Re: st: RE: Multilevel modeling

[Christina Wei <christinaw1211@gmail.com>]

Thank you so much Tim and Ariel.  I almost went down the wrong path
unknowingly.  Thanks you thank you thank you!

Sincerely,
Christina

On Mon, Dec 2, 2013 at 10:32 AM, Ariel Linden <ariel.linden@gmail.com> wrote:
> I will add to Tim's excellent discussion, by suggesting that Christina read
> the following article that describes the use of propensity score weighting
> for longitudinal data (using the GEE model).
>
> Hernán, M. A., Brumback, B. & Robins, J. M. (2002) Estimating the causal
> effect of zidovudine on CD4 count with a marginal structural model for
> repeated measures. Statistics in Medicine, 21, 1689–1709.
>
> Date: Mon, 2 Dec 2013 14:33:18 +0800
> From: Timothy Mak <tshmak@hku.hk>
> Subject: st: RE: Multilevel modeling
>
> The purpose of propensity-score weighting is to estimate what are called
> "Average causal effects", that is, these are effects defined as in:
>
> \sum_i CE_i / N
>
> where CE is the causal effect of subject i, and are assumed to be different
> for different individuals.
>
> Note that there are no "random effects" in the definition.
>
> In "random effects"/multilevel model, we are (strictly speaking) assuming
> that given the random effects, the individual effects are all the same. This
> is very different from the assumptions in the average causal effects model.
>
> If subjects are recruited in clusters, or if the independence assumption is
> being violated for whatever reason, then in the average causal effects
> model, we should account for the lack of independence across observations
> using Generalized Estimating Equations - These allow you to specify a
> correlation matrix which represent the correlation in effect estimates
> between individuals. This framework also naturally accounts for
> over/under-sampling in subpopulation.
>
> On another note, although PS-weighting can give consistent estimates of the
> average causal effects (provided your propensity scores are estimated
> consistently), the estimates are generally not efficient, and in certain
> cases, can lead to really big error. Thus, matching/stratification based on
> PS are generally preferred approaches.
>
> HTH,
> Tim
>
> - -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Christina Wei
> Sent: 30 November 2013 04:03
> To: statalist@hsphsun2.harvard.edu
> Subject: st: Multilevel modeling
>
> Hi everyone!  Happy Holidays :)
> I have a question about multilevel modeling.  I'm new to this type of
> analysis, and am planning on doing the analysis in Stata 12.
>
> The question is:  Is it possible to do a "propensity-score weighted"
> multilevel modeling?  I searched the literature and have found none.
>
> I plan to derive my propensity scores from a logistic regression model
> regressing baseline group membership on the baseline values of some
> selected covariates.  I have a panel/longitudinal data with 5 follow
> up visits, but I plan to just use baseline data to derive a "baseline
> cross-sectional" propensity score.  I plan to use the propensity
> scores to weight-adjust my final multi-level model.  Theoretically,
> the probability of being assigned to a specific treatment group is
> only "valid" at baseline (e.g. prior to initiation of treatment).
>
> I greatly appreciate any help I could get on this.
>
> ~Christina
>
>
>
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