Values Beyond "Health" in Budget-Constrained Healthcare Systems.

OBJECTIVES: Most current methods to value healthcare treatments only incorporate measures such as quality-adjusted life-years, combining gains in health-related quality of life and life expectancy in specific ways. Failure of these methods to recognize other dimensions of value has led to calls for methods to include additional values that are associated with the healthcare treatments but not captured directly by quality-adjusted life-years. This article seeks to provide methodologically sound ways to incorporate additional health-related outcomes, focusing on budget-constrained healthcare systems, in which using standard welfare economics methods are often eschewed. METHODS: The analysis develops standard extra-welfarist approaches to maximizing aggregate health, subject to fixed-budget constraints, using Lagrange multiplier methods. Then, additional valuable health-related outcomes, eg, reduced caregiver burden, real option value, and market- and non-market productivity are introduced. The article also introduces a social welfare function approach to illuminate how disability, disease severity and other equity-related issues can be incorporated into complete welfare measures. RESULTS: Resulting analysis, fully developed in an Appendix in Supplemental Materials found at https://doi.org/10.1016/j.jval.2024.02.005 and summarized in the main text, show that understanding how average and marginal healthcare costs increase with output and how health augments "additional values" provides ways to assess willingness to pay for them in these fixed-budget situations. CONCLUSIONS: In budget-constrained healthcare systems, only from actual budget allocations can values both of health itself and "additional values" be inferred. These methods, combined with methodologically sound social welfare functions, demonstrate how to move from "health" to "welfare" in measuring the value of increased healthcare use.

On the (Near) Equivalence of Welfarist and Extra-Welfarist Methods to Value Healthcare With Implications for Assessing Equity.

OBJECTIVES: While welfarist economics (WE) methods rely wholly on individuals' valuations, extra-welfarist (EW) methods seek alternative measures of value. Major reviews of the EW literature conclude that EW studies almost universally replace "utility" with "health" as the maximand. This analysis seeks to understand what conclusions are necessary and sufficient to make EW and WE methods concurrent and discusses implications for measuring social value. METHODS: Using standard WE methods, I demonstrate that EW is equivalent to WE with 2 key restrictions-individuals have constant returns to health in producing utility and health budgets are fixed. Fixing budgets removes a key WE step, determining the marginal rate of substitution between consumption and health, the willingness to pay for health gains. RESULTS: Because EW methods equate with WE with these 2 restrictions, I show how formal models to construct aggregated social welfare functions (SWFs) in WE frameworks lead directly to SWF models using EW models of value. I also show that, in fixed-budget health systems, when SWFs place different values for improving health of different subpopulations, aggregate health output fails as a SWF criterion. I demonstrate how different societal values can and should enter EW SWF models using WE criteria. I also discuss the implications when either of these key restrictions does not properly represent people's preferences. CONCLUSIONS: Once EW methods are shown to be a restricted form of WE methods, those WE methods can illuminate how best to measure SWFs in EW environments.

Methods to Adjust Willingness-to-Pay Measures for Severity of Illness.

OBJECTIVES: Both private sector organizations and governmental health agencies increasingly use illness severity measures to adjust willingness-to-pay thresholds. Three widely discussed methods-absolute shortfall (AS), proportional shortfall (PS), and fair innings (FI)-all use ad hoc adjustments to cost-effectiveness analysis methods and "stair-step" brackets to link illness severity with willingness-to-pay adjustments. We assess how these methods compare with microeconomic expected utility theory-based methods to value health gains. METHODS: We describe standard cost-effectiveness analysis methods, the basis from which AS, PS, and FI make severity adjustments. We then develop how the Generalized Risk Adjusted Cost Effectiveness (GRACE) model assesses value for differing illness and disability severity. We compare AS, PS, and FI against value as defined by GRACE. RESULTS: AS, PS, and FI have major and unresolved differences between them in how they value various medical interventions. Compared with GRACE, they fail to properly incorporate illness severity or disability. They conflate gains in health-related quality of life and life expectancy incorrectly and confuse the magnitude of treatment gains with value per quality-adjusted life-year. Stair-step methods also introduce important ethical concerns. CONCLUSIONS: AS, PS, and FI disagree with each other in major ways, demonstrating that at most, one correctly describes patients' preferences. GRACE offers a coherent alternative, based on neoclassical expected utility microeconomic theory, and can be readily implemented in future analyses. Other approaches that depend on ad hoc ethical statements have yet to be justified using sound axiomatic approaches.

