PONE-D-19-16128

Applying univariate vs. multivariate statistics to investigate therapeutic efficacy in controlled preclinical neurotrauma trials: A Monte Carlo simulation study

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for inviting me to review the manuscript PONE-D-19-1628 entitled “Applying univariate vs. multivariate statistics to investigate the therapeutic efficacy in controlled preclinical neurotrauma trials: A Monte Carlo simulation study”. Motivated by a clinical trial, the manuscript studied the empirical power of different statistics analyzing the trial data with multiple correlated endpoints with repeated measures. In general ,the manuscript was well written and has many merits: addressing a real clinical issue, conducting a simulation study, providing good ground for manipulated factors, visual examination of treatment effect, clear description of software used providing reproducibility options, good definition of the evaluating criteria of assessing the models and statistics. There are some improvements needed before it’s ready for publication.

Abstract

It is not clear what “acceptable level” of power means.

What does “20 measurements per group” mean? Does it mean 20 subjects per group?

Methods

In general the simulation scenario needs to be clarified: did the author generated 4 groups (1 control, 3 treatments of different doses) and 7 endpoints each with 3 times of measurements (page 5)? Or did the authors generated 2 groups of 1 control and 1 treatment as Hotelling’s T square was used in the MANOVA analysis (page 10). If the simulation is in 4 groups, how is effect size Cohen’s d defined? Is it defined as difference between each two groups?

In general, how the MANOVA multivariate responses are defined is not clear: is it the different time points of a specific endpoints that are treated are multivariate (page 7)? Or is it the 7 different endpoints that are treated as multivariate for a specific time? From Appendix 1, it looks like both the endpoints and time points are used as different response, altogether 19 columns. Please clarify in the method section.

Simulation procedure: the way simulation was done is a good representation of the clinical trial by bootstrapping method, yet it can also make the results of the study biased to a specific trial and limit its generalizability.

Simulation factors: How was the simulation scenarios of 24 defined? There were 4 levels in n, 4 levels in ES (0, .2, .5, .8), 3 levels in variance. If effect size = 0 is not treated as a simulation condition, details need to be elaborated. What about the distribution of dependent variables that also include log transformation?

Multivariate dimensionality reduction techniques for pattern analysis: please explain why is huge treatment effects (Cohen’s d=2.0) chosen as an example.

Reviewer #2: This paper seeks to encourage potentially more appropriate analysis of data from preclinical experiments involving multiple outcomes and multiple experimental groups. The main hypothesis is that a multivariate consideration of the outcomes rather than multiple univariate tests may be more powerful and the goal is to identify which multivariate method might be most useful via a simulation study.

The goal of the paper is laudable, but there are several issues with the approach and holes that limit the validity of the conclusions.

The first set of issues is related to the comparison of univariate to multivariate tests with respect to type I error. The way empirical type I error is calculated is ill-conceived. As the authors state, the univariate tests will maintain close to nominal levels of type I error on a test-by-test basis. In practice, the concern would be for the case when there really is no effect, but a few outcomes have (unadjusted) p<0.05. That should be the comparison. How often does a set of univariate tests give a "wrong result" in terms of concluding the treatment has an effect based on one or more significant p-values (if that is the rule for finding a significant difference). But if one were to accept 1 or more significant p-values among any of the multiple tests as indicating difference, standard practice would be to adjust the p-values using a Bonferroni adjustment or some other method of controlling the type I error rate. There is a similar issue with how power is calculated.

I am also confused by the combination of repeated measurement of multiple outcomes into a single vector without taking any of that information into account in most of the multivariate analyses. In this setting, it would be (somewhat) uncommon to do univariate on all items separately. Mixed effects regression or repeated measures ANOVA would be the choice to make, and mixed effects models for multivariate repeated measures do exist. It would have been helpful to consider these alternatives. It would seem that ignoring knowledge about the data structure that comes from the experimental design might also severely hamper the performance of the PCA ANOVA and dimensionality reduction techniques.

Another issue with multivariate methods that is not addressed is missing data. MANOVA cannot handle data that are missing at random while mixed effects models can. Although not a focus of this analysis, the limitations of MANOVA in this regard would suggest opting for a more flexible method like mixed effects models for comparative purposes.

Perhaps the most important question is whether multivariate techniques, even if they improve power in some modest way, are of value in the preclinical setting since, as the authors state in the introduction, they have "increased complexity of analysis and interpretation of results." If separate modeling of each outcome provides an accurate representation of the effect of the treatment on that outcome, does a multivariate p-value or a data reduction technique help if we can't easily interpret the effect? Perhaps coupling a data analysis example to the simulations where all of the methods were applied to a real data set would help to clarify the analytic methods that were actually applied and the issues in interpretation that come with the methods.

Reviewer #3: The paper evaluates the performance of univariate ANOVA and Welch’s ANOVA tests versus multivariate techniques based on the simulation study, taking into account sample size/effect size, normality and homogeneity of variance. The idea makes sense intuitively (according to the statistical textbook/theory) and the result may be helpful for some researchers in application. However, the methodology is not novel and the broadness of the application may be not enough. It may be helpful to medical researchers. I have some concerns and comments as follows.

(1) I assume that this is more like a statistical research paper, not medical research paper

(2) It is not clear why does the title of this paper include “in controlled preclinical neurotrauma trials”? It seems that the result of the simulation study can be applied to different trials (or clinical trials), not just neurotrauma trials only.

(3) The results from the simulation study show that Welch’s ANOVA is as powerful as classical ANOVA tests with variance homogeneity and outperformed the remaining methods when this assumption was violated. However, most animal data are much more homogeneous (less variation), in comparison with the clinical data (human being). That is to say, the result from the simulation study may be helpful to a small clinical study (bigger variation), not just preclinical trials (smaller variation).

(4) In simulation factors section, a sample size of 5 – 20 may be too small. It will be interesting to see more scenarios with a ranged from 5 – 50 (say) to benefit more people (similar to my comment (2))

(5) In simulation factors section, the correlation in multivariate normal distribution of dependent variables is missing (which is important)

(6) In the simulation study, you may generate the data from other distributions (not normal/ log-normal distribution. This can be another factor in the simulation study. For example, a gamma distribution or Weibull distribution.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Applying univariate vs. multivariate statistics to investigate therapeutic efficacy in (pre)clinical trials: A Monte Carlo simulation study on the example of a controlled preclinical neurotrauma trial.

PONE-D-19-16128R1

Dear Dr. Gerber,

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