Clinical factors associated with patterns of endocrine therapy adherence in premenopausal breast cancer patients.

INTRODUCTION: Patients with hormone receptor positive breast cancer are recommended at least five years of adjuvant endocrine therapy, but adherence to this treatment is often suboptimal. We investigated longitudinal trends in adjuvant endocrine therapy (AET) adherence among premenopausal breast cancer patients and identified clinical characteristics, including baseline comorbidities and non-cancer chronic medication use, associated with AET adherence. METHODS: We included stage I-III premenopausal breast cancer patients diagnosed during 2002-2011 and registered in the Danish Breast Cancer Group clinical database who initiated AET. We used group-based trajectory modeling to describe AET adherence patterns. We also linked patients to Danish population-based registries and fit multinomial logistic models to compute odds ratios (ORs) and 95% confidence intervals (95% CIs) associating clinical characteristics with AET adherence patterns. RESULTS: We identified three adherence patterns among 4,353 women-high adherers (57%), slow decliners (36%), and rapid decliners (6.9%). Women with stage I disease (vs. stage II; OR: 1.9, 95% CI 1.5, 2.5), without chemotherapy (vs. chemotherapy; OR: 4.3, 95% CI 3.0, 6.1), with prevalent comorbid disease (Charlson Comorbidity Index score ≥ 1 vs. 0; OR: 1.6, 95% CI 1.1, 2.3), and with a history of chronic non-cancer medication use (vs. none; OR: 1.3, 95% CI 1.0, 1.8) were more likely to be rapid decliners compared with high adherers. CONCLUSIONS: Women with stage I cancer, no chemotherapy, higher comorbidity burden, and history of chronic non-cancer medication use were less likely to adhere to AET. Taking steps to promote adherence in these groups of women may reduce their risk of recurrence.

A MicroRNA Approach to Evaluating Elevated Prostate Cancer Risk in Cancer-Free Men.

Objective criteria are required for prostate cancer (PCa) risk assessment, treatment decisions, evaluation of therapy, and initial indications of recurrence. Circulating microRNAs were utilized as biomarkers to distinguish PCa patients from cancer-free subjects or those encountering benign prostate hyperplasia. A panel of 60 microRNAs was developed with established roles in PCa initiation, progression, metastasis, and recurrence. Utilizing the FirePlex® platform for microRNA analysis, we demonstrated the efficacy and reproducibility of a rapid, high-throughput, serum-based assay for PCa biomarkers that circumvents the requirement for extraction and fractionation of patient specimens supporting feasibility for expanded clinical research and diagnostic applications.

Non-differential misclassification of outcome under (near)-perfect specificity: a simulation study.

Mismeasurement of a dichotomous outcome yields an unbiased risk ratio estimate when there are no false positive cases (perfect specificity) and when sensitivity is non-differential with respect to exposure status. In studies where these conditions are expected, quantitative bias analysis may be considered unnecessary. We conducted a simulation study to explore the robustness of this special case to small departures from perfect specificity and stochastic departures from non-differential sensitivity. We observed substantial bias of the risk ratio with specificity values as high at 99.8%. The magnitude of bias increased directly with the true underlying risk ratio and was markedly stronger at lower baseline risk. Stochastic departure from non-differential sensitivity also resulted in substantial bias in most simulated scenarios; downward bias prevailed when sensitivity was higher among unexposed compared with exposed, and upward bias prevailed when sensitivity was higher among exposed compared with unexposed. Our results show that seemingly innocuous departures from perfect specificity (e.g., 0.2%) and from non-differential sensitivity can yield substantial bias of the risk ratio under outcome misclassification. We present a web tool permitting easy exploration of this bias mechanism under user-specifiable study scenarios.

Biomarkers predictive of a response to extended endocrine therapy in breast cancer: a systematic review and meta-analysis.