Reimagining Patient Data Access for Researchers.

OBJECTIVES: Widespread use of electronic health records (EHRs) now makes it feasible to expand beyond health insurance claims data to include full EHR data for health economics and outcomes research (HEOR) studies. We seek to develop ways to maximize researcher access to such data while strongly protecting patients' privacy rights. METHODS: We analyzed alternative organizational structures and intellectual property rights assignments as they now exist and compared these with structures and intellectual property rights assignments that would maximize access to data for HEOR studies and minimize transactions costs. We analyzed data protection requirements and financial incentives at 3 levels: patient decision making, patients' data aggregators, and final aggregation across patients' data. RESULTS: Creating new HEOR data systems requires new organizations and funding, while also protecting patients' data privacy rights. The Cures Act enables a new market for trusted third parties (TTPs) to aggregate patients' data. New secondary data aggregators must combine individuals' aggregated EHRs into usable HEOR databases. Maximal patient participation requires complete health insurance coverage of costs that healthcare providers charge for transmitting patients' data to TTPs. The new secondary system to aggregate data from many TTPs into usable HEOR optimally has external funding. CONCLUSIONS: Important steps remain uncompleted to achieve maximally available HEOR data while protecting patients' privacy rights. HEOR information is a public good, so private incentives to support creation and operation of this new system remain incomplete. Public and private support can expand this system to optimally improve people's health.

Health Technology Assessment With Diminishing Returns to Health: The Generalized Risk-Adjusted Cost-Effectiveness (GRACE) Approach.

OBJECTIVES: Cost-effectiveness analysis (CEA) embeds an assumption at odds with most economic analysis-that of constant returns to health in the creation of happiness (utility). We aim to reconcile it with the bulk of economic theory. METHODS: We generalize the traditional CEA approach, allow diminishing returns to health, and align CEA with the rest of the health economics literature. RESULTS: This simple change has far-reaching implications for the practice of CEA. First, optimal cost-effectiveness thresholds should systematically rise for more severe diseases and fall for milder ones. We provide formulae for estimating how these thresholds vary with health-related quality of life (QoL) in the sick state. Practitioners can also use our approach to account for treatment outcome uncertainty. Holding average benefits fixed, risk-averse consumers value interventions more when they reduce outcome uncertainty ('insurance value') and/or when they provide a chance at positively skewed outcomes ('value of hope'). Finally, we provide a coherent way to combine improvements in QoL and life expectancy (LE) when people have diminishing returns to QoL. CONCLUSION: This new approach obviates the need for increasingly prevalent and ad hoc exceptions to CEA for end-of-life care, rare disease, and very severe disease (eg, cancer). Our methods also show that the value of improving QoL for disabled people is greater than for comparable non-disabled people, thus resolving an ongoing and mathematically legitimate objection to CEA raised by advocates for disabled people. Our Generalized Risk-Adjusted Cost-Effectiveness (GRACE) approach helps align HTA practice with realistic preferences for health and risk.

Estimating optimal willingness to pay thresholds for cost-effectiveness analysis: A generalized method.

