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Hi Terry, I think the answer is...."no." The reason is that no number of covariates can address some key unknown confounders, like attitude or intent. Imagine a study of healthy lifestyle--one may find identical demographics and comorbidity etc but an attitude towards health behaviors may, either in and of itself be predictive of outcome or at least be associated with a number of unknown confounders. In theory if one knew everything then the answer could be, I suppose, yes. But one cant know everything or enough. And obs studies often address questions that are different from those in trials. Miguel Hernan has written a lot about this, that I find quite useful. For example, though not exactly on topic, the considerations are related http://annals.org/article.aspx?articleid=1735165 best, Rich Saitz On 21 March 2014 09:40, Mayer, Dan <[log in to unmask]> wrote: > Hi Terry and list. > > > This is a great question. I've always thought of propensity matching as > a way of doing a pre-test adjustment and so apply the rules that I would > use in looking at data that has been adjusted for whatever. > > > > I have two main rules: > > 1. The number of outcome events should be at least 20 times the > number of variables that are used in the adjustment. > > 2. There should not be any 'obvious' variables that were 'left out' > of the adjustment process. This is a bit subjective and I always ask > about educational level and socioeconomic status of the participants, > things that are usually not added to any adjustments and which in some > situations are likely would lead to biased results. > > > > The bottom line is that observational studies need to take so many more > precautions in order to have low levels of bias and that is why they are > classified as low quality to start out on the GRADE quality criteria. > > > > Hope that this helps. > > Best wishes, > > > > Dan > > > > > > Dan Mayer, MD > > Professor of Emergency Medicine > > Albany Medical College > > Mail Code 34 > > 47 New Scotland Ave. > > Albany, NY, 12208 > > phone: 518-262-6180 > > FAX: 518-262-5029 > > Mobile: 518-461-3191 > > > > > > > > *From:* Evidence based health (EBH) [mailto: > [log in to unmask]] *On Behalf Of *Shaneyfelt, Terry > *Sent:* Thursday, March 20, 2014 12:24 PM > *To:* [log in to unmask] > *Subject:* Re: [EXTERNAL] RE: Is propensity matching equivalent to > randomization? > > > > *Is there a number of confounding variables you can account for after > which you consider the likelihood of unrecognized confounding variables is > extremely low?* > > > > This is really the crux of my question. I cant remember the exact > reference but we read a large propensity matched study for journal club and > a lot of variables were in the propensity model. As I thought about it I > couldn't think of anything else that could be a confounder that was not > covered by what they put in the propensity model. I know the propensity > model doesn't predict the outcome but it contained everything that could be > a potential confounder of the outcome. The residents asked me if this was > as good as a RCT. I wonder the same thing. > > > > > > *From:* Brian Alper MD [mailto:[log in to unmask] <[log in to unmask]>] > *Sent:* Thursday, March 20, 2014 10:26 AM > *To:* Shaneyfelt, Terry > *Cc:* [log in to unmask] > *Subject:* [EXTERNAL] RE: Is propensity matching equivalent to > randomization? > > > > Randomization balances the unrecognized variables (or at least increases > the likelihood that the unrecognized variables will be balanced and not > biasing results in an unseen way). > > > > How many recognized covariaties must be balanced to have a "balancing" of > the unrecognized variables? > > > > It seems to me like the more propensity matching that is done the more > balancing you get but how do you know when you have "reasonable certainty" > for the effect on the unknown variables? > > > > Is there a number of confounding variables you can account for after which > you consider the likelihood of unrecognized confounding variables is > extremely low? > > > > I don't have a statistical answer - just trying to rephrase the question > to understand it in terms of reaching high likelihood of certainty in the > estimates of effect. > > > > *From:* Evidence based health (EBH) [ > mailto:[log in to unmask]<[log in to unmask]>] > *On Behalf Of *Shaneyfelt, Terry > *Sent:* Thursday, March 20, 2014 11:10 AM > *To:* [log in to unmask] > *Subject:* Is propensity matching equivalent to randomization? > > > > This may seem like a silly question but is propensity matching in a large > observational study equivalent to randomization? Consider the propensity > score to have been developed using a lot of covariates (everything you > could think of and measure). As you have probably seen, large propensity > matched cohorts will be similar to each other within tenths of percentage > points on various characteristics. If you develop your propensity score > with enough covariates what else is potentially missing that could be a > confounder of the effect? > > ------------------------------ > ----------------------------------------- CONFIDENTIALITY NOTICE: This > email and any attachments may contain confidential information that is > protected by law and is for the sole use of the individuals or entities to > which it is addressed. If you are not the intended recipient, please notify > the sender by replying to this email and destroying all copies of the > communication and attachments. 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