Causal Inference in Adjuvant Endocrine Therapy for Breast Cancer
Bibliographic record
Abstract
Adjuvant endocrine therapy (AET) plays a critical role in the management of estrogen receptor (ER) positive breast cancer. However, early discontinuation is common and may compromise treatment effectiveness. While observational data offer valuable opportunities for understanding AET patterns and effects on cancer outcomes, robust causal evidence on AET adherence and the heterogeneity of treatment response remains limited. This thesis aims to address these challenges by applying target trial emulation (TTE) study design and causal inference methods to strengthen evidence derived from real-world observational data. Chapter 2 systematically reviews the implementation of TTE in the literature. A scoping review of 96 original studies revealed a rapid rise in TTE applications since 2018 across diseases such as cancer, cardiovascular, and infectious diseases. However, only about half of the studies clearly specified all three foundational components of TTE—time zero, treatment assignment, and comparison strategy—raising concerns about methodological rigor. The review also highlighted key limitations such as residual confounding and lack of generalizability, underscoring the need for improved guidance and evaluation tools. This work provides a foundation for the application of TTE in Chapter 3. Chapter 3 applies the TTE study design to estimate the causal effect of early AET discontinuation on survival among post-menopausal women with non-metastatic ER positive breast cancer using population-based data from the Alberta Cancer Registry and electronic health records. Among 6,823 eligible patients diagnosed between 2010 and 2015, early discontinuation was associated with significantly increased risks of overall mortality, recurrence, and breast cancer–specific death, particularly in patients with higher-stage disease. However, patients discontinuing AET between 4~5 years had similar survival experience compared to those completed five years of AET. Risk factors for early discontinuation included older age, low-stage disease, HER2-negative status, and lack of chemotherapy and radiotherapy. Chapter 4 examines the heterogeneous effects of AET on survival outcomes across varying levels of estrogen receptor (ER) and progesterone receptor (PR) expression. A weighted pooled logistic regression model was used to estimate effect modification by ER and PR expression. TTE was implemented to support causal inference. This work aims to generate insights that may inform more personalized and effective treatment strategies. Overall, this thesis advances both the application of causal inference methods and the generation of updated real-world evidence on AET use in treating postmenopausal patients with ER positive breast cancer. It underscores the potential and limitations of deriving causal insights from observational data and contributes to a deeper understanding of AET effectiveness and opportunities for treatment personalization in this patient population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.235 | 0.549 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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 source (direct Gemma or distilled Codex), 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".