The Health Inequality Impact of a New Cancer Therapy Given Treatment and Disease Characteristics.

OBJECTIVES: This study aimed to perform a simulation study to quantify the health inequality impact of a cancer therapy given cancer and treatment characteristics using the distributional cost-effectiveness framework. METHODS: The following factors were varied in 10 000 simulations: lifetime risk of the disease, median overall survival (OS) with standard of care (SOC), difference in OS between non-Hispanic (NH)-Black and NH-White patients (prognostic effect), treatment effect of the new therapy relative to SOC, whether the treatment effect differs between NH-Black and NH-White patients (effect modification), health utility, drug costs, and preprogression and postprogression costs. Based on these characteristics, the incremental population net health benefits were calculated for the new therapy and applied to a US distribution of quality-adjusted life expectancy at birth. The health inequality impact was quantified as the difference in the degree of inequality in the "post-new therapy" versus "pre-new therapy" quality-adjusted life expectancy distributions. RESULTS: For cancer types characterized by relatively large lifetime risk, large median OS with SOC, large treatment effect, and large effect modification, the direction of the impact of the new therapy on inequality is easy to predict. When effect modification is minor or absent, which is a realistic scenario, the direction of the inequality impact is difficult to predict. Larger incremental drug costs have a worsening effect on health inequality. CONCLUSIONS: The findings provide a guide to help decision makers and other stakeholders make an initial assessment whether a new therapy with known treatment effects for a specific tumor type can have a positive or negative health inequality impact.

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.

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.

OBJECTIVES: Several methods for unanchored population-adjusted indirect comparisons (PAICs) are available. Exploring alternative adjustment methods, depending on the available individual patient data (IPD) and the aggregate data (AD) in the external study, may help minimize bias in unanchored indirect comparisons. However, methods for time-to-event outcomes are not well understood. This study provides an overview and comparison of methods using a case study to increase familiarity. A recent method is applied to marginalize conditional hazard ratios, which allows for the comparisons of methods, and a doubly robust method is proposed. METHODS: The following PAIC methods were compared through a case study in third-line small cell lung cancer, comparing nivolumab with standard of care based on a single-arm phase II trial (CheckMate 032) and real-world study (Flatiron) in terms of overall survival: IPD-IPD analyses using inverse odds weighting, regression adjustment, and a doubly robust method; IPD-AD analyses using matching-adjusted indirect comparison, simulated treatment comparison, and a doubly robust method. RESULTS: Nivolumab extended survival versus standard of care with hazard ratios ranging from 0.63 (95% CI 0.44-0.90) in naive comparisons (identical estimates for IPD-IPD and IPD-AD analyses) to 0.69 (95% CI 0.44-0.98) in the IPD-IPD analyses using regression adjustment. Regression-based and doubly robust estimates yielded slightly wider confidence intervals versus the propensity score-based analyses. CONCLUSIONS: The proposed doubly robust approach for time-to-event outcomes may help to minimize bias due to model misspecification. However, all methods for unanchored PAIC rely on the strong assumption that all prognostic covariates have been included.

Multi-state network meta-analysis of progression and survival data.

Multiple randomized controlled trials, each comparing a subset of competing interventions, can be synthesized by means of a network meta-analysis to estimate relative treatment effects between all interventions in the evidence base. Here we focus on estimating relative treatment effects for time-to-event outcomes. Cancer treatment effectiveness is frequently quantified by analyzing overall survival (OS) and progression-free survival (PFS). We introduce a method for the joint network meta-analysis of PFS and OS that is based on a time-inhomogeneous tri-state (stable, progression, and death) Markov model where time-varying transition rates and relative treatment effects are modeled with parametric survival functions or fractional polynomials. The data needed to run these analyses can be extracted directly from published survival curves. We demonstrate use by applying the methodology to a network of trials for the treatment of non-small-cell lung cancer. The proposed approach allows the joint synthesis of OS and PFS, relaxes the proportional hazards assumption, extends to a network of more than two treatments, and simplifies the parameterization of decision and cost-effectiveness analyses.

