Preference of Patients and Physicians Concerning Treatment Options for Relapsed Follicular Lymphoma: A Discrete Choice Experiment.
Bibliographic record
Abstract
Abstract Abstract 4350 Background Patients with symptomatic relapsed follicular lymphoma, together with their physicians, must choose between a variety of treatment options, including conventional chemotherapy, radioimmunotherapy, and high-dose chemotherapy with subsequent autologous or allogeneic transplantation. These treatments vary with respect to effectiveness and tolerability. The purpose of this study was to elicit preference for the treatment options particular to relapsed follicular lymphoma and the attributes associated with the treatment options amongst patients in Alberta, and hematologists and medical oncologists in Canada who treat lymphoma, using a discrete choice experiment (DCE). Methods Background information and a questionnaire containing the DCE was mailed to 180 patients age 18 to 65 years and 252 hematologists and medical oncologists. The treatment attributes chosen for the DCE included: administration protocol, toxicity, average remission length and cost. For administration protocol and toxicity, there were 4 categorical levels describing the varying scenarios for standard chemotherapy (CT), radio-immunotherapy (RIT), high dose chemotherapy and autologous stem cell transplantation (AUTO), and allogeneic stem cell transplantation (ALLO). For average remission length, there were two numerical levels: 1 year (CT/RIT) and 5 years (AUTO/ALLO). For cost, four numerical levels were chosen to reflect current clinic/hospital expenditure: $5000 (CT), $25,000 (RIT), $50,000 (AUTO) and $150,000 (ALLO). A fractional factorial design was chosen to examine main effects and a fold-over technique was used to create multiple choice questions. In the series of multiple choice questions, respondents were asked to choose between two unlabeled treatment options, described according to the attributes where the attribute levels were different for each option. Descriptive statistics were applied to participant demographic and clinical data. The DCE was analyzed using a random effects logit model. Marginal rates of substitution calculated from regression coefficients provided information about preference for the treatment attributes. A post-estimation technique was used to predict uptake of the four treatment options. Results 81 patients (45%) and 48 physicians (19%) completed the questionnaire. Responding patients had a mean age of 54.7 years and were on average 4.4 years from initial diagnosis. 93% of patients received prior chemotherapy and 24% had received a prior stem cell transplant. 48% of patients had not yet relapsed and 33% were currently symptomatic. Physicians were predominantly hematologists (93%) who have been in practice on average for 12.2 years. 46% of physicians reported being in a practice that includes stem cell transplantation. Preferences of patients and physicians were similar. For all participants, remission length and the lesser toxicity of RIT compared with CT were found to be positive influences on choice (p<0.0001 and p=0.057, respectively). Negative influences on choice included toxicity of both AUTO and ALLO compared with CT (p=0.047 and p<0.0001, respectively), and cost (p<0.0001). Participants required a remission length of 0.6 years to accept the toxicity of AUTO but 3.9 years to accept the toxicity of ALLO. In relation to cost, participants thought it was acceptable for the health care system to pay $340, 000 per each 1 year increase in remission length. Post-estimation technique to evaluate distribution of preference for treatment options revealed that patients were most likely to choose AUTO (69%) and less likely to choose RIT (14%), CT (11%), and ALLO (7%). The distribution for physicians was similar: AUTO (56%), RIT (20%), CT (19%), and ALLO (4%). Conclusions This discrete choice experiment shows that patients with relapsed follicular lymphoma are able to consider the advantages and disadvantages of various treatment options, and appear to be willing to trade off the toxicity associated with autologous transplantation in order to benefit from increased remission length. Disclosures: Shafey: Glaxo-Smith-Kline: Research Funding. Stewart:Glaxo-Smith-Kline: Research Funding. Do:Roche: Honoraria; Sanofi-Aventis: Honoraria, Research Funding.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".