PURPOSE: Extension of adjuvant endocrine therapy beyond five years confers only modest survival benefit in breast cancer patients and carries risk of toxicities. This systematic review investigates the role of biomarker tests in predicting the clinical response to an extension of endocrine therapy. METHODS: We searched Ovid MEDLINE, Ovid Embase, Global Index Medicus, and the Cochrane Central Register of Controlled Trials using an iterative approach to identify full-text articles related to breast cancer, endocrine therapy, and biomarkers. RESULTS: Of the 1,217 unique reports identified, five studies were deemed eligible. Four investigated the Breast Cancer Index (BCI) assay in three distinct study populations. These studies consistently showed that BCI score was predictive of response to extended endocrine therapy among 1,946 combined patients, who were predominately non-Hispanic white and postmenopausal. CONCLUSIONS: Evidence in the setting of predictive tests for extended endocrine therapy is sparse. Most relevant studies investigated the use of BCI, but these study populations were largely restricted to a single age, race, and ethnicity group. Future studies should evaluate a variety of biomarkers in diverse populations. Without sufficient evidence, physicians and patients face a difficult decision in balancing the benefits and risks of endocrine therapy extension.

Genetic variants and social benefit receipt in premenopausal women with breast cancer treated with docetaxel: a Danish population-based cohort study.

PURPOSE: Breast cancer patients' need for social benefits may increase following taxane-based chemotherapy, due to long-lasting side effects. Specific single nucleotide polymorphisms (SNPs) may mediate such side effects. We investigated the association between SNPs related to taxane metabolism, transport, toxicity, or DNA and neural repair, and receipt of social benefits. METHODS: From the Danish Breast Cancer Group, we identified premenopausal women diagnosed with stage I-III breast cancer during 2007-2011 and treated with docetaxel-based chemotherapy. We genotyped 21 SNPs from archived breast tumors using TaqMan assays. We ascertained social benefit payments from 1 year before to 5 years after diagnosis, using nationwide, administrative registry data. For each week, we categorized women as receiving health-related benefits (including sick leave and disability pension), labor market-related benefits (including unemployment benefits), or as being self-supporting. We computed rate ratios (RRs) of social benefit receipt for variant carriers (heterozygotes plus homozygotes) vs. non-carriers, using negative binominal regression with robust variance estimation. RESULTS: Among 2430 women, 12% received health-related benefits before diagnosis, 80% at diagnosis, and ~ 24% 2 to 5 years after diagnosis. Labor market-related benefits were uncommon (3-6%). All RRs were near-null and/or imprecise. CONCLUSION: We found no clinically meaningful impact of the selected SNPs on social benefit receipt among premenopausal breast cancer survivors treated with docetaxel.

The Population-level Effect of Adjuvant Therapies on Breast Cancer Recurrence: Application of the Trend-in-Trend Design.

PURPOSE: Breast cancer has an average 10-year relative survival reaching 84%. This favorable survival is due, in part, to the introduction of biomarker-guided therapies. We estimated the population-level effect of the introduction of two adjuvant therapies-tamoxifen and trastuzumab-on recurrence using the trend-in-trend pharmacoepidemiologic study design. METHODS: We ascertained data on women diagnosed with nonmetastatic breast cancer who were registered in the Danish Breast Cancer Group clinical database. We used the trend-in-trend design to estimate the population-level effect of the introduction of (1) tamoxifen for postmenopausal women with estrogen receptor (ER)-positive breast cancer in 1982, (2) tamoxifen for premenopausal women diagnosed with ER-positive breast cancer in 1999, and (3) trastuzumab for women <60 years diagnosed with human epidermal growth factor receptor 2-positive breast cancer in 2007. RESULTS: For the population-level effect of the introduction of tamoxifen among premenopausal women diagnosed with ER-positive breast cancer in 1999, the risk of recurrence decreased by nearly one-half (OR = 0.52), consistent with evidence from clinical trials; however, the estimate was imprecise (95% confidence interval [CI] = 0.25, 1.85). We observed an imprecise association between tamoxifen use and recurrence from the time it was introduced in 1982 (OR = 1.24 95% CI = 0.46, 5.11), inconsistent with prior knowledge from clinical trials. For the introduction of trastuzumab in 2007, the estimate was also consistent with trial evidence, though imprecise (OR = 0.51; 95% CI = 0.21, 22.4). CONCLUSIONS: We demonstrated how novel pharmacoepidemiologic analytic designs can be used to evaluate the routine clinical care and effectiveness of therapeutic advancements in a population-based setting while considering some limitations of the approach.

