Hill criteria -application in the literature

We started a small series on the criteria of AB Hill for causal inference some time ago we talked  about the use of these criteria,  the original paper by AB Hill that describe these so called criteria, and a discussion on their orginis in comparison to Hume by A Morabia. In this post we will discuss some examples of review articles that not discuss the use of the criteria, but who apply the criteria in a search for an answer on the presumed causal mechanism discussed in that particular article. We discuss some examples in the hope that there is one that is related to your favorite topic of research. Some examples might be outdated, but it is interesting to see how these criteria are used in the course of history.

// breast cancer research

in this 1987 paper Godwin and Boyd appraise the evidence for a causal relationship between dietary fat intake and breast cancer. They conclude “The remaining criteria, with the exception of temporality and epidemiological coherence, were not satisfied. Insufficient evidence existed to conclude a causal association existed between dietary fat and breast cancer risk in humans.” PMID:  3476790 

Another example, also by Godwin, but this time together with Steinberg, discusses the evidence for a causal relationship between alcohol and breast cancer. PMID: 1838016  

// genital ulcer disease and HIV transmission

Dickerson and colleagues discuss the causal relationship between genetic ulcer disease as a causal factor in the transmission of HIV, of course independent of all other factors. It is interesting to see how the authors do a review of all evidence by first discussion the individual studies, their design and analyses etc, before discussing the Hill criteria. This combination is nice, because the authors conclude “When applying the literature to Hill’s criteria, all nine criteria for causal inference were met, providing additional evidence that genital ulcers are associated with an increased risk for the development of HIV infection.” Additional evidence…interesting phrasing when it comes to providing a definite answer on the causal question: apparently, these authors do not regard the Hill criteria to be sufficient? PMID:  8885077  

// antipsychotic drugs as cause of Neuroleptic malignant syndrome

in this article by Gilman, a classic question comes to mind: are the drugs we give to a particular kind of patient also the cause of one of the related disorders, of tis the other disorder just a common disorder found in these patient irrespective of treatment. Gilman writes “The formal criteria for substantiating cause-effect relationships in medical science, established by Hill, are applied to NMS and, for comparison, also to malignant hyperthermia and serotonin toxicity.” Whereas it hurts a little to see him use the term “formal criteria”, Gilman applies a nice negative control concept. Perhaps we should read some more on the concept of negative controls later on this blog? Ah…. we already did! PMID:  20623765  

Causality tweets I

As you know, this blog can also be followed via twitter. We tweet about our recent post but also keep on look out on anything that is remotely related to causal inference in epidemiology. Here some of the tweets and twitterers that we find/found interesting. Don’t want to check out all the possibilities to follow guys on twitter but dont want to miss out on the good stuff? No Worries, we are here to help with a new series in which we highlight some of our favorites.

// We follow statsblog, who “aggregates blogs on statistics, R, data visualization etc”

//We follow Matthew Hankins, who is responsible for the #stillnotsgnificant tweets on the over reliance, and wrongful use of p values dichotimisation.

// We follow Kate,a “Newbie epidemiologist with an interest in clinical outcomes research, risk prediction, e-health, data literacy and sci comms” who blogs at the normally distributed blog. She has some serious comments and entries, but sometimes a nice little gem as this one comes along.

// everyday we get a stat-tip of the day via @statfact

// we also follow some journals, such as Epidemiology

What are your favorites? Please talk to us via @causalityblog.

The Hazard of Hazard Ratios (including music video)

A new entry for our journalclub section: The hazard of Hazard Ratios. Miguel Hernan argues why hazard ratios are not a good causal estimates with two arguments: 1) Hazard ratios may change over time and 2) hazard ratios has got a build in selection bias which makes it difficult to interpret them. He uses the well-known  HRT example to illustrate his point. The article can eb found on PubMed central, and is also free to aces through the website of Epidemiology.

Do you wonder how this build in selection bias works? Miguel Hernan explains in the intro of this music video of the epi-hit “Baby got Dag”.


We’ve tweeted the video before… be sure to follow us via @causalityblog and join 200+ followers!

The Journal of Causal inference, a new open acces journal

A new open access journal has been launched! But this really news , because a lot of open access journals are started everyday hoping to make some money during the open access boom. This can even lead to predatory publishing, a term coined by Jeffrey Beall, a activist librarian trying to keep process of disseminating research data a clean process.

