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      string(179) "NCT02136134 - Phase 3 Study Comparing Daratumumab, Bortezomib and Dexamethasone (DVd) vs Bortezomib and Dexamethasone (Vd) in Subjects With Relapsed or Refractory Multiple Myeloma"
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  ["project_title"]=>
  string(137) "Validating Calibrated Transportation for Pre-Market Health Technology Assessment: An Individual Patient Data Analysis of the CASTOR Trial"
  ["project_narrative_summary"]=>
  string(850) "When HTA agencies decide whether to fund new therapies, they must predict real-world performance from clinical trial evidence. Trials enrol selected patients who differ from those treated routinely, creating an "efficacy-effectiveness gap" biasing cost-effectiveness predictions. We developed "Calibrated Transportation," using common comparators observed in both trial and registry settings to calibrate predictions for novel therapies. Using aggregate CASTOR data and the Australia & New Zealand Myeloma and Related Diseases Registry, we showed improved prediction accuracy for daratumumab-bortezomib-dexamethasone in Australian myeloma patients. This proposal requests individual patient data from CASTOR to validate and extend our method, enabling covariate adjustment and economic evaluation. Findings will inform global HTA decision-making."
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  string(12) "scien_public"
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  array(7) {
    ["first_name"]=>
    string(4) "Adam"
    ["last_name"]=>
    string(6) "Irving"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(17) "Monash University"
    ["email"]=>
    string(22) "adam.irving@monash.edu"
    ["state_or_province"]=>
    string(8) "Victoria"
    ["country"]=>
    string(9) "Australia"
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      ["p_pers_f_name"]=>
      string(6) "Dennis"
      ["p_pers_l_name"]=>
      string(6) "Petrie"
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      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(17) "Monash University"
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      string(19) "0000-0002-3882-2531"
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      string(5) "Laura"
      ["p_pers_l_name"]=>
      string(7) "Fanning"
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      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(17) "Monash University"
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      ["p_pers_f_name"]=>
      string(5) "Peter"
      ["p_pers_l_name"]=>
      string(7) "Ghijben"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(17) "Monash University"
      ["p_pers_scop_id"]=>
      string(19) "0000-0002-7532-7847"
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    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
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  ["property_scientific_abstract"]=>
  string(1496) "Background: HTA agencies must predict real-world effectiveness from trial evidence, yet population differences between trial and target populations can bias these predictions. We developed "Calibrated Transportation," which uses common comparator treatments to adjust for systematic population differences. Using aggregate CASTOR data, we demonstrated improved prediction accuracy for daratumumab-bortezomib-dexamethasone (DVd) in Australian multiple myeloma patients.



Objective: Validate Calibrated Transportation using individual patient data from CASTOR and assess whether covariate adjustment further improves predictions.



Study Design: Methodological research combining trial and registry data.



Participants: CASTOR trial patients (DVd: n=251; Vd: n=247) combined with 2,735 second-line multiple myeloma patients from the MRDR (2012–2019). Validation against 541 DVd patients from the MRDR (2020–2025).



Primary Outcome: Best clinical response per IMWG uniform response criteria, modelled as a six-category ordinal outcome.



Secondary Outcomes: Prediction accuracy (mean absolute error) and cost-effectiveness (incremental cost-effectiveness ratio).



Statistical Analysis: Ordered logistic regression combining trial and registry data, with bootstrap confidence intervals (500 iterations). Comparison of aggregate versus covariate-adjusted approaches, validated against held-out post-market outcomes."
  ["project_brief_bg"]=>
  string(3269) "The Efficacy-Effectiveness Gap in HTA

Drug regulatory agencies and HTA bodies worldwide face a fundamental challenge: the benefits observed in clinical trials often fail to match real-world treatment effects. This "efficacy-effectiveness gap" arises because trials enrol selected populations under controlled conditions, while real-world patients differ in age, comorbidities, prior treatments, and disease characteristics [1,2]. In oncology, strict eligibility criteria excluding patients with poor performance status or extensive prior treatment may exacerbate this gap [1]. For HTA decision-making, this gap has substantial implications. Cost-effectiveness analyses parameterised primarily with trial data implicitly assume that trial outcomes generalise to target populations—an assumption that may not hold when effect modifiers differ [3]. Inaccurate predictions can lead to inefficient resource allocation or inequitable access.



Limitations of Existing Methods

The statistical literature provides rigorous transportability methods for adjusting trial results to target populations [4–7]. However, these methods require individual patient data on the treatment of interest in both trial and target populations—a practical impossibility for pre-market HTA, where real-world data on novel treatments do not yet exist. Population adjustment methods (matching-adjusted indirect comparison, simulated treatment comparison) address cross-trial treatment comparisons but face a similar limitation: they use individual patient data from the novel treatment's trial to adjust for population differences [8–10]. These methods also rely heavily on measured covariates explaining outcome differences—an assumption that often fails in practice.



