Estimation for the nonlinear errors-in-variables model

Estimation of the parameters of the functional nonlinear measurement error model is considered. A simulation bias adjusted (SIMBA) estimation procedure is presented. In the SIMBA procedure, internal Monte Carlo simulation based on the sample data is used to adjust a naive estimator, such as the ordinary least squares estimator, for bias. Let the measurement error variance s2un be a sequence depending on the sample size n, and assume s2un → 0 as n → infinity. Under some regularity conditions, the order in probability convergence rate for the SIMBA estimator is max s4un , n-1/2, while the order in probability convergence rate for the ordinary least squares estimator is max s2un , n-1/2. Monte Carlo simulation is conducted to test the performance of SIMBA for four models: linear model, quadratic model, cosine model and logistic model. Monte Carlo simulation shows that the SIMBA estimation procedure outperforms or is comparable to methods such as simulation extrapolation, regression calibration and adjusted least squares. An example application of SIMBA estimation for the logistic regression model with errors in variables is given. In the example, the relation between minerals from dietary intake and the supplement use for people over 50 is studied. The data are from the two surveys: the Third National Health and Nutrition Examination Survey and the related Supplemental Nutrition Survey. One interesting result is that people whose dietary intake of minerals is high are more likely to take supplements

Assessing the commonly used assumptions in estimating the principal causal effect in clinical trials

In addition to the average treatment effect (ATE) for all randomized patients, sometimes it is important to understand the ATE for a principal stratum, a subset of patients defined by one or more post-baseline variables. For example, what is the ATE for those patients who could be compliant with the experimental treatment? Commonly used assumptions include monotonicity, principal ignorability, and cross-world assumptions of principal ignorability and principal strata independence. Most of these assumptions cannot be evaluated in clinical trials with parallel treatment arms. In this article, we evaluate these assumptions through a 2x2 cross-over study in which the potential outcomes under both treatments can be observed, provided there are no carry-over and study period effects. From this example, it seemed the monotonicity assumption and the within-treatment principal ignorability assumptions did not hold well. On the other hand, the assumptions of cross-world principal ignorability and cross-world principal stratum independence conditional on baseline covaraites seemed to hold well. With the latter assumptions, we estimated the ATE for principal strata, defined by whether the blood glucose standard deviation increased in each treatment period, without relying on the cross-over feature. These estimates were very close to the ATE estimate when exploiting the cross-over feature of the trial. To the best of our knowledge, this article is the first attempt to evaluate the plausibility of commonly used assumptions for estimating ATE for principal strata using the setting of a cross-over trial.Comment: 25 pages, 4 table

Accurate collection of reasons for treatment discontinuation to better define estimands in clinical trials

Background: Reasons for treatment discontinuation are important not only to understand the benefit and risk profile of experimental treatments, but also to help choose appropriate strategies to handle intercurrent events in defining estimands. The current case report form (CRF) commonly in use mixes the underlying reasons for treatment discontinuation and who makes the decision for treatment discontinuation, often resulting in an inaccurate collection of reasons for treatment discontinuation. Methods and results: We systematically reviewed and analyzed treatment discontinuation data from nine phase 2 and phase 3 studies for insulin peglispro. A total of 857 participants with treatment discontinuation were included in the analysis. Our review suggested that, due to the vague multiple-choice options for treatment discontinuation present in the CRF, different reasons were sometimes recorded for the same underlying reason for treatment discontinuation. Based on our review and analysis, we suggest an intermediate solution and a more systematic way to improve the current CRF for treatment discontinuations. Conclusion: This research provides insight and directions on how to optimize the CRF for recording treatment discontinuation. Further work needs to be done to build the learning into Clinical Data Interchange Standards Consortium standards.Comment: 13 pages, 3 figures, 1 tabl

Using principal stratification in analysis of clinical trials

The ICH E9(R1) addendum (2019) proposed principal stratification (PS) as one of five strategies for dealing with intercurrent events. Therefore, understanding the strengths, limitations, and assumptions of PS is important for the broad community of clinical trialists. Many approaches have been developed under the general framework of PS in different areas of research, including experimental and observational studies. These diverse applications have utilized a diverse set of tools and assumptions. Thus, need exists to present these approaches in a unifying manner. The goal of this tutorial is threefold. First, we provide a coherent and unifying description of PS. Second, we emphasize that estimation of effects within PS relies on strong assumptions and we thoroughly examine the consequences of these assumptions to understand in which situations certain assumptions are reasonable. Finally, we provide an overview of a variety of key methods for PS analysis and use a real clinical trial example to illustrate them. Examples of code for implementation of some of these approaches are given in supplemental materials