No, the newsworhtiness of this message is that the new journal is the Journal of Causal Inference (JCI), edited by Pearl, Petersen, Sekhon and van der Laan. The journal is published by de Gruyter, a publisher that is not even close to appearing on the  black list of Beall.

So what is this journal all about? A lot of interesting stuff, as we can read from their website:

Aims and Scope

Journal of Causal Inference (JCI) publishes papers on theoretical and applied causal research across the range of academic disciplines that use quantitative tools to study causality.

The past two decades have seen causal inference emerge as a unified field with a solid theoretical foundation, useful in many of the empirical and behavioral sciences. Journal of Causal Inference aims to provide a common venue for researchers working on causal inference in biostatistics and epidemiology, economics, political science and public policy, cognitive science and formal logic, and any field that aims to understand causality. The journal serves as a forum for this growing community to develop a shared language and study the commonalities and distinct strengths of their various disciplines’ methods for causal analysis.

Existing discipline-specific journals tend to bury causal analysis in the language and methods of traditional statistical methodologies, creating the inaccurate impression that causal questions can be handled by routine methods of regression or simultaneous equations, glossing over the special precautions demanded by causal analysis. In contrast, JCI highlights both the uniqueness and interdisciplinary nature of causal research.

Submissions

Journal of Causal Inference encourages submission of applied and theoretical work from across the range of rigorous causal paradigms. Research articles may focus on advances in one or more of the following steps of causal inference: research design, causal model and target parameter specification, identifiability, statistical estimation, or sensitivity analysis/interpretation. The journal also provides a venue for quantitative statistical papers that include a full formal elaboration of causal methods in applied data analyses beyond the abbreviated format typical in many applied journals.
In addition to significant original research articles, JCI also welcomes submissions that synthesize and assess cross-disciplinary methodological research, as well as submissions that discuss the history of the causal inference field and its philosophical underpinnings. Areas of emerging consensus and ongoing controversy are highlighted in editorials and invited commentaries. The journal further encourages submission of unsolicited short communications on topics that aim to stimulate public debate and bring unorthodox perspectives to open questions.

The first edition was just recently published , but when more is published we will get you up to speed. Also, the journalclubsuggestions will feature some of these papers in the next period of time, so that we all can get to know this journal a bit better.

Our guess is that the editors and the editorial board is working hard on the second edition. By the way… some of the names in the editorial board featured on this blog earlier:

Abadie, Abbring,  Bollen, DawidGreen, GreenlandHalpern, Heckman, HernanHill, HitchcockImai, Joffe, Kuroki, Miguel, Moodie, Oakes, RichardsonRigdon, Robins, Rosenblum, Rotnitsky, Small, Sobel, Sprites, Stuart, Tchetgen Tchetgen, TianvanderWeele, VansteelandtVytlacil, West, White, Winship.

announcement: masterclass advances in epidemiologic analysis

This summer will, as always, be a summer with the NIHES summer courses. This year, they have a great line up for their usual Masterclass series. Rothman, Egger, Lemeshow and Vanderweele will give their acte de presence. Be quick to enroll on with the NIHES. All information below is also from their website.

Kenneth-Rothman               Matthias-Egger       Professor Stanley LemeshowProf-Tyler-VanderWeele

Monday August 12, 2013

The public perception of Epidemiology

Prof. Kenneth J. Rothman Distinguished Fellow and VP for Epidemiology Research, RTI Health Solutions, Research Triangle Park, North Carolina, USA and Professor of Epidemiology Boston University School of Public Health, Boston, MA, USA

  • Tuesday August 13, 2013

    Spurious precision? Meta-Analysis of Observational Studies

    Prof. Matthias Egger Professor of Epidemiology and Public Health and Director, Inst. Social and Preventive Medicine (ISPM), Univ. Bern, Switzerland; visiting Professo

    r of Clinical Epidemiology, Dept. of Social Medicine, Univ. Bristol, UK; visiting Professor of Epidemiology, School of Public Health, Univ. Cape Town, South Africa

  • Wednesday August 14, 2013

    Methods for Assessing the Scale of Continuous Covariates in Logistic Regression Modeling

    Prof. Stanley Lemeshow Professor of Biostatistics, The Ohio State University, Columbus, USA

  •  Thursday August 15, 2013

    Sensitivity analysis for unmeasured confounding

    Prof. Tyler VanderWeele Harvard School of Public Health, Departments of Epidemiology and Biostatistics