|
|
Good Research Practices For Cost-Effectiveness Analysis Alongside Clinical Trials:
The ISPOR RCT-CEA Task Force Report |
|||||||||||||||||||||||||||||||
Task Force Co-Chairs
This report is published
in Value in Health. The citation for this report is: GOOD RESEARCH PRACTICES FOR COST-EFFECTIVENESS ANALYSIS ALONGSIDE CLINICAL TRIALS: THE ISPOR RCT-CEA TASK FORCE REPORT Scott Ramsey1, Richard Willke2, Andrew Briggs3, Ruth Brown4, Martin Buxton5, Anita Chawla6, John Cook7, Henry Glick8, Bengt Liljas9, Diana Petitti10, Shelby Reed11 March 1, 2005 1Fred Hutchinson Cancer Research Center, Seattle, USA; 2 fizer, Inc., Bridgewater, USA; 3University of Oxford, Oxford, UK; 4MEDTAP International, London, UK; 5Brunel University, Uxbridge, Middlesex, UK ; '6Genentech, San Francisco, USA; 7Merck & Co., Inc, Blue Bell, USA; 8University of Pennsylvania, Philadelphia, USA; 9AstraZeneca, Lund, Sweden; 10Kaiser Permanente, Pasadena, USA; 11Duke Clinical Research Institute, Durham, USA Ramsey S, Wilke R, Briggs A, et al. Good research practices for cost-effectiveness analysis alongside clinical trial: The ISPOR RCT-CEA task force report. Value Health 2005;8:521-33. ABSTRACT Objectives: A growing number of prospective clinical trials include economic endpoints. Recognizing the variation in methodology and reporting of these studies, ISPOR chartered a Task Force on Good Research Practices: Randomized Clinical Trials - Cost-Effectiveness Analysis. Its goal was to develop a guidance document for designing, conducting, and reporting of cost-effectiveness analysis conducted as a part of clinical trials. Methods: Task Force co-chairs were selected by the ISPOR Board of Directors. Co-chairs invited panel members to participate. Panel members included representatives from academia, the pharmaceutical industry, and health insurance plans. An outline and draft report developed by the panel were presented at the 2004 International and European ISPOR Meetings, respectively. The manuscript was then submitted to a Reference Group for review and comment. Results: The report addresses issues related to trial design, selecting data elements, database design and management, analysis, and reporting of results. Task Force members agreed that trials should be designed to evaluate effectiveness (rather than efficacy), should include clinical outcome measures, and should obtain health resource use and health state utilities directly from study subjects. Economic data collection should be fully integrated into the study. Analyses should be guided by an analysis plan and hypotheses. An incremental analysis should be conducted using an intention to treat approach. Uncertainty should be characterized. Manuscripts should adhere to established standards for reporting results of cost-effectiveness analyses Conclusions: Trial-based cost-effectiveness studies have appeal due to their high internal validity and timeliness. Improving the quality and uniformity of these studies will increase their value to decision makers who consider evidence of economic value along with clinical efficacy when making resource allocation decisions. I. INTRODUCTION Clinical trials evaluating medicines, medical devices and procedures now commonly assess economic value of these interventions. The growing number of prospective clinical/economic trials reflects both widespread interest in economic information for new technologies and the regulatory and reimbursement requirements of many countries that now consider evidence of economic value along with clinical efficacy. In recent years, research has also improved the methods for design, conduct, and analysis of economic data collected alongside clinical trials. Despite these advances, the literature reveals a great deal of variation in methodology and reporting of these studies. Improving the quality of these studies will enhance the credibility and usefulness of cost-effectiveness analyses to decision-makers worldwide. To foster improvements in the conduct and reporting of trial-based economic analysis, the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) chartered the ISPOR Task Force on Good Research Practices: Randomized Clinical Trials - Cost-Effectiveness Analysis (RCT-CEA). The Task Force co-chairs were selected by the ISPOR Board of Directors, and the co-chairs invited the other panel members to participate. The panel was first assembled in January 2004, communicated monthly, and agreed on an outline and preliminary content that was presented for comment by ISPOR membership at the May 2004 Annual Meeting. The draft report was then written and presented to the ISPOR membership at the October 2004 European Meeting. A volunteer Reference Group of ISPOR members provided valuable comments on the draft report which supported the completion of the final report in February 2005. The purpose of this report is to state the consensus position of this Task Force. The goal for the panel was to develop a guidance document for the design, conduct, and reporting of cost-effectiveness analyses alongside clinical trials. The intended audiences