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  1. Article ; Online: A cautionary tale: an evaluation of the performance of treatment switching adjustment methods in a real world case study.

    Latimer, Nicholas R / Dewdney, Alice / Campioni, Marco

    BMC medical research methodology

    2024  Volume 24, Issue 1, Page(s) 17

    Abstract: Background: Treatment switching in randomised controlled trials (RCTs) is a problem for health technology assessment when substantial proportions of patients switch onto effective treatments that would not be available in standard clinical practice. ... ...

    Abstract Background: Treatment switching in randomised controlled trials (RCTs) is a problem for health technology assessment when substantial proportions of patients switch onto effective treatments that would not be available in standard clinical practice. Often statistical methods are used to adjust for switching: these can be applied in different ways, and performance has been assessed in simulation studies, but not in real-world case studies. We assessed the performance of adjustment methods described in National Institute for Health and Care Excellence Decision Support Unit Technical Support Document 16, applying them to an RCT comparing panitumumab to best supportive care (BSC) in colorectal cancer, in which 76% of patients randomised to BSC switched onto panitumumab. The RCT resulted in intention-to-treat hazard ratios (HR) for overall survival (OS) of 1.00 (95% confidence interval [CI] 0.82-1.22) for all patients, and 0.99 (95% CI 0.75-1.29) for patients with wild-type KRAS (Kirsten rat sarcoma virus).
    Methods: We tested several applications of inverse probability of censoring weights (IPCW), rank preserving structural failure time models (RPSFTM) and simple and complex two-stage estimation (TSE) to estimate treatment effects that would have been observed if BSC patients had not switched onto panitumumab. To assess the performance of these analyses we ascertained the true effectiveness of panitumumab based on: (i) subsequent RCTs of panitumumab that disallowed treatment switching; (ii) studies of cetuximab that disallowed treatment switching, (iii) analyses demonstrating that only patients with wild-type KRAS benefit from panitumumab. These sources suggest the true OS HR for panitumumab is 0.76-0.77 (95% CI 0.60-0.98) for all patients, and 0.55-0.73 (95% CI 0.41-0.93) for patients with wild-type KRAS.
    Results: Some applications of IPCW and TSE provided treatment effect estimates that closely matched the point-estimates and CIs of the expected truths. However, other applications produced estimates towards the boundaries of the expected truths, with some TSE applications producing estimates that lay outside the expected true confidence intervals. The RPSFTM performed relatively poorly, with all applications providing treatment effect estimates close to 1, often with extremely wide confidence intervals.
    Conclusions: Adjustment analyses may provide unreliable results. How each method is applied must be scrutinised to assess reliability.
    MeSH term(s) Humans ; Panitumumab/therapeutic use ; Proto-Oncogene Proteins p21(ras) ; Treatment Switching ; Computer Simulation ; Probability ; Randomized Controlled Trials as Topic
    Chemical Substances Panitumumab (6A901E312A) ; Proto-Oncogene Proteins p21(ras) (EC 3.6.5.2)
    Language English
    Publishing date 2024-01-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 2041362-2
    ISSN 1471-2288 ; 1471-2288
    ISSN (online) 1471-2288
    ISSN 1471-2288
    DOI 10.1186/s12874-024-02140-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments.

    Chang, Jen-Yu Amy / Chilcott, James B / Latimer, Nicholas R

    PharmacoEconomics

    2024  Volume 42, Issue 5, Page(s) 487–506

    Abstract: With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving ...

    Abstract With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving into their interplay and nuances that go beyond economic model structures. We synthesised a 'roadmap' of literature from key methodological studies, highlighting the evolution of recent advances and emerging research themes. These insights were compared against HTA guidelines to identify potential avenues for future research. Our findings reveal a spectrum of challenges in sequence evaluation, encompassing selecting appropriate decision-analytic modelling approaches and comparators, deriving appropriate clinical effectiveness evidence in the face of data scarcity, scrutinising effectiveness assumptions and statistical adjustments, considering treatment displacement, and optimising model computations. Integrating methodologies from diverse disciplines-statistics, epidemiology, causal inference, operational research and computer science-has demonstrated promise in addressing these challenges. An updated review of application studies is warranted to provide detailed insights into the extent and manner in which these methodologies have been implemented. Data scarcity on the effectiveness of treatment sequences emerged as a dominant concern, especially because treatment sequences are rarely compared in clinical trials. Real-world data (RWD) provide an alternative means for capturing evidence on effectiveness and future research should prioritise harnessing causal inference methods, particularly Target Trial Emulation, to evaluate treatment sequence effectiveness using RWD. This approach is also adaptable for analysing trials harbouring sequencing information and adjusting indirect comparisons when collating evidence from heterogeneous sources. Such investigative efforts could lend support to reviews of HTA recommendations and contribute to synthesising external control arms involving treatment sequences.
    MeSH term(s) Technology Assessment, Biomedical/methods ; Humans ; Interdisciplinary Research ; Decision Support Techniques ; Models, Economic ; Research Design
    Language English
    Publishing date 2024-04-01
    Publishing country New Zealand
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ZDB-ID 1100273-6
    ISSN 1179-2027 ; 1170-7690
    ISSN (online) 1179-2027
    ISSN 1170-7690
    DOI 10.1007/s40273-024-01363-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Extrapolation beyond the end of trials to estimate long term survival and cost effectiveness.

