Case-control studies do the following:
Admittedly, the last two steps are not strictly required for a well-designed case-control study. They are just extremely popular. I'm being insensitive to people who conduct case-control studies here. Hopefully none of them will murder me. Because I hear that's a thing now, killing people who mock your irrational beliefs. [*]
And thinking case-control studies are a good idea is nothing if not an irrational belief. That very nice article on 538 takes the case-control results from Swedish studies and applies it to the American population over the past couple of decades. Since cell phone use has increased dramatically, you can compute an expected increase in the rare brain cancer that the Swedes say is increased by cell phone use, and you can see trivially that no such increase has occurred.
Ergo, cell phones don't cause brain cancer.
But why would anyone believe they did in the first place?
The problem with case-control studies is that you are attributing any difference between your cases and your controls to the causes you don't control for. And for rare events there will almost always be differences. There are ways around this that I'll get to below, but let's first look at how it happens.
To illustrate it I wrote a little Python code:
import numpy as np import random import scipy.stats.mstats # a bunch of characteristics with mean 10 and width 5 fMean = 10.0 fWidth = 5.0 nCharacteristics = 10 # one characteristic is going to have a trivial boost # in the case population, just 'cause randomness happens fCorrelation = 1.05 # three match criteria, three possible "causes" nTestCriteria = 3 nMatchCriteria = 3 nPatients = 500 nControls = 2*nPatients for nShots in range(0, 100): lstIndices = list(range(0,10)) random.shuffle(lstIndices) lstPatients =  for nI in range(0, nPatients): lstPatients.append(np.random.normal(loc = fMean, scale = fWidth, size=nCharacteristics)) for nIndex in lstIndices[-nTestCriteria:]: # causes are tweaked lstPatients[-1][nIndex] *= fCorrelation lstControls =  while len(lstControls) < nControls: lstTest = np.random.normal(loc = fMean, scale = fWidth, size=nCharacteristics) for lstPatient in lstPatients: nCount = sum([abs(lstTest[nI]-lstPatient[nI]) < 1 for nI in lstIndices[0:nMatchCriteria]]) if nCount == nMatchCriteria: # match on uncorrelated criteria lstControls.append(lstTest) break lstRatio =  # odds of rare events are based on tails of distributions! for nIndex in lstIndices[-nTestCriteria:]: nPatientCount = 0 for lstPatient in lstPatients: if lstPatient[nIndex] > fMean+2*fWidth: nPatientCount += 1 nControlCount = 0 for lstControl in lstControls: if lstControl[nIndex] > fMean+2*fWidth: nControlCount += 1 lstRatio.append((float(nPatientCount)/nPatients)/(float(nControlCount)/nControls)) nJMax = 0 # now take the BIGGEST difference in effect! fRatioMax = 0 for (nJ, fRatio) in enumerate(lstRatio): if fRatio > fRatioMax: nJMax = nJ fRatioMax = fRatio # compare the distributions... are they different? nJMax -= -nTestCriteria lstPatientData =  for lstPatient in lstPatients: lstPatientData.append(lstPatient[nJMax]) lstControlData =  for lstControl in lstControls: lstControlData.append(lstControl[nJMax]) fT, fProb = scipy.stats.mstats.ttest_ind(lstPatientData, lstControlData) print(fRatioMax, fT, fProb)
This is an illustrative cheat, nothing more. I could have built a fancier model but there's only so much time you can spend on irrational nutjobs, like people who believe in case-control studies.
The results of the simulation are shown below:
The point is that with an undetectably small tweak to the underlying distribution (the T-test p-values are almost all > 0.05) it is trivially easy to get factors of two or more difference between case and control groups.
This is possible in part because I've allowed multiple possible causes and selected and reported on the one that showed an effect. This is a criminally bad thing to do, utterly illegitimate and wrong. If you're going to do it, you need to a) define the categories of cause beforehand and b) correct all your p-values for the fact that you've gone on a fishing expedition. The odds of something being correlated with your effect are as near as anything to a certainty. The more different things you look at as "possible causes" the more likely it is that you will find one that is correlated by chance.
The importance of the stunt I've pulled here is that by any ordinary statistical standard (and the T-test is as ordinary as you can possibly get) the distributions are not different, but the specific procedure used to tease out the effect results in an apparently dramatic consequences. Statistically identical distributions are generating factors of two or more differences!
This is another way of saying: if you need a case-control study to detect the effect you are looking for, it is probably so small as to be irrelevant to public policy. The money spent on all those case-control studies on cell phones and brain cancer would have saved far more lives had it been spent on almost anything else: auto safety, anti-smoking campaigns, etc.