are researchers in academics, industry, and government who design and implement these studies, decision makers who evaluate clinical and economic evidence for formulary and insurance coverage policies, and students of the area. The panel recognizes that advances in methodology for joint clinical/economic analyses will continue, and that clinical/economic trials are heterogeneous in nature. Therefore, the report highlights areas of consensus, emerging methodologies with a diversity of professional opinions, and issues where further research and development are needed. The focus of this report is cost-effectiveness analysis conducted alongside randomized clinical trials designed to test the efficacy or effectiveness for drugs, devices, surgical procedures, or screening interventions, including pragmatic trials. Clinical trials are artificial treatment environments, and do not provide all the economic information needed by decision makers. Trial populations do not commonly reflect patient groups treated in clinical practice, and the time horizon for trials often does not reflect the duration of impact of the intervention. These issues are commonly addressed with modeling. The reader is referred to an earlier guidance document addressing these issues. [1] There are also some common issues in cost-effectiveness analysis that are fundamental to all studies of this nature that will not be addressed in this manuscript. These include study perspective, choice of discount rate for costs and outcomes, type of analysis (e.g., cost-utility, cost-benefit), types of costs that will be included (direct medical, non-medical, etc), and marginal versus average costing. These issues apply to all economic analyses, not just economic studies within clinical trials, and are well described in the literature. II. INITIAL TRIAL DESIGN ISSUESThe quality of economic information that is derived from trials depends on the attributes of the trial’s design. That economic analyses are often described as being conducted ‘alongside’ clinical trials is indicative of an important practical design issue. Economic analysis is rarely the primary purpose of an experimental study. Nevertheless, it is important that the analyst contributes to the design of the study to ensure that the structure of the trial will provide the data necessary for a high quality economic study. Appropriate trial design The distinction between pragmatic and exploratory trials and the corresponding distinction between effectiveness and efficacy is well understood.[2,3] It is generally acknowledged that pragmatic effectiveness trials are the best vehicle for economic studies; however, it is usually necessary to undertake economic evaluations earlier in the development cycle where the focus is on efficacy (including phase III or even phase II drug trials), in order to provide timely information for pricing and reimbursement and our report is meant to apply to both types of trials. Large simple trials [4] are efficient for addressing clinical questions because they capture the main effects of treatments that have small to moderate impacts on large potential populations. They will also be efficient for answering economic questions for diseases or treatments where the bulk of costs derive from primary outcomes that are measured in the trial and for which the quality of life impacts are persistent, and thus can be measured infrequently. An ideal follow-up period for an economic study is independent of the occurrence of clinical events, whether study related or not. All patients should be followed for a common length of time or the full duration of the trial. Discontinuation of data collection due to a clinical event will fail to capture the important aspects of the disease under study: the adverse effects of the event on quality of life, resource use and cost. Sample size and power In an ideal world the economic appraisal would be factored into sample size calculations using standard methods [5,6] based on asymptotic normality, or by simulation.[7] However, it is common for the sample size of the trial to be based on the primary clinical outcomes alone. As a consequence, it is possible that the economic comparisons will be underpowered. Analysts should calculate the likely power of the study at the design stage in order to ascertain whether, given the proposed sample size, it makes sense to undertake an economic appraisal. In many cases, sample size restrictions will necessitate focus on estimation rather than hypothesis testing of the economic outcomes. In cases where researchers wish to set up formal hypotheses for the economic analyses, these should be stated a priori including the thresholds (e.g., $50,000 or $100,000/QALY) and the power to detect when incremental analysis meets or exceeds those thresholds.