Analysis of uncertainty in health care cost-effectiveness studies: an introduction to statistical issues and methods

Cost-effectiveness analysis is now an integral part of health technology assessment and addresses the question of whether a new treatment or other health care program offers good value for money. In this paper we introduce the basic framework for decision making with cost-effectiveness data and then review recent developments in statistical methods for analysis of uncertainty when cost-effectiveness estimates are based on observed data from a clinical trial. Although much research has focused on methods for calculating confidence intervals for cost-effectiveness ratios using bootstrapping or Fieller’s method, these calculations can be problematic with a ratio-based statistic where numerator and=or denominator can be zero. We advocate plotting the joint density of cost and effect differences, together with cumulative density plots known as cost-effectiveness acceptability curves (CEACs) to summarize the overall value-for-money of interventions. We also outline the net-benefit formulation of the cost-effectiveness problem and show that it has particular advantages over the standard incremental cost-effectiveness ratio formulation

On the probability of cost-effectiveness using data from randomized clinical trials

BACKGROUND: Acceptability curves have been proposed for quantifying the probability that a treatment under investigation in a clinical trial is cost-effective. Various definitions and estimation methods have been proposed. Loosely speaking, all the definitions, Bayesian or otherwise, relate to the probability that the treatment under consideration is cost-effective as a function of the value placed on a unit of effectiveness. These definitions are, in fact, expressions of the certainty with which the current evidence would lead us to believe that the treatment under consideration is cost-effective, and are dependent on the amount of evidence (i.e. sample size). METHODS: An alternative for quantifying the probability that the treatment under consideration is cost-effective, which is independent of sample size, is proposed. RESULTS: Non-parametric methods are given for point and interval estimation. In addition, these methods provide a non-parametric estimator and confidence interval for the incremental cost-effectiveness ratio. An example is provided. CONCLUSIONS: The proposed parameter for quantifying the probability that a new therapy is cost-effective is superior to the acceptability curve because it is not sample size dependent and because it can be interpreted as the proportion of patients who would benefit if given the new therapy. Non-parametric methods are used to estimate the parameter and its variance, providing the appropriate confidence intervals and test of hypothesis

The Viborg vascular (VIVA) screening trial of 65-74 year old men in the central region of Denmark: study protocol

<p>Abstract</p> <p>Background</p> <p>Screening for abdominal aortic aneurysm (AAA) of men aged 65-74 years reduces the AAA-related mortality and is generally considered cost effective. Despite of this only a few national health care services have implemented permanent programs.</p> <p>Around 10% of men in this group have peripheral arterial disease (PAD) defined by an ankle brachial systolic blood pressure index (ABI) below 0.9 resulting in an increased mortality-rate of 25-30%. In addition well-documented health benefits may be achieved through primary prophylaxis by initiating systematic cholesterol-lowering, smoking cessation, low-dose acetylsalicylic acid (aspirins), exercise, a healthy diet and blood-pressure control altogether reducing the increased risks for cardiovascular disease by at least 20-25%.</p> <p>The benefits of combining screening for AAA and PAD seem evident; yet they remain to be established. The objective of this study is to assess the efficacy and the cost-effectiveness of a combined screening program for AAA, PAD and hypertension.</p> <p>Methods</p> <p>The Viborg Vascular (VIVA) screening trial is a randomized, clinically controlled study designed to evaluate the benefits of vascular screening and modern vascular prophylaxis in a population of 50,000 men aged 65-74 years. Enrolment started October 2008 and is expected to stop in October 2010. The primary outcome is all-cause mortality. The secondary outcomes are cardiovascular mortality, AAA-related mortality, hospital services related to cardiovascular conditions, prevalence of AAA, PAD and potentially undiagnosed hypertension, health-related quality of life and cost effectiveness. Data analysis by intention to treat.</p> <p>Results</p> <p>Major follow-up will be performed at 3, 5 and 10 years and final study result after 15 years.</p> <p>Trial registration</p> <p>ClinicalTrials.gov NCT00662480</p

Dynamic Health Policies for Controlling the Spread of Emerging Infections: Influenza as an Example

The recent appearance and spread of novel infectious pathogens provide motivation for using models as tools to guide public health decision-making. Here we describe a modeling approach for developing dynamic health policies that allow for adaptive decision-making as new data become available during an epidemic. In contrast to static health policies which have generally been selected by comparing the performance of a limited number of pre-determined sequences of interventions within simulation or mathematical models, dynamic health policies produce “real-time” recommendations for the choice of the best current intervention based on the observable state of the epidemic. Using cumulative real-time data for disease spread coupled with current information about resource availability, these policies provide recommendations for interventions that optimally utilize available resources to preserve the overall health of the population. We illustrate the design and implementation of a dynamic health policy for the control of a novel strain of influenza, where we assume that two types of intervention may be available during the epidemic: (1) vaccines and antiviral drugs, and (2) transmission reducing measures, such as social distancing or mask use, that may be turned “on” or “off” repeatedly during the course of epidemic. In this example, the optimal dynamic health policy maximizes the overall population's health during the epidemic by specifying at any point of time, based on observable conditions, (1) the number of individuals to vaccinate if vaccines are available, and (2) whether the transmission-reducing intervention should be either employed or removed

