Reviewer response for version 1
This article reports on a probabilistic cost effectiveness of stockpiling neuraminidase inhibitor antiviral drugs to mitigate against pandemic influenza deaths.
The study is methodologically sound. 
The decision-theoretic approach which selects the optimal course of action based on the utility of each outcome and the probability of each outcome conditional on each decision is appropriate. 
In this case the actions are whether or not to stockpile NAIs for use in a pandemic, and the outcome is the cost of the antiviral stockpile and the expected number of deaths during the shelf-life period of the antivirals.
The model of the expected number of pandemic deaths identifies is structurally sound and uses various appropriate data sources to quantify uncertainties present in all the parameters.
The conclusion that stockpiling NAIs is cost-effective for a sufficiently high willingness-to-pay cost per QALY follows from the model and the data used.
However, I think that more attention needs to be drawn to dependency of this result to the crucial antiviral effectiveness parameter. 
The methodology where all parameters are treated probabilistically in a uniform way is excellent; however additional one- or two- way sensitivity analyses are still valuable for providing insight into the effect of the most important parameters. 
The authors have done this for the proportion of hospitalised cases receiving antivirals; it seems appropriate to also do this for NAI effectiveness, given the ongoing debate on the subject.
I have divided further comments into three sections: major essential revisions, commentary with discretionary additions, and minor technical clarifications needed.
Essential Revision
------------------
- As stated above, the reader would be well served with an additional figure similar to Figure 3, but plotting QALY threshold against mean NAI effectiveness. 
In my shallow experimentation with the author's model, it looks like at 20,000 GBP / QALY, NAIs cease being cost effective at around 0.94 effectiveness (relative risk, compared to 0.89).
Commentary
----------
The suggestions made below I think might improve the article however I think the authors can best judge whether the additional effort and added complexity would be worthwhile or would be too diverting.
I commend the authors for including the model code in the Appendix, I managed to run this code with relatively little effort.
- The methodology of using expert opinion to mitigate potential bias in the studies estimating NAI effectiveness is a practical measure that is probably worthwhile. 
Some additional detail on the process would be appreciated. 
For example: how were assessors selected? 
How much time did the reviewers take in their bias estimates?
- In the discussion it could be noted that in a future pandemic with a large CAR or CFR, the proportion of severe cases receiving hospital care and the level of care are likely to be lower, simple due to hospitals being overwhelmed. 
The estimates of proportion of deaths occurring in hospital are from the 2009 pandemic which was very mild.
- If NAIs have any effect in preventing further transmission, e.g if they shorten the period of viral shedding, then mass administration of antivirals may reduce the overall attack rate and consequent mortality even if NAIs are not effective for mortality reduction of severe cases. 
As the study's model does not capture this, this is another way in which the study is conservative.
Minor Technical Revisions
-------------------------
- The CAR and CFR parameters used in the model are for a pandemic without NAI usage. 
Given that NAIs were used in the 2009 pandemic, should the CAR and CFR estimates for 2009 be included along side those of previous pandemics? 
If the 2009 CAR and CFR estimates are for example based on global data where NAI usage might be negligible that would be OK; but if they are based primarily on UK or US data they should possibly be excluded.
- In the Appendix page 4 there is a citation [20] that isn't given in a reference list.
- In the last sentence of the 3rd paragraph, the RR derived based on the OR and 10% mortality is stated as 0.89. 
This is the same as the bias-corrected RR given in the next paragraph. 
Is this intentional? 
Or should it be the RR value based on the OR and 10% mortality (but without bias correction), in which case it should be 0.825 (from the formula).
- The R / BUGS code in the Appendix worked almost without alteration. 
I found that I had to: Install BUGS (OpenBGUS). 
Hoist the npv function to the top. 
Remove the codaPkg=TRUE setting to obtain a result object. 
(also the "obs" and "qaly" values appear to be dead code) 
If F1000 allows additional appendix files this could be supplied as an additional plain ASCII file, to avoid scraping the text from the PDF and correcting resulting formatting.
- I can't find the support for the n_hosp data value of 136. 
The tot_hosp value of 125 appears in the Donaldson BMJ paper. 
That paper gives 138 for the total number of confirmed deaths due to pandemic influenza.
Structure
Recap
Strength
Strength
Recap
Strength
Recap
Todo
Strength
Todo
Other
Structure
Structure
Todo
Recap
Structure
Structure
Other
Strength
Strength
Todo
Todo
Todo
Todo
Weakness
Other
Weakness
Structure
Structure
Recap
Todo
Todo
Weakness
Recap
Recap
Todo
Todo
Strength
Other
Other
Other
Other
Todo
Weakness
Other
Other