| Secondary |
Number of Dose Reductions |
Number of dose reduction was reported as number of participants who underwent one or more number of dose reductions. As planned, data for this endpoint was analyzed and collected till Primary completion date. |
From date of first administration of study drug up to approximately 2 years 6 months |
|
| Secondary |
Time to First Dose Reduction |
Time to First Dose Reduction was analyzed using the Kaplan-Meier method. As planned, data for this endpoint was analyzed and collected till Primary completion date. |
From date of first administration of study drug up to approximately 2 years 6 months |
|
| Secondary |
Model Predicted Apparent Total Clearance (CL/F) Following Oral Dosing of Lenvatinib |
Sparse pharmacokinetic (PK) samples (approximately 9 per participant) were collected and analyzed using a population PK approach to estimate PK parameters. Lenvatinib total plasma concentration data were pooled with data from studies E7080-G000-303 (NCT01321554) and E7080-G000-201 (NCT00784303), and a population PK model was applied to the pooled dataset. Individual predicted CL/F for lenvatinib was then derived from the PK model by starting dose. |
Cycle 1 Day 1: 0.5-4 hours and 6-10 hours postdose; Cycle 1 Day 8: predose, Cycle 1 Day 15: predose, 0.5-4 hours and 6-10 hours postdose; Cycle 1 Day 22: optionally at predose; Cycle 2 Day 1: predose and 2-12 hours postdose (Cycle length=28 days) |
|
| Secondary |
Model Predicted Area Under the Plasma Drug Concentration-time Curve (AUC) for Lenvatinib |
Sparse PK samples (approximately 9 per participant) were collected and analyzed using a population PK approach to estimate PK parameters. Lenvatinib total plasma concentration data were pooled with data from studies E7080-G000-303 (NCT01321554) and E7080-G000-201 (NCT00784303), and a population PK model was applied to the pooled dataset. Individual predicted AUC for lenvatinib was then derived from the PK model by starting dose. |
Cycle 1 Day 1: 0.5-4 hours and 6-10 hours postdose; Cycle 1 Day 8: predose, Cycle 1 Day 15: predose, 0.5-4 hours and 6-10 hours postdose; Cycle 1 Day 22: optionally at predose; Cycle 2 Day 1: predose and 2-12 hours postdose (Cycle length=28 days) |
|
| Secondary |
Parameter Estimates From the Population Pharmacokinetic/Pharmacodynamic (PK/PD) Model Describing the Relationship Between Lenvatinib Exposure (AUC) and Thyroglobulin Levels |
The relationship between exposure to lenvatinib and change from baseline in thyroglobulin was planned to be analyzed using a model-based approach. |
Cycle 1 Day 1: 0.5-4 hours and 6-10 hours postdose; Cycle 1 Day 8: predose, Cycle 1 Day 15: predose, 0.5-4 hours and 6-10 hours postdose; Cycle 1 Day 22: optionally at predose; Cycle 2 Day 1: predose and 2-12 hours postdose (Cycle length=28 days) |
|
| Secondary |
Parameter Estimates From the Population Pharmacokinetic/Pharmacodynamic (PK/PD) Model Describing the Relationship Between Lenvatinib Exposure (AUC) and Thyroid-Stimulating Hormone (TSH) Levels |
The relationship between exposure to lenvatinib and change from baseline in TSH was planned to be analyzed using a model-based approach. |
Cycle 1 Day 1: 0.5-4 hours and 6-10 hours postdose; Cycle 1 Day 8: predose, Cycle 1 Day 15: predose, 0.5-4 hours and 6-10 hours postdose; Cycle 1 Day 22: optionally at predose; Cycle 2 Day 1: predose and 2-12 hours postdose (Cycle length=28 days) |
|
| Secondary |
Baseline Level Estimates From the Population PK/PD Model Describing the Relationship Between Lenvatinib Exposure (AUC) and Vascular Endothelial Growth Factor (VEGF), Soluble Tie-2, Angiopoietin-2 (Ang-2) and Fibroblast Growth Factor-23 (FGF23) Levels |
Per the planned population PK/PD analysis for this endpoint, Arms/Groups were combined and lenvatinib total plasma concentration and serum biomarker data for VEGF, Ang-2, soluble Tie-2, and FGF23 from this study were combined with data from study E7080-G000-303 (NCT01321554). The relationship between lenvatinib exposure at the time of measurement of biomarker was described using PK/PD modelling. Initially, PK/PD models were developed individually for each biomarker and then combined into a single combined model. Changes in biomarker levels over time related to lenvatinib exposure were best described by an indirect response, sigmoidal Emax model. For the final combined model, baseline level estimates were determined separately for each biomarker. The data presented are the model predicted baseline estimates, with Measure Type "Number." They are population PK/PD model predictions and have been estimated using non-linear mixed effects modelling. |
