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Principal stratification

This article will address the topic of Principal stratification, which has generated great interest in different areas in recent years. Since its emergence, Principal stratification has captured the attention of experts and the general public due to its relevance and impact in today's society. Throughout this writing, different aspects related to Principal stratification will be explored, such as its origin, evolution, influence and possible future implications. Likewise, different perspectives and approaches will be analyzed that will allow us to better understand the importance of Principal stratification in the current context.

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Principal stratification is a statistical technique used in causal inference when adjusting results for post-treatment covariates. The idea is to identify underlying strata and then compute causal effects only within strata. It is a generalization of the local average treatment effect (LATE) in the sense of presenting applications besides all-or-none compliance.  The LATE method, which was independently developed by Imbens and Angrist (1994)[1] and Baker and Lindeman (1994)[2] also included the key exclusion restriction and monotonicity assumptions for identifiability. For the history of early developments see Baker, Kramer, Lindeman.[3]

Example

An example of principal stratification is where there is attrition in a randomized controlled trial. With a binary post-treatment covariate (e.g. attrition) and a binary treatment (e.g. "treatment" and "control") there are four possible strata in which subjects could be:

  1. those who always stay in the study regardless of which treatment they were assigned
  2. those who would always drop-out of the study regardless of which treatment they were assigned
  3. those who only drop-out if assigned to the treatment group
  4. those who only drop-out if assigned to the control group

If the researcher knew the stratum for each subject then the researcher could compare outcomes only within the first stratum and estimate a valid causal effect for that population. The researcher does not know this information, however, so modelling assumptions are required to use this approach.

Using the principal stratification framework also permits providing bounds for the estimated effect (under different bounding assumptions), which is common in situations with attrition.

In applied evaluation research, principal strata are commonly referred to as "endogenous" strata or "subgroups" and involve specialized methods of analysis for examining the effects of interventions or treatments in the medical and social sciences.

See also

References

  1. ^ Imbens, Guido W.; Angrist, Joshua D. (March 1994). "Identification and Estimation of Local Average Treatment Effects". Econometrica. 62 (2): 467. doi:10.2307/2951620. ISSN 0012-9682.
  2. ^ Baker, Stuart G.; Lindeman, Karen S. (1994-11-15). "The paired availability design: A proposal for evaluating epidural analgesia during labor". Statistics in Medicine. 13 (21): 2269–2278. doi:10.1002/sim.4780132108. ISSN 0277-6715.
  3. ^ Baker, Stuart G.; Kramer, Barnett S.; Lindeman, Karen S. (2018-10-30). "Correction to "Latent class instrumental variables: A clinical and biostatistical perspective"". Statistics in Medicine. 38 (5): 901–901. doi:10.1002/sim.8035. ISSN 0277-6715.


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