Calibrated Transportation

We developed Calibrated Transportation to address these limitations. Phase 3 trials include standard-of-care comparator arms, and these comparators have typically accumulated real-world evidence through registries. By observing how the common comparator performs differently between trial and real-world settings, we can quantify systematic population differences and calibrate predictions for the novel treatment. Using aggregate CASTOR trial data combined with the MRDR, we found that a validation model showed negligible population differences after controlling for treatment, and that Calibrated Transportation predictions for DVd aligned more closely with observed post-market outcomes than trial-based predictions.



Significance of This Proposal

Individual patient data from CASTOR will substantially strengthen this work. First, IPD allows testing whether adjusting for baseline characteristics improves predictions beyond the aggregate approach. Second, comparing aggregate and IPD approaches provides guidance for settings where IPD is unavailable. Third, detailed population comparisons between CASTOR and MRDR will improve interpretation and generalisability. This research directly addresses calls from regulators for better methods to bridge efficacy-effectiveness gaps [1]. The methodology has broad applicability beyond multiple myeloma to any setting where common comparators link trial and real-world data."
  ["project_specific_aims"]=>
  string(999) "Aim 1: Characterise population differences between trial and registry settings. We will compare baseline characteristics descriptively and use propensity score analysis to quantify population overlap.



Aim 2: Validate Calibrated Transportation using individual patient data. We will fit ordered logistic regression models combining IPD from CASTOR with MRDR registry data.



Aim 3: Assess whether covariate adjustment improves prediction accuracy.  We will compare an aggregate model (population and treatment indicators only) against a covariate-adjusted model (adding age, sex, ECOG, ISS stage, prior lines, prior treatments), assessing prediction accuracy using mean absolute error against the validation cohort.



Aim 4: Evaluate economic implications of prediction method choice. We will conduct parallel economic evaluations using the EpiMAP Myeloma discrete-event simulation model, comparing ICERs against those derived from observed MRDR DVd outcomes."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(8) "meth_res"
    ["label"]=>
    string(23) "Methodological research"
  }
  ["project_purposes"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(37) "develop_or_refine_statistical_methods"
      ["label"]=>
      string(37) "Develop or refine statistical methods"
    }
  }
  ["project_research_methods"]=>
  string(1223) "CASTOR Trial (Requested via YODA): Individual patient-level data from the CASTOR trial (NCT02136134), including baseline characteristics (age, sex, ECOG performance status, ISS staging, prior lines of therapy, prior treatments received, refractory status, response to previous treatment), treatment assignment (DVd vs Vd), best clinical response per IMWG criteria, and time-to-event outcomes (progression-free survival, time to response). Inclusion: All randomised patients in the intention-to-treat population who were evaluable for response (received at least one dose and had baseline plus at least one post-baseline disease assessment). Exclusion: Patients not evaluable for response.



Australia & New Zealand Myeloma and Related Diseases Registry (MRDR): We already hold individual patient-level data from the MRDR, a prospective population-based registry capturing multiple myeloma patients across Australia and New Zealand since 2012 (ethics: Monash University Project ID 49430). We will use a pre-funding cohort (2012–2019, n=2,735 second-line patients including n=70 receiving Vd) for model fitting and a validation cohort (2020–2025, n=541 DVd patients) for out-of-sample validation.

"
  ["project_main_outcome_measure"]=>
  string(820) "Primary: Best clinical response (BCR), assessed using IMWG uniform response criteria [14] and categorised as complete response (CR), very good partial response (VGPR), partial response (PR), minimal response (MR), stable disease (SD), or progressive disease (PD). BCR is modelled as a six-category ordinal outcome and is directly comparable between CASTOR and MRDR, as both use IMWG criteria.



Secondary: Prediction accuracy, measured as mean absolute error comparing predicted versus observed BCR distributions in the validation cohort. Cost-effectiveness metrics, including incremental cost-effectiveness ratio (ICER) for DVd versus pre-funding standard of care, quality-adjusted life years (QALYs), total healthcare costs, and probability of cost-effectiveness at standard willingness-to-pay thresholds."
  ["project_main_predictor_indep"]=>
  string(408) "Primary Independent Variable: A binary population indicator (MRDR) coded 1 for registry patients and 0 for CASTOR trial patients. This variable captures systematic differences between trial and real-world populations after controlling for treatment. A coefficient near zero supports the transportability assumption; a significant coefficient indicates systematic population differences requiring calibration."
  ["project_other_variables_interest"]=>
  string(920) "Treatment Indicators: Binary indicators for DVd and Vd, with MRDR non-Vd regimens (including Rd, Pd, Kd, and others) as the reference group. The treatment coefficients capture each regimen's effect relative to this reference, and their difference (β_DVd − β_Vd) provides a within-model relative treatment effect comparable to the trial estimate.



