When assessing causal effects, determining the target population to which the results are intended to generalize is a critical decision. Randomized and observational studies each have strengths and ...limitations for estimating causal effects in a target population. Estimates from randomized data may have internal validity but are often not representative of the target population. Observational data may better reflect the target population, and hence be more likely to have external validity, but are subject to potential bias due to unmeasured confounding. While much of the causal inference literature has focused on addressing internal validity bias, both internal and external validity are necessary for unbiased estimates in a target population. This article presents a framework for addressing external validity bias, including a synthesis of approaches for generalizability and transportability, and the assumptions they require, as well as tests for the heterogeneity of treatment effects and differences between study and target populations.
Treatment-resistant depression exacts a heavy price in treatment costs and lost productivity, reaching into the tens of billions of dollars, but its effects on the lives of patients are just as ...devastating. In this literature review, the authors summarize 62 studies documenting the disease’s toll on quality of life, personal financial resources, and general health. The average patient in the included studies had experienced nearly four earlier episodes of depression, had not responded to 4.7 drug trials, and continued to meet or nearly meet criteria for severe depression.
ObjectiveThis literature review assessed the burden of treatment-resistant depression in the United States by compiling published data about the clinical, societal, and economic outcomes associated with failure to respond to one or more adequate trials of drug therapy.MethodsPubMed and the Tufts Cost-Effectiveness Analyses Registry were searched for English-language articles published between January 1996 and August 2013 that collected primary data about treatment-resistant depression. Two researchers independently assessed study quality and extracted data.ResultsSixty-two articles were included (N=59,462 patients). Patients with treatment-resistant depression had 3.8±2.1 prior depressive episodes and illness duration of 4.4±3.3 years and had completed 4.7±2.7 unsuccessful drug trials involving 2.1±.3 drug classes. Response rates for treatment-resistant depression were 36%±1%. A total of 17%±6% of patients had prior suicide attempts (1.1±.2 attempts per patient). Quality-of-life scores (scale of 0–1, with 0 indicating death and 1 indicating perfect health) for patients with treatment-resistant depression were .41±.8 and .26±.8 points lower, respectively, than for patients who experienced remission or response. Annual costs for health care and lost productivity were $5,481 and $4,048 higher, respectively, for patients with treatment-resistant versus treatment-responsive depression.ConclusionsTreatment-resistant depression exacts a substantial toll on patients’ quality of life. At current rates of 12%–20% among all depressed patients, treatment-resistant depression may present an annual added societal cost of $29–$48 billion, pushing up the total societal costs of major depression by as much as $106–$118 billion. These findings underscore the need for research on the mechanisms of depression, new therapeutic targets, existing and new treatment combinations, and tests to improve the efficacy of and adherence to treatments for treatment-resistant depression.
•Personalized medicine and orphan drugs face similar reimbursement challenges.•Both lack strong evidence at the time of submission.•Payers give more lenient coverage decisions to orphan ...drugs.•Clearer payer guidelines on what constitutes “quality evidence” are needed.•Incentivizing socially relevant development requires a more transparent approach.
Personalized medicine and orphan drugs share many characteristics—both target small patient populations, have uncertainties regarding efficacy and safety at payer submission, and frequently have high prices. Given personalized medicine's rising importance, this review summarizes international coverage and pricing strategies for personalized medicine and orphan drugs as well as their impact on therapy development incentives, payer budgets, and therapy access and utilization.
PubMed, Health Policy Reference Center, EconLit, Google Scholar, and references were searched through February 2017 for articles presenting primary data.
Sixty-nine articles summarizing 42 countries’ strategies were included. Therapy evaluation criteria varied between countries, as did patient cost-share. Payers primarily valued clinical effectiveness; cost was only considered by some. These differences result in inequities in orphan drug access, particularly in smaller and lower-income countries. The uncertain reimbursement process hinders diagnostic testing. Payer surveys identified lack of comparative effectiveness evidence as a chief complaint, while manufacturers sought more clarity on payer evidence requirements. Despite lack of strong evidence, orphan drugs largely receive positive coverage decisions, while personalized medicine diagnostics do not.
As more personalized medicine and orphan drugs enter the market, registries can provide better quality evidence on their efficacy and safety. Payers need systematic assessment strategies that are communicated with more transparency. Further studies are necessary to compare the implications of different payer approaches.
