An opportunity cost approach to sample size calculation in cost-effectiveness analysis

Incorporating Portfolio Uncertainty in Decision Rules for Healthcare Resource Allocation

Cost-effectiveness analysis is widely adopted as a means to inform policy and decision makers in setting priorities for healthcare resource allocation. In resource-constrained settings, decision makers are confronted with healthcare resource reallocation decisions, e.g., moving funds from one or more existing healthcare programs to fund new healthcare programs. The decision-making plane (DMP) has been developed as a means to graphically present the results of reallocating available healthcare resources when healthcare program costs and effects are uncertain. Mapping a value function over the DMP allows the analyst to value all possible combinations of net costs and net effects that may result from reallocating available healthcare resources under conditions of uncertainty. In this paper, we extend this approach to include a change in portfolio risk, stemming from a change in the portfolios of funded healthcare programs, as an additional source of uncertainty, and demonstrate how this can be incorporated into the value function over net costs and net effects for a risk-averse decision maker. The methodology presented in this paper is of particular interest to decision makers who are risk averse, as it will help to better incorporate their preferences in the process of deciding how to best allocate scarce healthcare resources.

Bayesian sample size determination for cost-effectiveness studies with censored data

Cost-effectiveness models are commonly utilized to determine the combined clinical and economic impact of one treatment compared to another. However, most methods for sample size determination of cost-effectiveness studies assume fully observed costs and effectiveness outcomes, which presents challenges for survival-based studies in which censoring exists. We propose a Bayesian method for the design and analysis of cost-effectiveness data in which costs and effectiveness may be censored, and the sample size is approximated for both power and assurance. We explore two parametric models and demonstrate the flexibility of the approach to accommodate a variety of modifications to study assumptions.

Cost-benefit and extended cost-effectiveness analysis of a comprehensive adolescent pregnancy prevention program in Zambia: study protocol for a cluster randomized controlled trial

Background

Early marriages, pregnancies and births are the major cause of school drop-out among adolescent girls in sub-Saharan Africa. Birth complications are also one of the leading causes of death among adolescent girls. This paper outlines a protocol for a cost-benefit analysis (CBA) and an extended cost-effectiveness analysis (ECEA) of a comprehensive adolescent pregnancy prevention program in Zambia. It aims to estimate the expected costs, monetary and non-monetary benefits associated with health-related and non-health outcomes, as well as their distribution across populations with different standards of living.

Methods

The study will be conducted alongside a cluster-randomized controlled trial, which is testing the hypothesis that economic support with or without community dialogue is an effective strategy for reducing adolescent childbearing rates. The CBA will estimate net benefits by comparing total costs with monetary benefits of health-related and non-health outcomes for each intervention package. The ECEA will estimate the costs of the intervention packages per unit health and non-health gain stratified by the standards of living. Cost data include program implementation costs, healthcare costs (i.e. costs associated with adolescent pregnancy and birth complications such as low birth weight, pre-term birth, eclampsia, medical abortion procedures and post-abortion complications) and costs of education and participation in community and youth club meetings. Monetary benefits are returns to education and averted healthcare costs. For the ECEA, health gains include reduced rate of adolescent childbirths and non-health gains include averted out-of-pocket expenditure and financial risk protection. The economic evaluations will be conducted from program and societal perspectives.

Discussion

While the planned intervention is both comprehensive and expensive, it has the potential to produce substantial short-term and long-term health and non-health benefits. These benefits should be considered seriously when evaluating whether such a program can justify the required investments in a setting with scarce resources. The economic evaluations outlined in this paper will generate valuable information that can be used to guide large-scale implementation of programs to address the problem of the high prevalence of adolescent childbirth and school drop-outs in similar settings.

Trial registration

ClinicalTrials.gov, NCT02709967. Registered on 2 March 2016. ISRCTN, ISRCTN12727868. Registered on 4 March 2016.

The Cost Implications of Less Tight Versus Tight Control of Hypertension in Pregnancy (CHIPS Trial)

The CHIPS randomized controlled trial (Control of Hypertension in Pregnancy Study) found no difference in the primary perinatal or secondary maternal outcomes between planned "less tight" (target diastolic 100 mm Hg) and "tight" (target diastolic 85 mm Hg) blood pressure management strategies among women with chronic or gestational hypertension. This study examined which of these management strategies is more or less costly from a third-party payer perspective. A total of 981 women with singleton pregnancies and nonsevere, nonproteinuric chronic or gestational hypertension were randomized at 14 to 33 weeks to less tight or tight control. Resources used were collected from 94 centers in 15 countries and costed as if the trial took place in each of 3 Canadian provinces as a cost-sensitivity analysis. Eleven hospital ward and 24 health service costs were obtained from a similar trial and provincial government health insurance schedules of medical benefits. The mean total cost per woman-infant dyad was higher in less tight versus tight control, but the difference in mean total cost (DM) was not statistically significant in any province: Ontario ($30 191.62 versus $24 469.06; DM $5723, 95% confidence interval, -$296 to $12 272; P=0.0725); British Columbia ($30 593.69 versus $24 776.51; DM $5817; 95% confidence interval, -$385 to $12 349; P=0.0725); or Alberta ($31 510.72 versus $25 510.49; DM $6000.23; 95% confidence interval, -$154 to $12 781; P=0.0637). Tight control may benefit women without increasing risk to neonates (as shown in the main CHIPS trial), without additional (and possibly lower) cost to the healthcare system.

