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]
Exampleedit
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:
those who always stay in the study regardless of which treatment they were assigned
those who would always drop-out of the study regardless of which treatment they were assigned
those who only drop-out if assigned to the treatment group
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.
^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.
^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.
^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.
Frangakis, Constantine E.; Rubin, Donald B. (March 2002). "Principal stratification in causal inference". Biometrics. 58 (1): 21–9. doi:10.1111/j.0006-341X.2002.00021.x. PMC4137767. PMID 11890317. Preprint
Zhang, Junni L.; Rubin, Donald B. (2003) "Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by "Death"", Journal of Educational and Behavioral Statistics, 28: 353–368 doi:10.3102/10769986028004353
Roy, Jason; Hogan, Joseph W.; Marcus, Bess H. (2008) "Principal stratification with predictors of compliance for randomized trials with 2 active treatments", Biostatistics, 9 (2), 277–289. doi:10.1093/biostatistics/kxm027
Egleston, Brian L.; Cropsey, Karen L.; Lazev, Amy B.; Heckman, Carolyn J.; (2010) "A tutorial on principal stratification-based sensitivity analysis: application to smoking cessation studies", Clinical Trials, 7 (3), 286–298. doi:10.1177/1740774510367811
Peck, L. R.; (2013) "On estimating experimental impacts on endogenous subgroups: Part one of a methods note in three parts", American Journal of Evaluation, 34 (2), 225–236. doi:10.1177/1098214013481666
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principal, stratification, statistical, technique, used, causal, inference, when, adjusting, results, post, treatment, covariates, idea, identify, underlying, strata, then, compute, causal, effects, only, within, strata, generalization, local, average, treatme. 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 editAn 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 those who always stay in the study regardless of which treatment they were assigned those who would always drop out of the study regardless of which treatment they were assigned those who only drop out if assigned to the treatment group those who only drop out if assigned to the control groupIf 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 editInstrumental variable Rubin causal modelReferences edit 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 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 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 Frangakis Constantine E Rubin Donald B March 2002 Principal stratification in causal inference Biometrics 58 1 21 9 doi 10 1111 j 0006 341X 2002 00021 x PMC 4137767 PMID 11890317 Preprint Zhang Junni L Rubin Donald B 2003 Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by Death Journal of Educational and Behavioral Statistics 28 353 368 doi 10 3102 10769986028004353 Barnard John Frangakis Constantine E Hill Jennifer L Rubin Donald B 2003 Principal Stratification Approach to Broken Randomized Experiments Journal of the American Statistical Association 98 299 323 doi 10 1198 016214503000071 Roy Jason Hogan Joseph W Marcus Bess H 2008 Principal stratification with predictors of compliance for randomized trials with 2 active treatments Biostatistics 9 2 277 289 doi 10 1093 biostatistics kxm027 Egleston Brian L Cropsey Karen L Lazev Amy B Heckman Carolyn J 2010 A tutorial on principal stratification based sensitivity analysis application to smoking cessation studies Clinical Trials 7 3 286 298 doi 10 1177 1740774510367811 Peck L R 2013 On estimating experimental impacts on endogenous subgroups Part one of a methods note in three parts American Journal of Evaluation 34 2 225 236 doi 10 1177 1098214013481666 nbsp This statistics related article is a stub You can help Wikipedia by expanding it vte Retrieved from https en wikipedia org w index php title Principal stratification amp oldid 1136208052, wikipedia, wiki, book, books, library,
