A Systematic Literature Review of Predictors of Erythropoiesis-Stimulating Agent Failure in Lower-Risk Myelodysplastic Syndromes.

Erythropoiesis-stimulating agents (ESAs) are the first-line treatment option for anemia in patients with lower-risk myelodysplastic syndromes (LR-MDS). A systematic literature review was conducted to identify evidence of the association between prognostic factors and ESA response/failure in LR-MDS. MEDLINE, Embase, and relevant conferences were searched systematically for studies assessing the association between prognostic factors and ESA response/failure in adult patients. Of 1566 citations identified, 38 were included. Patient risk status in studies published from 2000 onwards was commonly assessed using the International Prognostic Scoring System (IPSS) or revised IPSS. ESA response was generally assessed using the International Working Group MDS criteria. Among the included studies, statistically significant relationships were found, in both univariate and multivariate analyses, between ESA response and the following prognostic factors: higher hemoglobin levels, lower serum erythropoietin levels, and transfusion independence. Furthermore, other prognostic factors such as age, bone marrow blasts, serum ferritin level, IPSS risk status, and karyotype status did not demonstrate statistically significant relationships with ESA response. This systematic literature review has confirmed prognostic factors of ESA response/failure. Guidance to correctly identify patients with these characteristics could be helpful for clinicians to provide optimal treatment.

Implementing Multilevel Network Meta-Regression for Time-To-Event Outcomes: A Case Study in Relapsed Refractory Multiple Myeloma.

OBJECTIVES: Multilevel network meta-regression (ML-NMR) leverages individual patient data (IPD) and aggregate data from a network of randomized controlled trials (RCTs) to assess the comparative efficacy of multiple treatments, while adjusting for between-study differences. We provide an overview of ML-NMR for time-to-event outcomes and apply it to an illustrative case study, including example R code. METHODS: The case study evaluated the comparative efficacy of idecabtagene vicleucel (ide-cel), selinexor+dexamethasone (Sd), belantamab mafodotin (BM), and conventional care (CC) for patients with triple-class exposed relapsed/refractory multiple myeloma in terms of overall survival. Single-arm clinical trials and real-world data were naively combined to create an aggregate data artificial RCT (aRCT) (MAMMOTH-CC versus DREAMM-2-BM versus STORM-2-Sd) and an IPD aRCT (KarMMa-ide-cel versus KarMMa-RW-CC). With some assumptions, we incorporated continuous covariates with skewed distributions, reported as median and range. The ML-NMR models adjusted for number of prior lines, triple-class refractory status, and age and were compared using the leave-one-out information criterion. We summarized predicted hazard ratios and survival (95% credible intervals) in the IPD aRCT population. RESULTS: The Weibull ML-NMR model had the lowest leave-one-out information criterion. Ide-cel was more efficacious than Sd, BM, and CC in terms of overall survival. Effect modifiers had minimal impact on the model, and only triple-class refractory was a prognostic factor. CONCLUSIONS: We demonstrate an application of ML-NMR for time-to-event outcomes and introduce code that can be used to aid implementation. Given its benefits, we encourage practitioners to utilize ML-NMR when population adjustment is necessary for comparisons of multiple treatments.

Artificial Intelligence to Automate Health Economic Modelling: A Case Study to Evaluate the Potential Application of Large Language Models.

BACKGROUND: Current generation large language models (LLMs) such as Generative Pre-Trained Transformer 4 (GPT-4) have achieved human-level performance on many tasks including the generation of computer code based on textual input. This study aimed to assess whether GPT-4 could be used to automatically programme two published health economic analyses. METHODS: The two analyses were partitioned survival models evaluating interventions in non-small cell lung cancer (NSCLC) and renal cell carcinoma (RCC). We developed prompts which instructed GPT-4 to programme the NSCLC and RCC models in R, and which provided descriptions of each model's methods, assumptions and parameter values. The results of the generated scripts were compared to the published values from the original, human-programmed models. The models were replicated 15 times to capture variability in GPT-4's output. RESULTS: GPT-4 fully replicated the NSCLC model with high accuracy: 100% (15/15) of the artificial intelligence (AI)-generated NSCLC models were error-free or contained a single minor error, and 93% (14/15) were completely error-free. GPT-4 closely replicated the RCC model, although human intervention was required to simplify an element of the model design (one of the model's fifteen input calculations) because it used too many sequential steps to be implemented in a single prompt. With this simplification, 87% (13/15) of the AI-generated RCC models were error-free or contained a single minor error, and 60% (9/15) were completely error-free. Error-free model scripts replicated the published incremental cost-effectiveness ratios to within 1%. CONCLUSION: This study provides a promising indication that GPT-4 can have practical applications in the automation of health economic model construction. Potential benefits include accelerated model development timelines and reduced costs of development. Further research is necessary to explore the generalisability of LLM-based automation across a larger sample of models.

