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The ICH E9 (R1) guidance and the related estimand framework propose to clearly define and separate the clinical question of interest formulated as estimand from the estimation method. With that it becomes important to assess the validity of the estimation method and the assumptions that must be made. When going beyond the intention to treat analyses that can rely on randomization, causal inference is usually used to discuss the validity of estimation methods for the estimand of interest. In pharmacometrics, mixed‐effects models are routinely used to analyze longitudinal clinical trial data; however, they are rarely discussed as a method for causal inference. Here, we evaluate nonlinear mixed‐effects modeling and simulation (NLME M&S) in the context of causal inference as a standardization method for longitudinal data in the presence of confounders. Standardization is a well‐known method in causal inference to correct for confounding by analyzing and combining results from subgroups of patients. We show that nonlinear mixed‐effects modeling is a particular implementation of standardization that conditions on individual parameters described by the random effects of the mixed‐effects model. As an example, we use a simulated clinical trial with within‐subject dose titration. Being interested in the outcome of the hypothetical situation that patients adhere to the planned treatment schedule, we put assumptions in a causal diagram. From the causal diagram, conditional independence assumptions are derived either by conditioning on the individual parameters or on earlier outcomes. With both conditional independencies unbiased estimates can be obtained. [ABSTRACT FROM AUTHOR]

Rationale: CFTR (cystic fibrosis transmembrane conductance regulator) dysfunction is associated with mucus accumulation and worsening chronic obstructive pulmonary disease (COPD) symptoms. Objectives: The aim of this phase IIb dose-finding study was to compare a CFTR potentiator, icenticaftor (QBW251), with placebo in patients with COPD and chronic bronchitis. Methods: Patients with COPD on triple therapy for at least three months were randomized to six treatment arms (icenticaftor 450, 300, 150, 75, or 25mg or placebo twice daily [b.i.d.]) in a 24-week, multicenter, parallel-group, double-blind study. The primary endpoint was change from baseline in trough FEV1 after 12weeks. Secondary endpoints included change from baseline in trough FEV1 and Evaluating Respiratory Symptoms in COPD (E-RS) total and cough and sputum scores after 24weeks. Multiple comparison procedure-modeling was conducted to characterize dose-response relationship. Rescue medication use, exacerbations, and change in serum fibrinogen concentration after 24weeks were assessed in exploratory and post hoc analyses, respectively. Measurements and Main Results: Nine hundred seventy-four patients were randomized. After 12weeks of icenticaftor treatment, no dose-response relationship for change from baseline in trough FEV1 was observed; however, it was observed for E-RS cough and sputum score. A dose-response relationship was observed after 24weeks for trough FEV1, E-RS cough and sputum and total scores, rescue medication use, and fibrinogen. A dose of 300mg b.i.d. was consistently the most effective. Improvements for 300mg b.i.d. versus placebo were also seen in pairwise comparisons of these endpoints. All treatments were well tolerated. Conclusions: The primary endpoint was negative, as icenticaftor did not improve trough FEV1 over 12weeks. Although the findings must be interpreted with caution, icenticaftor improved trough FEV1; reduced cough, sputum, and rescue medication use; and lowered fibrinogen concentrations at 24weeks. [ABSTRACT FROM AUTHOR]

We propose a frequentist testing procedure that maintains a defined coverage and is optimal in the sense that it gives maximal power to detect deviations from a null hypothesis when the alternative to the null hypothesis is sampled from a pre-specified distribution (the prior distribution). Selecting a prior distribution allows to tune the decision rule. This leads to an increased power, if the true data generating distribution happens to be compatible with the prior. It comes at the cost of losing power, if the data generating distribution or the observed data are incompatible with the prior. We illustrate the proposed approach for a binomial experiment, which is sufficiently simple such that the decision sets can be illustrated in figures, which should facilitate an intuitive understanding. The potential beyond the simple example will be discussed: the approach is generic in that the test is defined based on the likelihood function and the prior only. It is comparatively simple to implement and efficient to execute, since it does not rely on Minimax optimization. Conceptually it is interesting to note that for constructing the testing procedure the Bayesian posterior probability distribution is used. [ABSTRACT FROM AUTHOR]

Discusses the use of the two-dimensional adaptive umbrella sampling with the first umbrella coordinate equal to the potential energy of the system and the second umbrella coordinate equal to a function that discriminates important folded conformations from unfolded conformations to determine the equilibrium properties of complex biomolecular systems.