165

The answers so far have focused on the data itself, which makes sense with the site this is on, and the flaws about it. But I'm a computational/mathematical epidemiologist by inclination, so I'm also going to talk about the model itself for a little bit, because it's also relevant to the discussion. In my mind, the biggest problem with the paper is not the ...


110

My primary concern with this paper is that it focuses primarily on Google search results. It is a well-established fact that smartphone use is on the rise (Pew Internet, Brandwatch), and traditional computer sales are declining (possibly just due to old computers still functioning) (Slate, ExtremeTech), as more people use smartphones to access the internet. ...


60

Well, this paper establishes the fact that the number of Google searches on Facebook fits a certain curve nicely. So at best it can predict that the searches on Facebook will decline by 80%. Which might be feasible, because Facebook might become so ubiquitous that nobody would need to search about it. The problem with such type of models is that they ...


37

For the US data: You are confusing two important but different concepts in epidemiology: prevalence and incidence. A Wikipedia page describes the difference. The anti-smoking warning that you show says that 9 of every 10 lung cancers that occur are caused by smoking. That's the incidence of smoking-related lung cancers among all lung cancers that occur. ...


25

Not to give a complete or authoritative answer, but just to stimulate ideas, I will report on a quick analysis I made for a lab exercise in a spatial stats course I was teaching ten years ago. The purpose was to see what effect an accurate accounting of likely travel pathways (on foot), compared to using Euclidean distances, would have on a relatively ...


24

What you're asking about is called the "Population Attributable Fraction"—the number of cases in the entire population that can be attributed to the exposure (in this case, smoking). The formula for this is: $$ PAF = \frac{P_{{\rm pop}}\times (RR-1)}{P_{{\rm pop}}\times (RR-1)+1} $$ Here, $P_{{\rm pop}}$ is the proportion of exposed subjects in the ...


19

In [1,ยง3.2], David Freedman suggests an essentially negative answer to your question. That is, no (mere) statistical model or algorithm could solve John Snow's problem. Snow's problem was to develop a critical argument supporting his theory that cholera is a water-borne infectious disease, against the prevailing miasma theory of his day. (Chapter 3 in [1], ...


14

Google Trend in my opinion can't produce a good data set for this case of study. Google trend shows how often a term is searched with Google so there are at least two reasons for raising some doubts about the prevision: We don't know if the user searches on Google Facebook to log in or if he searches information about Facebook Facebook is not only a site ...


14

There are several points that you can improve in the code Wrong boundary conditions Your model is fixed to I=1 for time zero. You can either changes this point to the observed value or add a parameter in the model that shifts the time accordingly. init <- c(S = N-1, I = 1, R = 0) # should be init <- c(S = N-Infected[1], I = Infected[1], R = 0) ...


12

A few basic issues stand out with this paper: It assumes correlation of search engine queries about a rising social network with the membership increases. This may have correlated in the past, but may not in the future. There are very few new large social networks. You can almost count them on one hand. Friendster, Myspace, Facebook, Google+. Also, Stack ...


11

First, observational studies can have control. Like prospective cohort studies (people choosing to smoke versus people choosing not to) or case-control studies (people with outcome versus people without outcome.) A more proper contrast for observational studies is probably "intervention studies" or "experimental studies", in which researchers get to assign ...


11

Survival bias and competing risks. Also, elderly having a high value of a risk factor who have not been affected by that risk factor have demonstrated a robustness to that particular factor in general. This is why age $\times$ risk factor interactions can be important to pre-specify in a model.


9

I too speculate at the prevalence of logistic models in the literature when a relative risk model would be more appropriate. We as statisticians are all too familiar with adherence to convention or sticking to "drop-down-menu" analyses. These create far more problems than they solve. Logistic regression is taught as a "standard off the shelf tool" for ...


9

Two thoughts in addition to the other answers: prevalence is a fraction (ratio), not a rate. a rate is a fraction where the units in enumerator and denominator differ. The difference is usually a time (duration) in the denominator. Examples: incidence rate, growth rate, decay rate. e.g. incidence rate: number of newly diagnosed disease X cases per (...


9

First, it's worth recognizing that you cannot typically change sensitivity independently of specificity, and vice versa. This is the point of a ROC curve. Given the nature of the data generating process, and your specific data and model, you will always be stuck with some tradeoff between sensitivity and specificity. You would of course prefer to have 100%...


