Outcome
| Type |
Measure |
Description |
Time frame |
Safety issue |
| Other |
ctHPVDNA detectability |
Due to the limited sample size, these analyses will be hypothesis generating and descriptive in nature. Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
At post-operative day 1 or 2 |
|
| Other |
Salivary ctHPVDNA analysis for recurrence risk and surveillance |
Salivary samples will be analyzed pre-treatment, post-op, and at the time of recurrence to determine whether salivary ctHPVDNA may further inform recurrence risk and surveillance in HPV(+) oropharyngeal squamous cell carcinoma. Due to the limited sample size, these analyses will be hypothesis generating and descriptive in nature. Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Other |
Characterization of post-operative drain fluid |
We will characterize post-operative drain fluid and compare rates of detectability to blood and saliva in order with the aim to determine whether the regional drain represents a separate regional compartment for analysis. Due to the limited sample size, these analyses will be hypothesis generating and descriptive in nature. Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Other |
Quantification of pre-treatment imaging |
We will prospectively quantify pretreatment imaging for number of involved nodes, radiographic extranodal extension as it relates to pathologic findings and risk of recurrence. Due to the limited sample size, these analyses will be hypothesis generating and descriptive in nature. Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Other |
Percentage of tumor infiltrating lymphocytes (TILs) |
We will analyze within category of low intermediate, high intermediate, and high-risk patients the percentage of TILs and association with recurrence as well as differences across treatment groups. Due to the limited sample size, these analyses will be hypothesis generating and descriptive in nature. Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Other |
Assessment of integrated vs episomal HPV |
Comparison of methods of surveillance as defined by the date of diagnosis of recurrence by ctHPVDNA testing as compared to clinical examination and imaging. Sensitivity, specificity, negative predictive value, and positive predictive value of each will be reported. Measure whether HPV is integrated vs episomal for each patient and the relationship of ctHPVDNA detectability and outcomes. These analyses will be hypothesis generating and descriptive in nature. Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Other |
Assess Molecular Markers Using FFPE |
We will investigate molecular markers on formalin-fixed paraffin-embedded (FFPE) from primary surgical specimens. Due to the limited sample size, these analyses will be hypothesis generating and descriptive in nature. Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Primary |
Progression-free survival (PFS) |
Progression-free survival (PFS), the time from treatment initiation until disease progression or worsening. PFS at specific timepoints will be estimated using Kaplan-Meier methodology. |
From registration to the first of either disease progression/recurrence or death, assessed up to 5 years |
|
| Secondary |
Progression-free survival (PFS) follow-up |
Progression-free survival (PFS) is defined as the time from treatment initiation until disease progression or worsening. Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response versus (vs.) no response), we'll use logistic regression models. |
At years 1, 2, 3, 4, and 5 |
|
| Secondary |
Disease-free survival (DFS) |
Disease-free survival (DFS) is the measure of time after treatment during which no sign of cancer is found. Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
From time of surgery and also from end of all treatment across all the groups, assessed up to 5 years |
|
| Secondary |
Overall survival (OS) |
Overall survival (OS) is defined as the duration of patient survival from the time of treatment initiation. Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
At years 1, 2, 3, 4, and 5 |
|
| Secondary |
Patient reported outcomes (PROs) |
Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Secondary |
Incidence of adverse events |
Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Secondary |
Patterns of recurrence and rate of salvage therapy for Low Intermediate Risk (GROUP 1) and multiple segment radiation therapy (MSRT) + de-escalated adjuvant radiation therapy (DART) |
Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Secondary |
Functional outcomes - modified barium swallow study (MBSS) |
Measured by modified barium swallow (MBSS). Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Secondary |
Functional outcomes - PROs |
Measure by patient reported outcomes (PROs). Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Secondary |
Proton vs photon treatment toxicity - PROs |
Assessed by PROs. Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Secondary |
Proton vs photon treatment toxicity - MBSS |
Assessed by MBSS. Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Secondary |
Proton vs photon treatment toxicity - dosimetric differences |
Assessed by dosimetric differences including to organs at risk and the primary tumor bed in the case of mucosal sparing. Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Secondary |
Costs of return visits for surveillance |
Presented descriptively and ANOVA models with tukey's adjustment for pairwise comparison will be utilized to test difference in arms. |
Up to 5 years |
|
| Secondary |
Assessment of surveillance circulating human papillomavirus deoxyribonucleic acid (ctHPVDNA) preceding clinical or radiologic detection of recurrence |
Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Secondary |
Matched analysis of patients by clinical and pathologic risk factor to MC1273 and MC1675 de-escalation arms to overall GROUP 1 and GROUP 2 cohorts including 2-year PFS |
Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Secondary |
Evaluation of return to work |
Will be assessed by by the Work Productivity and Activity Impairment Questionnaire (WPAI). Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Secondary |
Evaluation of end of treatment treatment circulating human papillomavirus deoxyribonucleic acid (ctHPVDNA) detectability as a marker of risk of progression |
Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Secondary |
Functional outcomes of DART alone vs. DART + MSRT |
Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
| Secondary |
Feasibility of DART regimen outside of Mayo Clinic |
Descriptive statistics will be summarized and the exploratory data will be correlated with clinical endpoints (PFS, time-to-progression, etc.). For time-to-event data, the Kaplan-Meier method will be used. For categorical data, we'll use the Fisher's exact test. For biomarker data used to predict binary outcomes (i.e. response vs. no response), we'll use logistic regression models. |
Up to 5 years |
|
