on data from article (or author of)
* Impact of Folic Acid Fortification of the US Food Supply on the Occurrence of Neural Tube Defects
* Incidence of open neural tube defects in Nova Scotia after folic acid fortification
|I||[USA data] Restrict your comparison to spina bifida.|
|a||Formally compare the observed rates pre and post (exclude data from the transition period). Do the same but using the numbers of cases [and denominators] explicitly. What is your best estimate (and 95% CI) of the difference that might be attributed to the fortification?|
|b||Delevop (and fit) a single incidence model that covers all 3 periods and that that allows you to estimate the changes in incidence over these 3 periods. In a deluxe model [not required for this exercise!] you would probably like to 'round the corners'. For this exercise, as a rough approximation consider a simpler one with 3 (connected) straight lines (or curves if you use a rate-ratio model). This is as much an exrecise in how to represent 3 connected straight lines as it is about rates and counts.|
|c||Is there evidence of over-dispersion (ie extra-binomial or extra-Poisson variation)? [the difference between binomial and Poisson is trivial here, since the binomial approaches the Poisson in the limiting case when, as here, the probability of an event is low].|
|d||Does it change your estimates very much whether you use an additive (rate difference)
or a multiplicative (rate ratio) model?
Does it matter very much if you use monthly or quarterly data?
|e||If we had the corresponding data for Canada (1/10th the population and numbers of births, with say with same incidence and same source of data), how much wider would the CI's be?|
|f||Estimate, for various size reductions in the incidence, the statistical power of a study that -- with an alpha of 0.05 2-sided -- compares (in the absence of other time trends) Quebec incidence in the 5 years pre with the incidence in the 5 years post introduction of fortification. Do so (a) without considering overdispersion, so that a regular Binomial or Poisson model is appropriate and (b) with the same amount of over-dispersion as is seen in the 'pre' series in the US. [of course if there is overdispersion, and it is ignored in the analysis, it tends to increase both the power and the probability of a type I error! [we won't know which!].|
|II||[Nova Scotia data] The Results section of the paper gives several point estimates, confidence intervals, and p-values. the authors did not have a statistical analysis paragraph so you will have to try to determine how they calculated these items -- or come up with your own.|
|a||Calculate these items by 'tabular' (i.e., calculator) methods.
Should you/they worry about the appropriateness of Gaussian-based inferences? [Remember that the chi-square statistic with 1 df is the same as the square of a z-statistic.. so whatever is accurate or inaccurate about one is equally so about the other]
|b||Calculate these items by 'regression' methods. Again, is use of Gaussian-based inferences OK?|
|III||Why do you think the estimates of impact differ so much between the USA and Nova Scotia? What soes this say about any concerns you had in in IIf above?|