CLINICAL TRIAL REGISTRATION

URL: http://www.clinicaltrials.gov. Unique identifier: NCT01192412.

Trial-based economic evaluations in occupational health: principles, methods, and recommendations

To allocate available resources as efficiently as possible, decision makers need information on the relative economic merits of occupational health and safety (OHS) interventions. Economic evaluations can provide this information by comparing the costs and consequences of alternatives. Nevertheless, only a few of the studies that consider the effectiveness of OHS interventions take the extra step of considering their resource implications. Moreover, the methodological quality of those that do is generally poor. Therefore, this study aims to help occupational health researchers conduct high-quality trial-based economic evaluations by discussing the theory and methodology that underlie them, and by providing recommendations for good practice regarding their design, analysis, and reporting. This study also helps consumers of this literature with understanding and critically appraising trial-based economic evaluations of OHS interventions.

Cost–utility analysis conducted alongside randomized controlled trials: Are economic end points considered in sample size calculations and does it matter?

Background

Many randomized controlled trials (RCTs) collect cost-effectiveness data. Without appropriate sample size calculations, patient recruitment may cease before the cost-effectiveness of the intervention can be established or continue after the cost-effectiveness of the intervention is established beyond doubt.

Purpose

We determined the frequency with which cost-effectiveness is considered in sample size calculations and whether RCT-based economic evaluations are likely to come to inconclusive results at odds with the clinical findings.

Methods

We searched the National Health Service Economic Evaluation Database (NHS EED) to identify RCT-based cost-utility analyses. RCTs that collected individual patient data on costs and quality-adjusted life years (QALYs) were eligible. Studies using models to extrapolate the results of RCTs or with insufficient information on incremental costs and QALYs were excluded.

Results

In total, 38 trials met eligibility criteria. Only one considered cost-effectiveness in sample size calculations. RCTs were less likely to reach definitive conclusions based on the cost-effectiveness results than the primary clinical outcome (15.8% vs. 42.1%; McNemar; p = 0.01). In trials that provided sufficient data, exploratory analysis indicated that the median power to detect important differences was 29.5% for QALYs, 94.1% for costs, and 78.7% for the primary clinical outcome. In three trials (7.9%), a definitely more effective intervention was found to be expensive and probably not cost-effective.

Limitations

Our results reflect trials where authors considered within-trial estimates of cost-effectiveness to be meaningful. In focusing on one primary clinical outcome from each RCT, we have simplified the clinical effectiveness results, although the primary outcome will usually be one that policy makers use in judging the ‘success’ of the intervention.

Conclusions

Economic evaluations conducted alongside RCTs are valuable, but often present inconclusive evidence. Trial results may lead to discordant messages when the most effective intervention is probably not the most cost-effective. Despite methodological advances, trialists rarely assessed the extent to which their trial might resolve the key uncertainties about the cost-effectiveness of interventions. We recommend that grant funders should do more to encourage trialists to include economic end points in sample size calculations, particularly when the majority of costs and benefits of the intervention occur within the time frame of the trial.

Sample size and power for cost-effectiveness analysis (part 1)

Basic sample size and power formulae for cost-effectiveness analysis have been established in the literature. These formulae are reviewed and the similarities and differences between sample size and power for cost-effectiveness analysis and for the analysis of other continuous variables such as changes in blood pressure or weight are described. The types of sample size and power tables that are commonly calculated for cost-effectiveness analysis are also described and the impact of varying the assumed parameter values on the resulting sample size and power estimates is discussed. Finally, the way in which the data for these calculations may be derived are discussed.

Finding legitimacy for the role of budget impact in drug reimbursement decisions

OBJECTIVES

Research has shown that effectiveness, cost-effectiveness, and severity of illness each play a role in drug reimbursement decisions. However, the role of budget impact in such decisions is less obvious. Policy makers almost always demand a budget impact estimate yet seem reluctant to formally include budget impact as a rationing criterion. Health economists even reject budget impact as a legitimate criterion. For these reasons, it is important to examine its use in rationing decisions, and rationales underlying its use.

METHODS

We trace several rationales supporting the use of budget impact through a literature review, supplemented by semistructured interviews with eleven key stakeholders involved in drug reimbursement decisions in the Netherlands.

RESULTS

Budget impact arguments are used in certain instances, although policy makers appear uncomfortable with its use because well described rationales still are lacking. In addition, we identify the following rationales to support budget impact as a rationing criterion: opportunity costs, loss aversion, uncertainty and equal opportunity.

CONCLUSIONS

Budget impact plays a role in drug reimbursement decisions and has rationales to support its use. However, policy makers do not easily admit that they consider budget impact and are even reluctant to explicitly use budget impact as a formal criterion. A debate would strengthen the theoretical foundation of budget impact as a legitimate criterion in the context of drug reimbursement decisions. Such discussion of budget impact's role will also enhance policy-makers' accountability.