*3.2.3 Bayesian methods*

*Beyond LEO - Human Health Issues for Deep Space Exploration*

the realm of possibility to further refine the analysis.

*3.2.1 Alternative uses of common statistical models*

means they share no common ancestor.

specific questions to be asked of existing datasets.

literature on astronaut mortality to demonstrate this idea.

**3.2 Alternative analytic approaches**

and machine learning.

about problems.

*3.1.4 Alternative hypotheses*

independencies, which in turn have implications for what is and is not possible in the system from which the data were acquired, and thus can help guide critical thinking

A final epistemological realignment is to define specific, sensible hypotheses given the question at hand, which may or may not conform to the typical NHST two-tailed tests of significance. Examples of such alternatives include equivalence testing, inferiority testing, and a still more exotic choice, the *modus tollens*. All of these ask different questions than whether the central tendency of a sample shows enough difference to evince a significant p-value for the given sample size and variance. By changing the testable hypothesis to be more specific to what we really would like to know, we can often obtain an answer that is not only more sensible, but often more statistically powerful too, which might then bring NHST back into

Yet another strategy for learning from data is the use of more-sophisticated analytic methods which do not necessarily rely on NHST. This includes exploiting properties of known statistical tests for alternative hypotheses, Bayesian methods,

With a good understanding of common statistical models, it is possible to exploit their properties to conduct atypical investigations. Here we use an example from the

Using data on US astronauts and Soviet and Russian cosmonauts, Reynolds et al. [5] demonstrated that mortality from cancer and cardiovascular disease have no common causes in this population. This in turn was taken as evidence that doses of ionizing radiation received in space cannot have been sufficient to affect mortality from both of these causes. This was achieved by showing that a naïve analysis of survival curves (where competing causes of death were treated as censoring events) were not markedly different from survival curves that account for competing risks. That is, the causes of death displayed statistical independence which, in DAG terms,

In this example, the authors exploited the implications of different statistical methods for computing survival in presence of competing risks to make inferences regarding the structure of causal relationships. This is but one example, and undoubtedly others exist for those who can think broadly and conceptually about

The advancements in computing power over the last several decades have made possible more sophisticated forms of analysis, not least among them being simulation. We refer here to several different well-established approaches, all of which have found use in various domains such as statistics, business, and engineering. Markov-chain Monte Carlo simulation (MCMC) has been used for decades in engineering for probabilistic risk assessment. Agent-based simulation has found increasing popularity in epidemiology for modeling community-level effects of policy

**6**

*3.2.2 Simulation*

Though certainly not new, Bayesian methods are still underutilized in research in general and in space medicine in particular. This is primarily owing to the unfamiliarity of most researchers with these methods, which in turn is due to the lack of graduate-level training on them in most scientific programs other than statistics. Historically, this was sensible: their mathematical complexity and need for computing power made them difficult to implement for all but the simplest of applications. Fortunately, computer science and computer hardware have both evolved to where these methods are easy to implement, creating a large opportunity for researchers to work with smaller datasets in meaningful and rigorous ways without reliance on NHST and p-values.