Operationalizing cost-effectiveness analysis (CEA) requires that decisionmakers select maximum willingness to pay thresholds (K). We generalize previous methods used to estimate K using highly flexible hyperbolic absolute risk aversion (HARA) utility functions that encompass a wide range of risk behavior. For HARA utility, we calculate formulas for relative risk aversion (r*) and relative prudence (π∗ ), using literature-based estimates to calibrate our HARA model. We then assess optimal WTP thresholds (K) in absolute value and relative to income (K/M). Across the most-plausible range of risk preference parameters (r* and π∗ ), optimal K/M ratios sit (approximately) in the range of 1 to 3, although we cannot readily rule out larger K/M values. The optimal K always increases with income, while K/M falls with income if utility has increasing relative risk aversion. Results of this more-general model of economic utility are broadly consistent with previous work using more-restrictive Weibull functions. More precision in measuring the key parameters-particularly relative prudence (π∗ ) will narrow down the range of K/M estimates. The highly general HARA structure illuminates why and how optimal CEA thresholds change with income. An appendix illuminates how relative risk aversion and relative prudence relate to each other.

Vision for a systems architecture to integrate and transform population health.

Entities involved in population health often share a common mission while acting independently of one another and perhaps redundantly. Population health is in everybody's interest, but nobody is really in charge of promoting it. Across governments, corporations, and frontline operations, lack of coordination, lack of resources, and lack of reliable, current information have often impeded the development of situation-awareness models and thus a broad operational integration for population health. These deficiencies may also affect the technical, organizational, policy, and legal arrangements for information sharing, a desired practice of high potential value in population health. In this article, we articulate a vision for a next-generation modeling effort to create a systems architecture for broadly integrating and visualizing strategies for advancing population health. This multipurpose systems architecture would enable different views, alerts, and scenarios to better prepare for and respond to potential degradations in population health. We draw inspiration from systems engineering and visualization tools currently in other uses, including monitoring the state of the economy (market performance), security (classified intelligence), energy (power generation), transportation (global air traffic control), environment (weather monitoring), jobs (labor market dynamics), manufacturing and supply chain (tracking of components, parts, subassemblies, and products), and democratic processes (election analytics). We envision the basic ingredients for a population health systems architecture and its visualization dashboards to eventually support proactive planning and joint action among constituents. We intend our ambitious vision to encourage the work needed for progress that the population deserves.

The impact of gene expression profile testing on confidence in chemotherapy decisions and prognostic expectations.

PURPOSE: Little is known about whether gene expression profile (GEP) testing and specific recurrence scores (e.g., medium risk) improve women's confidence in their chemotherapy decision or perceived recurrence risk. We evaluate the relationship between these outcomes and GEP testing. METHODS: We surveyed women eligible for GEP testing (stage I or II, Gr1-2, ER+, HER2-) identified through the Surveillance, Epidemiology, and End Results (SEER) Registry of Washington or Kaiser Permanente Northern California from 2012 to 2016, approximately 0-4 years from diagnosis (N = 904, RR = 45.4%). Confidence in chemotherapy was measured as confident (Very, completely) versus Not Confident (Somewhat, A little, Not At All); perceived risk recurrence was recorded numerically (0-100%). Women reported their GEP test receipt (Yes, No, Unknown) and risk recurrence score (High, Intermediate, Low, Unknown). In our analytic sample (N = 833), we propensity score weighted the three test receipt cohorts and used propensity weighted multivariable regressions to examine associations between the outcomes and the three test receipt cohorts, with receipt stratified by score. RESULTS: 29.5% reported an unknown GEP test receipt; 86% being confident. Compared to no test receipt, an intermediate score (aOR 0.34; 95% CI 0.20-0.58), unknown score (aOR 0.09; 95% CI 0.05-0.18), and unknown test receipt (aOR 0.37; 95% CI 0.24-0.57) were less likely to report confidence. Most women greatly overestimated their recurrence risk regardless of their test receipt or score. CONCLUSIONS: GEP testing was not associated with greater confidence in chemotherapy decisions. Better communication about GEP testing and the implications for recurrence risk may improve women's decisional confidence.

A New Method to Determine the Optimal Willingness to Pay in Cost-Effectiveness Analysis.

