ArticleInternational journal of methods in psychiatric research2016
Model-based imputation of latent cigarette counts using data from a calibration study.
Article in International journal of methods in psychiatric research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Model-based imputation of latent cigarette counts using data from a calibration study.International journal of methods in psychiatric research · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
In addition to dichotomous measures of abstinence, smoking studies may use daily cigarette consumption as an outcome variable. These counts hold the promise of more efficient and detailed analyses than dichotomous measures, but present serious quality issues - measurement error and heaping - if obtained by retrospective recall. A doubly-coded dataset with a retrospective recall measurement (timeline followback, TLFB) and a more precise instantaneous measurement (ecological momentary assessment, EMA) serves as a calibration dataset, allowing us to predict EMA given TLFB and baseline factors. We apply this model to multiply impute precise cigarette counts for a randomized, placebo-controlled trial of bupropion with only TLFB measurements available. To account for repeated measurements on a subject, we induce correlation in the imputed counts. Finally, we analyze the imputed data in a longitudinal model that accommodates random subject effects and zero inflation. Both raw and imputed data show a significant drug effect for reducing the odds of non-abstinence and the number of cigarettes smoked among non-abstainers, but the imputed data provide efficiency gains. This method permits the analysis of daily cigarette consumption data previously deemed suspect due to reporting error and is applicable to other self-reported count data sets for which calibration samples are available. Copyright © 2015 John Wiley & Sons, Ltd.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.