An imputation based empirical likelihood approach to pretest–posttest studies
Bibliographic record
Abstract
Abstract Pretest–posttest studies are an important and popular method for assessing treatment effects or the effectiveness of an intervention in many areas of scientific research. There are two distinct features for this type of study: availability of baseline information for all subjects in the study and missingness by design of measures of the responses. Important recent research advances on this topic include Leon et al. (2003) on efficient estimation of the treatment effect, and Huang et al. (2008) on a semi‐parametric estimation procedure based on empirical likelihood (EL) where the mean responses for the treatment group and the control group are handled separately. EL ratio confidence intervals or tests for the treatment effect, however, cannot be constructed under the approach used by Huang et al. (2008). In this paper, we use an alternative EL formulation, which directly involves the parameter of interest, i.e., the treatment effect, and incorporates baseline information through an imputation approach. Our focus is to derive the EL ratio confidence intervals and tests for the treatment effect under the proposed imputation‐based framework. Theoretical results are developed, and finite sample performances of the proposed methods with comparison to existing approaches are investigated through simulation studies. An application to a real data set is also presented. The Canadian Journal of Statistics 43: 378–402; 2015 © 2015 Statistical Society of Canada
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".