OBJECTIVE: To provide a new approach to estimate optimal willingness to pay (WTP) for health technology assessment (HTA). STUDY DESIGN: This analysis specified utility as a function of income and calibrated it using estimates of relative risk aversion, from which the optimal WTP (K) can be determined using Garber and Phelps' results (1997). METHODS: This analysis used the highly flexible Weibull utility function, calibrated with estimates of relative risk aversion (r*) derived from multiple data sources. The analysis centered on r* = 1 and conducted sensitivity analysis on r* and key Weibull parameters. For a range of income (M), graphs demonstrated how K/M and K vary with M. Results were compared with estimates of K and K/M from alternative models. Extrapolation from a representative individual to population-wide health plans was discussed. RESULTS: Using r* = 1 and central values of other key parameters, K/M (at average income for developed nations) was approximately 2× annual income. Both K and K/M rose with income. Sensitivity analysis showed that results depend moderately on the chosen value of r* and specific Weibull utility function parameters. At average income, the optimal K/M ratio (2×) was modestly lower than many standard recommendations (typically 3× average income) and substantially lower than estimates using value-of-statistical-life approaches. CONCLUSIONS: The new model, although not yet perfected, provides a different way to identify the WTP cutoff for HTA. Extrapolation to more than twice the calibration income ($50 000) is advised against. Analysis of other approaches to estimate the optimal K reveal potential upward biases.

Valuing Health: Evolution, Revolution, Resistance, and Reform.

A number of methods have sought to determine the value of interventions and services that promote health, even when no agreement exists on the proper way to determine and define "value." Previous valuation efforts began simply by counting deaths or measuring life expectancy, slowly evolving to the widespread use of cost-effectiveness analysis (CEA) as the de facto normative standard for medical interventions. Users of CEA recognize that the method is incomplete. Further, no meaningful agreement exists on how best to apply CEA in decision settings because of either inadequacies in the CEA framework or lack of consensus on how to use it in a setting with budget constraints. Yet efforts to value health still predominantly use (and continue to recommend) this limited framework. Is this owing to a lack of new ideas and motivation, resistance to change, or an aversion to embrace more comprehensive systems approaches? We argue that tools of systems engineering can advance our capabilities, but they have had only limited use in health policy. We identify some reasons and specifically highlight the promise of systems-analytic platforms-such as multicriteria decision support systems-and the need to make them more accessible for different uses in real situations with real consequences. We also explore the need for comparative testing of different multicriteria approaches (including direct comparisons with CEA) to learn when and by how much the recommendations differ and what the consequences might be.

Defining Elements of Value in Health Care-A Health Economics Approach: An ISPOR Special Task Force Report [3].

The third section of our Special Task Force report identifies and defines a series of elements that warrant consideration in value assessments of medical technologies. We aim to broaden the view of what constitutes value in health care and to spur new research on incorporating additional elements of value into cost-effectiveness analysis (CEA). Twelve potential elements of value are considered. Four of them-quality-adjusted life-years, net costs, productivity, and adherence-improving factors-are conventionally included or considered in value assessments. Eight others, which would be more novel in economic assessments, are defined and discussed: reduction in uncertainty, fear of contagion, insurance value, severity of disease, value of hope, real option value, equity, and scientific spillovers. Most of these are theoretically well understood and available for inclusion in value assessments. The two exceptions are equity and scientific spillover effects, which require more theoretical development and consensus. A number of regulatory authorities around the globe have shown interest in some of these novel elements. Augmenting CEA to consider these additional elements would result in a more comprehensive CEA in line with the "impact inventory" of the Second Panel on Cost-Effectiveness in Health and Medicine. Possible approaches for valuation and inclusion of these elements include integrating them as part of a net monetary benefit calculation, including elements as attributes in health state descriptions, or using them as criteria in a multicriteria decision analysis. Further research is needed on how best to measure and include them in decision making.

Approaches to Aggregation and Decision Making-A Health Economics Approach: An ISPOR Special Task Force Report [5].

