When estimating the treatment effect in an observational study, we use a semiparametric locally efficient dimension reduction approach to assess both the treatment assignment mechanism and the average responses in both treated and nontreated groups. We then integrate all results through imputation, inverse probability weighting and doubly robust augmentation estimators. Doubly robust estimators are locally efficient while imputation estimators are super-efficient when the response models are correct. To take advantage of both procedures, we introduce a shrinkage estimator to automatically combine the two, which retains the double robustness property while improving on the variance when the response model is correct. We demonstrate the performance of these estimators through simulated experiments and a real dataset concerning the effect of maternal smoking on baby birth weight.

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Authors: Apoorva Anandan, MD (1,2), Trinetri Ghosh (3), Jiwei Zhao (3), Kari Wisinski, MD (1,2) (1) Department of Medicine, Section of Hematology/Oncology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI (2) University of Wisconsin Carbone Cancer Center, Madison, WI (3) University of Wisconsin Department of Biostatistics and Medical Informatics Madison, WI Background: A combined analysis of the SOFT/TEXT trials demonstrated an overall survival (OS) benefit to endocrine therapy in addition to OFS compared to endocrine therapy alone, further establishing the importance of adequate OFS in premenopausal women with HR+ breast cancers. Factors associated with inadequate OFS are ambiguous, but younger age, type of chemotherapy, high body mass index (BMI), and time from treatment completion seem to be important. Additionally, data remains limited regarding whether monitoring of OFS should be routinely performed and optimal timing of hormone levels. We sought to identify predictors of inadequate OFS among an AYA population of hormone receptor positive breast cancer receiving OFS at our institution. Methods: A retrospective, descriptive study was conducted looking at AYA patients (pts) aged 18-39 with a diagnosis of HR+ breast cancer who previously and/or currently received oncologic care for management of their breast cancer at the institution. Data was collected from pts diagnosed between 2000-2022. Patients who had previously received or are currently receiving OFS in the adjuvant and/or metastatic setting were included in the study. Data was collected regarding age, BMI, chemotherapy regimen received in the neoadjuvant and/or adjuvant setting, type, dose, and frequency of ovarian suppression, number of times estradiol was monitored, and frequency of estradiol levels >20. This was used as the cut off based on a comprehensive review of data consistently categorizing levels >20 as not to be postmenopausal. Results: 74 AYA patients who received OFS were included with median age of 28 (range 20-39) and average BMI 27.7 (range 15-45). 70% of the population was Caucasian, 10% African American, and 20% Hispanic. Estradiol levels were monitored in 46 of the 74 pts (62%). The frequency of estradiol monitored ranged from 1-22 times. 16 out of the 46 (35%) pts had estradiol checked only once, 10 (22%) twice, and 16 (35%) four or more times. 22 out of 46 pts (48%) had estradiol levels >20 when checked at least once. 9 out of 22 (41%) had estradiol >20 when checked more than four times. Out of 74 pts, 36 received OFS every month (49%), 32 received OFS every three months (43%). Only 4 out of 74 pts (5%) switched from monthly to every three months, meanwhile only 1 (1%) switched from every three months to monthly. 18 out of the 36 pts (50%) receiving monthly OFS had estradiol levels checked with 9 (50%) having estradiol >20. Meanwhile, 20 out of the 32 pts who received OFS every 3 months (63%) had estradiol levels checked with 10 (50%) having estradiol >20. The average age of pts with estradiol >20 was 33.6 while the average BMI of those with estradiol >20 was 29.2. Finally, 64 out of 74 pts (86%) received chemotherapy in the neoadjuvant and/or adjuvant setting. 41 out of 64 (64%) had estradiol levels checked. 18 out of 41 (44%) had estradiol >20. Discussion: Our data indicates a high degree of clinician variability in monitoring estradiol levels in AYA pts treated with OFS. Lack of adequate OFS was seen in nearly half of pts in this cohort. Higher BMI and young age may be predictors of lack of adequate ovarian suppression, supporting the findings of other studies. An algorithm for routine monitoring of estradiol may improve outcomes with OFS especially in young pts or those with a high BMI. Citation Format: Apoorva Anandan, Trinetri Ghosh, Jiwei Zhao, Kari B. Wisinski. Predictors of Incomplete Ovarian Function Suppression (OFS) in Adolescent and Young Adult (AYA) Women with Hormone Receptor Positive (HR+) Breast Cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-03-24.

To maximize clinical benefit, clinicians routinely tailor treatment to the individual characteristics of each patient, where individualized treatment rules are needed and are of significant research interest to statisticians. In the covariate-adjusted randomization clinical trial with many covariates, we model the treatment effect with an unspecified function of a single index of the covariates and leave the baseline response completely arbitrary. We devise a class of estimators to consistently estimate the treatment effect function and its associated index while bypassing the estimation of the baseline response, which is subject to the curse of dimensionality. We further develop inference tools to identify predictive covariates and isolate effective treatment region. The usefulness of the methods is demonstrated in both simulations and a clinical data example.

We propose a new modeling and estimation approach to select the optimal treatment regime from different options through constructing a robust estimating equation. The method is protected against misspecification of the propensity score model, the outcome regression model for the non-treated group, or the potential non-monotonic treatment difference model. Our method also allows residual errors to depend on covariates. A single index structure is incorporated to facilitate the nonparametric estimation of the treatment difference. We then identify the optimal treatment through maximizing the value function. Theoretical properties of the treatment assignment strategy are established. We illustrate the performance and effectiveness of our proposed estimators through extensive simulation studies and a real dataset on the effect of maternal smoking on baby birth weight.