Cycle 1 Day 1: 0.5-4 hours and 6-10 hours postdose; Cycle 1 Day 8: predose, Cycle 1 Day 15: predose, 0.5-4 hours and 6-10 hours postdose; Cycle 1 Day 22: optionally at predose; Cycle 2 Day 1: predose and 2-12 hours postdose (Cycle length=28 days) |
|
| Secondary |
Mean Residence Time (MRT) Estimates From the Population PK/PD Model Describing the Relationship Between Lenvatinib Exposure (AUC) and VEGF, Soluble Tie-2, Ang-2 and FGF23 Levels |
Per the planned population PK/PD analysis for this endpoint, Arms/Groups were combined and lenvatinib total plasma concentration and serum biomarker data for VEGF, Ang-2, soluble Tie-2, and FGF23 from this study were combined with data from study E7080-G000-303 (NCT01321554). The relationship between lenvatinib exposure at the time of measurement of biomarker was described using PK/PD modelling. Initially, PK/PD models were developed individually for each biomarker and then combined into a single combined model. Changes in biomarker levels over time related to lenvatinib exposure were best described by an indirect response, sigmoidal Emax model. For the final combined model, MRT estimates were determined separately for each biomarker. The data presented are the model predicted MRT estimates, with Measure Type "Number." They are population PK/PD model predictions and have been estimated using non-linear mixed effects modelling. |
Cycle 1 Day 1: 0.5-4 hours and 6-10 hours postdose; Cycle 1 Day 8: predose, Cycle 1 Day 15: predose, 0.5-4 hours and 6-10 hours postdose; Cycle 1 Day 22: optionally at predose; Cycle 2 Day 1: predose and 2-12 hours postdose (Cycle length=28 days) |
|
| Secondary |
Hill Coefficient Estimates From the Population PK/PD Model Describing the Relationship Between Lenvatinib Exposure (AUC) and VEGF, Soluble Tie-2, Ang-2 and FGF23 Levels |
Per the planned population PK/PD analysis for this endpoint, Arms/Groups were combined and lenvatinib total plasma concentration and serum biomarker data for VEGF, Ang-2, soluble Tie-2, and FGF23 from this study were combined with data from study E7080-G000-303 (NCT01321554). The relationship between lenvatinib exposure at the time of measurement of biomarker was described using PK/PD modelling. Initially, PK/PD models were developed individually for each biomarker and then combined into a single combined model. Changes in biomarker levels over time related to lenvatinib exposure were best described by an indirect response, sigmoidal Emax model. For the final combined model, Hill Coefficient estimates were determined separately for each biomarker. The data presented are the model predicted Hill Coefficient estimates, with Measure Type "Number." They are population PK/PD model predictions and (&) have been estimated using non-linear mixed effects modelling. |
Cycle 1 Day 1: 0.5-4 hours and 6-10 hours postdose; Cycle 1 Day 8: predose, Cycle 1 Day 15: predose, 0.5-4 hours and 6-10 hours postdose; Cycle 1 Day 22: optionally at predose; Cycle 2 Day 1: predose and 2-12 hours postdose (Cycle length=28 days) |
|
| Secondary |
Parameter Estimates From the PK/PD Model for Tumor Growth Inhibition and Serum Biomarkers Tie-2 and Ang-2 |
Per the planned analysis, Arms/Groups were combined & tumor-growth inhibition models based on lenvatinib & placebo data from this study combined with study NCT01321554. Effects of tumor growth rate, drug effects, tumor resistance, & tumor size reduction related to biomarker response were assessed. Longitudinal data of sum of the longest diameter for target lesion by investigator assessment in this study & independent reviewer assessment in study NCT01321554 were used. Changes in Ang-2 & soluble Tie-2 were evaluated, individually & in combination for their impact on tumor size. The concomitant use of lenvatinib & biomarker changes due to drug effects as predictors of tumor size were also evaluated. The final integrated model for tumor growth & biomarkers included effects of lenvatinib exposure & tumor growth reduction related to Tie-2 & Ang-2 biomarkers as significant predictors. Data presented are the parameters defining this non-linear mixed effects model, with Measure Type "Number." |
Baseline up to week 120 |
|
| Secondary |
Lenvatinib Mean AUC Resulting in 50% of the Emax (EC50) Estimate From the PK/PD Model for Tumor Growth Inhibition and Serum Biomarkers Tie-2 and Ang-2 |