Exploring the presence of bovine leukemia virus among breast cancer tumors in a rural state.

PURPOSE: The bovine leukemia virus (BLV) is a deltaretrovirus that causes malignant lymphoma and lymphosarcomas in cattle globally and has high prevalence among large scale U.S. dairy herds. Associations between presence of BLV DNA in human mammary tissue and human breast cancer incidence have been reported. We sought to estimate the prevalence of BLV DNA in breast cancer tissue samples in a rural state with an active dairy industry. METHODS: We purified genomic DNA from 56 fresh-frozen breast cancer tissue samples (51 tumor samples, 5 samples representing adjacent normal breast tissue) banked between 2016 and 2019. Using nested PCR assays, multiple BLV tax sequence primers and primers for the long terminal repeat (LTR) were used to detect BLV DNA in tissue samples and known positive control samples, including the permanently infected fetal lamb kidney cell line (FLK-BLV) and blood from BLV positive cattle. RESULTS: The median age of patients from which samples were obtained at the time of treatment was 60 (40-93) and all were female. Ninety percent of patients had invasive ductal carcinoma. The majority were poorly differentiated (60%). On PCR assay, none of the tumor samples tested positive for BLV DNA, despite having consistent signals in positive controls. CONCLUSION: We did not find BLV DNA in fresh-frozen breast cancer tumors from patients presenting to a hospital in Vermont. Our findings suggest a low prevalence of BLV in our patient population and a need to reevaluate the association between BLV and human breast cancer.

Sampling Validation Data to Achieve a Planned Precision of the Bias-Adjusted Estimate of Effect.

Data collected from a validation substudy permit calculation of a bias-adjusted estimate of effect that is expected to equal the estimate that would have been observed had the gold standard measurement been available for the entire study population. In this paper, we develop and apply a framework for adaptive validation to determine when sufficient validation data have been collected to yield a bias-adjusted effect estimate with a prespecified level of precision. Prespecified levels of precision are decided a priori by the investigator, based on the precision of the conventional estimate and allowing for wider confidence intervals that would still be substantively meaningful. We further present an applied example of the use of this method to address exposure misclassification in a study of transmasculine/transfeminine youth and self-harm. Our method provides a novel approach to effective and efficient estimation of classification parameters as validation data accrue, with emphasis on the precision of the bias-adjusted estimate. This method can be applied within the context of any parent epidemiologic study design in which validation data will be collected and modified to meet alternative criteria given specific study or validation study objectives.

Preexisting stress-related diagnoses and mortality: A Danish cancer cohort study.

BACKGROUND: This study evaluated the association between preexisting stress-related diagnoses and mortality in a Danish population-based cancer cohort. METHODS: This study included Danish patients with cancer diagnosed in 1995-2011 who had a stress-related diagnosis before their cancer diagnosis. Cancer patients without a prior stress-related diagnosis were matched 5:1 to the stress disorder cohort by cancer site, age group, calendar period, and sex. The 5-year cumulative incidence of cancer-specific and all-cause mortality was computed by stress-related diagnosis category. Hazard ratios and 95% confidence intervals (CIs) associating stress-related diagnoses with mortality were computed by follow-up time, stress-related diagnosis category, stage, comorbidity status, and cancer type. RESULTS: This study identified 4437 cancer patients with a preexisting stress-related diagnosis and 22,060 matched cancer cohort members. The 5-year cumulative risk of cancer-specific mortality was 33% (95% CI, 32%-35%) for those with a preexisting stress-related diagnosis and 29% (95% CI, 28%-29%) for those without a prior stress-related diagnosis. Cancer patients with a preexisting stress-related diagnosis had a 1.3 times higher cancer-specific mortality rate than the comparison cohort members (95% CI, 1.2-1.5). This increase persisted across categories of stress-related diagnosis. The association varied by stage and cancer type, with more pronounced associations found among those with a late stage at diagnosis and hematological malignancies. CONCLUSIONS: Cancer patients with preexisting stress-related diagnoses had increased rates of cancer-specific and all-cause mortality. The results suggest that psychiatric comorbidities may be an important consideration for cancer prognosis, and cancer treatment informed by a patient's history may improve outcomes.