The Health Inequality Impact of Liquid Biopsy to Inform First-Line Treatment of Advanced Non-Small Cell Lung Cancer: A Distributional Cost-Effectiveness Analysis.

OBJECTIVES: To perform a distributional cost-effectiveness analysis of liquid biopsy (LB) followed by, if needed, tissue biopsy (TB) (LB-first strategy) relative to a TB-only strategy to inform first-line treatment of advanced non-small cell lung cancer (aNSCLC) from a US payer perspective by which we quantify the impact of LB-first on population health inequality according to race and ethnicity. METHODS: With a health economic model, quality-adjusted life-years (QALYs) and costs per patient were estimated for each subgroup. Given the lifetime risk of aNSCLC, and assuming equally distributed opportunity costs, the incremental net health benefits of LB-first were calculated, which were used to estimate general population quality-adjusted life expectancy at birth (QALE) by race and ethnicity with and without LB-first. The degree of QALYs and QALE differences with the strategies was expressed with inequality indices. Their differences were defined as the inequality impact of LB-first. RESULTS: LB-first resulted in an additional 0.21 (95% uncertainty interval: 0.07-0.39) QALYs among treated patients, with the greatest gain observed among Asian patients (0.31 QALYs [0.09-0.61]). LB-first resulted in an increase in relative inequality in QALYs among patients, but a minor decrease in relative inequality in QALE. CONCLUSIONS: LB-first to inform first-line aNSCLC therapy can improve health outcomes. With current diagnostic performance, the benefit is the greatest among Asian patients, thereby potentially widening racial and ethnic differences in survival among patients with aNSCLC. Assuming equally distributed opportunity costs and access, LB-first does not worsen and, in fact, may reduce inequality in general population health according to race and ethnicity.

A Comparison of Alternative Network Meta-Analysis Methods in the Presence of Nonproportional Hazards: A Case Study in First-Line Advanced or Metastatic Renal Cell Carcinoma.

OBJECTIVES: Network meta-analysis (NMA) of time-to-event outcomes based on constant hazard ratios can result in biased findings when the proportional hazards (PHs) assumption does not hold in a subset of trials. We aimed to summarize the published non-PH NMA methods for time-to-event outcomes, demonstrate their application, and compare their results. METHODS: The following non-PH NMA methods were compared through an illustrative case study in oncology of 4 randomized controlled trials in terms of progression-free survival and overall survival: (1) 1-step or (2) 2-step multivariate NMAs based on traditional survival distributions or fractional polynomials, (3) NMAs with restricted cubic splines for baseline hazard, and (4) restricted mean survival NMA. RESULTS: For progression-free survival, the PH assumption did not hold across trials and non-PH NMA methods better reflected the relative treatment effects over time. The most flexible models (fractional polynomials and restricted cubic splines) fit better to the data than the other approaches. Estimated hazard ratios obtained with different non-PH NMA methods were similar at 5 years of follow-up but differed thereafter in the extrapolations. Although there was no strong evidence of PH violation for overall survival, non-PH NMA methods captured this uncertainty in the relative treatment effects over time. CONCLUSIONS: When the PH assumption is questionable in a subset of the randomized controlled trials, we recommend assessing alternative non-PH NMA methods to estimate relative treatment effects for time-to-event outcomes. We propose a transparent and explicit stepwise model selection process considering model fit, external constraints, and clinical validity. Given inherent uncertainty, sensitivity analyses are suggested.

Comparative effectiveness and safety of pharmaceuticals assessed in observational studies compared with randomized controlled trials.

