We show that the method is equivalent or asymptotically equivalent to method-of-moments estimation in linear measurement error modelling. Simulation studies are presented showing that the method produces estimators that are nearly asymptotically unbiased and efficient in standard and nonstandard logistic regression models. An oversimplified but fairly accurate description of the method is that it is method-of-moments estimation using Monte Carlo derived estimating equations.

Trial-based cost-effectiveness studies have appeal because of 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.

A growing number of jurisdictions now request economic data in support of their decision-making procedures for the pricing and/or reimbursement of health technologies. Because more jurisdictions request economic data, the burden on study sponsors and researchers increases. There are many reasons why the cost-effectiveness of health technologies might vary from place to place. Therefore, this report of an ISPOR Good Practices Task Force reviews what national guidelines for economic evaluation say about transferability, discusses which elements of data could potentially vary from place to place, and recommends good research practices for dealing with aspects of transferability, including strategies based on the analysis of individual patient data and based on decision-analytic modeling.

This paper provides theoretical support for the simulation-based estimation procedure, SIMEX, introduced by Cook and Stefanski (1992) for measurement error models. We do so by establishing a strong relationship between SIMEX estimation and jackknife estimation. A resultant of our investigations is the identification of a variance estimation method for SIMEX that parallels jackknife variance estimation. It is shown that the variance estimator is asymptotically valid in simple linear regression measurement error models. Data from the Framingham Heart Study are used to illustrate the variance estimation procedure in logistic regression measurement error models.

We describe a simulation-based method of inference for parametric measurement error models in which the measurement error variance is known or at least well estimated. The method entails adding additional measurement error in known increments to the data, computing estimates from the contaminated data, establishing a trend between these estimates and the variance of the added errors, and extrapolating this trend back to the case of no measurement error.We show that the method is equivalent or asymptotically equivalent to method-of-moments estimation in linear measurement error modelling. Simulation studies are presented showing that the method produces estimators that are nearly asymptotically unbiased and efficient in standard and nonstandard logistic regression models. An oversimplified but fairly accurate description of the method is that it is method-of-moments estimation using Monte Carlo derived estimating equations.