Baseline Covariates (Aim 3 models): Age (continuous, centred, with quadratic term), sex, time since diagnosis, ECOG performance status (categorical: 0, 1, 2+), ISS stage (I, II, III), R-ISS stage if available, prior lines of therapy (1, 2, 3+), prior bortezomib, prior IMiD therapy, prior autologous stem cell transplant, refractory status, and response to last line of therapy.



Stratification Variables: For subgroup analyses: age (<65, 65–74, ≥75), prior lines (1, 2–3, 4+), ECOG (0–1, 2+), and ISS stage (I–II, III).

"
  ["project_stat_analysis_plan"]=>
  string(2710) "Primary Analysis: Ordered Logistic Regression. The primary analysis models best clinical response (BCR) as a six-category ordinal outcome using ordered logistic regression:



logit[P(BCR ≤ k)] = αₖ - β_Pop× MRDR - β_DVd× DVd - β_Vd× Vd - γ'X

Where:

	k ∈ {1,2,3,4,5} indexes BCR category cut-points

	MRDR is a population indicator (1 = MRDR, 0 = CASTOR)

	DVd, Vd are treatment indicators

	X is a vector of baseline covariates (Aim 3 models)



The coefficient β_Pop tests for systematic population differences after controlling for treatment. Predictions for real-world DVd outcomes are generated at MRDR=1, DVd=1, combining baseline response distributions, population calibration (if β_Pop is significant), and the DVd treatment effect.



We specify two models: Model A (aggregate) includes population and treatment indicators only; Model B (covariate-adjusted) adds age, sex, ECOG, ISS, prior lines, and prior response.



Validation. We compare predicted BCR distributions against the held-out MRDR DVd validation cohort (n=541) using mean absolute error: 



MAE = (1/6) × Σⱼ |Predictedⱼ - Observedⱼ|.



Uncertainty Quantification. Bootstrap resampling (500 iterations) of MRDR patients with CASTOR held fixed, constructing 95% percentile confidence intervals for model coefficients, predicted BCR distributions, and mean absolute error.



Sensitivity Analyses. We will assess robustness through inverse probability weighting to balance populations, propensity score stratification, restriction to patients meeting CASTOR eligibility criteria, and subgroup analyses by key prognostic factors. We will also explore progression-free survival as an alternative outcome if data permit.



Economic Evaluation

We will conduct parallel economic evaluations using the EpiMAP Myeloma v2.1 discrete-event simulation model [11,12], which projects individual patient trajectories through up to 9 lines of therapy using BCR as a time-varying risk factor parameterised with 30 risk equations from MRDR data. For each prediction method (trial-based, Calibrated Transportation aggregate, Calibrated Transportation IPD-adjusted), we will input BCR predictions for DVd at line 2, simulate 10,000 patients through the complete disease course with 500 bootstrap iterations, and calculate incremental costs, QALYs, and ICERs versus pre-funding standard of care. The evaluation using observed MRDR DVd outcomes serves as the post-market benchmark. The analysis adopts an Australian healthcare system perspective with a lifetime horizon and 5% annual discounting."
  ["project_software_used"]=>
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    [0]=>
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      ["label"]=>
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  ["project_timeline"]=>
  string(597) "Data access approved and received	Month 1

Data preparation, merging, quality checks	Month 2

Descriptive analyses and population comparison	Month 3

Primary analysis (Aims 1–3): ordered logistic regression models	Months 3–4

Validation analysis: prediction accuracy assessment	Month 5

Economic evaluation (Aim 4)	Months 5–6

Sensitivity analyses	Month 7

Manuscript preparation	Months 8–9

Internal review and co-author feedback	Month 10

Manuscript submitted for publication	Month 11

Results reported to YODA Project	Month 12"
  ["project_dissemination_plan"]=>
  string(914) "The primary manuscript will present the validation of Calibrated Transportation using individual patient data, comparing prediction accuracy across methods and demonstrating economic implications. Target journals (in order of preference): Health Economics, Medical Decision Making, Value in Health, PharmacoEconomics.



Conference presentations will be submitted to the Australian Health Economics Society (AHES) and the International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Analysis code will be shared via GitHub (CHE-Monash/EpiMAP-Myeloma repository), and the YODA Project data sharing agreement will be acknowledged in all publications.