[8] Study endpoints The choice of primary endpoint in a clinical study may not correspond with the ideal endpoint for economic evaluation. For example, the use of composite clinical endpoints is common in clinical trials (e.g., fatal events and non fatal events combined) in order to provide greater statistical power. However, cost per composite clinical endpoint is often an unsatisfactory summary measure for an economic analysis, in part because the different outcomes are rarely of equal importance. It is recommended that clinical endpoints used in economic evaluations be presented in disaggregated form. We recommend weighting endpoints (e.g., by utilities) so they yield a measure of quality-adjusted life-years (QALYs) in the case of cost-effectiveness analysis, or a monetary benefit measure in the case of cost-benefit analysis. Alternatively, quality of life values may be obtained within the trial at regular intervals and the QALYs estimated as one of the outcomes of the trial. If possible, one should avoid using intermediate endpoints (e.g., percent LDL reduction) as the measure of benefit; however, intermediate outcome measures are often employed when the costs of conducting a long-term trial are prohibitive. When use of intermediate outcomes is unavoidable, additional evidence is needed to link them with long term costs and outcomes. If such a link is not reliable or is unavailable, the analyst should argue for follow-up sufficient to include clinically meaningful disease endpoints. Appropriate follow-up Economic analyses ideally include lifetime costs and outcomes of treatment. However, clinical trials rarely extend beyond a few years and are often conducted over much shorter periods. In practice, consideration of the follow-up period for the trial involves the relationship between intermediate endpoints gathered in the short run and long term disease outcomes – the stronger that relationship, the more a reliance on intermediate endpoints can be justified. III. DATA ELEMENTS The design issues discussed above will impact decisions about which resource use and outcome measures to collect, how to collect them, and how to value them. To begin, we recommend developing a description of the clinical processes for the intervention and how the intervention may impact resource use in the short-term and long-term.[9] In this process, study perspective affects the types of resource use -- both medical and non-medical -- that should be considered for inclusion in the study. For example, the societal perspective might include patients’ costs for transportation, time spent undergoing treatment, caregiver time, and non-medical goods and services attributable to the disease or treatment. After resources have been identified, logistical and cost considerations often require prioritization as to which data elements will be collected. We recommend that analysts focus on ‘big ticket’ items as well as resources that are expected to differ between treatment arms.[10] For the items chosen, the study should collect information on all resource use, not just that considered to be disease- or intervention-related.[11,12] If necessary, the distinction between costs related and unrelated to the disease can be attempted at the analysis phase. For each resource, prospectively determine the level of aggregation desired. As an example, hospitalizations could be considered in disaggregated units, such as nursing time, operating room time, and supplies, or in highly aggregated units such as numbers of hospitalizations or days in the hospital. The decision is typically driven by the characteristics of the intervention under study, resource use patterns expected, and availability of unit costs (also called unit prices or price weights). For practical reasons, the level of aggregation may be varied by whether the resource use is thought to be disease or intervention related. In some trial settings, secondary data such as hospital
bills or claims data are available. These data sources can provide
an inexpensive, detailed accounting of some resources consumed by
patients, and should be used when available.[13] Unit costs should be consistent with measured resource use, the study’s perspective, and its time horizon. For example, if the level of resource aggregation is hospital days that include ICU and non-ICU days, the unit costs should reflect the costs of each type of service [13,14]; if the study is conducted from the societal perspective, unit costs should reflect social opportunity cost. In selecting a costing approach, analysts should weigh issues of accuracy/bias, cost, feasibility, and generalizability.[15] For a more thorough discussion on costing issues, we refer readers to Drummond et al.[16] and Luce et al.[9] Unit cost estimates are rarely derived via direct observation of patients in trials. Most often they are derived from substudies that are divorced from the trial itself. Sometimes, unit costs will be estimated from trial centers, but more commonly they are derived from national data.[17-20] If a reliable method for cost imputation exists (e.g., diagnostic group weights), one can combine the two methods by collecting a limited set of unit costs in a number of countries and imputing the remainder of costs.