The fifth section of our Special Task Force report identifies and discusses two aggregation issues: 1) aggregation of cost and benefit information across individuals to a population level for benefit plan decision making and 2) combining multiple elements of value into a single value metric for individuals. First, we argue that additional elements could be included in measures of value, but such elements have not generally been included in measures of quality-adjusted life-years. For example, we describe a recently developed extended cost-effectiveness analysis (ECEA) that provides a good example of how to use a broader concept of utility. ECEA adds two features-measures of financial risk protection and income distributional consequences. We then discuss a further option for expanding this approach-augmented CEA, which can introduce many value measures. Neither of these approaches, however, provide a comprehensive measure of value. To resolve this issue, we review a technique called multicriteria decision analysis that can provide a comprehensive measure of value. We then discuss budget-setting and prioritization using multicriteria decision analysis, issues not yet fully resolved. Next, we discuss deliberative processes, which represent another important approach for population- or plan-level decisions used by many health technology assessment bodies. These use quantitative information on CEA and other elements, but the group decisions are reached by a deliberative voting process. Finally, we briefly discuss the use of stated preference methods for developing "hedonic" value frameworks, and conclude with some recommendations in this area.

A Health Economics Approach to US Value Assessment Frameworks-Summary and Recommendations of the ISPOR Special Task Force Report [7].

This summary section first lists key points from each of the six sections of the report, followed by six key recommendations. The Special Task Force chose to take a health economics approach to the question of whether a health plan should cover and reimburse a specific technology, beginning with the view that the conventional cost-per-quality-adjusted life-year metric has both strengths as a starting point and recognized limitations. This report calls for the development of a more comprehensive economic evaluation that could include novel elements of value (e.g., insurance value and equity) as part of either an "augmented" cost-effectiveness analysis or a multicriteria decision analysis. Given an aggregation of elements to a measure of value, consistent use of a cost-effectiveness threshold can help ensure the maximization of health gain and well-being for a given budget. These decisions can benefit from the use of deliberative processes. The six recommendations are to: 1) be explicit about decision context and perspective in value assessment frameworks; 2) base health plan coverage and reimbursement decisions on an evaluation of the incremental costs and benefits of health care technologies as is provided by cost-effectiveness analysis; 3) develop value thresholds to serve as one important input to help guide coverage and reimbursement decisions; 4) manage budget constraints and affordability on the basis of cost-effectiveness principles; 5) test and consider using structured deliberative processes for health plan coverage and reimbursement decisions; and 6) explore and test novel elements of benefit to improve value measures that reflect the perspectives of both plan members and patients.

Resource allocation in decision support frameworks.

BACKGROUND: Cost-benefit and cost-effectiveness analysis place limits on the dimensions of value that the models can incorporate. Cost-benefit analysis requires monetization of all measures of value (including life), a task sometimes deemed either difficult to accomplish or even repugnant. Cost-effectiveness analyses include health care gains in natural units (e.g., quality-adjusted life years or QALYs) rather than purely monetizing them (e.g., in dollars) and offers an efficiency perspective based on the ratio of cost per QALYs or similar health measures. These two methods use different rules for investment. Cost-benefit analysis says to invest whenever benefits exceed costs. Cost-effectiveness analysis says to invest if the intervention has a cost per QALY that meets-or is below-a designated cutoff value. METHODS: Multi-criteria frameworks expand decision analyses by considering value tradeoffs from decision makers, and then producing a synthetic measure that summarizes the performance of investment options. This evaluation is done across all chosen dimensions of value, based on the weights provided by the decision makers, but this flexibility comes at a cost. To date, no approach is widely accepted to suggest how much to invest (how to determine a budget constraint) using multi-attribute models. Moreover, there is no agreed-upon method to measure willingness to pay for incremental multi-attribute value improvements. Our paper proposes a way forward. RESULTS: Based on existing dollar estimates of willingness to pay for QALYs, our concept creates a comparable cutoff for multi-criteria value measures. Our proposed method expands the acceptable cost per QALYs in proportion to how much of the total measure is accounted for by the QALY component. Agreed-upon values for cost per QALY are thus extrapolated to account for extra value created by non-QALY attributes of each intervention. CONCLUSION: Using our proposed methods, the cost per QALY cutoff can serve as a benchmark toward creating a resource allocation cutoff in multi-criteria frameworks.

Oncologist and organizational factors associated with variation in breast cancer multigene testing.

