_Editor’s Note: I recently posted about a paper in Nature that purported to predict the H-index. The authors contacted me to get my criticisms, then responded to those criticisms. They have requested the opportunity to respond publicly, and I think it is a totally reasonable request. Until there is a better comment generating mechanism at the journal level, this seems like as good a forum as any to discuss statistical papers. I will post an extended version of my criticisms here and give them the opportunity to respond publicly in the comments. _
The paper in question is a clearly a clever idea and the kind that would get people fired up. Quantifying researchers output is all the rage and being able to predict this quantity in the future would obviously make a lot of evaluators happy. I think it was, in that sense, a really good idea to chase down these data, since it was clear that if they found anything at all it would be very widely covered in the scientific/popular press.
- Lack of reproducibility. The code/data are not made available either through Nature or on your website. This is a critical component of papers based on computation and has led to serious problems before. It is also easily addressable.
- No training/test set. You mention cross-validation (and maybe the R^2 is the R^2 using the held out scientists?) but if you use the cross-validation step to optimize the model parameters and to estimate the error rate, you could see some major overfitting.
- The R^2 values are pretty low. An R^2 of 0.67 is obviously superior to the h-index alone, but (a) there is concern about overfitting, and (b) even without overfitting, that low of R^2 could lead to substantial variance in predictions.
- The prediction error is not reported in the paper (or in the online calculator). How far off could you be at 5 years, at 10? Would the results still be impressive with those errors reported?
- You use model selection and show only the optimal model (as described in the last paragraph of the supplementary), but no indication of the potential difficulties with this model selection are made in the text.
- You use a single regression model without any time variation in the coefficients and without any potential non-linearity. Clearly when predicting several years into the future there will be variation with time and non-linearity. There is also likely heavy variance in the types of individuals/career trajectories, and outliers may be important, etc.
Our formula is particularly useful for funding agencies, peer reviewers and hiring committees who have to deal with vast
<div> numbers of applications and can give each only a cursory examination. Statistical techniques have the advantage of returning </div> <div> results instantaneously and in an unbiased way. </div>