[21] Ideally, unit costs to be used for resource costing should be finalized prior to unblinding the trial data. Because relative costs can affect resource use, in general one should use unit cost estimates that are specific to the precise intervention under study and specific events of interest in the trial, but which are generalizable to the population that the study is intended to inform. (e.g., the pooled analysis, when resource use in a country is multiplied by unit costs from that country), results have to be converted to a common currency if they are to be compared appropriately. Purchasing power parity adjustments are recommended for such conversion.[22,23] Selecting and Tracking Measures of Outcomes Because cost-utility analyses are widely accepted, we recommend that analysts collect preference weights as part of clinical trials. The most common method of assessing preferences is the use of a preference-weighted health state classification system such as the EuroQol-5D [24,25], one of the 3 versions of the Health Utilities Index (HUI) [26-29], or the Quality of Well-Being Scale (QWB) [30]. Analysts may also consider the inclusion of a rating scale to measure patient-based preferences.[31] Frequency and timing of these assessments should capture changes in patients’ quality of life that may be affected by the treatment, but will be influenced by the disease severity of the study population, the study duration, the timing of trial visits, and patient burden.[32] Other options for collecting preference data include direct-elicitation methods such as standard gamble or time-tradeoff exercises. These methods have certain theoretical advantages, however their use in clinical trials is often difficult. Trained interviewers or computerized applications are routinely used to conduct such exercises.[16,33] Also, many respondents have difficulty understanding and completing the exercises.[34-36] Finally, there is some evidence that these measures are generally unresponsive to changes in health status.[37-40] At present, the balance between the feasibility and desirability of using direct elicitation methods in clinical trials remains an issue to be decided on a case-by-case basis. IV. DATABASE DESIGN AND MANAGEMENT Ideally, collection and management of the economic data should be fully integrated into the clinical data. As such, there should be no distinction between the clinical and economic data elements. As with any prospective study, there should be a plan for ongoing data quality monitoring to address missing and poor quality data issues immediately. Queries should be managed on an ongoing basis rather than at the end of the trial to maximize data completeness and quality, and the timeliness of final analysis. Informed consent for clinical studies does not routinely include provisions for collection economic data, particularly from third party databases. Therefore, explicit language should be included in trial consent documents. The consent forms should allow for capture of pre- and post-trial economic data if such data are necessary for the economic analysis. Collection of economic data may reveal adverse events, such as hospitalization, not otherwise found in the clinical data. Data handling procedures are necessary to maintain consistency between economic and safety databases. In reality, the clinical analysts are usually separate from the economic analysts. The clinical data elements and data formatting procedures needed for the economic analysis should be pre-specified such that transfer of all necessary data for the economics study is timely and complete. V. ANALYSIS Guiding Principles The analysis of economic measures should be guided by a data analysis plan. A pre-specified plan is particularly important if formal tests of hypotheses are planned. Any tests of hypotheses that are not specified within the plan should be reported as exploratory. In writing the plan, specify whether regression or other multivariable analyses will be used to improve precision and to adjust for treatment group imbalances. The plan should also identify any selected subgroups and state whether a non-intention to treat analysis will be conducted. While the specific analytic methods used in the analysis of resource use, cost and cost-effectiveness are likely to differ, there are several analysis features that should be common to all economic analyses alongside clinical trials:
The purpose of clinical trial cost analysis is to estimate costs, cost differences associated with treatment, the variability of differences and whether the differences occurred by chance. Once resources have been identified and valued, differences between groups must be summarized. Arithmetic mean cost differences are generally considered the most appropriate and robust measure.[41] However, cost data often do not conform to the assumptions for standard statistical tests for comparing differences in arithmetic means.[42-44] They are usually right skewed and truncated at zero due to small numbers of high-resource-use patients, many patients who incur no costs, and the impossibility of incurring costs less than zero. In most cases, the nonparametric bootstrap is an appropriate method to compare means and calculate confidence intervals.