    Latimer, Nicholas R / Adler, Amanda I

    BMJ medicine

    2022  Volume 1, Issue 1, Page(s) e000094

    Language English
    Publishing date 2022-03-10
    Publishing country England
    Document type Journal Article
    ISSN 2754-0413
    ISSN (online) 2754-0413
    DOI 10.1136/bmjmed-2021-000094
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Adjusting for Nonadherence or Stopping Treatments in Randomized Clinical Trials.

    Adler, Amanda I / Latimer, Nicholas R

    JAMA

    2021  Volume 325, Issue 20, Page(s) 2110–2111

    MeSH term(s) Breast Neoplasms/prevention & control ; Data Interpretation, Statistical ; Diet, Fat-Restricted ; Female ; Humans ; Intention to Treat Analysis ; Patient Compliance ; Randomized Controlled Trials as Topic/statistics & numerical data
    Language English
    Publishing date 2021-05-25
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2958-0
    ISSN 1538-3598 ; 0254-9077 ; 0002-9955 ; 0098-7484
    ISSN (online) 1538-3598
    ISSN 0254-9077 ; 0002-9955 ; 0098-7484
    DOI 10.1001/jama.2021.2433
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Treatment switching in oncology trials and the acceptability of adjustment methods.

    Latimer, Nicholas R

    Expert review of pharmacoeconomics & outcomes research

    2015  Volume 15, Issue 4, Page(s) 561–564

    Abstract: Treatment switching has become an important issue in the development and approval of new drugs, particularly in oncology. Randomized controlled trials (RCTs) represent the gold standard for evaluating the effectiveness of interventions, but often ... ...

    Abstract Treatment switching has become an important issue in the development and approval of new drugs, particularly in oncology. Randomized controlled trials (RCTs) represent the gold standard for evaluating the effectiveness of interventions, but often patients randomized to the control group are permitted to switch onto the experimental treatment at some point during the trial. This is important, because standard statistical approaches used to analyze RCTs compare groups as randomized, based upon an intention-to-treat principle. When patients in both groups receive the new drug, such analyses do not provide an accurate estimate of the comparative effectiveness of the two treatments. This may lead to inappropriate decision-making - cost-effective drugs may not be approved. Limited healthcare finances may be used inefficiently. Health-related quality-of-life and lives may be lost.
    MeSH term(s) Antineoplastic Agents/administration & dosage ; Antineoplastic Agents/economics ; Antineoplastic Agents/therapeutic use ; Comparative Effectiveness Research/methods ; Cost-Benefit Analysis ; Decision Making ; Drug Design ; Humans ; Neoplasms/drug therapy ; Neoplasms/economics ; Quality of Life ; Randomized Controlled Trials as Topic/methods
    Chemical Substances Antineoplastic Agents
    Language English
    Publishing date 2015
    Publishing country England
    Document type Editorial ; Research Support, Non-U.S. Gov't
    ZDB-ID 2208481-2
    ISSN 1744-8379 ; 1473-7167
    ISSN (online) 1744-8379
    ISSN 1473-7167
    DOI 10.1586/14737167.2015.1037835
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: A Systematic Review of Methods to Incorporate External Evidence into Trial-Based Survival Extrapolations for Health Technology Assessment.

    Bullement, Ash / Stevenson, Matthew D / Baio, Gianluca / Shields, Gemma E / Latimer, Nicholas R

    Medical decision making : an international journal of the Society for Medical Decision Making

    2023  Volume 43, Issue 5, Page(s) 610–620

    Abstract: Background: External evidence is commonly used to inform survival modeling for health technology assessment (HTA). While there are a range of methodological approaches that have been proposed, it is unclear which methods could be used and how they ... ...