Artificial Intelligence to Automate Network Meta-Analyses: Four Case Studies to Evaluate the Potential Application of Large Language Models.

BACKGROUND: The emergence of artificial intelligence, capable of human-level performance on some tasks, presents an opportunity to revolutionise development of systematic reviews and network meta-analyses (NMAs). In this pilot study, we aim to assess use of a large-language model (LLM, Generative Pre-trained Transformer 4 [GPT-4]) to automatically extract data from publications, write an R script to conduct an NMA and interpret the results. METHODS: We considered four case studies involving binary and time-to-event outcomes in two disease areas, for which an NMA had previously been conducted manually. For each case study, a Python script was developed that communicated with the LLM via application programming interface (API) calls. The LLM was prompted to extract relevant data from publications, to create an R script to be used to run the NMA and then to produce a small report describing the analysis. RESULTS: The LLM had a > 99% success rate of accurately extracting data across 20 runs for each case study and could generate R scripts that could be run end-to-end without human input. It also produced good quality reports describing the disease area, analysis conducted, results obtained and a correct interpretation of the results. CONCLUSIONS: This study provides a promising indication of the feasibility of using current generation LLMs to automate data extraction, code generation and NMA result interpretation, which could result in significant time savings and reduce human error. This is provided that routine technical checks are performed, as recommend for human-conducted analyses. Whilst not currently 100% consistent, LLMs are likely to improve with time.

Retrospective Comparison of Survival Projections for CAR T-Cell Therapies in Large B-Cell Lymphoma.

BACKGROUND: Durable remission has been observed in patients with relapsed or refractory (R/R) large B-cell lymphoma (LBCL) treated with chimeric antigen receptor (CAR) T-cell therapy. Consequently, hazard functions for overall survival (OS) are often complex, requiring the use of flexible methods for extrapolations. OBJECTIVES: We aimed to retrospectively compare the predictive accuracy of different survival extrapolation methods and evaluate the validity of goodness-of-fit (GOF) criteria-based model selection for CAR T-cell therapies in R/R LBCL. METHODS: OS data were sourced from JULIET, ZUMA-1, and TRANSCEND NHL 001. Standard parametric, mixture cure, cubic spline, and mixture models were fit to multiple database locks (DBLs), with varying follow-up durations. GOF was assessed using the Akaike information criterion and Bayesian information criterion. Predictive accuracy was calculated as the mean absolute error (MAE) relative to OS observed in the most mature DBL. RESULTS: For all studies, mixture cure and cubic spline models provided the best predictive accuracy for the least mature DBL (MAE 0.013‒0.085 and 0.014‒0.128, respectively). The predictive accuracy of the standard parametric and mixture models showed larger variation (MAE 0.024‒0.162 and 0.013‒0.176, respectively). With increasing data maturity, the predictive accuracy of standard parametric models remained poor. Correlation between GOF criteria and predictive accuracy was low, particularly for the least mature DBL. CONCLUSIONS: Our analyses demonstrated that mixture cure and cubic spline models provide the most accurate survival extrapolations of CAR T-cell therapies in LBCL. Furthermore, GOF should not be the only criteria used when selecting the optimal survival model.

Challenges in conducting fractional polynomial and standard parametric network meta-analyses of immune checkpoint inhibitors for first-line advanced renal cell carcinoma.