[45,46] Other common nonparametric tests (e.g. Wilcoxon) compare medians and not means, and thus are not appropriate.[47-49] Transformations to normalize the distribution are not straightforward and are often sensitive to departures from distributional assumptions. Re-transformation to the original scale of costs must include transformation of error terms. [50-54] The same distributional issues that affect univariate tests of costs also affect use of costs as a dependent variable in a multivariate regression analyses. The underlying distribution of costs should be carefully assessed to determine the most appropriate approach to conduct statistical inference on the costs between treatment groups.[55] The choice of the multivariate model requires careful consideration: ordinary least squares (OLS) and generalized linear models (GLM) perform differently in terms of bias and efficiency of estimation, depending upon the underlying data distribution.[51] If differences in resource use or subsets of costs are to be estimated, similar considerations regarding the appropriateness of statistical tests based on distributional assumptions should be applied. When study participants use large amounts of medical services that are unrelated to the disease or treatment under study, it may be difficult to detect the influence of the treatment on total health care costs. One approach to addressing this problem is to conduct secondary analyses that evaluate costs that are considered to be related to the disease or treatment under study. If such analyses are performed, it is important to identify services that were deemed ‘disease related’ versus those deemed ‘unrelated,’ and to display costs for each component in the treatment and control arms. Outcomes When one of the trial’s clinical endpoints is also used as the outcome for the cost-effectiveness analysis (e.g., in-trial mortality), it is generally most transparent to adopt the methods used in the clinical analysis for the primary analysis plan, particularly if the clinical result is cited in product labeling or a publication. In some cases, the clinical analysis methods are not appropriate for economic analysis (e.g., the clinical analyis may focus on relative treatment differences while the economic analysis needs absolute treatment differences); if other outcomes are used for the economic analysis, the linkage between the clinical and economic measures should be clearly specified. Analyses of outcomes for the cost-effectiveness study may employ multivariable or other methods that are consistent with the cost analysis or otherwise appropriate for the data.[56-60] Cost-effectiveness analysis should still be performed when the clinical study fails to demonstrate a statistically significant difference in clinical endpoints. In situations where cost-minimization analysis is conducted, the analyst should also conduct joint analysis of costs and outcomes to convey information about the likelihood of an intervention being cost-effective. Using non-clinical effectiveness endpoints, such as QALY’s, involves both construction and analysis. Health state utilities, either collected directly from trial patients or imputed based on observed health states, can be transformed into QALYs using standard area-under-the-curve methods [16; 61]; a recent consideration involves adjusting for changes in health [62]. Simple analysis of means is the usual starting point; refinements may include adjusting for ceiling effects [63] and modeling of longitudinal effects [64,65]. Missing and Censored Data Missing data are inevitable in economic analyses conducted alongside trials. Such data can include item-level missingness and missingness due to censoring. In analyzing datasets with missing data, one must determine the nature of the missing data and then define an approach for dealing with the missing data. Missing data may bear no relation to observed or unobserved factors in the population (missing completely at random), have a relationship to observed variables (missing at random), or be related to unobserved factors (not missing at random).[66] Eliminating cases with missing data is not recommended as it may introduce bias or severely reduce power to test hypotheses. However, ignoring small amounts of missing data is acceptable if a reasonable case can be made that doing so is unlikely to bias treatment group comparisons. Imputation refers to replacing missing fields with estimates. If one chooses to impute missing data, most experts recommend multiple imputation approaches, as they reflect the uncertainty that is inherent when replacing missing data.[67-69] Most commonly used statistical software packages include programs for imputation of missing data. A review of these programs can be found at www.multiple-imputation.com.[70] Censoring can be addressed with a number of approaches. Most assume that censoring is either completely at random [71] or at random [72-76]. However, non-random censoring is common and external data sources for similar patients may be required to both identify and address it. Summary Measures One or more summary measures should be used to characterize the relative value of the treatments in the clinical trial. Three general classes of summary measures are available that differ in how the incremental costs and outcomes are combined into a single metric:
