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Status: OngoingResearch Proposal
Project Title: Longitudinal Adverse Event Analysis using ToxT in The MAIA Study
Scientific Abstract: Background: Daratumumab, Lenalidomide, and dexamethasone is the current standard of care treatments in transplant-ineligible patients with multiple myeloma (MM). Given the continuous nature of this treatment, there is a risk of cumulative toxicity, which can lead to treatment discontinuation is a subset of patients. Currently, toxicity analysis in clinical trials focuses on high grade adverse events (AEs) and does not provide information on timing and duration of symptoms. In addition, this approach overlooks chronic lower grade toxicities, which may have significant impact on quality of life and account for premature treatment discontinuation in a subset of patients, particularly older adults. Toxicity over Time (ToxT) is an approach to AE analysis that describes AEs over time. To our knowledge, ToxT has not been previously applied to a clinical trial in MM. Objective: We propose a study to apply ToxT for analysis of toxicity and patient-reported outcomes for both treatment arms in the MAIA study. Study design and participants: We will include data from transplant-ineligible patients age >=18 years patients treated in the MAIA study with available data on toxicity. For both treatment arms, we will analyze toxicity over time for toxicities graded using CTCAE and for patient-reported outcomes using the EORTC QLQ30 tool. Primary and secondary outcome measures: the primary outcome is longitudinal toxicity over time. The secondary outcomes are: time-to-dose reduction (TTDR), time-to-treatment discontinuation (TTTD), and dose delay. Statistical analysis: We will format data using ToxT statistical package created by Dr. Amylou Deck's lab at Mayo Clinic. Time-to-event modeling and generalized linear mixed model regression analyses will be used to determine the association between time-dependent AUC and TTDR, TTTD, and treatment delay.
Brief Project Background and Statement of Project Significance:
Background:
Multiple myeloma (MM) is the second most common hematologic malignancy in the US, accounting for 2% of all cancers. Over the past two decades, there has been a rapid expansion in the therapeutic armamentarium in MM with the introduction of novel therapies, which was reflected in markedly improved survival outcomes. However, MM remains an incurable disease, and most patients will receive several lines of treatment throughout their disease course. Thus, patients may experience increased symptom burden due to cumulative side effects of treatment. Treatment-related toxicities are recognized as one of the main causes for early treatment discontinuation, particularly in older patients, which compromises the efficacy of treatment. The combination of daratumumab, lenalidomide and dexamethasone (DRD) is the current standard of care regimen in newly diagnosed patients with MM who are transplant-ineligible, which is was based on the results of the MAIA study, which demonstrated superiority over lenalidomide and dexamethasone (RD). This combination has also demonstrated tolerability and superiority over Rd in older patients, including those who are frail. However, this treatment is administered continuously until progression, which can increase the risk of cumulative toxicity. At this time, toxicity data in clinical trials is reported as the rate of adverse events (AE)s experienced during the entire trial as defined by the Common Terminology Criteria for Adverse Events (CTCAE), with an emphasis on high-grade toxicities (grade 3-4). However, this type of analysis does not provide information on the timing of onset of side effects, their duration, and severity at a given timepoint. Furthermore, this method focuses on high grade toxicities and overlooks chronic lower grade toxicities, which may have significant impact on quality of life and account for premature treatment discontinuation in a subset of patients, particularly older adults. Toxicity over Time (ToxT) is an approach to AE analysis that describes adverse events over time. This was developed by Dr. Gita Thanarajasingam and her team at Mayo Clinic. This approach has been previously applied to several clinical trials involving gastrointestinal tumors and lymphoma, providing a richer description of the side effect profiles of chemotherapy. In addition to AEs graded by CTCAE, this approach has been extrapolated to patient-reported outcomes to provide time-dependent measures of toxicity and quality of life from the patient perspective. We propose a study to apply ToxT for analysis of toxicity and patient-reported outcomes for both treatment arms in the MAIA study.
Study Rationale and significance:
A standardized, consensus approach to analyzing toxicity over time is an important unmet need in MM clinical trials. To our knowledge, ToxT has not been previously applied to a clinical trial in MM. multiple myeloma clinical trials. The current standard of care in TI patients with MM is continuous treatment given until progression. Thus, assessing toxicity over time is imperative in MM trials. As DRD is the current standard of care in TI patients, the application of ToxT in the MAIA study has the potential to provide important information that can impact practice. Assessing AEs over time can separate potentially overlapping toxicities from multi-drug regimens based on different time of presentation. Furthermore, defining the time profile of AEs can guide appropriate timing symptom control interventions. This is directly important for patients and could in turn improve their ability to continue to receive therapeutic doses of a planned treatment. Comparison of defined toxicity-related endpoints over time can become important secondary aims in MM clinical trials. Evaluation of toxicity over the time has the potential to transform the landscape of AE evaluation and improve the comprehensiveness, accuracy and patient-centeredness of oncology clinical trials.
