{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T16:25:27Z","timestamp":1791649527001,"version":"4.3.4"},"reference-count":58,"publisher":"Walter de Gruyter GmbH","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,11,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>A number of sophisticated estimators of longitudinal effects have been proposed for estimating the intervention-specific mean outcome. However, there is a relative paucity of research comparing these methods directly to one another. In this study, we compare various approaches to estimating a causal effect in a longitudinal treatment setting using both simulated data and data measured from a human immunodeficiency virus cohort. Six distinct estimators are considered: (i) an iterated conditional expectation representation, (ii) an inverse propensity weighted method, (iii) an augmented inverse propensity weighted method, (iv) a double robust iterated conditional expectation estimator, (v) a modified version of the double robust iterated conditional expectation estimator, and (vi) a targeted minimum loss-based estimator. The details of each estimator and its implementation are presented along with nuisance parameter estimation details, which include potentially pooling the observed data across all subjects regardless of treatment history and using data adaptive machine learning algorithms. Simulations are constructed over six time points, with each time point steadily increasing in positivity violations. Estimation is carried out for both the simulations and applied example using each of the six estimators under both stratified and pooled approaches of nuisance parameter estimation. Simulation results show that double robust estimators remained without meaningful bias as long as at least one of the two nuisance parameters were estimated with a correctly specified model. Under full misspecification, the bias of the double robust estimators remained better than that of the inverse propensity estimator under misspecification, but worse than the iterated conditional expectation estimator. Weighted estimators tended to show better performance than the covariate estimators. As positivity violations increased, the mean squared error and bias of all estimators considered became worse, with covariate-based double robust estimators especially susceptible. Applied analyses showed similar estimates at most time points, with the important exception of the inverse propensity estimator which deviated markedly as positivity violations increased. Given its efficiency, ability to respect the parameter space, and observed performance, we recommend the pooled and weighted targeted minimum loss-based estimator.<\/jats:p>","DOI":"10.1515\/ijb-2017-0054","type":"journal-article","created":{"date-parts":[[2019,2,28]],"date-time":"2019-02-28T04:01:50Z","timestamp":1551326510000},"source":"Crossref","is-referenced-by-count":38,"title":["Double Robust Efficient Estimators of Longitudinal Treatment Effects: Comparative Performance in Simulations and a Case Study"],"prefix":"10.1515","volume":"15","author":[{"given":"Linh","family":"Tran","sequence":"first","affiliation":[{"name":"Department of Biostatistics , University of California Berkeley , Berkeley , CA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Constantin","family":"Yiannoutsos","sequence":"additional","affiliation":[{"name":"Department of Biostatistics , Indiana University Richard M Fairbanks School of Public Health , Indianapolis , IN , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kara","family":"Wools-Kaloustian","sequence":"additional","affiliation":[{"name":"Infectious Diseases , Howard Hughes Medical Institute - Indiana University School of Medicine , Indianapolis , IN , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abraham","family":"Siika","sequence":"additional","affiliation":[{"name":"Moi University , Eldoret , Kenya"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"van der Laan","sequence":"additional","affiliation":[{"name":"University of California at Berkeley , Berkeley , CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maya","family":"Petersen","sequence":"additional","affiliation":[{"name":"University of California at Berkeley , Berkeley , CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2019,2,26]]},"reference":[{"key":"2025121208564043716_j_ijb-2017-0054_ref_001_w2aab3b7e2045b1b6b1ab2b1b1Aa","doi-asserted-by":"crossref","unstructured":"Robins JM. 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