17,798 research outputs found
Cost-effectiveness analysis in R using a multi-state modelling survival analysis framework: a tutorial
This tutorial provides a step-by-step guide to performing cost-effectiveness analysis using a multi-state modelling approach. Alongside the tutorial we provide easy-to-use functions in the statistics package R. We argue this multi-state modelling approach using a package such as R has advantages over approaches where models are built in a spreadsheet package. In particular, using a syntax-based approach means there is a written record of what was done and the calculations are transparent. Reproducing the analysis is straightforward as the syntax just needs to be run again. The approach can be thought of as an alternative way to build a Markov decision analytic model, which also has the option to use a state-arrival extended approach if the Markov property does not hold. In the state-arrival extended multi-state model a covariate that represents patients’ history is included allowing the Markov property to be tested. We illustrate the building of multi-state survival models, making predictions from the models and assessing fits. We then proceed to perform a cost-effectiveness analysis including deterministic and probabilistic sensitivity analyses. Finally, we show how to create two common methods of visualising the results, namely cost-effectiveness planes and cost-effectiveness acceptability curves. The analysis is implemented entirely within R. It is based on adaptions to functions in the existing R package mstate, to accommodate parametric multi-state modelling which facilitates extrapolation of survival curves
Methods for Population Adjustment with Limited Access to Individual Patient Data: A Review and Simulation Study
Population-adjusted indirect comparisons estimate treatment effects when
access to individual patient data is limited and there are cross-trial
differences in effect modifiers. Popular methods include matching-adjusted
indirect comparison (MAIC) and simulated treatment comparison (STC). There is
limited formal evaluation of these methods and whether they can be used to
accurately compare treatments. Thus, we undertake a comprehensive simulation
study to compare standard unadjusted indirect comparisons, MAIC and STC across
162 scenarios. This simulation study assumes that the trials are investigating
survival outcomes and measure continuous covariates, with the log hazard ratio
as the measure of effect. MAIC yields unbiased treatment effect estimates under
no failures of assumptions. The typical usage of STC produces bias because it
targets a conditional treatment effect where the target estimand should be a
marginal treatment effect. The incompatibility of estimates in the indirect
comparison leads to bias as the measure of effect is non-collapsible. Standard
indirect comparisons are systematically biased, particularly under stronger
covariate imbalance and interaction effects. Standard errors and coverage rates
are often valid in MAIC but the robust sandwich variance estimator
underestimates variability where effective sample sizes are small. Interval
estimates for the standard indirect comparison are too narrow and STC suffers
from bias-induced undercoverage. MAIC provides the most accurate estimates and,
with lower degrees of covariate overlap, its bias reduction outweighs the loss
in effective sample size and precision under no failures of assumptions. An
important future objective is the development of an alternative formulation to
STC that targets a marginal treatment effect.Comment: 73 pages (34 are supplementary appendices and references), 8 figures,
2 tables. Full article (following Round 4 of minor revisions). arXiv admin
note: text overlap with arXiv:2008.0595
A General Framework for Updating Belief Distributions
We propose a framework for general Bayesian inference. We argue that a valid
update of a prior belief distribution to a posterior can be made for parameters
which are connected to observations through a loss function rather than the
traditional likelihood function, which is recovered under the special case of
using self information loss. Modern application areas make it is increasingly
challenging for Bayesians to attempt to model the true data generating
mechanism. Moreover, when the object of interest is low dimensional, such as a
mean or median, it is cumbersome to have to achieve this via a complete model
for the whole data distribution. More importantly, there are settings where the
parameter of interest does not directly index a family of density functions and
thus the Bayesian approach to learning about such parameters is currently
regarded as problematic. Our proposed framework uses loss-functions to connect
information in the data to functionals of interest. The updating of beliefs
then follows from a decision theoretic approach involving cumulative loss
functions. Importantly, the procedure coincides with Bayesian updating when a
true likelihood is known, yet provides coherent subjective inference in much
more general settings. Connections to other inference frameworks are
highlighted.Comment: This is the pre-peer reviewed version of the article "A General
Framework for Updating Belief Distributions", which has been accepted for
publication in the Journal of Statistical Society - Series B. This article
may be used for non-commercial purposes in accordance with Wiley Terms and
Conditions for Self-Archivin
Deductive semiparametric estimation in Double-Sampling Designs with application to PEPFAR
Non-ignorable dropout is common in studies with long follow-up time, and it
can bias study results unless handled carefully. A double-sampling design
allocates additional resources to pursue a subsample of the dropouts and find
out their outcomes, which can address potential biases due to non-ignorable
dropout. It is desirable to construct semiparametric estimators for the
double-sampling design because of their robustness properties. However,
obtaining such semiparametric estimators remains a challenge due to the
requirement of the analytic form of the efficient influence function (EIF), the
derivation of which can be ad hoc and difficult for the double-sampling design.
Recent work has shown how the derivation of EIF can be made deductive and
computerizable using the functional derivative representation of the EIF in
nonparametric models. This approach, however, requires deriving the mixture of
a continuous distribution and a point mass, which can itself be challenging for
complicated problems such as the double-sampling design. We propose
semiparametric estimators for the survival probability in double-sampling
designs by generalizing the deductive and computerizable estimation approach.
In particular, we propose to build the semiparametric estimators based on a
discretized support structure, which approximates the possibly continuous
observed data distribution and circumvents the derivation of the mixture
distribution. Our approach is deductive in the sense that it is expected to
produce semiparametric locally efficient estimators within finite steps without
knowledge of the EIF. We apply the proposed estimators to estimating the
mortality rate in a double-sampling design component of the President's
Emergency Plan for AIDS Relief (PEPFAR) program. We evaluate the impact of
double-sampling selection criteria on the mortality rate estimates
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