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      string(258) "NCT02407236 - A Phase 3, Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter Protocol to Evaluate the Safety and Efficacy of Ustekinumab Induction and Maintenance Therapy in Subjects With Moderately to Severely Active Ulcerative Colitis"
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      string(242) "https://dev-yoda.pantheonsite.io/clinical-trial/nct02407236-a-phase-3-randomized-double-blind-placebo-controlled-parallel-group-multicenter-protocol-to-evaluate-the-safety-and-efficacy-of-ustekinumab-induction-and-maintenance-therapy-in-subj/"
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  ["project_title"]=>
  string(73) "The Impact of Anchoring on MAIC Variance: Lessons from Ulcerative Colitis"
  ["project_narrative_summary"]=>
  string(802) "Matching-adjusted indirect comparisons (MAICs) compare the effectiveness of two treatments when no head-to-head trial exists, using patient-level data from one treatment and published data from the other. MAICs can be anchored (with a shared control like placebo) or unanchored (no common comparator). Anchored MAICs are more reliable and require fewer assumptions, but unanchored MAICs are still widely used, especially in scientfic meetings. This project compares the precision of estimates from anchored vs unanchored MAIC. Ulcerative colitis trials provide a useful case, as they are similarly designed and use placebo. Using patient-level data from the UNIFI study for ustekinumab and summary data for other treatments, we will assess inferential differences between anchored and unanchored MAICs."
  ["project_learn_source"]=>
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  array(7) {
    ["first_name"]=>
    string(4) "Bret"
    ["last_name"]=>
    string(6) "Zeldow"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(22) "Genesis Research Group"
    ["email"]=>
    string(25) "Bret.Zeldow@genesisrg.com"
    ["state_or_province"]=>
    string(2) "ME"
    ["country"]=>
    string(3) "USA"
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    ["label"]=>
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  ["property_scientific_abstract"]=>
  string(1626) "Background: Matching-adjusted indirect comparisons (MAICs) are used to compare treatment effectiveness between two treatments in the absence of a head-to-head trial. Individual patient data is available for one treatment, but only summary statistics are available for the other. MAICs can be anchored or unanchored depending on whether the trials share a common comparator.



Objective: To assess differences in precision (e.g., variance) resulting from anchored and unanchored comparisons using the same data.



Study Design: This study applies anchored and unanchored MAIC to clinical trial data and published summary statistics to compare resulting precision.



Participants: This study will include patients with moderately to severely active ulcerative colitis (UC). Individual patient data will be used from the induction portion of the UNIFI study, which compares ustekinumab against placebo, both administered intravenously. Summary statistics from studies examining other UC treatments will comprise the comparator arms.



Primary and Secondary Outcome Measures: The primary outcome will be clinical response to induction treatment; the secondary outcome will be clinical remission to induction treatment.



Statistical Analysis: Individual patient data from UNIFI are weighed to match published summary statistics using logistic regression and method of moments. Anchored comparisons include both ustekinumab and placebo; unanchored will exclude placebo. Point estimates and 95% confidence intervals for risk differences and odds ratios are reported. "
  ["project_brief_bg"]=>
  string(3253) "Matching-adjusted indirect comparisons (MAICs) are widely used to estimate the relative effectiveness of treatments when head-to-head trials are unavailable (Signorovitch et al. 2012). MAIC leverages individual patient data (IPD) from one trial and adjusts it to match the aggregate baseline characteristics reported in another, enabling indirect treatment comparisons. This method is particularly useful where therapies emerge rapidly and direct comparisons are infeasible.



There are two main forms of MAIC: anchored and unanchored. Anchored MAICs rely on a common comparator arm (e.g., placebo or standard of care) across trials, while unanchored MAICs do not. Anchored approaches require weaker assumptions; they require no imbalance of unobserved effect modifiers between the trial population. However, unanchored MAICs require no imbalance in effect modifiers and prognostic factors (Phillippo et al. 2018). Thus, anchored MAICs require effect modifiers be balanced between studies; unanchored MAICs require both effect modifiers and prognostic factors be balanced, a much stronger assumption.