Per the planned analysis, Arms/Groups were combined & tumor-growth inhibition models based on lenvatinib & placebo data from this study combined with study NCT01321554. Effects of tumor growth rate, drug effects, tumor resistance, & tumor size reduction related to biomarker response were assessed. Longitudinal data of the sum of the longest diameter for target lesion by investigator assessment in this study & independent reviewer assessment in study NCT01321554 were used. Changes in Ang-2 & soluble Tie-2 were evaluated, individually & in combination for their impact on tumor size. The concomitant use of lenvatinib & biomarker changes due to drug effects as predictors of tumor size were also evaluated. The final integrated model for tumor growth & biomarkers included effects of lenvatinib exposure & tumor growth reduction related to Tie-2 & Ang-2 biomarkers as significant predictors. Data presented are EC50 estimated using non-linear mixed effects modeling, with Measure Type "Number." |
Baseline up to week 120 |
|
| Secondary |
Scale Factor Estimate for Final Parametric Time to Event PK/PD Model for PFS |
Per planned analysis, Arms/Groups were combined & PK/PD analysis for PFS was based on lenvatinib & placebo data from this study combined with NCT01321554. Relationship between lenvatinib exposure & PFS was assessed using Kaplan-Meier plots. A parametric survival model (proportional hazard model) with Weibull distribution structure was developed to estimate the probability distribution of time from study start to progression, as a function of covariates including baseline disease characteristics, demographics, lenvatinib exposure, changes in biomarker time profiles, model predicted change from baseline in tumor size & change in tumor size time-profiles. Significant (p<0.01) covariates from the univariate analysis were added to the model simultaneously & significant predictors were retained according to backward exclusion criteria (log likelihood ratio test, p-value of 0.001). Data presented are scale factor estimated using non-linear mixed effects modeling, with Measure Type "Number." |
Time from the date of randomization to the date of first documentation of PD, or date of death, whichever occurred first up to approximately 3 years 3 months |
|
| Secondary |
Shape Factor Estimate for Final Parametric Time to Event PK/PD Model for PFS |
Per planned analysis, Arms/Groups were combined & PK/PD analysis for PFS was based on lenvatinib & placebo data from this study combined with NCT01321554. Relationship between lenvatinib exposure & PFS was assessed using Kaplan-Meier plots. A parametric survival model (proportional hazard model) with Weibull distribution structure was developed to estimate the probability distribution of time from study start to progression, as a function of covariates including baseline disease characteristics, demographics, lenvatinib exposure, changes in biomarker time profiles, model predicted change from baseline in tumor size & change in tumor size time-profiles. Significant (p<0.01) covariates from the univariate analysis were added to the model simultaneously & significant predictors were retained according to backward exclusion criteria (log likelihood ratio test, p-value of 0.001). Data presented are shape factor estimated using non-linear mixed effects modeling, with Measure Type "Number." |
Time from the date of randomization to the date of first documentation of PD, or date of death, whichever occurred first up to approximately 3 years 3 months |
|
| Secondary |
Lenvatinib AUC Exposure Effect Estimate for Final Parametric Time to Event PK/PD Model for PFS |
Per planned analysis, Arms/Groups were combined & PK/PD analysis for PFS was based on lenvatinib & placebo data from this study combined with NCT01321554. Relationship between lenvatinib exposure & PFS was assessed using Kaplan-Meier plots. A parametric survival model (proportional hazard model) with Weibull distribution structure was developed to estimate probability distribution of time from study start to progression, as a function of covariates including baseline disease characteristics, demographics, lenvatinib exposure, changes in biomarker time profiles, model predicted change from baseline in tumor size & change in tumor size time-profiles. Significant (p<0.01) covariates from the univariate analysis were added to the model simultaneously & significant predictors retained according to backward exclusion criteria (log likelihood ratio test, p-value of 0.001). Data presented are AUC exposure effect estimated using non-linear mixed effects modeling, with Measure Type "Number." |
Time from the date of randomization to the date of first documentation of PD, or date of death, whichever occurred first up to approximately 3 years 3 months |
|
| Secondary |
Predicted Percent Change in Tumor Size Estimate for Final Parametric Time to Event PK/PD Model for PFS |