    Abstract Background: External evidence is commonly used to inform survival modeling for health technology assessment (HTA). While there are a range of methodological approaches that have been proposed, it is unclear which methods could be used and how they compare.
    Purpose: This review aims to identify, describe, and categorize established methods to incorporate external evidence into survival extrapolation for HTA.
    Data sources: Embase, MEDLINE, EconLit, and Web of Science databases were searched to identify published methodological studies, supplemented by hand searching and citation tracking.
    Study selection: Eligible studies were required to present a novel extrapolation approach incorporating external evidence (i.e., data or information) within survival model estimation.
    Data extraction: Studies were classified according to how the external evidence was integrated as a part of model fitting. Information was extracted concerning the model-fitting process, key requirements, assumptions, software, application contexts, and presentation of comparisons with, or validation against, other methods.
    Data synthesis: Across 18 methods identified from 22 studies, themes included use of informative prior(s) (
    Limitations: As only studies with a specific methodological objective were included, methods proposed as part of another study type (e.g., an economic evaluation) were excluded from this review.
    Conclusions: Several methods were identified in this review, with common themes based on typical data sources and analytical approaches. Of note, no evidence was found comparing the identified methods to one another, and so an assessment of different methods would be a useful area for further research.HighlightsThis review aims to identify methods that have been used to incorporate external evidence into survival extrapolations, focusing on those that may be used to inform health technology assessment.We found a range of different approaches, including piecewise methods, Bayesian methods using informative priors, and general population adjustment methods, as well as a variety of "other" approaches.No studies attempted to compare the performance of alternative methods for incorporating external evidence with respect to the accuracy of survival predictions. Further research investigating this would be valuable.
    MeSH term(s) Humans ; Technology Assessment, Biomedical ; Bayes Theorem ; Neoplasms ; Cost-Benefit Analysis
    Language English
    Publishing date 2023-04-26
    Publishing country United States
    Document type Systematic Review ; Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ZDB-ID 604497-9
    ISSN 1552-681X ; 0272-989X
    ISSN (online) 1552-681X
    ISSN 0272-989X
    DOI 10.1177/0272989X231168618
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Perils of Randomized Controlled Trial Survival Extrapolation Assuming Treatment Effect Waning: Why the Distinction Between Marginal and Conditional Estimates Matters.

    Jennings, Angus C / Rutherford, Mark J / Latimer, Nicholas R / Sweeting, Michael J / Lambert, Paul C

    Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research

    2023  Volume 27, Issue 3, Page(s) 347–355

    Abstract: Objectives: A long-term, constant, protective treatment effect is a strong assumption when extrapolating survival beyond clinical trial follow-up; hence, sensitivity to treatment effect waning is commonly assessed for economic evaluations. Forcing a ... ...

    Abstract Objectives: A long-term, constant, protective treatment effect is a strong assumption when extrapolating survival beyond clinical trial follow-up; hence, sensitivity to treatment effect waning is commonly assessed for economic evaluations. Forcing a hazard ratio (HR) to 1 does not necessarily estimate loss of individual-level treatment effect accurately because of HR selection bias. A simulation study was designed to explore the behavior of marginal HRs under a waning conditional (individual-level) treatment effect and demonstrate bias in forcing a marginal HR to 1 when the estimand is "survival difference with individual-level waning".
    Methods: Data were simulated under 4 parameter combinations (varying prognostic strength of heterogeneity and treatment effect). Time-varying marginal HRs were estimated in scenarios where the true conditional HR attenuated to 1. Restricted mean survival time differences, estimated having constrained the marginal HR to 1, were compared with true values to assess bias induced by marginal constraints.
    Results: Under loss of conditional treatment effect, the marginal HR took a value >1 because of covariate imbalances. Constraining this value to 1 lead to restricted mean survival time difference bias of up to 0.8 years (57% increase). Inflation of effect size estimates also increased with the magnitude of initial protective treatment effect.
    Conclusions: Important differences exist between survival extrapolations assuming marginal versus conditional treatment effect waning. When a marginal HR is constrained to 1 to assess efficacy under individual-level treatment effect waning, the survival benefits associated with the new treatment will be overestimated, and incremental cost-effectiveness ratios will be underestimated.
    MeSH term(s) Humans ; Proportional Hazards Models ; Randomized Controlled Trials as Topic
    Language English
    Publishing date 2023-12-26
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1471745-1
    ISSN 1524-4733 ; 1098-3015
    ISSN (online) 1524-4733
    ISSN 1098-3015
    DOI 10.1016/j.jval.2023.12.008
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Evaluation of Flexible Parametric Relative Survival Approaches for Enforcing Long-Term Constraints When Extrapolating All-Cause Survival.

    Lee, Sangyu / Lambert, Paul C / Sweeting, Michael J / Latimer, Nicholas R / Rutherford, Mark J

    Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research

    2023  Volume 27, Issue 1, Page(s) 51–60

    Abstract: Objectives: Parametric models are used to estimate the lifetime benefit of an intervention beyond the range of trial follow-up. Recent recommendations have suggested more flexible survival approaches and the use of external data when extrapolating. Both ...

