ABSTRACT This paper focuses on detecting the existence of a treatment effect when comparing multiple treatments using discrete observational data. A new test statistic, based on a divergence measure, is introduced, and its theoretical properties are obtained. Stochastic dominance tests are also proposed, illustrating relevant practical cases of treatment‐level effects ordering patterns. Numerical simulations show the effectiveness of the proposed tests, in terms of both significance level and power. A clinical application indicates that our methods can significantly enhance the evaluation of multitreatments in real‐world scenarios using observational data.
ABSTRACT Clinical audits are essential for monitoring and improving the quality of healthcare with statistical outlier detection used to identify healthcare units whose performance deviates from expected standards. Two commonly used outlier‐detection approaches are Common Mean Model (CMM) and Random Effects Logistic Regression (RELR). However, these methods can identify different healthcare units as outliers when applied to the same dataset. Incorrectly flagging a unit as an outlier can have serious consequences, including reputational harm, decreased staff morale, unnecessary regulatory scrutiny, diversion of resources, and loss of public trust. This article investigates the reasons for these discrepancies through a simulation study evaluating the performance of the CMM and RELR under a range of scenarios. The results show that differences in outlier detection are primarily driven by the degree to which a normality assumption holds for each method. When these assumptions are violated, for one or both methods, the two approaches may produce markedly different outlier detection. To choose the most appropriate approach for a given dataset, we recommend assessing each normality assumption, by combining visual diagnostics, such as QQ plots, with quantitative measures, such as Lin's concordance correlation coefficient (CCC).
ABSTRACT The multi‐arm two‐stage (MATS) design combines proof‐of‐concept and dose optimization in a single study, which starts from parallel enrollments of participants across multiple cohorts (indications) at a dose selected from the dose‐escalation study, which is usually the maximum tolerable dose (MTD) in traditional oncology dose‐escalation studies, followed by randomizing subjects into two dose levels only for those “efficaciously promising” cohorts at the second stage. Here, we communicate a variant of the MATS design by “reversing” the two stages so that randomization to two dose levels for each indication‐specific cohort precedes potential further enrollment at the dose selected for optimal efficacy/safety profile based on the randomization stage. This “Reversed MATS” (RevMATS) design fits into certain situations when prior data evidence indicates two doses may result in similar activities or there is not enough evidence to favor one dose over the other in terms of preliminary activities, thus justifying earlier randomization of two doses instead of delaying to the second stage. It also fits when one dose has revealed promising early efficacy signals in a heterogenous population and there is reasonable belief of effectiveness across indications (so that a larger upfront investment is justified so that the second stage of MATS may be skipped). The RevMATS design further improves the underlying Bayesian hierarchical model in the MATS design by accounting for broader (potential) efficacy profiles of candidate doses and explicitly incorporating collective data evidence, such as estimated dose–response curves (from dose escalation), to boost efficiency in decision‐making.