Aim: Network meta-analyses (NMAs) increasingly feature time-varying hazards to account for non-proportional hazards between different drug classes. This paper outlines an algorithm for selecting clinically plausible fractional polynomial NMA models. Methods: The NMA of four immune checkpoint inhibitors (ICIs) + tyrosine kinase inhibitors (TKIs) and one TKI therapy for renal cell carcinoma (RCC) served as case study. Overall survival (OS) and progression free survival (PFS) data were reconstructed from the literature, 46 models were fitted. The algorithm entailed a-priori face validity criteria for survival and hazards, based on clinical expert input, and predictive accuracy against trial data. Selected models were compared with statistically best-fitting models. Results: Three valid PFS and two OS models were identified. All models overestimated PFS, the OS model featured crossing ICI + TKI versus TKI curves as per expert opinion. Conventionally selected models showed implausible survival. Conclusion: The selection algorithm considering face validity, predictive accuracy, and expert opinion improved the clinical plausibility of first-line RCC survival models.

Cost-Effectiveness of Nivolumab Plus Ipilimumab for the First-Line Treatment of Intermediate/Poor-Risk Advanced and/or Metastatic Renal Cell Carcinoma in Switzerland.

OBJECTIVE: This study assessed the cost-effectiveness of nivolumab plus ipilimumab versus both sunitinib and pazopanib for the treatment of first-line unresectable advanced renal cell carcinoma (aRCC) from a healthcare system perspective in Switzerland. METHODS: A three-state partitioned survival model, consisting of progression-free, progressed disease, and death, was constructed. Efficacy estimates were based on data from the CheckMate 214 trial (NCT02231749) with a minimum follow-up of 42 months. Two Swiss oncologists were consulted to determine disease management resource use. Costs were derived from the Swiss tariff lists for outpatient (TARMED Online Browser 1.09) and inpatient (2020 data from Swiss diagnosis-related groups) treatments. Drug acquisition costs (ex-factory prices) were obtained from the March 2020 price list published by the Swiss Federal Office of Public Health. Treatment-specific EQ-5D-3L-based utilities were derived from CheckMate 214 using a French value set as a proxy for Switzerland. The model utilized a 1-week cycle length and a 40-year time horizon, with costs and effects discounted by 3.0% per annum. One-way sensitivity analyses, probabilistic analysis, and scenario analyses assessed the robustness of the results. RESULTS: Nivolumab plus ipilimumab yielded incremental 1.43 life-years and 1.36 lifetime discounted quality-adjusted life-years (QALYs) relative to sunitinib and pazopanib at an additional cost of 147,453 Swiss Francs (CHF) and CHF145,643, respectively. With an incremental cost-utility ratio of CHF108,326 per QALY gained versus sunitinib, and CHF106,996 per QALY gained versus pazopanib, the nivolumab plus ipilimumab combination can be considered a cost-effective option for the treatment of patients with aRCC in Switzerland, with a willingness-to-pay threshold of CHF200,000. Sensitivity and scenario analyses confirmed the robustness of the deterministic results. CONCLUSIONS: This study showed that nivolumab plus ipilimumab, which represents one of the standard-of-care first-line treatments for intermediate- or poor-risk aRCC patients, is a life-extending and cost-effective treatment option for patients in Switzerland.

A Guide to Selecting Flexible Survival Models to Inform Economic Evaluations of Cancer Immunotherapies.

OBJECTIVES: Parametric models are routinely used to estimate the benefit of cancer drugs beyond trial follow-up. The advent of immune checkpoint inhibitors has challenged this paradigm, and emerging evidence suggests that more flexible survival models, which can better capture the shapes of complex hazard functions, might be needed for these interventions. Nevertheless, there is a need for an algorithm to help analysts decide whether flexible models are required and, if so, which should be chosen for testing. This position article has been produced to bridge this gap. METHODS: A virtual advisory board comprising 7 international experts with in-depth knowledge of survival analysis and health technology assessment was held in summer 2021. The experts discussed 24 questions across 6 topics: the current survival model selection procedure, data maturity, heterogeneity of treatment effect, cure and mortality, external evidence, and additions to existing guidelines. Their responses culminated in an algorithm to inform selection of flexible survival models. RESULTS: The algorithm consists of 8 steps and 4 questions. Key elements include the systematic identification of relevant external data, using clinical expert input at multiple points in the selection process, considering the future and the observed hazard functions, assessing the potential for long-term survivorship, and presenting results from all plausible models. CONCLUSIONS: This algorithm provides a systematic, evidence-based approach to justify the selection of survival extrapolation models for cancer immunotherapies. If followed, it should reduce the risk of selecting inappropriate models, partially addressing a key area of uncertainty in the economic evaluation of these agents.

Estimating and Extrapolating Survival Using a State-Transition Modeling Approach: A Practical Application in Multiple Myeloma.

