An efficient analysis of covariance model for crossover thorough QT studies with period‐specific pre‐dose baselines

Lu, Kaifeng

doi:10.1002/pst.1647

Cited by 4 publications

(10 citation statements)

References 15 publications

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“…This paper advocates a regression model including average baseline measurements as a covariate. It is shown in Lu (2014) that this approach is consistent with the joint baseline and postbaseline measurement model advocated in Kenward and Roger (2010) and Meng et al. (2010).…”

Section: Data Examplesupporting

confidence: 71%

“…These trials have complicated designs in an attempt to minimize the sample size. This complexity has, in turn, led to a long‐standing debate and several suggestions on how to best model the resulting data, and in particular how to use baseline measurements (Kenward & Roger, 2010; Lu, 2014; Orihashi & Kumagai, 2021; Orihashi et al., 2021; Schall & Ring, 2011). For a standard TQT study, healthy volunteers (International Council for Harmonisation, 2019) are enrolled with the purpose of obtaining electrocardiograms (ECGs) from each subject under different treatment conditions.…”

Section: Introductionmentioning

confidence: 99%

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Unbiased and efficient estimation of causal treatment effects in crossover trials

2023

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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.

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Section: Data Examplesupporting

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Unbiased and efficient estimation of causal treatment effects in crossover trials

2023

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“…The generalization to factorial designs with more than two cross-classified or nested factors works analogously and is, therefore, not discussed here. 4 The Wild Bootstrap for the White-ANCOVA model Especially in small sample size scenarios, the White-ANCOVA test statistic and the corresponding asymptotic result stated in (3) might not yield satisfactory results in terms of maintaining the prespecified type I error probability, see our simulation study in Section 5 below. A resampling procedure such as the bootstrap might remedy this problem.…”

Section: Applicability Of the White-ancova Model To Real-life Datamentioning

confidence: 99%

“…1 For this purpose, the analysis of covariance (ANCOVA) is an appropriate tool, which is used with the aim of increasing the inferential power, and reducing bias and variance of the effect estimators. 2 The ANCOVA has been applied in many research disciplines, ranging from studies about Alzheimer’s disease 3 to pharmaceutical issues, 4 educational 5 and fishery research, 6 just to name a few.…”

Section: Introductionmentioning

confidence: 99%

Small-sample performance and underlying assumptions of a bootstrap-based inference method for a general analysis of covariance model with possibly heteroskedastic and nonnormal errors

Zimmermann

Pauly

Bathke

2019

Stat Methods Med Res

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It is well known that the standard F test is severely affected by heteroskedasticity in unbalanced analysis of covariance models. Currently available potential remedies for such a scenario are based on heteroskedasticity-consistent covariance matrix estimation (HCCME). However, the HCCME approach tends to be liberal in small samples. Therefore, in the present paper, we propose a combination of HCCME and a wild bootstrap technique, with the aim of improving the small-sample performance. We precisely state a set of assumptions for the general analysis of covariance model and discuss their practical interpretation in detail, since this issue may have been somewhat neglected in applied research so far. We prove that these assumptions are sufficient to ensure the asymptotic validity of the combined HCCME-wild bootstrap analysis of covariance. The results of our simulation study indicate that our proposed test remedies the problems of the analysis of covariance F test and its heteroskedasticity-consistent alternatives in small to moderate sample size scenarios. Our test only requires very mild conditions, thus being applicable in a broad range of real-life settings, as illustrated by the detailed discussion of a dataset from preclinical research on spinal cord injury. Our proposed method is ready-to-use and allows for valid hypothesis testing in frequently encountered settings (e.g., comparing group means while adjusting for baseline measurements in a randomized controlled clinical trial).

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“…For the two‐day covariance structure, total variance is kept constant, and variances of subject, subject × day, subject × time, and error terms are changed, so that the correlations between different time points within a day ( ρ 1 ) and between matched time points on different days ( ρ 2 ) vary in six scenarios as shown in Table 5. The values in Table 5 are based on the settings in the literatures 8,17‐19 and our past experiences. The standard deviation of random slopes is set at a constant value of 0.24 ms/μg/mL so that they vary 30% in the positive scenario at 600 mg mean C max .…”

Section: Simulation Planmentioning

confidence: 99%

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

Orihashi

Kumagai

Shiosakai

2020

Pharmaceutical Statistics

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The QTc interval of the electrocardiogram is a pharmacodynamic biomarker for drug‐induced cardiac toxicity. The ICH E14 guideline Questions and Answers offer a solution for evaluating a concentration‐QTc relationship in early clinical studies as an alternative to conducting a thorough QT/QTc study. We focused on covariance structures of QTc intervals on the baseline day and dosing day (two‐day covariance structure,) and proposed a two‐day QTc model to analyze a concentration‐QTc relationship for placebo‐controlled parallel phase 1 single ascending dose studies. The proposed two‐day QTc model is based on a constrained longitudinal data analysis model and a mixed effects model, thus allowing various variance components to capture the two‐day covariance structure. We also propose a one‐day QTc model for the situation where no baseline day or only a pre‐dose baseline is available and models for multiple ascending dose studies where concentration and QTc intervals are available over multiple days. A simulation study shows that the proposed models control the false negative rate for positive drugs and have both higher accuracy and power for negative drugs than existing models in a variety of settings for the two‐day covariance structure. The proposed models will promote early and accurate evaluation of the cardiac safety of new drugs.

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An efficient analysis of covariance model for crossover thorough QT studies with period‐specific pre‐dose baselines

Cited by 4 publications

References 15 publications

Unbiased and efficient estimation of causal treatment effects in crossover trials

Unbiased and efficient estimation of causal treatment effects in crossover trials

Small-sample performance and underlying assumptions of a bootstrap-based inference method for a general analysis of covariance model with possibly heteroskedastic and nonnormal errors

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

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