The target audience includes health economists and HTA analysts, HTA agency methodologists (NICE, PBAC, HAS, CADTH), clinical trialists designing RCTs with HTA endpoints, and outcomes researchers using registry data.

"
  ["project_bibliography"]=>
  string(3108) "

Eichler HG, Abadie E, Breckenridge A, et al. Bridging the efficacy-effectiveness gap: a regulator’s perspective on addressing variability of drug response. Nat Rev Drug Discov. 2011;10(7):495-506.
Rothwell PM. Factors that can affect the external validity of randomised controlled trials. PLoS Clin Trials. 2006;1(1):e9.
Stuart EA, Ackerman B, Westreich D. Generalizability of randomized trial results to target populations: design and analysis possibilities. Res Soc Work Pract. 2018;28(5):532-537.
Cole SR, Stuart EA. Generalizing evidence from randomized clinical trials to target populations: the ACTG 320 trial. Am J Epidemiol. 2010;172(1):107-115.
Westreich D, Edwards JK, Lesko CR, Stuart E, Cole SR. Transportability of trial results using inverse odds of sampling weights. Am J Epidemiol. 2017;186(8):1010-1014.
Dahabreh IJ, Robertson SE, Tchetgen Tchetgen EJ, Stuart EA, Hernán MA. Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals. Biometrics. 2019;75(2):685-694.
Ling AY, Montez-Rath ME, Carita P, et al. An overview of current methods for real-world applications to generalize or transport clinical trial findings to target populations of interest. Epidemiology. 2023;34(5):627-636.
Phillippo DM, Ades AE, Dias S, Palmer S, Abrams KR, Welton NJ. Methods for population-adjusted indirect comparisons in health technology appraisal. Med Decis Making. 2018;38(2):200-211.
Signorovitch JE, Sikirica V, Erder MH, et al. Matching-adjusted indirect comparisons: a new tool for timely comparative effectiveness research. Value Health. 2012;15(6):940-947.
Zhang L, Bujkiewicz S, Jackson D. Four alternative methodologies for simulated treatment comparison: How could the use of simulation be re-invigorated? Res Synth Methods. 2024;15(2):227-241.
Irving A, Petrie D, Harris A, et al. Developing and validating a discrete-event simulation model of multiple myeloma disease outcomes and treatment pathways using a national clinical registry. PLoS One. 2024;19(8):e0308812.
Irving A, Petrie D, Harris A, et al. A Post-Market Economic Evaluation of Bortezomib, Lenalidomide and Dexamethasone Versus Pre-funding Standard of Care for Newly Diagnosed Multiple Myeloma Using Registry Data. Pharmacoeconomics. 2025;43:1-13.
Palumbo A, Chanan-Khan A, Weisel K, et al. Daratumumab, bortezomib, and dexamethasone for multiple myeloma. N Engl J Med. 2016;375(8):754-766.
Durie BGM, Harousseau JL, Miguel JS, et al. International uniform response criteria for multiple myeloma. Leukemia. 2006;20(9):1467-1473.
Wang SV, Schneeweiss S, Berger ML, et al. Reporting to improve reproducibility and facilitate validity assessment for healthcare database studies V1.0. Pharmacoepidemiol Drug Saf. 2017;26(9):1018-1032.
Ghijben P, Petrie D, Zavarsek S, Chen G, Lancsar E. Healthcare funding decisions and real-world benefits: reducing bias by matching untreated patients. Pharmacoeconomics. 2021;39(7):741-756.

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    string(34) "Data-Request-Approved-Notice-1.pdf"
    ["filesize"]=>
    int(195663)
    ["url"]=>
    string(83) "https://yoda.yale.edu/wp-content/uploads/2025/12/Data-Request-Approved-Notice-1.pdf"
    ["link"]=>
    string(77) "https://yoda.yale.edu/data-request/2025-0876/data-request-approved-notice-67/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(31) "data-request-approved-notice-67"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18443)
    ["date"]=>
    string(19) "2026-02-16 20:05:16"
    ["modified"]=>
    string(19) "2026-02-16 20:05:16"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_review_link"]=>
  bool(false)
  ["project_highlight_button"]=>
  string(0) ""
  ["request_data_partner"]=>
  string(0) ""
}
data partner
array(1) {
  [0]=>
  string(0) ""
}


pi country
array(0) {
}


pi affil
array(0) {
}


products
array(0) {
}


num of trials
array(1) {
  [0]=>
  string(1) "0"
}


res
array(1) {
  [0]=>
  string(1) "3"
}