69 research outputs found
Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data
Comment on ``Demystifying Double Robustness: A Comparison of Alternative
Strategies for Estimating a Population Mean from Incomplete Data''
[arXiv:0804.2958]Comment: Published in at http://dx.doi.org/10.1214/07-STS227B the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Interim Monitoring of Sequential Multiple Assignment Randomized Trials Using Partial Information
The sequential multiple assignment randomized trial (SMART) is the gold
standard trial design to generate data for the evaluation of multi-stage
treatment regimes. As with conventional (single-stage) randomized clinical
trials, interim monitoring allows early stopping; however, there are few
methods for principled interim analysis in SMARTs. Because SMARTs involve
multiple stages of treatment, a key challenge is that not all enrolled
participants will have progressed through all treatment stages at the time of
an interim analysis. Wu et al. (2021) propose basing interim analyses on an
estimator for the mean outcome under a given regime that uses data only from
participants who have completed all treatment stages. We propose an estimator
for the mean outcome under a given regime that gains efficiency by using
partial information from enrolled participants regardless of their progression
through treatment stages. Using the asymptotic distribution of this estimator,
we derive associated Pocock and O'Brien-Fleming testing procedures for early
stopping. In simulation experiments, the estimator controls type I error and
achieves nominal power while reducing expected sample size relative to the
method of Wu et al. (2021). We present an illustrative application of the
proposed estimator based on a recent SMART evaluating behavioral pain
interventions for breast cancer patients
Linear mixed models with flexible distributions of random effects for longitudinal data.
SUMMARY. Normality of random effects is a routine assumption for the linear mixed model, but it may be unrealistic, obscuring important features of among-individual variation. We relax this assumption by approximating the random effects density by the seminonparameteric (SNP) representation of Gallant and Nychka (1987, Econometrics 55, 363-390), which includes normality as a special case and provides flexibility in capturing a broad range of nonnormal behavior, controlled by a user-chosen tuning parameter. An advantage is that the marginal likelihood may be expressed in closed form, so inference may be carried out using standard optimization techniques. We demonstrate that standard information criteria may be used to choose the tuning parameter and detect departures from normality, and we illustrate the approach via simulation and using longitudinal data from the Framingham study
Estimating optimal treatment regimes from a classification perspective
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135136/1/sta4124.pd
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