One of the reasons I don't work in radiotherapy any more, after a brief and productive stint in the field in the early '90's, is that I realized all the money we were spending would be far better put into anti-smoking and other campaigns against the small number of things we knew pretty well caused cancer, instead of marginally improving radiotherapy treatment, which was a) already pretty good and b) showed no significant likelihood of improving much (spoiler: it didn't.)
There are ways case-control studies can be improved to generate results that are more reliable guides to reality. In particular, any decent case-control study should look at exactly one possible cause, or correct very aggressively for multiple experiments. It is hard to overstate how rapidly the statistical power of data decreases as hypotheses multiply, particularly if they are allowed to work in combination, or if the data are sub-setted, so instead of looking at "brain cancer" you end up looking at "this particularly rare form of brain cancer".
Secondly, additional non-causal variables should be investigated that have similar scope to the potentially causal ones, and their distributions should be analyzed and reported alongside the purportedly causal ones. Ideally this should be done blindly.
That is, if you're investigating cell phone use and brain cancer, you should also question participants on how often they talk to their mother, or how often they go out with friends, or what their favourite colour is, and so on. Everything is correlated with everything else, of course, so it'll be difficult to find truly independent variables... which should give you pause when executing a hyper-sensitive test for correlations. Because maybe cell phone use correlates with how often you talk to your mother, or how often you go out with friends, or what your favourite colour is (seriously: colour preferences exhibit age and cultural differences that could easily correlate with cell phone use.)
By measuring and reporting nominally unrelated variables, the ridiculousness of supposedly positive results will be highlighted.
Thirdly, in the "Methods" section, the rate at which case-control studies produce results that are later shown to be nonsense should be mentioned. A simple sentence like, "Case-control studies have been used in this area of research for the past 20 years. We have found 253 studies in the literature. Only three of them identified effects that were later confirmed by more reliable and robust forms of investigation."
If you expect me to believe a result, you need to show me that the method you are using has a good track record of confirmed results in the past. It is true that because of their hyper-sensitivity it will be very difficult to confirm many results from case-control studies by other means, but again: that suggests perhaps redirecting scarce research funding toward areas that have a big enough impact on human life to actually measure.
Fourthly: commit to publishing all results, and get a commitment from your institution's PR people to make the same amount of noise when you find no association as when you do find an association. Put that message, "New study shows no correlation between cell phone use and scurvy!" out there. Try to ensure the same amount of money is spent promoting negative results as positive. Yeah, I know, I'm into the realm of total fantasy here.
Finally: case-control studies should where possible focus strongly on the dose-response curve. In the absence of randomized controlled trials, the dose-response curve is by far the best indicator of causation. If the effect can be graded by levels of severity then the level of severity should be correlated with the level of the cause. If it is not, then the results are probably noise. This may not be possible in all cases, but when it is, not doing it is inexcusable.
Case-control studies do have a use in guiding future research, but they are so fraught with problematic aspects that they should never be used to imply causation without a strong dose-response result. This review is rather more generous to them than I am. I am not aware of any research into how often case-control studies are confirmed on follow-up, and any evidence-based researcher (and what other kind is there?) should be bothered by that.
Here is a nice example of a case-control study that doesn't do everything wrong. They have a single hypothesis, they have a causal account, they do what they can to poke at their results within the limits of their data, and they don't draw grandiose conclusions (I'd like to see the press release associated with the work, though, which probably says something about mothers taking anti-depressants killing babies.)
Even so, tests that are hyper-sensitive to correlations should come with an outsized warning regarding the lack of correlation between correlation and causation, and whenever you read about a case-control study you should think, "This is more likely than not due to random chance and poor research methods, and even if the effect is real it is so small they had to use a test that was hyper-sensitive to correlations to find it, and in any case they don't show any dose-response data so it doesn't constitute more than the tiniest incremental evidence in favour of the proposition under test."
[*] Yes, I am still pretty much incandescently angry regarding the murders by blasphemophobes last week in Paris, and am likely to remain so for a good long time. I get that way when irrational people take it upon themselves to kill people specifically because of characteristics I have. That means I'm still a monkey underneath, rooting for members of my troop, and I won't deny it. Oook. That said, I have also been angry for a long time at the killings perpetrated by the US and others against innocents in the Middle East and elsewhere--being more-or-less an innocent myself--and have written and spoken about it extensively, so this is not cherry-picking. Dropping the innocent and focusing just on the largest monkey-troop of all--human beings--here's something I wrote on Facebook after the death of Osama bin Laden: "I do not celebrate the death of a human being. The impulse to solve our problems by killing people is what got us into this mess. It will not get us out of it." But though I do not support killing, I reserve the right to be absolutely furious with killers.