Per planned analysis, Arms/Groups were combined & PK/PD analysis for PFS based on lenvatinib & placebo data from this study combined with NCT01321554. Relationship between lenvatinib exposure & PFS was assessed using Kaplan-Meier plots. A parametric survival model (proportional hazard model) with Weibull distribution structure was developed to estimate probability distribution of time from study start to progression, as a function of covariates including baseline disease characteristics, demographics, lenvatinib exposure, changes in biomarker time profiles, model predicted change from baseline tumor size & change in tumor size time-profiles. Significant (p<0.01) covariates from univariate analysis were added to the model simultaneously & significant predictors retained according to backward exclusion criteria (log likelihood ratio test, p-value = 0.001). Data presented are the predicted change in tumor size estimated using non-linear mixed effects modeling, with Measure Type "Number." |
Time from the date of randomization to the date of first documentation of PD, or date of death, whichever occurred first up to approximately 3 years 3 months |
|
| Secondary |
Baseline Tumor Size Estimate for Final Parametric Time to Event PK/PD Model for PFS |
Per planned analysis Arms/Groups were combined & PK/PD analysis for PFS was based on lenvatinib & placebo data from this study combined with E7080-G000-303(NCT01321554).Relationship between lenvatinib exposure & PFS was assessed using Kaplan-Meier plots.Parametric survival model(proportional hazard model)with Weibull distribution structure was developed to estimate probability distribution of time from study start to progression as function of covariates including baseline disease characteristics,demographics,lenvatinib exposure,changes in biomarker time profiles,model predicted change from baseline tumor size & change in tumor size time-profiles.Significant(p<0.01)covariates from univariate analysis were added to model simultaneously & significant predictors retained as per backward exclusion criteria(log likelihood ratio test,p-value of 0.001).Data presented are predicted baseline tumor size from the model with Measure Type "Number"estimated using non-linear mixed effects modelling. |
Time from the date of randomization to the date of first documentation of PD, or date of death, whichever occurred first up to approximately 3 years 3 months |
|
| Secondary |
Input Rate Indirect Effect Model Estimate From Base/Final PK/PD Blood Pressure Model |
Per the planned analysis, Arms/Groups were combined and population PK/PD analysis for blood pressure was based on lenvatinib and placebo pooled data from this study combined with study E7080-G000-201 (NCT00784303) & study E7080-G000-303 (NCT01321554). The effect of lenvatinib exposure (AUC) at the time of blood pressure assessment on systolic and diastolic blood pressure was tested as a simultaneous indirect model where lenvatinib AUC was linked to the input rate of the indirect effect model by a linear slope factor function. Based on the results from model development, an indirect PK/PD model with a linear effect of lenvatinib exposure on both systolic and diastolic blood pressure was selected as the base model for subsequent univariate analysis. The data presented are the input rate indirect effect model estimate, with Measure Type "Number." They are population PK/PD model predictions and have been estimated using mixed effects non-linear modeling. |
From date of first administration of study drug up to 6 months |
|
| Secondary |
Drug Effect on Systolic and Diastolic Input Rate Estimates From Base/Final PK/PD Blood Pressure Model |
Per the planned analysis, Arms/Groups were combined and population PK/PD analysis for blood pressure was based on lenvatinib and placebo pooled data from this study combined with study E7080-G000-201 (NCT00784303) & study E7080-G000-303 (NCT01321554). The effect of lenvatinib exposure (AUC) at the time of blood pressure assessment on systolic and diastolic blood pressure was tested as a simultaneous indirect model where lenvatinib AUC was linked to the input rate of the indirect effect model by a linear slope factor function. Based on the results from model development, an indirect PK/PD model with a linear effect of lenvatinib exposure on both systolic and diastolic blood pressure was selected as the base model for subsequent univariate analysis. The data presented are the input rate indirect effect model estimate, with Measure Type "Number." They are population PK/PD model predictions and have been estimated using non-linear mixed effects modeling. |
From date of first administration of study drug up to 6 months |
|
| Secondary |
