Methods for clustered encouragement design studies with noncompliance and missing data
نویسندگان
چکیده
منابع مشابه
Methods for clustered encouragement design studies with noncompliance and missing data.
Encouragement design studies are particularly useful for estimating the effect of an intervention that cannot itself be randomly administered to some and not to others. They require a randomly selected group receive extra encouragement to undertake the treatment of interest, where the encouragement typically takes the form of additional information or incentives. We consider a "clustered encour...
متن کاملITT analysis of randomized encouragement design studies with missing data.
In this paper, we considered a missing outcome problem in causal inferences for a randomized encouragement design study. We proposed both moment and maximum likelihood estimators for the marginal distributions of potential outcomes and the local complier average causal effect (CACE) parameter. We illustrated our methods in a randomized encouragement design study on the effectiveness of flu shots.
متن کاملClustered encouragement designs with individual noncompliance: bayesian inference with randomization, and application to advance directive forms.
In many studies comparing a new 'target treatment' with a control target treatment, the received treatment does not always agree with assigned treatment-that is, the compliance is imperfect. An obvious example arises when ethical or practical constraints prevent even the randomized assignment of receipt of the new target treatment but allow the randomized assignment of the encouragement to rece...
متن کاملPerformance evaluation of different estimation methods for missing rainfall data
There are numerous methods to estimate missing values of which some are used depending on the data type and regional climatic characteristics. In this research, part of the monthly precipitation data in Sarab synoptic station, east Azerbaijan province, Iran was randomly considered missing values. In order to study the effectiveness of various methods to estimate missing data, by seven classic s...
متن کاملMissing data methods in Mendelian randomization studies with multiple instruments.
Mendelian randomization studies typically have low power. Where there are several valid candidate genetic instruments, precision can be gained by using all the instruments available. However, sporadically missing genetic data can offset this gain. The authors describe 4 Bayesian methods for imputing the missing data based on a missing-at-random assumption: multiple imputations, single nucleotid...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Biostatistics
سال: 2010
ISSN: 1465-4644,1468-4357
DOI: 10.1093/biostatistics/kxq065