RE: st: Using AIPW for missing data purposes in RCTs?

["Stephen Kay (TRUELIFE)" <Stephen.Kay@completetruelife.com>]

Thanks Steve for your clarifications to Ariel and also for the additional references and ado file that I was not aware of - most useful. 

Here's a further reference for you Ariel - free to download and a really good source to help in understanding various methods of handling missing data in clinical trials:

http://www.nap.edu/catalog.php?record_id=12955

Best,

Steve 

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Steve Samuels
Sent: 26 June 2013 22:46
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Using AIPW for missing data purposes in RCTs?

Ariel:

Adjustment for covariates in clinical trials can correct for chance imbalances and also reduce the variability of estimated treatment effects (Piantadosi, 2005, pp. 470-475). Adjutment can also be useful for creating prognostic models and for subgroup analyses (interactions with treatment), but the latter run into the multiple-comparison problem.

Reference: Steven Piantadosi. 2005. Clinical Trials: 
A Methodologic Perspective. Hoboken, NJ: Wiley-Interscience.

IPTW estimators, originally designed for observational stuies are  also applied to randomized studies:

http://www.ncbi.nlm.nih.gov/pubmed/22510495

http://biostatistics.oxfordjournals.org/content/early/2012/04/17/biostatistics.kxs009.abstract

http://www4.stat.ncsu.edu/~davidian/synergytalk.pdf

Van Der Laan, Mark. 2011. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer.


Stephen:

A "doubly-robust" estimator similar, but not identical to, Stata's -teffects aipw- estimator is Mark Lunt's -dr- which can be obtained by:

. net from http://personalpages.manchester.ac.uk/staff/mark.lunt
. net install dr


Steve
sjsamuels@gmail.com




On Jun 26, 2013, at 10:22 AM, Ariel Linden, DrPH wrote:

I am perplexed by your issue here. Why would you use adjustment strategies in an RCT? That is, unless your randomization failed? If so, you've got all sorts of other problems on your hands...

As for adjusting for missing data, I am not sure what you are specifically referring to? Missing data on patient characteristics, outcomes, censoring, what? Are you planning on analyzing your data longitudinally or as a point treatment study? If you do not describe your problem in sufficient detail, no one will be able to give you a helpful response.

Missing data can be imputed via Stata's -mi- suite of commands, if that makes sense in your particular situation.

As for IPTW and missing data, you are only partially correct. In the literature (particularly James Robins work) the reference to missing data involves handling of censoring. In short, there are two weights that are generated, (1) the weight based on the propensity score to model probability of receiving treatment, and (2) the weight based on the probability of being censored. The two weights are then combined.

There is a Stata Journal paper that came out a few years ago that shows how to perform this manually. 

Fewell Z  et al. Controlling for time-dependent confounding using marginal structural models. Stata Journal 2004;4( 4): 402-420.

I hope this helps

Ariel

Date: Tue, 25 Jun 2013 12:17:34 +0000
From: "Stephen Kay (TRUELIFE)" <Stephen.Kay@completetruelife.com>
Subject: st: Using AIPW for missing data purposes in RCTs?

Hello,

Stata 13 has augmented inverse probability weighting, AIPW, capabilities aimed at observational data. I don't have Stata 13 so cannot test it out.
Does anyone know if it can be used/modified  for missing data problems in RCTs (which is one area that AIPW is used for)? I have such a trial to analysis and it may be worth upgrading if so. Or can anyone point me to other ado files for Stata 12 that can do so?

Many thanks,

Steve 


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