OBJECTIVES: State-transition models (STMs) applied in oncology have given limited considerations to modeling postprogression survival data. This study presents an application of an STM focusing on methods to evaluate the postprogression transition and its impact on survival predictions. METHODS: Data from the lenalidomide plus dexamethasone arm of the ASPIRE trial was used to estimate transition rates for an STM. The model accounted for the competing risk between the progression and preprogression death events and included an explicit structural link between the time to progression and subsequent death. The modeled transition rates were used to simulate individual disease trajectories in a discrete event simulation framework, based on which progression-free survival and overall survival over a 30-year time horizon were estimated. Survival predictions were compared with the observed trial data, matched external data, and estimates obtained from a more conventional partitioned survival analysis approach. RESULTS: The rates of progression and preprogression death were modeled using piecewise exponential functions. The rate of postprogression mortality was modeled using an exponential function accounting for the nonlinear effect of the time to progression. The STM provided survival estimates that closely fitted the trial data and gave more plausible long-term survival predictions than the best-fitting Weibull model applied in a partitioned survival analysis. CONCLUSIONS: The fit of the STM suggested that the modeled transition rates accurately captured the underlying disease process over the modeled time horizon. The considerations of this study may apply to other settings and facilitate a wider use of STMs in oncology.

What Did Time Tell Us? A Comparison and Retrospective Validation of Different Survival Extrapolation Methods for Immuno-Oncologic Therapy in Advanced or Metastatic Renal Cell Carcinoma.

BACKGROUND: The immuno-oncologic (IO) mechanism of action may lead to an overall survival (OS) hazard that changes over time, producing shapes that standard parametric extrapolation methods may struggle to reflect. Furthermore, selection of the most appropriate extrapolation method for health technology assessment is often based on trial data with limited follow-up. OBJECTIVE: To examine this problem, we fitted a range of extrapolation methods to patient-level survival data from CheckMate 025 (NCT01668784, CM-025), a phase III trial comparing nivolumab with everolimus for previously treated advanced renal cell carcinoma (aRCC), to assess their predictive accuracy over time. METHODS: Six extrapolation methods were examined: standard parametric models, natural cubic splines, piecewise models combining Kaplan-Meier data with an exponential or non-exponential distribution, response-based landmark models, and parametric mixture models. We produced three database locks (DBLs) at minimum follow-ups of 15, 27, and 39 months to align with previously published CM-025 data. A three-step evaluation process was adopted: (1) selection of the distribution family for each method in each of the three DBLs, (2) internal validation comparing extrapolation-based landmark and mean survival with the latest CM-025 dataset (minimum follow-up, 64 months), and (3) external validation of survival projections using clinical expert opinion and long-term follow-up data from other nivolumab studies in aRCC (CheckMate 003 and CheckMate 010). RESULTS: All extrapolation methods, with the exception of mixture models, underestimated landmark and mean OS for nivolumab compared with CM-025 long-term follow-up data. OS estimates for everolimus tended to be more accurate, with four of the six methods providing landmark OS estimates within the 95% confidence interval of observed OS as per the latest dataset. The predictive accuracy of survival extrapolation methods fitted to nivolumab also showed greater variation than for everolimus. The proportional hazards assumption held for all DBLs, and a dependent log-logistic model provided reliable estimates of longer-term survival for both nivolumab and everolimus across the DBLs. Although mixture models and response-based landmark models provided reasonable estimates of OS based on the 39-month DBL, this was not the case for the two earlier DBLs. The piecewise exponential models consistently underestimated OS for both nivolumab and everolimus at clinically meaningful pre-specified landmark time points. CONCLUSIONS: This aRCC case study identified marked differences in the predictive accuracy of survival extrapolation methods for nivolumab but less so for everolimus. The dependent log-logistic model did not suffer from overfitting to early DBLs to the same extent as more complex methods. Methods that provide more degrees of freedom may accurately represent survival for IO therapy, particularly if data are more mature or external data are available to inform the long-term extrapolations.

Effectiveness and success factors of educational inhaler technique interventions in asthma & COPD patients: a systematic review.

