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Purpose: The aim of this study was to investigate the potential added value of combining propensity score (PS) methods with multivariable linear regression (MLR) in estimating the average treatment effect on the treated (ATT) in nonrandomized studies with health-related quality of life (HRQoL) outcomes., Methods: We first used simulations to compare the performances of different PS-based methods, either alone or in combination with further MLR adjustment, in estimating ATT. PS methods were, respectively, optimal pair (OPM) and full (OFM) PS matching, subclassification on the PS (SBC), and the inverse probability of treatment weighting (IPTW). We simulated several scenarios, according to different sample sizes, proportions of treated vs untreated subjects, and types of HRQoL outcomes. We also applied the same methods to a real clinical data set., Results: OPM and IPTW provided the closest Type I error to the nominal threshold α = 0.05 across all scenarios. Overall, both methods showed also lower variability in estimates than SBC and OFM. SBC performed worst, generally providing the highest levels of bias. Further MLR adjustment lessened bias for all methods, however providing higher Type I error for SBC and OFM. In the real case, all methods provided similar ATT estimates except for one outcome., Conclusions: Our findings suggest that for sample sizes up to n = 200, OPM and IPTW are to be preferred to OFM and SBC in estimating ATT on HRQoL outcomes. Specifically, OPM performed best in sample sizes of n ≥ 80, IPTW for smaller sample sizes. Additional MLR adjustment can further improve ATT estimates., (© 2019 John Wiley & Sons, Ltd.)

Under the three-treatment three-period crossover design with simple carry-over effects, we derive the least-squares estimators for period effects, treatment effects and carry-over effects, as well as their covariance matrix in closed and explicit expressions. Using Monte Carlo simulation, we compare the test procedure adjusting carry-over with that ignoring carry-over with respect to Type I error and power. We further compare interval estimators adjusting carry-over with those ignoring carry-over with respect to the coverage probability and the average length. When the variation of responses within patients is small, the test procedure and interval estimators ignoring carry-over can lose accuracy in the presence of carry-over effects. When the variation of responses within patients is large, this loss of accuracy may become small or even minimal. We note that the loss of efficiency due to the adjustment of carry-over under the simple carry-over three-period crossover design is moderate, and is much less than that found for a two-period crossover design. We use the double-blind three-period crossover trial comparing formoterol solution aerosol and salbutamol suspension aerosol with a placebo for patients suffering from exercise-induced asthma on the forced expiratory volume in one second (FEV1) to illustrate the use of test procedures and interval estimators discussed here.

Dropouts are common in longitudinal study. If the dropout probability depends on the missing observations at or after dropout, this type of dropout is called informative (or nonignorable) dropout (ID). Failure to accommodate such dropout mechanism into the model will bias the parameter estimates. We propose a conditional autoregressive model for longitudinal binary data with an ID model such that the probabilities of positive outcomes as well as the drop-out indicator in each occasion are logit linear in some covariates and outcomes. This model adopting a marginal model for outcomes and a conditional model for dropouts is called a selection model. To allow for the heterogeneity and clustering effects, the outcome model is extended to incorporate mixture and random effects. Lastly, the model is further extended to a novel model that models the outcome and dropout jointly such that their dependency is formulated through an odds ratio function. Parameters are estimated by a Bayesian approach implemented using the user-friendly Bayesian software WinBUGS. A methadone clinic dataset is analyzed to illustrate the proposed models. Result shows that the treatment time effect is still significant but weaker after allowing for an ID process in the data. Finally the effect of drop-out on parameter estimates is evaluated through simulation studies., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)