PURPOSE: Multigene testing for breast cancer recurrence risk became available in 2007, yet many eligible patients remain untested. This study evaluated variation in testing rates, and oncologist and organizational factors associated with variation, in a setting without financial influences on testing. METHODS: We conducted a retrospective cohort study using electronic data and oncologist surveys within Kaiser Permanente Northern California, a large integrated health care system. Analyses included all 2974 test eligible patients from 2013 to 2015, 113 oncologists, and 15 practice groups. Receipt of multigene testing was evaluated with generalized linear mixed models. RESULTS: Overall, 39% of eligible patients had multigene testing, but rates varied widely among practice groups, ranging from 24 to 48% after case mix adjustment. This 24% difference among practices was greater than the variation associated with most patient characteristics, including comorbidities and race/ethnicity, and similar to that associated with tumor size. Practice group and oncologist factors were statistically significant contributors to the variation in testing after adjusting for patient factors. Patients were more likely to be tested if they had a female oncologist (aOR 1.60, 95% CI 1.21-2.12) or were in a practice whose chief had a high testing rate (aOR 1.20, 95% CI 1.12-1.29 per 10% increase in the percent tested). CONCLUSIONS: Oncologist and leadership practices play a key role in the variation in genomic test use for cancer recurrence risk even in a healthcare system without financial barriers to testing and could be a leverage point for implementing desired practice changes for new genomic advances.

Using Multicriteria Approaches to Assess the Value of Health Care.

Practitioners of cost-utility analysis know that their models omit several important factors that often affect real-world decisions about health care options. Furthermore, cost-utility analyses typically reflect only single perspectives (e.g., individual, business, and societal), further limiting the value for those with different perspectives (patients, providers, payers, producers, and planners-the 5Ps). We discuss how models based on multicriteria analyses, which look at problems from many perspectives, can fill this void. Each of the 5Ps can use multicriteria analyses in different ways to aid their decisions. Each perspective may lead to different value measures and outcomes, whereas no single-metric approach (such as cost-utility analysis) can satisfy all these stakeholders. All stakeholders have unique ways to measure value, even if assessing the same health intervention. We illustrate the benefits of this approach by comparing the value of five different hypothetical treatment choices for five hypothetical patients with cancer, each with different preference structures. Nine attributes describe each treatment option. We add a brief discussion regarding the use of these approaches in group-based decisions. We urge that methods to value health interventions embrace the multicriteria approaches that we discuss, because these approaches 1) increase transparency about the decision process, 2) allow flight simulator-type evaluation of alternative interventions before actual investment or deployment, 3) help focus efforts to improve data in an efficient manner, 4) at least in some cases help facilitate decision convergence among stakeholders with differing perspectives, and 5) help avoid potential cognitive errors known to impair intuitive judgments.

Evaluating Frameworks That Provide Value Measures for Health Care Interventions.

The recent acceleration of scientific discovery has led to greater choices in health care. New technologies, diagnostic tests, and pharmaceuticals have widely varying impact on patients and populations in terms of benefits, toxicities, and costs, stimulating a resurgence of interest in the creation of frameworks intended to measure value in health. Many of these are offered by providers and/or advocacy organizations with expertise and interest in specific diseases (e.g., cancer and heart disease). To help assess the utility of and the potential biases embedded in these frameworks, we created an evaluation taxonomy with seven basic components: 1) define the purpose; 2) detail the conceptual approach, including perspectives, methods for obtaining preferences of decision makers (e.g., patients), and ability to incorporate multiple dimensions of value; 3) discuss inclusions and exclusions of elements included in the framework, and whether the framework assumes clinical intervention or offers alternatives such as palliative care or watchful waiting; 4) evaluate data sources and their scientific validity; 5) assess the intervention's effect on total costs of treating a defined population; 6) analyze how uncertainty is incorporated; and 7) illuminate possible conflicts of interest among those creating the framework. We apply the taxonomy to four representative value frameworks recently published by professional organizations focused on treatment of cancer and heart disease and on vaccine use. We conclude that each of these efforts has strengths and weaknesses when evaluated using our taxonomy, and suggest pathways to enhance the utility of value-assessing frameworks for policy and clinical decision making.