BACKGROUND: There have been ongoing efforts to understand when and how data from observational studies can be applied to clinical and regulatory decision making. The objective of this review was to assess the comparability of relative treatment effects of pharmaceuticals from observational studies and randomized controlled trials (RCTs). METHODS: We searched PubMed and Embase for systematic literature reviews published between January 1, 1990, and January 31, 2020, that reported relative treatment effects of pharmaceuticals from both observational studies and RCTs. We extracted pooled relative effect estimates from observational studies and RCTs for each outcome, intervention-comparator, or indication assessed in the reviews. We calculated the ratio of the relative effect estimate from observational studies over that from RCTs, along with the corresponding 95% confidence interval (CI) for each pair of pooled RCT and observational study estimates, and we evaluated the consistency in relative treatment effects. RESULTS: Thirty systematic reviews across 7 therapeutic areas were identified from the literature. We analyzed 74 pairs of pooled relative effect estimates from RCTs and observational studies from 29 reviews. There was no statistically significant difference (based on the 95% CI) in relative effect estimates between RCTs and observational studies in 79.7% of pairs. There was an extreme difference (ratio < 0.7 or > 1.43) in 43.2% of pairs, and, in 17.6% of pairs, there was a significant difference and the estimates pointed in opposite directions. CONCLUSIONS: Overall, our review shows that while there is no significant difference in the relative risk ratios between the majority of RCTs and observational studies compared, there is significant variation in about 20% of comparisons. The source of this variation should be the subject of further inquiry to elucidate how much of the variation is due to differences in patient populations versus biased estimates arising from issues with study design or analytical/statistical methods.

Comparing Efficacy, Safety, and Preinfusion Period of Axicabtagene Ciloleucel versus Tisagenlecleucel in Relapsed/Refractory Large B Cell Lymphoma.

Axicabtagene ciloleucel (axi-cel) and tisagenlecleucel (tisa-cel) are autologous anti-CD19 chimeric antigen receptor T (CAR T) cell therapies for the treatment of patients with relapsed/refractory large B cell lymphoma (RR-LBCL). Both can induce durable responses; however, cross-trial comparisons are difficult due to differences in study design. In this study, the registration trials of axi-cel and tisa-cel were compared using a matching adjusted indirect comparison (MAIC). A MAIC was performed to adjust for differences in patient characteristics between trials. The estimates for the ZUMA-1 (axi-cel) trial were adjusted using patient-level data to match the study population in JULIET (tisa-cel) for key variables: International Prognostic Index), Eastern Cooperative Oncology Group score, stage, refractoriness or relapsed disease, double/triple hit status, cell of origin, and number of prior lines of therapy. The endpoints analyzed were response, overall survival (OS), and adverse events. After adjusting for differences in patient characteristics between trials, axi-cel was associated with a greater objective response rate (relative risk [RR]=1.61; 95% confidence interval [CI], 1.29 to 2.01) and complete response (RR = 1.62; 95% CI, 1.16 to 2.27) than tisa-cel among patients who underwent infusion. The OS from infusion onward comparing axi-cel to tisa-cel had a hazard ratio of 0.51 (95% CI, 0.31 to 0.83). The indirect comparison showed a higher rate of grade 1 to 2 cytokine release syndrome (CRS) in ZUMA-1 compared with JULIET (RR = 2.03; 95% CI, 1.55 to 2.65) and similar rates of grade ≥3 CRS and neurologic events. In the absence of a direct head-to-head study, the MAIC statistical technique suggests axi-cel may have superior efficacy but a greater risk of grade 1 to 2 CRS. Future real-world studies can further inform the relative efficacy and safety of CAR T therapies in RR-LBCL.

The economic burden of kidney graft failure in the United States.

Despite improvements in outcomes for kidney transplant recipients in the past decade, graft failure continues to impose substantial burden on patients. However, the population-wide economic burden of graft failure has not been quantified. This study aims to fill that gap by comparing outcomes from a simulation model of kidney transplant patients in which patients are at risk for graft failure with an alternative simulation in which the risk of graft failure is assumed to be zero. Transitions through the model were estimated using Scientific Registry of Transplant Recipients data from 1987 to 2017. We estimated lifetime costs, overall survival, and quality-adjusted life-years (QALYs) for both scenarios and calculated the difference between them to obtain the burden of graft failure. We find that for the average patient, graft failure will impose additional medical costs of $78 079 (95% confidence interval [CI] $41 074, $112 409) and a loss of 1.66 QALYs (95% CI 1.15, 2.18). Given 17 644 kidney transplants in 2017, the total incremental lifetime medical costs associated with graft failure is $1.38B (95% CI $725M, $1.98B) and the total QALY loss is 29 289 (95% CI 20 291, 38 464). Efforts to reduce the incidence of graft failure or to mitigate its impact are urgently needed.

