Frontline Rituximab Monotherapy Induction Versus a Watch and Wait Approach For Asymptomatic Advanced Stage Follicular Lymphoma: A Cost-Effectiveness Analysis
Bibliographic record
Abstract
Abstract Background Follicular lymphoma is the most common indolent lymphoma, with median survival over 10 years. A large proportion of patients present with advanced, but asymptomatic disease. Three randomized controlled trials in the last 25 years have confirmed there is no detriment to deferred chemotherapy vs. upfront treatment with respect to overall survival in such patients. However, these studies were all comparing upfront toxic regimens. Single agent rituximab (R) is associated with less side-effects, thus there are advantages to giving it upfront and significantly delaying further more toxic and expensive treatments. Recently, Ardeshna et al. (2010) compared upfront rituximab to watchful waiting in asymptomatic stage II-IV follicular lymphoma, and demonstrated significant improvement in time to initiation of next treatment (TTINT). We performed a cost-effectiveness analysis comparing the two strategies. Methods We developed a Markov decision-analytic model to compare upfront R-induction (RI) X 4 weekly doses vs. a watch and wait (WW) strategy for a hypothetical cohort of 60 year old patients newly-diagnosed with asymptomatic low burden advanced stage follicular lymphoma. The model simulates the clinical course of patients over a lifetime horizon, with the end-points of quality-adjusted life years (QALYs) and incremental cost effectiveness ratio (ICER). The baseline probabilities used in the model were derived from a systematic review of published studies. Key health states include PF1 (progression-free 1), PD1 (1st progressive disease needing treatment), R-maintenance (2-year state progression-free post-PD1 receiving R q3mo), PF2 (post 2 years of R-maintenance), PD2/3/4 (subsequent progressions requiring salvage), PF3/4/5 (subsequent PF states post-salvage), palliation and death. The probabilities of transitioning from one state to another were evaluated on a 6-month cycle. The model incorporated data on health state utilities, which were derived from the literature. Direct costs were collected from a Canadian public health payer's perspective. Resource utilization was based on guidelines, literature and expert opinion. Cost information was obtained from hospital, provincial and national costing sources, as well as the literature, and presented in 2012 Canadian dollars. Costs and effects were discounted at 5%. All patients were assumed to be treated with bendamustine and rituximab (BR) upon PD1, followed by 2 years of R-maintenance. Patients were treated with a maximum of 3 lines of salvage therapy, after which they entered palliation for maximum of 2 cycles. Results The quality-adjusted life expectancy was 6.70 QALYs for the RI strategy vs. 6.66 QALYs for the WW strategy, yielding an expected benefit from RI of 0.04 QALYs. Over a lifetime horizon, the total cost of the RI arm is $59061 and $74531 for the WW arm. The RI strategy dominates, as it is $15469 cheaper than the WW approach (Table 1). In one-way sensitivity analyses of key variables, effectiveness was sensitive to probability of PD2, PF4, age and time horizon. The model particularly favours RI in people under the age of 65. However, even at extreme end values for these variables that remain reasonable from the literature, the largest effectiveness difference in favour of WW is 0.06 QALYs (22 days) over the lifetime horizon. Thus overall the difference in either direction is minimal, demonstrating that the quality-adjusted life expectancy is essentially equivalent between the two strategies. Probabilistic sensitivity analyses (10 000 simulations) were performed. For the commonly accepted willingness to pay threshold of $50000, RI is the more cost-effective strategy 82% of the time (figure 1). Conclusions In conclusion, rituximab monotherapy as an induction strategy for asymptomatic advanced stage follicular lymphoma is the dominant strategy from a cost-utility perspective over the standard watch and wait strategy, with neutral overall effectiveness, but significant cost minimization of fifteen thousand dollars per patient over the lifetime horizon. Particularly, it is the optimal strategy in patients under the age of 65 and should be recommended. Disclosures: No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".