Circulating lipids and breast cancer prognosis in the Malmö diet and cancer study.

PURPOSE: Examine the association between circulating lipids and breast cancer outcomes in patients enrolled in the Malmö Diet and Cancer Study (MDCS). PATIENTS AND METHODS: Circulating lipid levels were measured in blood sampled upon enrollment in the female MDCS cohort (N = 17,035). We identified all MDCS participants with incident invasive breast cancer diagnosed between 1991 and 2014. Follow-up time began at breast cancer diagnosis and continued until the first event of breast cancer recurrence, death, emigration, or 5 years of follow-up. We estimated the incidence rates of recurrence at 5 years and fit Cox regression models to compute crude and adjusted hazard ratios (HRs) with 95% confidence intervals (95% CI) of breast cancer recurrence as well as all-cause mortality according to cohort-specific tertiles of apolipoprotein A-1 (Apo A-1) and apolipoprotein B (Apo B). RESULTS: We enrolled 850 eligible patients. During the 5 years of follow-up, 90 invasive breast cancer recurrences were diagnosed over 3807 person-years. In multivariable analyses, high baseline levels of Apo B were associated with an increased rate of recurrence (tertile 3 vs. 1, HR = 2.30 [95% CI 1.13-4.68]). However, high baseline levels of Apo B were not associated with all-cause mortality (tertile 3 vs. 1, HR = 1.23 [95% CI 0.68-2.25]). We observed no associations between levels of Apo A-1 and recurrence (tertile 3 vs. 1, HR = 1.34 [95% CI 0.70-2.58]) or all-cause mortality (tertile 3 vs. 1, HR = 1.12 [95% CI 0.61-2.05]). CONCLUSION: High pre-diagnostic levels of Apo B were associated with an increased risk of recurrence among breast cancer patients. Circulating Apo A-1 was not associated with breast cancer outcomes.

Single-nucleotide polymorphisms and the effectiveness of taxane-based chemotherapy in premenopausal breast cancer: a population-based cohort study in Denmark.

PURPOSE: Taxane-based chemotherapy is the primary treatment for premenopausal breast cancer. Although being inconsistent, research suggests that variant alleles alter pharmacokinetics through reduced function of OATP transporters (limiting hepatic uptake), CYP-450 enzymes (hampering drug metabolism), and ABC transporters (decreasing clearance). Reduced function of DNA repair enzymes may hamper effectiveness through dose-limiting toxicities. We investigated whether single-nucleotide polymorphisms (SNPs) were associated with breast cancer recurrence or mortality in premenopausal women diagnosed with breast cancer. METHODS: We conducted a population-based cohort study of premenopausal women diagnosed with non-distant metastatic breast cancer in Denmark during 2007‒2011, when guidelines recommended adjuvant combination chemotherapy (taxanes, anthracyclines, and cyclophosphamide). Using archived formalin-fixed paraffin-embedded primary tumor tissue, we genotyped 26 SNPs using TaqMan assays. Danish health registries provided data on breast cancer recurrence (through September 25, 2017) and death (through December 31, 2019). We fit Cox regression models to calculate crude hazard ratios (HRs) and 95% confidence intervals (CIs) for recurrence and mortality across genotypes. RESULTS: Among 2,262 women, 249 experienced recurrence (cumulative incidence: 13%) and 259 died (cumulative incidence: 16%) during follow-up (median 7.0 and 10.1 years, respectively). Mortality was increased in variant carriers of GSTP1 rs1138272 (HR: 1.30, 95% CI 0.95-1.78) and CYP3A rs10273424 (HR: 1.33, 95% CI 0.98-1.81). SLCO1B1 rs2306283 (encoding OATP1B1) variant carriers had decreased recurrence (HR: 0.82, 95% CI 0.64-1.07) and mortality (HR: 0.77, 95% CI 0.60-0.98). CONCLUSION: Docetaxel effectiveness was influenced by SNPs in GSTP1, CYP3A, and SLCO1B1 in premenopausal women with non-distant metastatic breast cancer, likely related to altered docetaxel pharmacokinetics. These SNPs may help determine individual benefit from taxane-based chemotherapy.