Incorporating single-arm evidence into a network meta-analysis using aggregate level matching: Assessing the impact.

Increasingly, single-armed evidence is included in health technology assessment submissions when companies are seeking reimbursement for new drugs. While it is recognized that randomized controlled trials provide a higher standard of evidence, these are not available for many new agents that have been granted licenses in recent years. Therefore, it is important to examine whether alternative strategies for assessing this evidence may be used. In this work, we examine approaches to incorporating single-armed evidence formally in the evaluation process. We consider matching aggregate level covariates to comparator arms or trials and including this evidence in a network meta-analysis. We consider two methods of matching: (i) we include the chosen matched arm in the data set itself as a comparator for the single-arm trial; (ii) we use the baseline odds of an event in a chosen matched trial to use as a plug-in estimator for the single-arm trial. We illustrate that the synthesis of evidence resulting from such a setup is sensitive to the between-study variability, formulation of the prior for the between-design effect, weight given to the single-arm evidence, and extent of the bias in single-armed evidence. We provide a flowchart for the process involved in such a synthesis and highlight additional sensitivity analyses that should be carried out. This work was motivated by a hepatitis C data set, where many agents have only been examined in single-arm studies. We present the results of our methods applied to this data set.

R You Still Using Excel? The Advantages of Modern Software Tools for Health Technology Assessment.

Economic models are used in health technology assessments (HTAs) to evaluate the cost-effectiveness of competing medical technologies and inform the efficient use of healthcare resources. Historically, these models have been developed with specialized commercial software (such as TreeAge) or more commonly with spreadsheet software (almost always Microsoft Excel). Although these tools may be sufficient for relatively simple analyses, they put unnecessary constraints on the analysis that may ultimately limit its credibility and relevance. In contrast, modern programming languages such as R, Python, Matlab, and Julia facilitate the development of models that are (i) clinically realistic, (ii) capable of quantifying decision uncertainty, (iii) transparent and reproducible, and (iv) reusable and adaptable. An HTA environment that encourages use of modern software can therefore help ensure that coverage and pricing decisions confer greatest possible benefit and capture all scientific uncertainty, thus enabling correct prioritization of future research.

Integrating expert opinion with clinical trial data to extrapolate long-term survival: a case study of CAR-T therapy for children and young adults with relapsed or refractory acute lymphoblastic leukemia.

BACKGROUND: Long-term clinical outcomes are necessary to assess the cost-effectiveness of new treatments over a lifetime horizon. Without long-term clinical trial data, current practice to extrapolate survival beyond the trial period involves fitting alternative parametric models to the observed survival. Choosing the most appropriate model is based on how well each model fits to the observed data. Supplementing trial data with feedback from experts may improve the plausibility of survival extrapolations. We demonstrate the feasibility of formally integrating long-term survival estimates from experts with empirical clinical trial data to provide more credible extrapolated survival curves. METHODS: The case study involved relapsed or refractory B-cell pediatric and young adult acute lymphoblastic leukemia (r/r pALL) regarding long-term survival for tisagenlecleucel (chimeric antigen receptor T-cell [CAR-T]) with evidence from the phase II ELIANA trial. Seven pediatric oncologists and hematologists experienced with CAR-T therapies were recruited. Relevant evidence regarding r/r pALL and tisagenlecleucel provided a common basis for expert judgments. Survival rates and related uncertainty at 2, 3, 4, and 5 years were elicited from experts using a web-based application adapted from Sheffield Elicitation Framework. Estimates from each expert were combined with observed data using time-to-event parametric models that accounted for experts' uncertainty, producing an overall distribution of survival over time. These results were validated based on longer term follow-up (median duration 24.2 months) from ELIANA following the elicitation. RESULTS: Extrapolated survival curves based on ELIANA trial without expert information were highly uncertain, differing substantially depending on the model choice. Survival estimates between 2 to 5 years from individual experts varied with a fair amount of uncertainty. However, incorporating expert estimates improved the precision in the extrapolated survival curves. Predictions from a Gompertz model, which experts believed was most appropriate, suggested that more than half of the ELIANA patients treated with tisagenlecleucel will survive up to 5 years. Expert estimates at 24 months were validated by longer follow-up. CONCLUSIONS: This study provides an example of how expert opinion can be elicited and synthesized with observed survival data using a transparent and formal procedure, capturing expert uncertainty, and ensuring projected long-term survival is clinically plausible.