While much of the causal inference literature has focused on addressing internal validity biases, both internal and external validity are necessary for unbiased estimates in a target population of ...interest. However, few generalizability approaches exist for estimating causal quantities in a target population that is not well‐represented by a randomized study but is reflected when additionally incorporating observational data. To generalize to a target population represented by a union of these data, we propose a novel class of conditional cross‐design synthesis estimators that combine randomized and observational data, while addressing their estimates' respective biases—lack of overlap and unmeasured confounding. These methods enable estimating the causal effect of managed care plans on health care spending among Medicaid beneficiaries in New York City, which requires obtaining estimates for the 7% of beneficiaries randomized to a plan and 93% who choose a plan, who do not resemble randomized beneficiaries. Our new estimators include outcome regression, propensity weighting, and double robust approaches. All use the covariate overlap between the randomized and observational data to remove potential unmeasured confounding bias. Applying these methods, we find substantial heterogeneity in spending effects across managed care plans. This has major implications for our understanding of Medicaid, where this heterogeneity has previously been hidden. Additionally, we demonstrate that unmeasured confounding rather than lack of overlap poses a larger concern in this setting.
Patients with schizophrenia often fail to respond to an initial course of therapy. This study systematically reviewed the societal and economic burden of treatment-resistant schizophrenia (TRS). ...Studies that described patients with TRS published 1996–2012 were included if they collected primary data on clinical, social, or economic outcomes. All studies were independently reviewed and extracted by at least two investigators. Sixty-five studies were identified. Almost 60% (SD 18%) of patients failed to achieve response after 23 weeks on antipsychotic drug therapy. Patients with TRS had high rates of smoking (56%), alcohol abuse (51%), substance abuse (51%), and suicide ideation (44%). The incidence of severe adverse events to treatment was 4% (SD 7%). Mean quality of life for patients who were unresponsive or intolerant to treatment was ∼20% lower than that of patients in remission. Annual costs for patients with schizophrenia are $15 500–$22 300 and are 3–11-fold higher for patients with TRS. TRS remains common and costly, despite availability of many treatment options, and contributes to a significant loss in patient quality of life. Although estimates in the literature vary greatly, TRS conservatively adds more than $34 billion in annual direct medical costs in the USA.
Abstract Objectives Gene-expression profiling (GEP) reliably supplements traditional clinicopathological information on the tissue of origin (TOO) in metastatic or poorly differentiated cancer. A ...cost-effectiveness analysis of GEP TOO testing versus usual care was conducted from a US third-party payer perspective. Methods Data on recommendation changes for chemotherapy, surgery, radiation therapy, blood tests, imaging investigations, and hospice care were obtained from a retrospective, observational study of patients whose physicians received GEP TOO test results. The effects of chemotherapy recommendation changes on survival were based on the results of trials cited in National Comprehensive Cancer Network and UpToDate guidelines. Drug and administration costs were based on average doses reported in National Comprehensive Cancer Network guidelines. Other unit costs came from Centers for Medicare & Medicaid Services fee schedules. Quality-of-life weights were obtained from literature. Bootstrap analysis estimated sample variability; probabilistic sensitivity analysis addressed parameter uncertainty. Results Chemotherapy regimen recommendations consistent with guidelines for final tumor-site diagnoses increased significantly from 42% to 65% (net difference 23%; P <0.001). Projected overall survival increased from 15.9 to 19.5 months (mean difference 3.6 months; two-sided 95% confidence interval CI 3.2–3.9). The average increase in quality-adjusted life-months was 2.7 months (95% CI 1.5–4.3), and average third-party payer costs per patient increased by $10,360 (95% CI $2,982–$19,192). The cost per quality-adjusted life-year gained was $46,858 (95% CI $13,351–$104,269). Conclusions GEP TOO testing significantly altered clinical practice patterns and is projected to increase overall survival, quality-adjusted life-years, and costs, resulting in an expected cost per quality-adjusted life-year of less than $50,000.
Studies are often performed in samples that do not resemble the target populations relevant for policy, treatment, or other decisions. Much of the causal inference literature has focused on ...addressing internal validity bias; however, both internal and external validity are necessary for unbiased estimates in a target population. The generalizability methods presented in this thesis allow for inference on the population of interest rather than the one in the study. Chapter 1 presents a framework for addressing external validity bias, including a synthesis of approaches for generalizability and transportability, the assumptions they require, as well as tests for the heterogeneity of treatment effects and differences between study and target populations. The chapter concludes with practical guidance for researchers and practitioners.Chapter 2 presents an innovative class of estimators, conditional cross-design synthesis (CCDS), for combining randomized and observational data to eliminate their respective external and internal validity biases. CCDS uses the region of covariate overlap between data types to remove potential unmeasured confounding bias in the observational data in order to extend inference beyond the support of the randomized data to the full target population. We derive outcome regression, propensity weighting, and double robust approaches under the CCDS framework. We illustrate the methods to estimate the causal effect of health insurance plans on cost among New York City Medicaid enrollees. Chapter 3 introduces novel approaches for generalizing from an evaluation study of a voluntary intervention to estimate population average treatment effects for future treated individuals, which can accommodate nonparametric outcome regression approaches such as Bayesian Additive Regression Trees and Bayesian Causal Forests. The generalizability approach incorporates uncertainty regarding target population treated group membership into the posterior credible intervals to better-reflect the uncertainty of scaling up a voluntary intervention. In a simulation based on real data, we estimate impacts of a national scale-up of a voluntary health policy model and highlight the benefit of using flexible regression approaches for generalizability.