The difference measures and probability measures are calculated for specific values of “willingness to pay” or cost-effectiveness thresholds. Because these values may not be known and/or vary among health care decision-makers, one should evaluate the summary measure over a reasonable range of values. Uncertainty Results of economic assessments in trials are subject to a number of sources of uncertainty, including sampling uncertainty, uncertainty in parameters such as unit costs and the discount rate, and -- when missing data are present -- imputation-related uncertainty. Sampling Uncertainty: Because economic outcomes in trials are the result of a single sample drawn from the population, one should report the variability in these outcomes that arises from such sampling. Variability should be reported for within-group estimates of costs and outcomes, between-group differences in costs and outcomes, and the comparison of costs and outcomes. One of the most common measures of this variability is the confidence interval. Policy inferences about adoption of a therapy should be based on one's level of confidence that its cost for a unit of outcome, for example, a quality-adjusted life year, is less than one's maximum willingness to pay. Thus, one should report ranges of ceiling ratios for which one 1) is confident that the therapy is good value for the cost, 2) is confident that the therapy is not good value, and 3) cannot be confident that the therapies differ from one another. Policy makers can then draw inferences by identifying their maximum willingness to pay and determining into which of the ranges it falls. These ranges of ceiling ratios where one can and cannot be confident about a therapy's value can be calculated by use of confidence intervals for cost-effectiveness ratios [82,83], confidence intervals for net monetary benefit, or the acceptability curve. One advantage of the confidence interval for the cost-effectiveness ratio is that its limits directly define the boundaries between these ranges. One advantage of the acceptability curve is that it defines the boundaries between these ranges for varying levels of confidence that range from 0 to 100%. Parameter Uncertainty: Uncertainty related to parameter estimates such as unit costs and the discount rate should be assessed by use of sensitivity analysis. For example, if one uses a discount rate of 3%, one may want to assess the impact of this assumption by repeating the analysis, but using a 1% or a 5% rate. Analysts should evaluate all parameters which, when varied, have the potential to influence policy decisions. Measures of stochastic uncertainty and sensitivity analysis for parameter uncertainty are complements, not substitutes. Thus, when conducting sensitivity analysis, one should report both the revised point estimate and revised 95% confidence intervals that result from the sensitivity analysis. Imputation Uncertainty: Finally, some methods employed to address missing or censored data (e.g., use of an imputed mean) may artificially reduce estimates of stochastic uncertainty. One should make efforts to address this shrinkage when reporting stochastic uncertainty, for example, by bootstrapping the entire imputation and estimation process. Identifying and addressing threats to external validity/generalizability Due to the “artificiality” of most clinical trials, they have high internal, but may have low external validity. The threats to external validity come from:
The external validity can best be increased by making the trial more naturalistic during the design phase of the trial. [16,84] Additional threats arise with international trials, as treatment pathways, patient and health care provider behavior, supply and financing of health care, and unit costs (prices) can differ tremendously between countries.[85-92] Pooled results may not be representative of any one country, but the sample size is usually not large enough to analyze countries separately. It is common to apply country-specific unit costs for pooled trial resource use to estimate country-specific costs. In practice this approach yields few qualitative differences in summary measures of cost-effectiveness among countries of similar levels of economic development but may not adjust for important country-specific differences.