9

"If all you have is a hammer, everything looks like a nail." The dataset you have is small, possibly underrepresented, and of unknown quality, since it is argued that many cases could have not been diagnosed. You observe an exponential growth, a common phenomena in many natural and artificial processes. The curve fits well, but I'd bet that other similar ...


7

A propensity score isn't just a way of matching groups. There are other ways to use propensity scores - at its heart, its a way to characterize the probability of being exposed given covariates. When this is adjusted for in any one of a number of ways (including matching) you theoretically break one of the conditions necessary for confounding. The problem ...


7

I don't know that there is necessarily a book on ecological studies, but there are some decent articles written about it. I'd consider starting with the January 2012 issue of Epidemiology, Vol. 23 No. 1. The reason for this is two articles: Radon and Skin Cancer in Southwest England: An Ecologic Study by B. Wheeler et al. Commentary: A Niche for Ecologic ...


7

"All", "always", etc. are dangerous words. Most epidemiology studies are observational - as a field epidemiology tends to concern itself with study questions that are not amenable to randomization and controlled trials. The dominant form of studies one would encounter while doing graduate work in epidemiology or working in a public health department, or ...


7

I think the best example of this may likely be the controversy around hormone replacement therapy and cardiovascular risk - large cohort epidemiological studies seem to suggest a protective effect and health policy and physician recommendations were made on this information. Follow-up RCTs then seem to show that there's actually an increased risk of ...


7

I would be surprised if this result held up. Consider that the overall effect size is very small - the point estimate risk of a specific type of cancer (breast cancer) increased by 5% (alternatively: by a factor of 1.05) and was barely significant at the 95% level of confidence. Consider that data dredging indicated that the effect only held for a) pre-...


7

As already said by others, sensitivity and specificity don't depend on prevalence. Sensitivity is the proportion of true positives among all positives and specificity is proportion of true negatives among all negatives. So if sensitivity is 90%, then the test will be correct for 90% of the cases that are positive. Obviously 90% of something smaller and 90% ...


6

Your colleague is correct. In the US, 1.6 to 1.7 m is near the middle of the range of adult female heights. According to Wolfram Alpha, which summarizes NHANES 2006 data, the height distribution in this range should look close to this: This is extremely close to uniform: its mean is 1.649 m and its standard deviation is 0.0287 m (whereas a uniform ...


6

I propose modeling cancer occurrence as a Poisson process. Multiple events (appearance of tumors) are possible within the same individual over the time period of observation. If $\lambda$ is the rate of tumor appearance by year, the probability of 0 events is $e^{-\lambda}$, and the probability of 1 event or more is $p=1-e^{-\lambda}$. You follow $n$ ...


6

It sounds like a cross-sectional study: it's a descriptive study, only looking at one specific time point. That said, it could still be part of a prospective cohort study if the individuals in the 1985 birth cohort have been followed up over time. In that case, it would be cross-sectional study nested in a cohort study.


6

The question isn't "if" but "when". That it will end is already guaranteed. http://www.ted.com/talks/geoffrey_west_the_surprising_math_of_cities_and_corporations.html I take umbrage with the use of the SIR model. It comes with assumptions. One of the assumptions is that eventually everyone is "recovered". Infections are not perpetual, while technology ...


6

I'm going to weigh in as an Epidemiologist. I can see inertia setting in as researchers & professionals in the health care field move into middle management and beyond and are out of touch with new developments in statistics. First, I would strongly advise you not to assume this is simply inertia, either in the form of the discipline not wanting to ...


6

It is possible that there is a prejudice against anything that comes close to data mining, but there can also be good reasons for omitting this area. Without further information I would start with the assumption that there was a good reason. If you assume a good reason and there was actually a prejudice the debate starts as much more friendly and is likely ...


6

You can use the Delta method to obtain an approximate distribution of your relative risk, as shown by that link. Then you can define a pivot and use this to obtain a CI. I understand that there might be some confusion regarding the use of the Delta method, so here are a few simple steps that show how to construct an approximate CI for the relative risk. ...


6

I am going to confine my comments to the SEIR model - the issues for the SIR model are similar and it can be treated as a special limiting case of the SEIR model anyway (for large $\delta$). What you've done so far I've had a look at your MATLAB code, which seems absolutely fine to me. For a given set of model parameters, your code solves the SEIR ...


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