Treatment modifying factors of biologics for psoriatic arthritis: a systematic review and Bayesian meta-regression.

OBJECTIVES: The aim of this study was to explore factors that modify treatment effects of non-conventional biologics versus placebo in patients with psoriatic arthritis. METHODS: A systematic literature review and meta-regression was conducted. The biologics included etanercept, infliximab, adalimumab, golimumab, certolizumab, ustekinumab, tocilizumab, anakinra, abatacept, rituximab, and secukinumab. Outcomes included American College of Rheumatology (ACR) 20 and 50, Psoriasis Area Severity Index (PASI) 75, and 36-Item Short Form Health Survey (SF-36) Physical and Mental Component Summaries (PCS and MCS). RESULTS: Twelve RCTs were eligible for meta-regression. Treatment effects for ACR-20 at 12 weeks were higher in trials with longer disease durations (OR=2.94), and lower in trials enrolling older patients (OR=0.48), and those recently published (OR=0.49). Treatment effects for ACR-50 at 12 weeks were higher in trials with more males (OR=2.27), but lower in trials with high prior anti-TNF use (OR=0.28) and recently published trials (OR=0.37). For PASI-75, trials with more male patients (24 weeks: OR=2.56), and with higher swollen and tender joint counts (12 weeks: OR=8.33; 24 weeks: OR=14.44) showed higher treatment effects, and trials with high prior anti-TNF use had lower effects (OR=0.41). Treatment effects for SF-36 PCS at 24 weeks were higher in trials with longer psoriasis disease durations (OR=2.95) and PsA disease durations (OR=4.76), and those published earlier (OR=4.19). CONCLUSIONS: Our analyses show that differences in baseline characteristics may explain some of the differences in response to biologics versus placebo across different trials. Accounting for these factors in future studies will likely be important.

Meta-analysis of long-term joint structural deterioration in minimally treated patients with rheumatoid arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by inflammation and joint structural deterioration. Driven by recent expectations that patients in clinical trials randomized to placebo should be 'rescued' with active therapy within 6 months of starting treatment, the relative benefit of arresting joint damage with biologic agents beyond this period is unclear. With longer-term evidence of the rate of joint deterioration with minimal treatment, the efficacy of biologic agents and novel treatments might be projected beyond the placebo-controlled phase observed in clinical trials. The aim of this study was to estimate radiographic structural deterioration over time in patients with moderate-to-severe RA minimally treated with DMARDs. METHODS: A literature review identified evidence of joint structural deterioration in patients with (DMARD-IR population) and without (non-DMARD-IR population) a history of inadequate response to DMARDs. Patients were minimally treated with one non-biologic DMARD or palliative care (non-DMARD-IR population only). Outcomes of interest were the (modified) Total Sharp Score (TSS) and subscales (Erosion Subscore [ES] and Joint Space Narrowing [JSN] Subscore), and Larsen score. Pooled joint-deterioration curves over time were obtained with meta-analysis models. RESULTS: Mean change from baseline in TSS increased in the DMARD-IR population from 1.14 (95 % credible interval [CrI] 0.66, 1.67) to 9.84 (5.68, 14.46) at Weeks 12 and 104, respectively, and a non-linear increase of 1.56 (0.79, 2.34) and 5.13 (-1.35, 11.67) in the non-DMARD-IR population. At the same time points, mean changes (95 % CrI) were 0.51 (0.27, 0.83) and 4.43 (2.38, 7.21) for ES and 0.36 (0.09, 0.67) and 3.14 (0.80, 5.78) for JSN in the DMARD-IR population, whereas corresponding changes in the non-DMARD-IR population were 0.69 (0.31, 1.12) and 2.93 (0.92, 5.02), and 0.29 (0.17, 0.44) and 2.55 (1.45, 3.80), respectively. Larsen scores were only available for the non-DMARD-IR population, with mean changes (95 % CrI) of 0.08 (0.04, 0.11) and 0.65 (0.36, 0.96) at Weeks 12 and 104, respectively. CONCLUSION: Minimal treatment of RA with one non-biologic DMARD results in deterioration of joint structure in patients with or without a history of inadequate response to non-biologic DMARDs.

