123,864 research outputs found
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
Estimating the Expected Value of Partial Perfect Information in Health Economic Evaluations using Integrated Nested Laplace Approximation
The Expected Value of Perfect Partial Information (EVPPI) is a
decision-theoretic measure of the "cost" of parametric uncertainty in decision
making used principally in health economic decision making. Despite this
decision-theoretic grounding, the uptake of EVPPI calculations in practice has
been slow. This is in part due to the prohibitive computational time required
to estimate the EVPPI via Monte Carlo simulations. However, recent developments
have demonstrated that the EVPPI can be estimated by non-parametric regression
methods, which have significantly decreased the computation time required to
approximate the EVPPI. Under certain circumstances, high-dimensional Gaussian
Process regression is suggested, but this can still be prohibitively expensive.
Applying fast computation methods developed in spatial statistics using
Integrated Nested Laplace Approximations (INLA) and projecting from a
high-dimensional into a low-dimensional input space allows us to decrease the
computation time for fitting these high-dimensional Gaussian Processes, often
substantially. We demonstrate that the EVPPI calculated using our method for
Gaussian Process regression is in line with the standard Gaussian Process
regression method and that despite the apparent methodological complexity of
this new method, R functions are available in the package BCEA to implement it
simply and efficiently
Financial Services to the Unbanked: The Case of the Mzansi Intervention in South Africa
The Mzansi intervention is a major initiative designed to provide banking services to the unbanked South African population. This study investigates the underlying variables that define the choice of a Mzansi account from a consumer perspective. Unlike previous studies, we do not assume that demand for financial services is a given but instead that it is underlain by perceptions and attitudes. Financial attitudes and perceptions are found to exert significant effects on financial choices. In particular, aspirations and forward-looking values are instrumental in facilitating access to finance
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Lessons Learned and Next Steps in Energy Efficiency Measurement and Attribution: Energy Savings, Net to Gross, Non-Energy Benefits, and Persistence of Energy Efficiency Behavior
This white paper examines four topics addressing evaluation, measurement, and attribution of direct and indirect effects to energy efficiency and behavioral programs: Estimates of program savings (gross); Net savings derivation through free ridership / net to gross analyses; Indirect non-energy benefits / impacts (e.g., comfort, convenience, emissions, jobs); and, Persistence of savings
Invariant Causal Prediction for Nonlinear Models
An important problem in many domains is to predict how a system will respond
to interventions. This task is inherently linked to estimating the system's
underlying causal structure. To this end, Invariant Causal Prediction (ICP)
(Peters et al., 2016) has been proposed which learns a causal model exploiting
the invariance of causal relations using data from different environments. When
considering linear models, the implementation of ICP is relatively
straightforward. However, the nonlinear case is more challenging due to the
difficulty of performing nonparametric tests for conditional independence. In
this work, we present and evaluate an array of methods for nonlinear and
nonparametric versions of ICP for learning the causal parents of given target
variables. We find that an approach which first fits a nonlinear model with
data pooled over all environments and then tests for differences between the
residual distributions across environments is quite robust across a large
variety of simulation settings. We call this procedure "invariant residual
distribution test". In general, we observe that the performance of all
approaches is critically dependent on the true (unknown) causal structure and
it becomes challenging to achieve high power if the parental set includes more
than two variables. As a real-world example, we consider fertility rate
modelling which is central to world population projections. We explore
predicting the effect of hypothetical interventions using the accepted models
from nonlinear ICP. The results reaffirm the previously observed central causal
role of child mortality rates
Banking the unbanked: the Mzansi intervention in South Africa:
Purpose
This paper aims to understand household’s latent behaviour decision making in accessing financial services. In this analysis we look at the determinants of the choice of the pre-entry Mzansi account by consumers in South Africa.
Design/methodology/approach
We use 102 variables, grouped in the following categories: basic literacy, understanding financial terms, targets for financial advice, desired financial education and financial perception. Employing a computationally efficient variable selection algorithm we study which variables can satisfactorily explain the choice of a Mzansi account.
Findings
The Mzansi intervention is appealing to individuals with basic but insufficient financial education. Aspirations seem to be very influential in revealing the choice of financial services and to this end Mzansi is perceived as a pre-entry account not meeting the aspirations of individuals aiming to climb up the financial services ladder. We find that Mzansi holders view the account mainly as a vehicle for receiving payments, but on the other hand are debt-averse and inclined to save. Hence although there is at present no concrete evidence that the Mzansi intervention increases access to finance via diversification (i.e. by recruiting customers into higher level accounts and services) our analysis shows that this is very likely to be the case.
Originality/value
The issue of demand side constraints on access to finance have been largely ignored in the theoretical and empirical literature. This paper undertakes some preliminary steps in addressing this gap
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