In the previous blog there was an interesting link to a report by Yaron Hollander on the use and abuse of models in transport forecasting. His description of abuse of models can be seen in many sectors including the life sciences where it is arguably a bigger issue. Why? Other sectors have to some degree acknowledged the concept of structural uncertainty, which is a taboo subject for, most not all, modelers within the life sciences sector. By acknowledging there is a problem modelers within the other sectors have at least moved beyond the denial phase, the first phase of an addiction problem. This does not seem to be the case for most life sciences modelers. A typical example of this can be seen in a recent article by Zhou et al. from the University of Oxford which explores the mechanisms, through use of modelling and simulation, behind certain biological phenomena in cardiac myocytes termed alternans (alternating long and short action potentials)…

In the article, Zhou et al., claim that the mathematical/computational model being used within the study is the “gold standard” and has been “extensively validated”. Declaring a model as being the gold standard and extensively validated gives a licence to models being used to answer many questions which the model has not been tested for which will lead to all sorts of misuse of a model. Indeed the type of model used by Zhou et al. can never truly be tested due to its scale: 10’s of variables and 100’s of parameters. Such large models, which also include extensive non-linear functions, are almost impossible to test because they are so flexible. Thus, using such models for the type of analysis Zhou et al. conducted can be considered a classic example of model misuse. The authors applied the following analysis (more detail can be found in the article):

- A population of models is created by generating 10000 parameter sets by perturbing a subset of model parameters
- Of these a subset (~2500) are deemed acceptable according to some criteria
- Each of these parameter sets are then used to explore the alternan phenomena
- Parameter sets are then grouped by how they answer the following questions:
- Does a parameter set produce alternans or not
- Are the alternans eye or folk type

- Finally statistical tests are performed to ascertain whether the distributions of parameters are different between the groups created.

In essence they are applying statistical tests to simulated data, which has been discussed within ecology as something that should not be done. White et al. provide two reasons why statistical significance tests should not be used to interpret simulation results of which the first is most relevant here as the second is more a philosophical debate to some degree. The first reason revolves around power calculations: probability that a test correctly rejects the null hypothesis when the alternative is true. One of the key components of a power calculation is sample size! In brief, by using such a large sample size, numbers of simulations, Zhou et al. have powered their study to be able to detect the smallest of differences between groups. Indeed Zhou et al. can control the sample size and thus control the results of a statistical test; they could be accused of p-hacking. This brings into question the results seen by Zhou et al. In addition to the misuse of statistical hypothesis testing there is another more worrying issue about the first step of the approach: using large flexible models to explain variability in a dependent variable, measured experimentally, by varying a subset of model parameters. An obvious question is which parameters should be varied in such large models given how flexible they are? Furthermore, the bigger issue around structural uncertainty still hasn’t been addressed with such an approach. What consequences could these issues have? It will lead to a high number of false positives and waste experimental resources chasing hypotheses that were not worthwhile.

Finally on an even more cautionary note, if the type of approach, described by Zhou et al., were used to develop biomarkers and to guide clinical trials then this is likely to increase clinical trial failure rates rather than improve them. In an era where people within the healthcare industry are looking at systems approaches, real care must be taken as to what approaches are actually used within the industry. As modelers our duty is to remain questioning and skeptical.