The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations.

The PRISMA statement is a reporting guideline designed to improve the completeness of reporting of systematic reviews and meta-analyses. Authors have used this guideline worldwide to prepare their reviews for publication. In the past, these reports typically compared 2 treatment alternatives. With the evolution of systematic reviews that compare multiple treatments, some of them only indirectly, authors face novel challenges for conducting and reporting their reviews. This extension of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) statement was developed specifically to improve the reporting of systematic reviews incorporating network meta-analyses. A group of experts participated in a systematic review, Delphi survey, and face-to-face discussion and consensus meeting to establish new checklist items for this extension statement. Current PRISMA items were also clarified. A modified, 32-item PRISMA extension checklist was developed to address what the group considered to be immediately relevant to the reporting of network meta-analyses. This document presents the extension and provides examples of good reporting, as well as elaborations regarding the rationale for new checklist items and the modification of previously existing items from the PRISMA statement. It also highlights educational information related to key considerations in the practice of network meta-analysis. The target audience includes authors and readers of network meta-analyses, as well as journal editors and peer reviewers.

Critical appraisal of network meta-analyses evaluating the efficacy and safety of new oral anticoagulants in atrial fibrillation stroke prevention trials.

OBJECTIVES: To critically appraise published network meta-analyses (NMAs) evaluating the efficacy or safety of the new oral anticogulants (NOACs) dabigatran, rivaroxaban, and apixaban for the prevention of stroke in patients with nonvalvular atrial fibrillation (AF). METHODS: A systematic literature review was performed to identify the relevant NMAs using MEDLINE, EMBASE, Cochrane Library, Database of Abstracts of Reviews of Effects, and Health Technology Assessment. The synthesis studies were evaluated using the "Questionnaire to assess the relevance and credibility of the NMA." RESULTS: Eleven NMAs evaluating NOACs among adults with nonvalvular AF were identified. Most NMAs included three large phase III randomized controlled trials, comparing NOACs to adjusted-dose warfarin (Randomized Evaluation of Long-Term Anticoagulation Therapy [RE-LY], Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared With Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation [ROCKET-AF], and Apixaban for Reduction of Stroke and Other Thromboembolic Events in Atrial Fibrillation [ARISTOTLE]). The main differences identified related to potential treatment effect modifiers regarding the mean time spent in therapeutic range (TTR) in the warfarin arm, the risk of stroke or systemic embolism across the trials (mean CHADS2 score: C = congestive heart failure, H = hypertension, A = older than age 75 years, D = diabetes mellitus, S2 = prior stroke or history of transient ischemic attack) or primary versus secondary prevention, and type of populations used in the analysis. Kansal et al. [Kansal AR, Sharma M, Bradley-Kennedy C, et al. Dabigatran versus rivaroxaban for the prevention of stroke and systemic embolism in atrial fibrillation in Canada: comparative efficacy and cost-effectiveness. Thromb Haemost 2012;108:672-82] appropriately adjusted the ROCKET-AF TTR to match the RE-LY population on the basis of individual patient data. Meta-regressions are not expected to minimize confounding bias given limited data, whereas subgroup analyses had some impact on the point estimates for the treatment comparisons. CONCLUSIONS: Results of the synthesis studies were generally comparable and suggested that the NOACs had similar efficacy, although some differences were identified depending on the outcome. The extent to which differences in the distribution of TTR, CHADS2 score, or primary versus secondary prevention biased the results remains unclear.