Specific Aims of the Project:
Aim 1: To apply ToxT for analysis of toxicity using CTCAE and patient-reported outcomes using the EORTC QLQ30 for both treatment arms in the MAIA study.
Hypothesis 1: We hypothesize that longitudinal AE analysis using ToxT will provide valuable information that may be missed by conventional methods that focus on maximum grade alone.
Aim 2: To determine if time-dependent measures of toxicity from the ToxT are predictive of time-to-dose reduction (TTDR), time-to-treatment discontinuation (TTTD), and dose delay.
Hypothesis 2: We hypothesize that time-dependent measures of toxicity will predict TTDR, TTTD and dose delay.
Exploratory aim: To evaluate if the area under the curve (AUC) for longitudinal toxicity predicts TTDR, TTTD, and dose delay, better than conventional maximum grade AE evaluation.
Study Design: Individual trial analysis
What is the purpose of the analysis being proposed? Please select all that apply.: New research question to examine treatment safety
Software Used: RStudio
Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study:
Inclusion Criteria:
Patients >= 18 years
Diagnosis of newly diagnosed multiple myeloma.
Transplant ineligible patients
Patients received at least 1 dose of the study treatment in MAIA
Patients with available safety data
Exclusion criteria:
Patients non-evaluable for safety
Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study:
Time-to-dose reduction (TTDR)
Time-to-treatment discontinuation (TTTD)
Dose delay
Main Predictor/Independent Variable and how it will be categorized/defined for your study:
Incidence of adverse events graded using CTCAE
We will analyze the most common toxicities reported in the MAIA study:
Neutropenia, Anemia, Lymphopenia, Infections, Pneumonia, Diarrhea, Constipation, Fatigue, Asthenia, Peripheral edema, and Nausea.
Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: Patient-reported outcomes using EORTC QLC30 and EQ-5D-LD.
Statistical Analysis Plan:
For Aim 1, statistical summaries of the type and severity of toxicity will be constructed at each time point to identify trends over time. We will format data for use with the ToxT statistical package created by Dr. Amylou Deck's lab at Mayo Clinic, which combines multiple longitudinal methods into a readily applicable tool for toxicity analysis. We will demonstrate AE trajectory over time using stream plots depict AE trajectory over time in a format that is easily interpreted. Multiple toxicities from one regimen are depicted by cycle, and using the butterfly plot (i.e. common y-axis), mean grades of each AE between two different treatment arms are compared.
For Aim 2, time-to-event modeling and generalized linear mixed model regression analyses will be used to determine the association between time-dependent AUC and clinical outcomes (TTDR, TTTD, treatment delay, and QOL). Time-to-event models will be based on competing risk methodology (Fine-Gray regression) for TTDR, TTTD, and time to treatment delay. Competing risks will include end of treatment due to disease progression or death; end of treatment due to AEs/patient refusal; and end of treatment for all other reasons. For quality of life, linear mixed models will be used. In addition to baseline stratification factors and treatment arm, each model will use the planned cycle of assessment as the continuous time value. Unstructured covariance will initially be used, though alternative covariance structures will be investigated with the final covariance structure selected based on minimization of the Akaike information criterion. All regression models will include the baseline stratification variables and treatment arm.
Narrative Summary: At this time, toxicity analysis in clinical trials focuses on high grade toxicities and does not provide information on timing and the duration of side effects. This approach overlooks lower grade toxicities which are long lasting. These may significantly impact quality of life and lead to discontinuation of treatment in some patients. Toxicity over Time (ToxT) is an approach that describes toxicity of treatments over time. To our knowledge, ToxT has not been previously applied to a clinical trial in MM. We propose a study to apply ToxT for analyzing both toxicity and patient-reported outcomes in the MAIA study. By providing information on the timing and duration of adverse events, including low grade events, this approach can guide interventions for symptom control. This, in turn, can improve the ability of patients to continue therapeutic doses of a planned treatment.
Project Timeline: Project start date 05/30/2024. Analysis completion date: 07/30/2024. Date of first draft: 08/30/2024. Date of final manuscript draft: 10/30/2024. Date of submission for publication: 11/01/2024. Date results reported back to the YODA project: 01/30/2025.
Dissemination Plan: This study is expected to lead to an original research article. The target audience for this study is clinicians, trainees, and researchers interested in hematological disorders. Potentially suitable journals for submission include: Blood, American Journal of Hematology, Blood Cancer Journal, and Leukemia.
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