[93,94] Rather, inter-country differences in population characteristics and treatment patterns are more likely to influence summary measures between countries rather than differences in unit costs. Recommended approaches to address this issue include [93,95-97] : Modeling beyond the time horizon of the trial The cost-effectiveness observed within the trial may be substantially different from what would have been observed with continued follow-up. Modeling is used to estimate costs and outcomes that one projects would have been observed had observation been prolonged. When modeling beyond the follow-up period for the trial, it is important to project costs and outcomes over the expected duration of treatment and its effects. >Direct modeling of long term costs and outcomes is feasible when the trial period is long enough, or if at least a subset of patients are observed for a longer time and provide a basis for estimating other patients’ outcomes. Parametric survival models estimated on trial data are recommended for such projections. In cases where such direct modeling is not feasible, it may be possible to “marry” trial data to long-term observational data in a model. In either case, good modeling practices should be followed. The reader is referred to the consensus position of the ISPOR Task Force on Good Research Practices—Modeling Studies for discussion of modeling issues.[1] Cost-effectiveness ratios should be calculated at various time horizons (e.g., 2, 5, 10 yrs, or as appropriate for the disease), both to accommodate the needs of decision makers and to provide a “trajectory” of summary measures over time. The effects of long-term health care costs not directly related to treatment should be taken into account as well as possible. [98] As always, assumptions used must be described and justified, and the uncertainty associated with projections must be taken into account. Subgroup analysis The dangers of spurious sub-group effects are well known. For example, the probability of finding a difference due solely to random variation increases with the number of differences examined unless the alpha-level is scrupulously adjusted. Yet economics requires a marginal approach, so proper subgroup analysis can be vital to decision-makers. The focus should be on testing treatment interactions on the absolute scale, with a justification for choice of scale used. In cases where pre-specified clinical interactions are significant, subgroup analyses may be justified. Subgroup analysis based on pre-specified economic interactions should also be reported. VI. REPORTING THE METHODS AND RESULTS We anticipate that the results of an economic analysis will have a variety of audiences. Correspondingly, detailed and comprehensive information on the methods and results should be readily available to any interested reader. Journal word limits often necessitate parsimony in reporting; therefore, we recommend that detailed technical reports be made available on the world wide web. A number of organizations have developed minimum reporting standards for economic analyses (e.g., study perspective, discount rate, marginal versus average outcomes and analyses).[99,100] The principles in these should be adhered to in all economic studies. Here, we highlight issues particular to economic studies conducted alongside clinical trials. The report should include these elements: Trial Related Issues Data for the Economic Study Methods of Analysis Results When there are economic analyses alongside several clinical trials for a given intervention, attempts may be made to estimate a summary cost-effectiveness ratio across trials (although the methods for this are not perfectly straightforward, i.e., clearly not a simple average of the individual incremental cost-effectiveness ratios). Data from economic analyses done in the context of trials may also be used in independent cost-effectiveness models based on decision-analysis or meta-analyses.[84] To facilitate synthesis, report means and standard errors for the incremental costs and outcomes and their correlation. VII. CONCLUSIONS
As decision makers increasingly demand evidence of economic value for
health care interventions, conducting high quality economic analyses
alongside clinical studies is desirable because they provide timely
information with high internal validity. This ISPOR RCT-CEA Task
Force Report is intended to provide guidance to improve their quality
and consistency. The Task Force recognizes that there are areas where
future methodological research could further improve the quality and
usefulness of these studies. Examples here include (among many):
new approaches for pooling and analyzing data from multinational trials;
issues related to multiple trial analysis, such as Bayesian learning
designs, pooling of clinical trial data, or meta-analysis; design and
analysis in trials where outcomes are valued in monetary units (i.e.,
willingness-to-pay studies); methods for projecting trial findings;
appropriate methods for
a priori selection of items of resource use to be included in
trial protocols (e.g., whether to include outpatient services, non-study
drugs), and; selecting levels of aggregation of resources necessary for
discriminating between intervention and control (e.g., counts of
hospitalizations versus length of stay, etc.). REFERENCES Table 1. Core Recommendations for Conducting Economic Analyses Alongside Clinical Trials
|
|||||||||||||||||||||||||||||||
|
|
|
Contact ISPOR @
info@ispor.org
| View Legal Disclaimer |