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A potpourri of biostatistical research: Special Issue for ISCB ASC 2018

The motivation and themes for this Special Issue arose from a joint conference that combined the annual meeting of the International Society for Clinical Biostatistics (ISCB) and the biennial Australian Statistical Conference 2018, held in Melbourne (Australia) August 26–30, 2018. This was an important conference for extending the international reach of the ISCB, being the first time in its 39 years that the conference had been held in the southern hemisphere, and only the third time it had been held outside of Europe. It was also an important event for Australia, highlighting the recent growth of biostatistics Down Under, albeit in the face of relatively underdeveloped foundations and institutional support (Lee et al., 2019). The geographical diversity of the conference attendance is reflected in this Special Issue, with 6 of the 16 papers led by Australian authors, 5 from Europe, 3 from North America, and 2 from East Asia. Being a joint conference with the Statistical Society of Australia, the meeting also featured a wide diversity of topics, with themes relating to modern methods for highdimensional data and causal inference, statistical methods for precision medicine, and recent advances in the design and analysis of clinical trials. These themes were all reflected in three very well-received plenary sessions, featuring talks by Susan Murphy (Harvard), Louise Ryan (University of Technology Sydney), Thomas Lumley (University of Auckland), and Chris Holmes (Oxford). The call for this Special Issue sought and received a similarly wide range of submissions, emphasizing the same themes, but also covering other areas of strong recent interest such as methods for handling missing data. A total of 38 submissions were received, of which 16 were accepted for this issue. Starting with the elusive but very timely theme of “big data,” an original perspective from Hwang and Ryan showcases that statisticians have a few tricks up their sleeves to reduce computational time when analyzing massive datasets, complementing the growing prominence of computer scientists in the world of data. Befitting the traditional focus of biostatistics, the majority of the papers accepted for the Special Issue develop, extend, and evaluate methods for the design and analysis of clinical trials. In a particularly innovative contribution, Xu et al. examine the evaluation of dynamic treatment regimes as individualized treatment strategies for managing chronic periodontitis. Specifically, they develop SMARTp—a two-stage sequential multiple assignment randomized trial design that accommodates the complex data typical of this setting, including clustering, non-Gaussianity, and non-random missingness. In another contribution to novel methods for trial design, Edelmann et al. address a problem commonly seen in cancer studies, that the population under consideration is highly heterogeneous in response probability. In this paper, two modifications of Simon’s optimal two-stage design are derived to correct for heterogeneous populations, utilizing clinical, demographical, and biological covariates. These are evaluated in a simulation study. Tapping the recent increase in interest in Bayesian methods for clinical trials, Guo and Liu describe a Bayesian predictive probability design for phase II clinical trials that is particularly suited for the evaluation of novel therapies, such as molecularly targeted agents and immunotherapy, which typically involve complicated outcomes such as ordinal, nested, and co-primary endpoints. Basket trials simultaneously evaluate the effect of one or more drugs on a defined biomarker or molecular target in a variety of disease subtypes. These frequently lack the power to detect the effect of drugs in each subtype. Fujikawa et al. propose a novel Bayesian basket trial design that incorporates predictive sample size determination, early termination for inefficacy and efficacy, and the borrowing of information across strata. In their paper, Funatogawa and Funatogawa consider longitudinal models for outcome measures of individually randomized trials. They consider the impact of the violation of assumptions about the equality of the covariance matrices of outcome measures across treatment groups on the properties of estimators. In recent years there has been much interest in whether external information from previous clinical trials can be leveraged to increase the statistical power of a new trial. Kopp-Schneider et al. show that in situations where a uniformly most powerful test exists, borrowing information cannot improve statistical power if Type I error control is desired. Given their result, they discuss the important implications for how external information might be