The limitations of unanchored MAICs are well-established. Hatswell et al. (2020) showed unanchored MAIC can yield unbiased and accurate estimates under ideal conditions, but validity is highly sensitive to unmet assumptions. Jiang and Ni (2020) found unanchored MAIC can produce unbiased estimates for time-to-event outcomes if all prognostic factors and effect modifiers are properly accounted for. However, even small omissions can introduce bias and compromise confidence interval coverage. Park et al. (2024) noted the strong assumption required for unanchored MAIC. Ren et al. (2024) reported unanchored MAICs suffer when prognostic factors are omitted and suggested unanchored simulated treatment comparisons as an alternative. Despite these limitations, unanchored MAICs remain common in early health technology assessments, conference abstracts, and in settings with sparse data or evolving standards of care. However, validity is often unclear. Notably, unanchored MAICs often yield narrower confidence intervals than anchored counterparts, even though they do not use information from a comparator arm and rely on more stringent assumptions. This apparent gain in precision can be misleading and should not be mistaken for increased confidence.



Ulcerative colitis (UC) offers a convenient context in which to explore this gain in precision. Induction trials for UC typically have similar eligibility criteria and include placebo arms, allowing both anchored and unanchored MAICs from a shared source. The trial of ustekinumab induction and maintenance therapy included a placebo arm, making it suitable for indirect comparison with other approved therapies. I will use UNIFI IPD to conduct both anchored and unanchored MAICs against published trial summaries, evaluating differences in point estimates and interval widths.



By directly comparing anchored and unanchored MAICs under controlled conditions, this study aims to caution researchers and policymakers regarding results from unanchored MAICs. I aim to promote more transparency and rigor in comparative effectiveness research."
  ["project_specific_aims"]=>
  string(1124) "The aim of this project is to compare results from anchored and unanchored matching-adjusted indirect comparisons (MAICs) using the same underlying data sources. Both methods will be applied to individual patient data from UNIFI and published summary statistics from comparator trials. The objective is to assess how methodological choice influences treatment effect estimates, with a particular focus on precision (e.g., confidence interval width).



In practice, analysts often have no choice but to use unanchored MAICs when no shared comparator exists. However, it remains important to understand the risks and limitations of this approach to avoid false confidence in the results.



I hypothesize that unanchored MAICs, despite relying on stronger assumptions and omitting a comparator arm, will produce much narrower confidence intervals than anchored MAICs. This raises concerns about the interpretability of unanchored results. By applying both methods to the same dataset, this study aims to quantify inferential differences and support more thoughtful use of unanchored MAICs in research."
  ["project_study_design"]=>
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    ["value"]=>
    string(5) "other"
    ["label"]=>
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  ["project_study_design_exp"]=>
  string(168) "The study will use individual patient data from UNIFI to compare to published summary statistics from several other trials using matching-adjusted indirect comparisons."
  ["project_purposes"]=>
  array(1) {
    [0]=>
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      ["value"]=>
      string(37) "develop_or_refine_statistical_methods"
      ["label"]=>
      string(37) "Develop or refine statistical methods"
    }
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  ["project_research_methods"]=>
  string(868) "The source of the individual patient data will be from UNIFI. Patients who were randomized to either the placebo arm or the ~6mg/kg ustekinumab arm during the induction phase will be included.



Summary statistics from additional trials will be compiled from relevant publications as cited herein. No additional eligibility criteria can be applied to these patients. These include studies for adalimumab (W. Sandborn, Van Assche, and Reinisch 2013), vedolizumab (Feagan et al. 2013), tofacitinib (W. J. Sandborn et al. 2017), upadacitinib (W. J. Sandborn et al. 2020), ozanimod (W. J. Sandborn et al. 2021), etrasimod (W. J. Sandborn et al. 2023), and mirikizumab (D’Haens et al. 2023).  Summary statistics will be extracted from the published manuscripts and manually entered into R for analysis. No raw datasets will be uploaded from external sources."
  ["project_main_outcome_measure"]=>
  string(172) "The primary outcome for this study will be clinical response to induction treatment. The secondary outcome for this study will be clinical remission to induction treatment."
  ["project_main_predictor_indep"]=>
  string(1208) "The primary independent variable will be induction treatment. For individual patient data (IPD), the treatment of interest is ustekinumab, administered intravenously during the induction phase of the UNIFI trial. For comparator treatments, the independent variable will be based on published summary-level data from clinical trials evaluating other induction therapies for moderately to severely active ulcerative colitis.

Comparator treatments may include adalimumab (W. Sandborn, Van Assche, and Reinisch 2013), vedolizumab (Feagan et al. 2013), tofacitinib (W. J. Sandborn et al. 2017), upadacitinib (W. J. Sandborn et al. 2020), ozanimod (W. J. Sandborn et al. 2021), etrasimod (W. J. Sandborn et al. 2023), and mirikizumab (D’Haens et al. 2023), depending on feasibility and data availability. All comparators will be included only if trials report relevant baseline characteristics and outcomes.