Ultrasound for the Objective Measurement of Breast Lymphedema.

OBJECTIVES: Breast lymphedema has supplanted upper extremity lymphedema as a common and debilitating sequela of breast cancer treatment, but has no objective measurement. We assessed the utility of ultrasound-measured difference in dermal thickness between affected and unaffected breasts as a measure of breast lymphedema. We associated this measure with patient characteristics, treatment parameters, and patient-reported impact on quality of life. METHODS: We enrolled 30 invasive breast carcinoma patients treated with breast-conserving surgery, sentinel lymph node biopsy, and radiotherapy, and 10 control patients evaluated for benign breast conditions without prior breast surgery or radiotherapy. Patient and treatment variables were ascertained from medical records and radiotherapy instruments. Impacts on quality of life were measured with a modified Disability of the Arm, Shoulder, and Hand questionnaire. We characterized breast lymphedema by calculating the difference in ultrasound-measured dermal thickness between affected and unaffected breasts. Associations with patient characteristics, treatment, and quality of life were quantified with log-binomial regression models. RESULTS: Breast lymphedema was defined as a dermal thickness difference of >0.3 mm. Nineteen patients in the invasive group (63%) had breast lymphedema by this definition. We observed positive associations between ultrasound-defined breast lymphedema and surgical factors (size of primary tumor, number of lymph nodes removed), radiotherapy factors (breast volume irradiated, receipt of radiation boost), and patient-reported outcomes (sleep quality and overall confidence). CONCLUSIONS: Difference in dermal thickness is an easy and inexpensive measurement for quantifying breast lymphedema, and correlates with treatment parameters and patient-reported impacts on quality of life.

Hypoxia-inducible factor-1α expression and breast cancer recurrence in a Danish population-based case control study.

BACKGROUND: Hypoxia-inducible factor-1α (HIF-1α) is a transcription factor that facilitates the adaptation of cancer cells to hypoxic conditions and may be prognostic of breast cancer recurrence. We evaluated the association of HIF-1α expression with breast cancer recurrence, and its association with timing of breast cancer recurrence. METHODS: In this population-based case-control study, we included women diagnosed with stage I-III breast cancer between 1985 and 2001, aged 35-69 years, registered in the Danish Breast Cancer Group. We identified 541 cases of breast cancer recurrence among women with estrogen receptor (ER)-positive disease who were treated with tamoxifen for at least 1 year (ER+ TAM+). We also enrolled 300 breast cancer recurrence cases among women with ER-negative disease, not treated with tamoxifen, who survived at least 1 year (ER-/TAM-). Controls were recurrence-free breast cancer patients at the time of case diagnosis, matched to recurrence cases on ER/TAM status, date of surgery, menopausal status, cancer stage, and county of residence. Expression of HIF-1α was measured by immunohistochemistry on tissue microarrays. We fitted logistic regression models to compute odds ratios (ORs) and 95% confidence intervals (CIs) associating HIF-1α expression with recurrence, and with timing of recurrence. RESULTS: HIF-1α expression was observed in 23% of cases and 20% of controls in the ER+/TAM+ stratum, and in 47% of cases and 48% of controls in the ER-/TAM- stratum. We observed a near-null association between HIF-1α expression in both ER/TAM groups (ER+/TAM+ OR = 1.21, 95%CI 0.88, 1.67 and ER-/TAM- OR = 0.97, 95%CI 0.68, 1.39). HIF-1α expression was not associated with time to recurrence among women in the ER+/TAM+ stratum, but was associated with early recurrence among women in the ER-/TAM- stratum. CONCLUSION: In this study, HIF-1α expression was not associated with breast cancer recurrence overall but may be associated with early recurrence among women diagnosed with ER- breast cancer.

