Targeted Learning Causal Inference for Observational and Experimental Data /
The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can...
| Κύριοι συγγραφείς: | , |
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| Συγγραφή απο Οργανισμό/Αρχή: | |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
New York, NY :
Springer New York,
2011.
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| Σειρά: | Springer Series in Statistics,
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| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- Models, Inference, and Truth
- The Open Problem
- Defining the Model and Parameter
- Super Learning
- Introduction to TMLE
- Understanding TMLE
- Why TMLE?
- Bounded Continuous Outcomes
- Direct Effects and Effect Among the Treated
- Marginal Structural Models
- Positivity
- Robust Analysis of RCTs Using Generalized Linear Models
- Targeted ANCOVA Estimator in RCTs
- Independent Case-Control Studies
- Why Match? Matched Case-Control Studies
- Nested Case-Control Risk Score Prediction
- Super Learning for Right-Censored Data
- RCTs with Time-to-Event Outcomes
- RCTs with Time-to-Event Outcomes and Effect Modification Parameters
- C-TMLE of an Additive Point Treatment Effect
- C-TMLE for Time-to-Event Outcomes
- Propensity-Score-Based Estimators and C-TMLE
- Targeted Methods for Biomarker Discovery
- Finding Quantitative Trait Loci Genes
- Case Study: Longitudinal HIV Cohort Data
- Probability of Success of an In Vitro Fertilization Program
- Individualized Antiretroviral Initiation Rules
- Cross-Validated Targeted Minimum-Loss-Based Estimation
- Targeted Bayesian Learning
- TMLE in Adaptive Group Sequential Covariate Adjusted RCTs
- Foundations of TMLE
- Introduction to R Code Implementation.
