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- Clustered Matched-pair Data (1)
- Confidence interval (1)
- Diagnostic medicine (1)
- Longitudinal Data Analysis and Time Series (1)
- Marginal probability (1)
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- Matched-pair binary data (1)
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- Sequential randomized controlled trials; targeted maximum likelihood estimation; semi-parametric estimation; efficient estimation; dynamic treatment regimes; longitudinal methods (1)
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- Tango's method (1)
Articles 1 - 3 of 3
Full-Text Articles in Applied Statistics
Big Data And The Future, Sherri Rose
Targeted Maximum Likelihood Estimation For Dynamic Treatment Regimes In Sequential Randomized Controlled Trials, Paul Chaffee, Mark J. Van Der Laan
Targeted Maximum Likelihood Estimation For Dynamic Treatment Regimes In Sequential Randomized Controlled Trials, Paul Chaffee, Mark J. Van Der Laan
Paul H. Chaffee
Sequential Randomized Controlled Trials (SRCTs) are rapidly becoming essential tools in the search for optimized treatment regimes in ongoing treatment settings. Analyzing data for multiple time-point treatments with a view toward optimal treatment regimes is of interest in many types of afflictions: HIV infection, Attention Deficit Hyperactivity Disorder in children, leukemia, prostate cancer, renal failure, and many others. Methods for analyzing data from SRCTs exist but they are either inefficient or suffer from the drawbacks of estimating equation methodology. We describe an estimation procedure, targeted maximum likelihood estimation (TMLE), which has been fully developed and implemented in point treatment settings, …
R Code: A Non-Iterative Implementation Of Tango's Score Confidence Interval For A Paired Difference Of Proportions, Zhao Yang
Zhao (Tony) Yang, Ph.D.
For matched-pair binary data, a variety of approaches have been proposed for the construction of a confidence interval (CI) for the difference of marginal probabilities between two procedures. The score-based approximate CI has been shown to outperform other asymptotic CIs. Tango’s method provides a score CI by inverting a score test statistic using an iterative procedure. In the developed R code, we propose an efficient non-iterative method with closed-form expression to calculate Tango’s CIs. Examples illustrate the practical application of the new approach.