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In the absence of formulae, simulation is the tool of choice, but often trial statisticians are not fully aware of how to do the simulations. Trial simulations in Stata are easily achieved by writing many lines of code, but I introduce a new command, tacts, that attempts to run the simulations within a single line of code. This command will handle many different types of design, such as MAMS, trials with longitudinal outcomes, sample size reestimation, multiple-outcome trials with multiplicity correction, and adaptive randomization. The command will also be able to gather the output of the simulations and produce a summary table of the results. To handle the complexity of all of these trials requires a complex syntax that is very flexible.

Analysts are now presented with multivariate data, sometimes measured repeatedly and often requiring the ability to model nonlinear relationships and hierarchical structures. In this talk, I will give an overview of the merlin command, which attempts to provide an extremely general framework for data analysis. From simple things, like fitting a linear regression model or a Weibull survival model to a three-level logistic mixed-effects model, or a multivariate joint model of multiple longitudinal outcomes (of different types) and a recurrent event and survival with nonlinear effects...merlin can fit them all. I’ll take a single dataset and attempt to show you the full range of capabilities of merlin before diving into some new tricks in the field of multistate survival modeling.

While it has considerable clinical relevance, there are also inherent limitations in relation to the fixed time point of 24 months and potential variation by treatment type and choice of comparison group. In this talk, I will highlight some of the methodical limitations and present the first results from a large population-based cohort of FL patients. National register-based information has been combined with detailed medical record data to create a unique cohort with detailed treatment and follow-up information. We allow progression to be time varying and estimate relative rates, as well as OS, by first-line treatment and timing of progression using an illness-death modeling approach. Stata packages merlin and multistate were applied, and example code will be presented. Our findings show that progression is associated with worse survival beyond the 24-month time point, illustrating the need for individualized management by timing of progression for optimal care of patients with FL.

We formulate the effects of potentially time-dependent covariates on the failure time through the familiar Cox proportional hazards model, under which the failure time distribution is completely arbitrary. We consider nonparametric maximum-likelihood estimation with an arbitrary number of examination times for each study subject. We present an EM algorithm that involves very simple calculations and converges stably for any dataset, even in the presence of time-dependent covariates. The resulting estimators for the regression parameters are consistent, asymptotically normal, and asymptotically efficient with an easily estimated covariance matrix. In addition, we extend the EM algorithm and the theoretical results to multivariate failure time data, in which there are multiple events per subjects or clustering of study subjects. Finally, we provide illustrations with real medical studies.

We present a new prototype command, mvintcox, for fitting multivariate interval-censored event-time models in Stata and demonstrate its use with several real-world applications.

Classically, it has been used for conventional pairwise meta-analysis to compare the effects between treatments based on head-to-head comparisons; however, data from direct comparisons are relatively limited, hampering the knowledge translation and clinical decision making. An alternative analytical approach called network meta-analysis (NMA) was developed to include in the meta-analysis not only direct comparisons but also indirect comparisons based on logical inference (and assumptions) from the network model. This approach is being used rapidly because it maximizes the way we use evidence to clinical decision making. In this lecture, we will introduce NMA definitions, relevant statistical concepts, and the frequentist NMA analytics process to be implemented using Stata with a practical example from the fibromyalgia literature.

However, most biostatistical and epidemiological researchers are not cognizant of the ways their understandings of race and ethnicity seep into their research designs and statistical analyses. It is important to consider how the social constructions of race and ethnicity influences the ways in which biostatisticians and other researchers measure racial and ethnic phenomena, including racial and ethnic disparities. This talk includes a discussion of different coding strategies and the promise of nested models for critical studies of race and racism using regression analysis.