Treatment status (e.g., ustekinumab vs. comparator) will be modeled as a binary variable in both anchored and unanchored MAICs. This will allow us to assess how inclusion or exclusion of a shared placebo comparator affects estimates of treatment effect for clinical response and remission."
  ["project_other_variables_interest"]=>
  string(1515) "Other variables of interest will include baseline demographic and clinical characteristics used to characterize the study sample and for covariate adjustment in the MAIC. These will be selected based on availability in the UNIFI individual patient data and in the published summary data for each comparator treatment.

The following candidate covariates are based on the summary statistics reported in Feagan et al. (2013):

    • Age

    • Sex

    • Race (e.g., White vs. non-White)

    • Body weight

    • Duration of ulcerative colitis

    • History of TNF antagonist failure (≥1 or ≥2 failures)

    • Prior exposure to immunosuppressants or corticosteroids

    • Disease location (e.g., left-sided, extensive colitis, or pancolitis)

    • Baseline disease severity (e.g., Mayo Clinic Score, Partial Mayo Score, IBDQ Score)

    • Baseline biomarker levels (e.g., fecal calprotectin, hemoglobin, white blood cell count)

    • Concomitant medications at baseline (e.g., glucocorticoids, immunosuppressants, or both)

    • Smoking status

The final list of covariates included in the MAIC will be determined based on overlap between the IPD and the summary-level data for each comparator, and selected using clinical relevance and availability of summary statistics. Variables will be used for weighting in the MAIC to match the aggregate characteristics of the comparator trial populations."
  ["project_stat_analysis_plan"]=>
  string(1762) "The analysis will use matching-adjusted indirect comparison (MAIC) methodology to compare the effectiveness of ustekinumab versus other induction treatments for moderately to severely active ulcerative colitis (UC). The analysis will include both anchored MAICs, which incorporate a shared placebo comparator, and unanchored MAICs, which compare treatments directly in the absence of a common comparator.

Patients in the ustekinumab arm of the UNIFI trial will be reweighted to match the published baseline characteristics from each comparator trial. Weights will be derived using logistic regression to estimate a propensity score that predicts trial membership, with coefficients estimated via the method of moments. The selection of covariates will depend on overlap in available baseline characteristics between UNIFI and each comparator trial.

For each comparator, I will estimate:

    • Risk differences and odds ratios for binary outcomes (clinical response and clinical remission)

    • 95% confidence intervals using the adjusted bootstrap percentile (BCa) method with 1,000 replications

The analysis will be repeated separately for anchored and unanchored MAICs:

    • In anchored MAICs, both the ustekinumab and placebo arms from UNIFI will be retained. The placebo-adjusted treatment effect will then be compared against the comparator trial’s published treatment effect.

    • In unanchored MAICs, only the ustekinumab arm will be retained, and outcomes will be compared directly with the comparator arm from the published data.

