Novel concentration-QTc models for early clinical studies with parallel placebo controls: A simulation study

Unbiased and efficient estimation of causal treatment effects in crossover trials

We introduce causal inference reasoning to crossover trials, with a focus on thorough QT (TQT) studies. For such trials, we propose different sets of assumptions and consider their impact on the modeling strategy and estimation procedure. We show that unbiased estimates of a causal treatment effect are obtained by a g-computation approach in combination with weighted least squares predictions from a working regression model. Only a few natural requirements on the working regression and weighting matrix are needed for the result to hold. It follows that a large class of Gaussian linear mixed working models lead to unbiased estimates of a causal treatment effect, even if they do not capture the true data-generating mechanism. We compare a range of working regression models in a simulation study where data are simulated from a complex data-generating mechanism with input parameters estimated on a real TQT data set. In this setting, we find that for all practical purposes working models adjusting for baseline QTc measurements have comparable performance. Specifically, this is observed for working models that are by default too simplistic to capture the true data-generating mechanism. Crossover trials and particularly TQT studies can be analyzed efficiently using simple working regression models without biasing the estimates for the causal parameters of interest.

Concentration-QTc analysis with two or more correlated baselines

The relationship between drug concentration and QTc interval is typically evaluated by applying the standard analysis model proposed in a scientific whitepaper by Garnett et al. ( https://doi.org/10.1007/s10928-017-9558-5 ). The model is a mixed effects model in which a baseline QTc interval is included as a covariate. Two or more baseline QTc intervals are sometimes observed for a study participant, such as time-matched baselines on a baseline day in parallel studies, or pre-dose baselines in each period in crossover studies. In such situations, the baseline adjustments are not straightforward because these baselines correlate with not only the corresponding QTc intervals after drug administration, but also other QTc intervals at different timepoints for parallel studies, or those in different periods for crossover studies. In this study, we compared three analysis models through simulations and clinical study examples in settings in which two or more baselines were observed for a subject. We compared a model without baseline adjustment, a model with baseline adjustment, and a model in which baseline and baseline mean were included as covariates. In the simulations and clinical study examples, the model with baseline and baseline mean as covariates demonstrated higher accuracy and power than the other models. This model assumed a specific covariance structure in QTc intervals, which well approximated the correlations between QTc intervals within and between days. When there are two or more baselines in concentration-QTc analyses, the baseline mean should be included as a covariate in addition to the corresponding baseline.

Concentration-QTc analysis for single arm studies

Concentration-QTc (C-QTc) modeling is being increasingly used in phase 1 studies. For studies without a placebo arm (single arm studies), the scientific whitepaper by Garnett et al. ( https://doi.org/10.1007/s10928-017-9558-5 ) states that time-matched baseline adjustments may minimize the effect of diurnal variation in QTc intervals, and categorical time effects are not needed in the model. However, how diurnal variations can be accounted for when only pre-dose baselines are available is unclear. This research investigates whether including categorical time effects in the model can adjust diurnal variation in single arm studies with pre-dose baselines, where QTc prolongation is evaluated at a concentration of interest based on ΔQTc at 24 h and ΔΔQTc (a model-derived difference in ΔQTc from concentration zero). To understand the operating characteristics for the models with and without categorical time effects, simulations were conducted under various scenarios considering oncology early phase studies. When the C-QTc relationship is linear, models without categorical time effects provided biased estimates for model parameters and inflated or decreased false negative rates (FNRs) depending on the pattern of diurnal variations in QTc intervals, whereas models with categorical time effects caused no biases and controlled the FNRs. For non-linear C-QTc relationships, ΔΔQTc estimations made using the model with categorical time effects were not robust. Thus, for single arm studies where only pre-dose baselines are available, we recommend collecting QTc measurements at 24 h and estimating ΔQTc at a concentration of interest at 24 h using the C-QTc model with categorical time effects.