    Abstract Objectives: Parametric models are used to estimate the lifetime benefit of an intervention beyond the range of trial follow-up. Recent recommendations have suggested more flexible survival approaches and the use of external data when extrapolating. Both of these can be realized by using flexible parametric relative survival modeling. The overall aim of this article is to introduce and contrast various approaches for applying constraints on the long-term disease-related (excess) mortality including cure models and evaluate the consequent implications for extrapolation.
    Methods: We describe flexible parametric relative survival modeling approaches. We then introduce various options for constraining the long-term excess mortality and compare the performance of each method in simulated data. These methods include fitting a standard flexible parametric relative survival model, enforcing statistical cure, and forcing the long-term excess mortality to converge to a constant. We simulate various scenarios, including where statistical cure is reasonable and where the long-term excess mortality persists.
    Results: The compared approaches showed similar survival fits within the follow-up period. However, when extrapolating the all-cause survival beyond trial follow-up, there is variation depending on the assumption made about the long-term excess mortality. Altering the time point from which the excess mortality is constrained enables further flexibility.
    Conclusions: The various constraints can lead to applying explicit assumptions when extrapolating, which could lead to more plausible survival extrapolations. The inclusion of general population mortality directly into the model-building process, which is possible for all considered approaches, should be adopted more widely in survival extrapolation in health technology assessment.
    MeSH term(s) Humans ; Survival Analysis
    Language English
    Publishing date 2023-10-17
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1471745-1
    ISSN 1524-4733 ; 1098-3015
    ISSN (online) 1524-4733
    ISSN 1098-3015
    DOI 10.1016/j.jval.2023.10.003
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Estimation of Transition Probabilities for State-Transition Models: A Review of NICE Appraisals.

    Srivastava, Tushar / Latimer, Nicholas R / Tappenden, Paul

    PharmacoEconomics

    2021  Volume 39, Issue 8, Page(s) 869–878

    Abstract: State transition models are used to inform health technology reimbursement decisions. Within state transition models, the movement of patients between the model health states over discrete time intervals is determined by transition probabilities (TPs). ... ...

    Abstract State transition models are used to inform health technology reimbursement decisions. Within state transition models, the movement of patients between the model health states over discrete time intervals is determined by transition probabilities (TPs). Estimating TPs presents numerous issues, including missing data for specific transitions, data incongruence and uncertainty around extrapolation. Inappropriately estimated TPs could result in biased models. There is limited guidance on how to address common issues associated with TP estimation. To assess current methods for estimating TPs and to identify issues that may introduce bias, we reviewed National Institute for Health and Care Excellence Technology Appraisals published from 1 January, 2019 to 27 May, 2020. Twenty-eight models (from 26 Technology Appraisals) were included in the review. Several methods for estimating TPs were identified: survival analysis (n = 11); count method (n = 9); multi-state modelling (n = 7); logistic regression (n = 2); negative binomial regression (n = 2); Poisson regression (n = 1); and calibration (n = 1). Evidence Review Groups identified several issues relating to TP estimation within these models, including important transitions being excluded (n = 5); potential selection bias when estimating TPs for post-randomisation health states (n = 2); issues concerning the use of multiple data sources (n = 4); potential biases resulting from the use of data from different populations (n = 2), and inappropriate assumptions around extrapolation (n = 3). These issues remained unresolved in almost every instance. Failing to address these issues may bias model results and lead to sub-optimal decision making. Further research is recommended to address these methodological problems.
    MeSH term(s) Cost-Benefit Analysis ; Humans ; Probability ; Survival Analysis ; Technology Assessment, Biomedical ; Uncertainty
    Language English
    Publishing date 2021-05-19
    Publishing country New Zealand
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 1100273-6
    ISSN 1179-2027 ; 1170-7690
    ISSN (online) 1179-2027
    ISSN 1170-7690
    DOI 10.1007/s40273-021-01034-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Not cost-effective at zero price: valuing and paying for combination therapies in cancer.

    Latimer, Nicholas R / Towse, Adrian / Henshall, Chris

    Expert review of pharmacoeconomics & outcomes research

    2021  Volume 21, Issue 3, Page(s) 331–333

    MeSH term(s) Antineoplastic Combined Chemotherapy Protocols/administration & dosage ; Antineoplastic Combined Chemotherapy Protocols/economics ; Cost-Benefit Analysis ; Drug Costs ; Humans ; Neoplasms/drug therapy ; Neoplasms/economics ; Technology Assessment, Biomedical
    Language English
    Publishing date 2021-02-09
    Publishing country England
    Document type Editorial
    ZDB-ID 2208481-2
    ISSN 1744-8379 ; 1473-7167
    ISSN (online) 1744-8379
    ISSN 1473-7167
    DOI 10.1080/14737167.2021.1879644
    Database MEDical Literature Analysis and Retrieval System OnLINE

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