Descriptive statistics will be reported for all baseline covariates before and after weighting, including standardized mean differences to assess balance."
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  ["project_timeline"]=>
  string(282) "I anticipate the study to commence once we have access to the data, ideally July 2025. Analysis should be complete by the end of October 2025. I expect to draft and submit a manuscript by the end of 2025. I can report results back to the YODA Project around that time or early 2026."
  ["project_dissemination_plan"]=>
  string(406) "I plan on writing a single manuscript and submitting it to a suitable journal. The target journal is Statistical Methods in Medical Research. Depending on timing, the research may be submitted as an abstract to a statistics-focused conference such as the Joint Statistical Meetings (JSM) or an industry-focused conference such as The Professional Society for Health Economics and Outcomes Research (ISPOR)."
  ["project_bibliography"]=>
  string(5717) "
 D’Haens, Geert, Marla Dubinsky, Taku Kobayashi, Peter M. Irving, Stefanie Howaldt, Juris Pokrotnieks, Kathryn Krueger, et al. 2023. “Mirikizumab as Induction and Maintenance Therapy for Ulcerative Colitis.” New England Journal of Medicine 388 (26): 2444–55. https://doi.org/10.1056/NEJMoa2207940.
 Feagan, Brian G., Paul Rutgeerts, Bruce E. Sands, Stephen Hanauer, Jean-Frédéric Colombel, William J. Sandborn, Gert Van Assche, et al. 2013. “Vedolizumab as Induction and Maintenance Therapy for Ulcerative Colitis.” New England Journal of Medicine 369 (8): 699–710. https://doi.org/10.1056/NEJMoa1215734.
 Gros, Beatriz, and Gilaad G Kaplan. 2023. “Ulcerative Colitis in Adults: A Review.” Jama 330 (10): 951–65.
 Hatswell, Anthony James, Nick Freemantle, and Gianluca Baio. 2020. “The Effects of Model Misspecification in Unanchored Matching-Adjusted Indirect Comparison: Results of a Simulation Study.” Value in Health 23 (6): 751–59.
 Jiang, Yawen, and Weiyi Ni. 2020. “Performance of Unanchored Matching-Adjusted Indirect Comparison (MAIC) for the Evidence Synthesis of Single-Arm Trials with Time-to-Event Outcomes.” BMC Medical Research Methodology 20 (1): 241. https://doi.org/10.1186/s12874-020-01124-6.
 Park, Julie E., Harlan Campbell, Kevin Towle, Yong Yuan, Jeroen P. Jansen, David Phillippo, and Shannon Cope. 2024. “Unanchored Population-Adjusted Indirect Comparison Methods for Time-to-Event Outcomes Using Inverse Odds Weighting, Regression Adjustment, and Doubly Robust Methods with Either Individual Patient or Aggregate Data.” Value in Health 27 (3): 278–86.
 Phillippo, David M, Anthony E Ades, Sofia Dias, Stephen Palmer, Keith R Abrams, and Nicky J Welton. 2018. “Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal.” Medical Decision Making 38 (2): 200–211.
 Ren, Shijie, Sa Ren, Nicky J. Welton, and Mark Strong. 2024. “Advancing Unanchored Simulated Treatment Comparisons: A Novel Implementation and Simulation Study.” Research Synthesis Methods 15 (4): 657–70.
 Sandborn, William J., Brian G. Feagan, Geert D’Haens, Douglas C. Wolf, Igor Jovanovic, Stephen B. Hanauer, Subrata Ghosh, et al. 2021. “Ozanimod as Induction and Maintenance Therapy for Ulcerative Colitis.” The New England Journal of Medicine 385 (14): 1280–91. https://doi.org/10.1056/NEJMoa2033617.
 Sandborn, William J., Subrata Ghosh, Julian Panes, Stefan Schreiber, Geert D’Haens, Satoshi Tanida, Jesse Siffledeen, Jeffrey Enejosa, Wen Zhou, and Ahmed A. Othman. 2020. “Efficacy of Upadacitinib in a Randomized Trial of Patients with Active Ulcerative Colitis.” Gastroenterology 158 (8): 2139–2149. e14.
 Sandborn, William J., Chinyu Su, Bruce E. Sands, Geert R. D’Haens, Séverine Vermeire, Stefan Schreiber, Silvio Danese, et al. 2017. “Tofacitinib as Induction and Maintenance Therapy for Ulcerative Colitis.” New England Journal of Medicine 376 (18): 1723–36. https://doi.org/10.1056/NEJMoa1606910.
 Sandborn, William J., Séverine Vermeire, Laurent Peyrin-Biroulet, Marla C. Dubinsky, Julian Panes, Andres Yarur, Timothy Ritter, et al. 2023. “Etrasimod as Induction and Maintenance Therapy for Ulcerative Colitis (ELEVATE): Two Randomised, Double-Blind, Placebo-Controlled, Phase 3 Studies.” The Lancet 401 (10383): 1159–71. https://doi.org/10.1016/S0140-6736(23)00061-2.
 Sandborn, W., G. Van Assche, and W. Reinisch. 2013. “Adalimumab in the Treatment of Moderate-to-Severe Ulcerative Colitis: ULTRA 2 Trial Results.” Gastroenterol Hepatol (NY) 9 (5): 317–20.
 Sands, Bruce E., William J. Sandborn, Remo Panaccione, Christopher D. O’Brien, Hongyan Zhang, Jewel Johanns, Omoniyi J. Adedokun, Katherine Li, Laurent Peyrin-Biroulet, and Gert Van Assche. 2019. “Ustekinumab as Induction and Maintenance Therapy for Ulcerative Colitis.” New England Journal of Medicine 381 (13): 1201–14.
 Signorovitch, James E., Vanja Sikirica, M. Haim Erder, Jipan Xie, Mei Lu, Paul S. Hodgkins, Keith A. Betts, and Eric Q. Wu. 2012. “Matching-Adjusted Indirect Comparisons: A New Tool for Timely Comparative Effectiveness Research.” Value in Health 15 (6): 940–47. https://doi.org/10.1016/j.jval.2012.05.004.
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  [0]=>
  string(1) "0"
}


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