Bias Analysis Gone Bad.

Quantitative bias analysis comprises the tools used to estimate the direction, magnitude, and uncertainty from systematic errors affecting epidemiologic research. Despite the availability of methods and tools, and guidance for good practices, few reports of epidemiologic research incorporate quantitative estimates of bias impacts. The lack of familiarity with bias analysis allows for the possibility of misuse, which is likely most often unintentional but could occasionally include intentional efforts to mislead. We identified 3 examples of suboptimal bias analysis, one for each common bias. For each, we describe the original research and its bias analysis, compare the bias analysis with good practices, and describe how the bias analysis and research findings might have been improved. We assert no motive to the suboptimal bias analysis by the original authors. Common shortcomings in the examples were lack of a clear bias model, computed example, and computing code; poor selection of the values assigned to the bias model's parameters; and little effort to understand the range of uncertainty associated with the bias. Until bias analysis becomes more common, community expectations for the presentation, explanation, and interpretation of bias analyses will remain unstable. Attention to good practices should improve quality, avoid errors, and discourage manipulation.

The Importance of Making Assumptions in Bias Analysis.

Quantitative bias analyses allow researchers to adjust for uncontrolled confounding, given specification of certain bias parameters. When researchers are concerned about unknown confounders, plausible values for these bias parameters will be difficult to specify. Ding and VanderWeele developed bounding factor and E-value approaches that require the user to specify only some of the bias parameters. We describe the mathematical meaning of bounding factors and E-values and the plausibility of these methods in an applied context. We encourage researchers to pay particular attention to the assumption made, when using E-values, that the prevalence of the uncontrolled confounder among the exposed is 100% (or, equivalently, the prevalence of the exposure among those without the confounder is 0%). We contrast methods that attempt to bound biases or effects and alternative approaches such as quantitative bias analysis. We provide an example where failure to make this distinction led to erroneous statements. If the primary concern in an analysis is with known but unmeasured potential confounders, then E-values are not needed and may be misleading. In cases where the concern is with unknown confounders, the E-value assumption of an extreme possible prevalence of the confounder limits its practical utility.

State-level rurality and cigarette smoking-associated cancer incidence and mortality: Do individual-level trends translate to population-level outcomes?

County-level analyses demonstrate that overall cancer incidence is generally lower in rural areas, though incidence and mortality from tobacco-associated cancers are higher than in non-rural areas and have experienced slower declines over time. The goal of our study was to examine state-level rurality and smoking-related cancer outcomes. We used publicly-available national data to quantify rurality, cigarette smoking prevalence, and smoking-attributable cancer incidence and mortality at the state level and to estimate the population-attributable fraction of cancer deaths attributable to smoking for each state, overall and by gender, for 12 smoking-associated cancers. Accounting for a 15-year lag between smoking exposure and cancer diagnosis, the median proportion of smoking-attributable cancer deaths was 28.2% in Virginia (24.6% rural) and ranged from 19.9% in Utah (9.4% rural) to 35.1% in Kentucky (41.6% rural). By gender, the highest proportion of smoking-attributable cancer deaths for women (29.5%) was in a largely urban state (Nevada, 5.8% rural) and for men (38.0%) in a largely rural state (Kentucky). Regression analyses categorizing state-level rurality into low (0-13.9%), moderate (15.3-29.9%) and high (33.6-61.3%) levels showed that high rurality was associated with 5.8% higher cigarette smoking prevalence, higher age-adjusted smoking-associated cancer incidence (44.3 more cases per 100,000 population), higher smoking-associated cancer mortality (29.8 more deaths per 100,000 population), and 3.4% higher proportion of smoking-attributable cancer deaths compared with low rurality. Our findings highlight the magnitude of the relationship between state-level rurality and smoking-attributable cancer outcomes and the importance of tobacco control in reducing cancer disparities in rural populations.