Neonatal hearing screening: modelling cost and effectiveness of hospital- and community-based screening

BACKGROUND: Children with congenital hearing impairment benefit from early detection and management of their hearing loss. These and related considerations led to the recommendation of universal newborn hearing screening. In 2001 the first phase of a national Newborn Hearing Screening Programme (NHSP) was implemented in England. Objective of this study was to assess costs and effectiveness for hospital and community-based newborn hearing screening systems in England based on data from this first phase with regard to the effects of alterations to parameter values. METHODS: Design: Clinical effectiveness analysis using a Markov Model. Outcome measure: quality weighted detected child months (QCM). RESULTS: Both hospital and community programmes yielded 794 QCM at the age of 6 months with total costs of £3,690,000 per 100,000 screened children in hospital and £3,340,000 in community. Simulated costs would be lower in hospital in 48% of the trials. Any statistically significant difference between hospital and community in prevalence, test sensitivity, test specificity and costs would result in significant differences in cost-effectiveness between hospital and community. CONCLUSION: This modelling exercise informs decision makers by a quantitative projection of available data and the explicit and transparent statements about assumptions and the degree of uncertainty. Further evaluation of the cost-effectiveness should focus on the potential differences in test parameters and prevalence in these two settings

Using the net benefit regression framework to construct cost-effectiveness acceptability curves: an example using data from a trial of external loop recorders versus Holter monitoring for ambulatory monitoring of "community acquired" syncope

BACKGROUND: Cost-effectiveness acceptability curves (CEACs) describe the probability that a new treatment or intervention is cost-effective. The net benefit regression framework (NBRF) allows cost-effectiveness analysis to be done in a simple regression framework. The objective of the paper is to illustrate how net benefit regression can be used to construct a CEAC. METHODS: One hundred patients referred for ambulatory monitoring with syncope or presyncope were randomized to a one-month external loop recorder (n = 49) or 48-hour Holter monitor (n = 51). The primary endpoint was symptom-rhythm correlation during monitoring. Direct costs were calculated based on the 2003 Ontario Health Insurance Plan (OHIP) fee schedule combined with hospital case costing of labour, materials, service and overhead costs for diagnostic testing and related equipment. RESULTS: In the loop recorder group, 63.27% of patients (31/49) had symptom recurrence and successful activation, compared to 23.53% in the Holter group (12/51). The cost in US dollars for loop recording was $648.50 and$212.92 for Holter monitoring. The incremental cost-effectiveness ratio (ICER) of the loop recorder was $1,096 per extra successful diagnosis. The probability that the loop recorder was cost-effective compared to the Holter monitor was estimated using net benefit regression and plotted on a CEAC. In a sensitivity analysis, bootstrapping was used to examine the effect of distributional assumptions. CONCLUSION: The NBRF is straightforward to use and interpret. The resulting uncertainty surrounding the regression coefficient relates to the CEAC. When the link from the regression's p-value to the probability of cost-effectiveness is tentative, bootstrapping may be used

Evaluation of a system of structured, pro-active care for chronic depression in primary care: a randomised controlled trial

Background: People with chronic depression are frequently lost from effective care, with resulting psychological, physical and social morbidity and considerable social and financial societal costs. This randomised controlled trial will evaluate whether regular structured practice nurse reviews lead to better mental health and social outcomes for these patients and will assess the cost-effectiveness of the structured reviews compared to usual care. The hypothesis is that structured, pro-active care of patients with chronic depression in primary care will lead to a cost-effective improvement in medical and social outcomes when compared with usual general practitioner (GP) care.Methods/Design: Participants were recruited from 42 general practices throughout the United Kingdom. Eligible participants had to have a history of chronic major depression, recurrent major depression or chronic dsythymia confirmed using the Composite International Diagnostic Interview (CIDI). They also needed to score 14 or above on the Beck Depression Inventory (BDI-II) at recruitment.Once consented, participants were randomised to treatment as usual from their general practice (controls) or the practice nurse led intervention. The intervention includes a specially prepared education booklet and a comprehensive baseline assessment of participants' mood and any associated physical and psycho-social factors, followed by regular 3 monthly reviews by the nurse over the 2 year study period. At these appointments intervention participants' mood will be reviewed, together with their current pharmacological and psychological treatments and any relevant social factors, with the nurse suggesting possible amendments according to evidence based guidelines. This is a chronic disease management model, similar to that used for other long-term conditions in primary care. The primary outcome is the BDI-II, measured at baseline and 6 monthly by self-complete postal questionnaire. Secondary outcomes collected by self-complete questionnaire at baseline and 2 years include social functioning, quality of life and data for the economic analyses. Health service data will be collected from GP notes for the 24 months before recruitment and the 24 months of the study.Discussion: 558 participants were recruited, 282 to the intervention and 276 to the control arm. The majority were recruited via practice database searches using relevant READ codes