With the current wealth of new inhalers available and insurance policy driven inhaler switching, the need for insights in optimal education on inhaler use is more evident than ever. We aimed to systematically review educational inhalation technique interventions, to assess their overall effectiveness, and identify main drivers of success. Medline, Embase and CINAHL databases were searched for randomised controlled trials on educational inhalation technique interventions. Inclusion eligibility, quality appraisal (Cochrane's risk of bias tool) and data extraction were performed by two independent reviewers. Regression analyses were performed to identify characteristics contributing to inhaler technique improvement. Thirty-seven of the 39 interventions included (95%) indicated statistically significant improvement of inhaler technique. However, average follow-up time was relatively short (5 months), 28% lacked clinical relevant endpoints and all lacked cost-effectiveness estimates. Poor initial technique, number of inhalation procedure steps, setting (outpatient clinics performing best), and time elapsed since intervention (all, p < 0.05), were shown to have an impact on effectiveness of the intervention, explaining up to 91% of the effectiveness variation. Other factors, such as disease (asthma vs. chronic obstructive pulmonary disease), education group size (individual vs. group training) and inhaler type (dry powder inhalers vs. pressurised metered dose inhalers) did not play a significant role. Notably, there was a trend (p = 0.06) towards interventions in adults being more effective than those in children and the intervention effect seemed to wane over time. In conclusion, educational interventions to improve inhaler technique are effective on the short-term. Periodical intervention reinforcement and longer follow-up studies, including clinical relevant endpoints and cost-effectiveness, are recommended.

At the heart of the problem - A person-centred, developmental perspective on the link between alcohol consumption and cardio-vascular events.

INTRODUCTION: The cardio-protective effect of alcohol has been the subject of a long-standing scientific controversy. Emerging evidence remains equivocal, as the validity of the dose-dependent J-shape association is tainted by conceptual, theoretical and methodological problems. A major impediment for a resolution on the matter is the lack of a life-long developmental approach to pinpoint alcohol's specific impact on the risk for cardio-vascular events (CVE). OBJECTIVE: Using retrospective and prospective individual-level data of alcohol consumption (AC) we applied a model-based clustering technique to uncover life-course trajectories of AC and explored their links to CVE. METHODS: Data stemmed from a random sub-cohort of a large-scale, longitudinal study conducted in the Netherlands (N=2288). Group Based Trajectory Model (GBTM) was applied to extract distinct progressions of AC over time. Stratified by sex, the association between the developmental trajectories and CVE was examined with multiple logistic regression models, with adjustment for traditional risk factors. RESULTS: GBTM analysis laid bare the heterogeneity of AC dynamics over the life-course, reiterating sex differences in drinking habits and CVE risk. AC temporal behaviors during adolescence and adulthood were diverse, but showed relative stability in in middle-age and elderly years. For males, adjusted odds for CVE differed among the uncovered developmental classes. CONCLUSIONS: The findings elicited supportive evidence for a J-shape, but with a new twist. Besides moderation the results indicate that onset, timing, duration and stability of AC over the life-course are major aspects to be accounted for when attempting to elucidate alcohol's cardio-vascular role.

Introducing the fit-criteria assessment plot - A visualisation tool to assist class enumeration in group-based trajectory modelling.

Background and objective Group-based trajectory modelling is a model-based clustering technique applied for the identification of latent patterns of temporal changes. Despite its manifold applications in clinical and health sciences, potential problems of the model selection procedure are often overlooked. The choice of the number of latent trajectories (class-enumeration), for instance, is to a large degree based on statistical criteria that are not fail-safe. Moreover, the process as a whole is not transparent. To facilitate class enumeration, we introduce a graphical summary display of several fit and model adequacy criteria, the fit-criteria assessment plot. Methods An R-code that accepts universal data input is presented. The programme condenses relevant group-based trajectory modelling output information of model fit indices in automated graphical displays. Examples based on real and simulated data are provided to illustrate, assess and validate fit-criteria assessment plot's utility. Results Fit-criteria assessment plot provides an overview of fit criteria on a single page, placing users in an informed position to make a decision. Fit-criteria assessment plot does not automatically select the most appropriate model but eases the model assessment procedure. Conclusions Fit-criteria assessment plot is an exploratory, visualisation tool that can be employed to assist decisions in the initial and decisive phase of group-based trajectory modelling analysis. Considering group-based trajectory modelling's widespread resonance in medical and epidemiological sciences, a more comprehensive, easily interpretable and transparent display of the iterative process of class enumeration may foster group-based trajectory modelling's adequate use.