Number of Participants With Weight Decrease Stratified by AUC Quartile (Q) Group |
Per the planned analysis for this endpoint, Arms/Groups were combined and a population PK/PD analysis of the relationship between lenvatinib exposure and occurrence of the TEAE weight decreased was based on placebo and lenvatinib pooled data from this study combined with study E7080-G000-201 (NCT00784303) and study E7080-G000-303 (NCT01321554). The relationship of occurrence probability of different grades of the TEAE weight decreased and lenvatinib exposure were evaluated by logistic regression model. The logit model was of the form: sum of intercept, of lenvatinib exposure, effects of covariates were explored, and random effects were used to describe between participant variability. Lenvatinib exposure was AUC based on the dose at the time of event. For each TEAE, probabilities of having no TEAE and a CTCAE Version 4.03 Grade 1 (Mild), Grade 2 (Moderate) or Grade 3 (severe or medically significant) TEAE were estimated as a function of lenvatinib or exposure. |
Up to 3 years 3 months |
|
| Secondary |
Number of Participants With Hypertension Stratified by AUC Quartile (Q) Group |
Per the planned analysis for this endpoint, Arms/Groups were combined & a population PK/PD analysis of the relationship between lenvatinib exposure & occurrence of the TEAE hypertension was based on placebo & lenvatinib pooled data from this study combined with study E7080-G000-201 (NCT00784303) & study E7080-G000-303 (NCT01321554). The relationship of occurrence probability of different grades of the TEAE hypertension & lenvatinib exposure were evaluated by logistic regression model. The logit model was of the form: sum of intercept, of lenvatinib exposure, effects of covariates were explored, & random effects were used to describe between participant variability. Lenvatinib exposure was AUC based on the dose at the time of event. For each TEAE, probabilities of having no TEAE & a CTCAE Version 4.03 Grade 1 (Mild), Grade 2 (Moderate), Grade 3 (severe or medically significant) or Grade 4 (life-threatening consequences) TEAE were estimated as a function of lenvatinib or exposure. |
Up to 3 years 3 months |
|
| Secondary |
Number of Participants With Proteinuria Stratified by AUC Quartile (Q) Group |
Per the planned analysis for this endpoint, Arms/Groups were combined and a population PK/PD analysis of the relationship between lenvatinib exposure and occurrence of the TEAE proteinuria was based on placebo and lenvatinib pooled data from this study combined with study E7080-G000-201 (NCT00784303) and study E7080-G000-303 (NCT01321554). The relationship of occurrence probability of different grades of the TEAE proteinuria and lenvatinib exposure were evaluated by logistic regression model. The logit model was of the form: sum of intercept, of lenvatinib exposure, effects of covariates were explored, and random effects were used to describe between participant variability. Lenvatinib exposure was AUC based on the dose at the time of event. For each TEAE, probabilities of having no TEAE and a CTCAE Version 4.03 Grade 1 (Mild), Grade 2 (Moderate), or Grade 3 (severe or medically significant) TEAE were estimated as a function of lenvatinib or exposure. |
Up to 3 years 3 months |
|
| Secondary |
Number of Participants With Fatigue Stratified by AUC Quartile (Q) Group |
Per the planned analysis for this endpoint, Arms/Groups were combined and a population PK/PD analysis of the relationship between lenvatinib exposure and occurrence of the TEAE fatigue was based on placebo and lenvatinib pooled data from this study combined with study E7080-G000-201 (NCT00784303) and study E7080-G000-303 (NCT01321554). The relationship of occurrence probability of different grades of the TEAE fatigue and lenvatinib exposure were evaluated by logistic regression model. The logit model was of the form: sum of intercept, of lenvatinib exposure, effects of covariates were explored, and random effects were used to describe between participant variability. Lenvatinib exposure was AUC based on the dose at the time of event. For each TEAE, probabilities of having no TEAE and a CTCAE Version 4.03 Grade 1 (Mild), Grade 2 (Moderate) or Grade 3 (severe or medically significant) TEAE were estimated as a function of lenvatinib or exposure. |
Up to 3 years 3 months |
|
| Secondary |
Number of Participants With Diarrhea Stratified by AUC Quartile (Q) Group |