The predominant cause of chronic lung allograft failure is small airway obstruction arising from bronchiolitis obliterans. However, clinical methodologies for evaluating presence and degree of small ...airway disease are lacking.
To determine if parametric response mapping (PRM), a novel computed tomography voxel-wise methodology, can offer insight into chronic allograft failure phenotypes and provide prognostic information following spirometric decline.
PRM-based computed tomography metrics quantifying functional small airways disease (PRM
) and parenchymal disease (PRM
) were compared between bilateral lung transplant recipients with irreversible spirometric decline and control subjects matched by time post-transplant (n = 22). PRM
at spirometric decline was evaluated as a prognostic marker for mortality in a cohort study via multivariable restricted mean models (n = 52).
Patients presenting with an isolated decline in FEV
(FEV
First) had significantly higher PRM
than control subjects (28% vs. 15%; P = 0.005), whereas patients with concurrent decline in FEV
and FVC had significantly higher PRM
than control subjects (39% vs. 20%; P = 0.02). Over 8.3 years of follow-up, FEV
First patients with PRM
greater than or equal to 30% at spirometric decline lived on average 2.6 years less than those with PRM
less than 30% (P = 0.004). In this group, PRM
greater than or equal to 30% was the strongest predictor of survival in a multivariable model including bronchiolitis obliterans syndrome grade and baseline FEV
predicted (P = 0.04).
PRM is a novel imaging tool for lung transplant recipients presenting with spirometric decline. Quantifying underlying small airway obstruction via PRM
helps further stratify the risk of death in patients with diverse spirometric decline patterns.
The multibiomarker disease activity (MBDA) blood test has been clinically validated as a measure of disease activity in patients with RA. We aimed to estimate the effect of the MBDA test on physical ...function for patients with RA (based on HAQ), quality-adjusted life years and costs over 10 years.
A decision analysis was conducted to quantify the effect of using the MBDA test on RA-related outcomes and costs to private payers and employers. Results of a clinical management study reporting changes to anti-rheumatic drug recommendations after use of the MBDA test informed clinical utility. The effect of treatment changes on HAQ was derived from 5 tight-control and 13 treatment-switch trials. Baseline HAQ scores and the HAQ score relationship with medical costs and quality of life were derived from published National Data Bank for Rheumatic Diseases data.
Use of the MBDA test is projected to improve HAQ scores by 0.09 units in year 1, declining to 0.02 units after 10 years. Over the 10 year time horizon, quality-adjusted life years increased by 0.08 years and costs decreased by US$457 (cost savings in disability-related medical costs, US$659; in productivity costs, US$2137). The most influential variable in the analysis was the effect of the MBDA test on clinician treatment recommendations and subsequent HAQ changes.
The MBDA test aids in the assessment of disease activity in patients with RA by changing treatment decisions, improving the functional status of patients and cost savings. Further validation is ongoing and future longitudinal studies are warranted.
Evaluations often inform future program implementation decisions. However, the implementation context may differ, sometimes substantially, from the evaluation study context. This difference leads to ...uncertainty regarding the relevance of evaluation findings to future decisions. Voluntary interventions pose another challenge to generalizability, as we do not know precisely who will volunteer for the intervention in the future. We present a novel approach for estimating target population average treatment effects among the treated by generalizing results from an observational study to projected volunteers within the target population (the treated group). Our estimation approach can accommodate flexible outcome regression estimators such as Bayesian Additive Regression Trees (BART) and Bayesian Causal Forests (BCF). Our generalizability approach incorporates uncertainty regarding target population treatment status into the posterior credible intervals to better reflect the uncertainty of scaling a voluntary intervention. In a simulation based on real data, we demonstrate that these flexible estimators (BCF and BART) improve performance over estimators that rely on parametric regressions. We use our approach to estimate impacts of scaling up Comprehensive Primary Care Plus, a health care payment model intended to improve quality and efficiency of primary care, and we demonstrate the promise of scaling to a targeted subgroup of practices.
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