Ibrutinib combined with bendamustine and rituximab compared with placebo, bendamustine, and rituximab for previously treated chronic lymphocytic leukaemia or small lymphocytic lymphoma (HELIOS): a randomised, double-blind, phase 3 study.

BACKGROUND: Most patients with chronic lymphocytic leukaemia or small lymphocytic lymphoma relapse after initial therapy. Bendamustine plus rituximab is often used in the relapsed or refractory setting. We assessed the efficacy and safety of adding ibrutinib, an oral covalent inhibitor of Bruton's tyrosine kinase (BTK), to bendamustine plus rituximab in patients with previously treated chronic lymphocytic leukaemia or small lymphocytic lymphoma. METHODS: The HELIOS trial was an international, double-blind, placebo-controlled, phase 3 study in adult patients (≥18 years of age) who had active chronic lymphocytic leukaemia or small lymphocytic lymphoma with measurable lymph node disease (>1·5 cm) by CT scan, and had relapsed or refractory disease following one or more previous lines of systemic therapy consisting of at least two cycles of a chemotherapy-containing regimen, an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1, and adequate bone marrow, liver, and kidney function. Patients with del(17p) were excluded because of known poor response to bendamustine plus rituximab. Patients who had received previous treatment with ibrutinib or other BTK inhibitors, refractory disease or relapse within 24 months with a previous bendamustine-containing regimen, or haemopoietic stem-cell transplant were also excluded. Patients were randomly assigned (1:1) by a web-based system to receive bendamustine plus rituximab given in cycles of 4 weeks' duration (bendamustine: 70 mg/m(2) intravenously on days 2-3 in cycle 1, and days 1-2 in cycles 2-6; rituximab: 375 mg/m(2) on day 1 of cycle 1, and 500 mg/m(2) on day 1 of cycles 2-6 for a maximum of six cycles) with either ibrutinib (420 mg daily orally) or placebo until disease progression or unacceptable toxicity. Patients were stratified according to whether they were refractory to purine analogues and by number of previous lines of therapy. The primary endpoint was independent review committee (IRC)-assessed progression-free survival. Crossover to ibrutinib was permitted for patients in the placebo group with IRC-confirmed disease progression. Analysis was by intention-to-treat and is continuing for further long-term follow-up. The trial is registered with ClinicalTrials.gov, number NCT01611090. FINDINGS: Between Sept 19, 2012, and Jan 21, 2014, 578 eligible patients were randomly assigned to ibrutinib or placebo in combination with bendamustine plus rituximab (289 in each group). The primary endpoint was met at the preplanned interim analysis (March 10, 2015). At a median follow-up of 17 months (IQR 13·7-20·7), progression-free survival was significantly improved in the ibrutinib group compared with the placebo group (not reached in the ibrutinib group (95% CI not evaluable) vs 13·3 months (11·3-13·9) in the placebo group (hazard ratio [HR] 0·203, 95% CI 0·150-0·276; p<0·0001). IRC-assessed progression-free survival at 18 months was 79% (95% CI 73-83) in the ibrutinib group and 24% (18-31) in the placebo group (HR 0·203, 95% CI 0·150-0·276; p<0·0001). The most frequent all-grade adverse events were neutropenia and nausea. 222 (77%) of 287 patients in the ibrutinib group and 212 (74%) of 287 patients in the placebo group reported grade 3-4 events; the most common grade 3-4 adverse events in both groups were neutropenia (154 [54%] in the ibrutinib group vs 145 [51%] in the placebo group) and thrombocytopenia (43 [15%] in each group). A safety profile similar to that previously reported with ibrutinib and bendamustine plus rituximab individually was noted. INTERPRETATION: In patients eligible for bendamustine plus rituximab, the addition of ibrutinib to this regimen results in significant improvements in outcome with no new safety signals identified from the combination and a manageable safety profile. FUNDING: Janssen Research & Development.