Per the planned analysis for this endpoint, Arms/Groups were combined and a population PK/PD analysis of the relationship between lenvatinib exposure and occurrence of the TEAE diarrhea was based on placebo and lenvatinib pooled data from this study combined with study E7080-G000-201 (NCT00784303) and study E7080-G000-303 (NCT01321554). The relationship of occurrence probability of different grades of the TEAE diarrhea and lenvatinib exposure were evaluated by logistic regression model. The logit model was of the form: sum of intercept, of lenvatinib exposure, effects of covariates were explored, and random effects were used to describe between participant variability. Lenvatinib exposure was AUC based on the dose at the time of event. For each TEAE, probabilities of having no TEAE and a CTCAE Version 4.03 Grade 1 (Mild), Grade 2 (Moderate) or Grade 3 (severe or medically significant) TEAE were estimated as a function of lenvatinib or exposure. |
Up to 3 years 3 months |
|
| Secondary |
Number of Participants With Nausea Stratified by AUC Quartile (Q) Group |
Per the planned analysis for this endpoint, Arms/Groups were combined and a population PK/PD analysis of the relationship between lenvatinib exposure and occurrence of the TEAE nausea was based on placebo and lenvatinib pooled data from this study combined with study E7080-G000-201 (NCT00784303) and study E7080-G000-303 (NCT01321554). The relationship of occurrence probability of different grades of the TEAE nausea and lenvatinib exposure were evaluated by logistic regression model. The logit model was of the form: sum of intercept, of lenvatinib exposure, effects of covariates were explored, and random effects were used to describe between participant variability. Lenvatinib exposure was AUC based on the dose at the time of event. For each TEAE, probabilities of having no TEAE and a CTCAE Version 4.03 Grade 1 (Mild), Grade 2 (Moderate) or Grade 3 (severe or medically significant) TEAE were estimated as a function of lenvatinib or exposure. |
Up to 3 years 3 months |
|
| Secondary |
Number of Participants With Vomiting Stratified by AUC Quartile (Q) Group |
Per the planned analysis for this endpoint, Arms/Groups were combined and a population PK/PD analysis of the relationship between lenvatinib exposure and occurrence of the TEAE vomiting was based on placebo and lenvatinib pooled data from this study combined with study E7080-G000-201 (NCT00784303) and study E7080-G000-303 (NCT01321554). The relationship of occurrence probability of different grades of the TEAE vomiting and lenvatinib exposure were evaluated by logistic regression model. The logit model was of the form: sum of intercept, of lenvatinib exposure, effects of covariates were explored, and random effects were used to describe between participant variability. Lenvatinib exposure was AUC based on the dose at the time of event. For each TEAE, probabilities of having no TEAE and a CTCAE Version 4.03 Grade 1 (Mild), Grade 2 (Moderate) or Grade 3 (severe or medically significant) TEAE were estimated as a function of lenvatinib or exposure. |
Up to 3 years 3 months |
|
| Secondary |
Change From Baseline in the Health-Related Quality of Life (HRQoL) Assessed by European Quality of Life (EuroQol) Five-Dimensional, 3-Level (EQ-5D-3L) Index Score and Visual Analogue Scale (VAS) |
The EQ-5D-3L is a health profile questionnaire assessing quality of life along 5 dimensions. Participants rate 5 dimensions of health (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression) by choosing from 3 answering options (1=no problems; 2=some problems; 3=extreme problems). The summed score ranges from 5-15 with "5" corresponding to no problems and "15" corresponding to severe problems in the 5 dimensions. EQ-5D-3L also included an EQ visual analogue scale (VAS) that ranges between 100 (best imaginable health) and 0 (worst imaginable health). Decrease from baseline in EQ-5D-3L signifies improvement. Total index EQ-5D-3L summary score was weighted with a range of -0.594 (worst) to 1.0 (best). EQ-5D-3L also included an EQ health utilities index (HUI) where 1 indicated full health while a score of 0 indicated worst health/death. |
Baseline, Week 8, 16, and 24 |
|
| Secondary |
Change From Baseline in the HRQoL Assessed by Functional Assessment of Cancer Therapy-General (FACT-G) Total Score |
The FACT-G is a 27-item questionnaire that measures the effect of cancer treatment on quality of life that has four areas of measurements (physical well-being, social/family well-being, emotional well-being and functional well-being). Each item has a 5-point scale response set (0: not at all; 1: a little bit; 2: somewhat; 3: quite a bit; and 4: very much). The FACT-G total score ranges between 0 and 108. Higher score indicates better quality of life. |
Baseline, Week 8, 16 and 24 |
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