Conditional distribution modeling as an alternative method for covariates simulation: comparison with joint multivariate normal and bootstrap techniques

Abstract

Clinical trial simulation (CTS) is a valuable tool in drug development. To obtain realistic scenarios, the subjects included in the CTS must be representative of the target population. Common ways of generating virtual subjects are based upon bootstrap (BS) procedures or multivariate normal distributions (MVND). Here, we investigated the performance of an alternative method based on multiple imputation (MI). Age, weight, serum creatinine, creatinine clearance, sex and race data from a hypertension drug development program were used. The methods were evaluated based on the original data set (internal evaluation) and on their ability to reproduce an older, unobserved population (extrapolation). Similar results were obtained in the internal evaluation in terms of summary statistics. However, BS was able to preserve the correlation structure of the empirical distribution, which was not adequately reproduced by MVND; MI was in between BS and MVND. BS does not allow to extrapolate to an unobserved population. Improved extrapolation performance of the continuous covariates was observed for MI over MVND, yet after removing the healthy subject data from the training data set, there was no clear difference between the methods. Sex was better predicted by MVND vs. MI, while similar results were obtained for race. If CTS is used to simulate within the range of the observed distribution, BS is the preferred method for covariates simulation. When extrapolating to new populations, a parametric method like MI/MVND is needed. As MVND rests on relatively strong assumptions, MI appears to be more robust when deviations from these assumptions occur.

Efficient and relevant stepwise covariate model building for pharmacometrics

“…In this work, the simulation setup used was more complicated than that usually seen in methodological studies. The covariate distributions in each simulated dataset were generated using CD modeling 9 : this approach retains the distributional properties of the real covariate data that was used as a template while still not resorting to random selection of complete individual covariate vectors. The combination of phase I studies with specific objectives (assessment of food effects and impact of CYP2D6 genotype) and large (phase III) patient studies mimic the common situation for which the SCM+, and particularly stage‐wise filtering, was designed for.…”

“…Covariate data were simulated using conditional distribution (CD) modeling 9 based on the observed covariate distribution from the three real studies used as templates. This method retains the original multivariate distribution of the covariates without reducing the number of unique covariate vectors (per simulated dataset) like a bootstrap approach would.…”

“…The same age and sex values were used across the simulated datasets to “seed” the simulations. 9 Only baseline values were considered for these covariates. CRCL was calculated from the baseline serum creatinine using the Cockcroft–Gault formula 10 capping CRCL at 150 ml/min.…”

Performance of Cox proportional hazard models on recovering the ground truth of confounded exposure–response relationships for large‐molecule oncology drugs

“…In addition, there are implicit correlations of these covariates representing worse health status with higher PK clearance and consequently lower exposures, implemented by the conditional distribution modeling method for covariate simulation. 8 For Confounding Reason 2, it is designed by adding explicit functions as the interaction term so that patient health status-related covariates have interactions with the maximum effect of drug trough concentration (PCMIN) on hazard rate of In this research, we used a historical study of trastuzumab emtansine (T-DM1) in human epidermal growth factor receptor 2-positive (HER2+) breast cancer patients (EMILIA study, ClinicalTrials.gov identifier NCT00829166 9 ) as a starting point of the simulation workflow. The steps taken to simulate and analyze survival datasets with confounded E-R relationships are outlined next.…”

“…10 The post hoc PK parameters have the implicit correlations with baseline covariates, so patients with lower clearance of large-molecule drugs are generally healthier with better prognostic factors, that is, lower TMBD, ECOG, and AST and higher ALBUM who also have a longer survival. 2,3 For Step 1, a novel clinical trial simulation approach by the multiple imputation by chained equations (MICE) method 8 was used to generate an augmented covariate matrix with 5000 patients that maintains the original distribution and implicit correlations among continuous covariates, categorical covariates, and individual post hoc PK parameters as in the original EMILIA study. This method is based on conditional distribution modeling to generate virtual patients with a covariate distribution similar to the original observed distribution.…”