Estimating the value of medical treatments to patients using probabilistic multi criteria decision analysis.

BACKGROUND: Estimating the value of medical treatments to patients is an essential part of healthcare decision making, but is mostly done implicitly and without consulting patients. Multi criteria decision analysis (MCDA) has been proposed for the valuation task, while stated preference studies are increasingly used to measure patient preferences. In this study we propose a methodology for using stated preferences to weigh clinical evidence in an MCDA model that includes uncertainty in both patient preferences and clinical evidence explicitly. METHODS: A probabilistic MCDA model with an additive value function was developed and illustrated using a case on hypothetical treatments for depression. The patient-weighted values were approximated with Monte Carlo simulations and compared to expert-weighted results. Decision uncertainty was calculated as the probability of rank reversal for the first rank. Furthermore, scenario analyses were done to assess the relative impact of uncertainty in preferences and clinical evidence, and of assuming uniform preference distributions. RESULTS: The patient-weighted values for drug A, drug B, drug C, and placebo were 0.51 (95% CI: 0.48 to 0.54), 0.51 (95% CI: 0.48 to 0.54), 0.54 (0.49 to 0.58), and 0.15 (95% CI: 0.13 to 0.17), respectively. Drug C was the most preferred treatment and the rank reversal probability for first rank was 27%. This probability decreased to 18% when uncertainty in performances was not included and increased to 41% when uncertainty in criterion weights was not included. With uniform preference distributions, the first rank reversal probability increased to 61%. The expert-weighted values for drug A, drug B, drug C, and placebo were 0.67 (95% CI: 0.65 to 0.68), 0.57 (95% CI: 0.56 to 0.59), 0.67 (95% CI: 0.61 to 0.71), and 0.19 (95% CI: 0.17 to 0.21). The rank reversal probability for the first rank according to experts was 49%. CONCLUSIONS: Preferences elicited from patients can be used to weigh clinical evidence in a probabilistic MCDA model. The resulting treatment values can be contrasted to results from experts, and the impact of uncertainty can be quantified using rank probabilities. Future research should focus on integrating the model with regulatory decision frameworks and on including other types of uncertainty.

A process for assessing the feasibility of a network meta-analysis: a case study of everolimus in combination with hormonal therapy versus chemotherapy for advanced breast cancer.

BACKGROUND: The aim of this study is to outline a general process for assessing the feasibility of performing a valid network meta-analysis (NMA) of randomized controlled trials (RCTs) to synthesize direct and indirect evidence for alternative treatments for a specific disease population. METHODS: Several steps to assess the feasibility of an NMA are proposed based on existing recommendations. Next, a case study is used to illustrate this NMA feasibility assessment process in order to compare everolimus in combination with hormonal therapy to alternative chemotherapies in terms of progression-free survival for women with advanced breast cancer. RESULTS: A general process for assessing the feasibility of an NMA is outlined that incorporates explicit steps to visualize the heterogeneity in terms of treatment and outcome characteristics (Part A) as well as the study and patient characteristics (Part B). Additionally, steps are performed to illustrate differences within and across different types of direct comparisons in terms of baseline risk (Part C) and observed treatment effects (Part D) since there is a risk that the treatment effect modifiers identified may not explain the observed heterogeneity or inconsistency in the results due to unexpected, unreported or unmeasured differences. Depending on the data available, alternative approaches are suggested: list assumptions, perform a meta-regression analysis, subgroup analysis, sensitivity analyses, or summarize why an NMA is not feasible. CONCLUSIONS: The process outlined to assess the feasibility of an NMA provides a stepwise framework that will help to ensure that the underlying assumptions are systematically explored and that the risks (and benefits) of pooling and indirectly comparing treatment effects from RCTs for a particular research question are transparent.