Identifying key barriers to effective breast cancer control in rural settings.

Breast cancer is the most common cancer and the second most common cause of cancer mortality among women in the United States. Efforts to promote breast cancer control in rural settings face specific challenges. Access to breast cancer screening, diagnosis, and treatment services is impaired by shortages of primary care and specialist providers, and geographic distance from medical facilities. Women in rural areas have comparable breast cancer mortality rates compared to women in urban settings, but this is due in large part to lower incidence rates and masks a substantial rural/urban disparity in breast cancer survival among women diagnosed with breast cancer. Mammography screening utilization rates are slightly lower among rural women than their urban counterparts, with a corresponding increase in late stage breast cancer. Differences in breast cancer survival persist after controlling for stage at diagnosis, largely due to disparities in access to treatment. Travel distance to treatment centers is the most substantial barrier to improved breast cancer outcomes in rural areas. While numerous interventions have been demonstrated in controlled studies to be effective in promoting treatment access and adherence, widespread dissemination in public health and clinical practice remains lacking. Efforts to improve breast cancer control in rural areas should focus on implementation strategies for improving access to breast cancer treatments.

Bayesian Pathway Analysis for Complex Interactions.

Modern epidemiologic studies permit investigation of the complex pathways that mediate effects of social, behavioral, and molecular factors on health outcomes. Conventional analytical approaches struggle with high-dimensional data, leading to high likelihoods of both false-positive and false-negative inferences. Herein, we describe a novel Bayesian pathway analysis approach, the algorithm for learning pathway structure (ALPS), which addresses key limitations in existing approaches to complex data analysis. ALPS uses prior information about pathways in concert with empirical data to identify and quantify complex interactions within networks of factors that mediate an association between an exposure and an outcome. We illustrate ALPS through application to a complex gene-drug interaction analysis in the Predictors of Breast Cancer Recurrence (ProBe CaRe) Study, a Danish cohort study of premenopausal breast cancer patients (2002-2011), for which conventional analyses severely limit the quality of inference.

Statin use and breast cancer recurrence in postmenopausal women treated with adjuvant aromatase inhibitors: a Danish population-based cohort study.

PURPOSE: To examine the association between statin use and risk of breast cancer recurrence in a national Danish cohort of postmenopausal breast cancer patients receiving aromatase inhibitors (AI) in the adjuvant setting. PATIENTS AND METHODS: We enrolled all postmenopausal patients diagnosed with stage I-III estrogen receptor positive breast cancer during the years 2007-2017, assigned adjuvant AI treatment, and registered in both the Danish Breast Cancer Group database and the Danish Cancer Registry. We ascertained incident statin exposure (≥ 1 prescription post-diagnosis) from the Danish National Prescription Registry and modeled statins as a time-varying exposure lagged by 6 months. Follow-up began 7 months after diagnosis and continued to the first event of recurrence, death, emigration, 5 years elapsed, or 25th September 2018. We estimated incidence rates of recurrence at 5 years and used Cox regression models to compute crude and adjusted hazard ratios (HRs) with 95% confidence intervals (95% CI), comparing statin exposure with non-exposure. RESULTS: We enrolled 14,773 eligible patients. During the 5 years of follow-up, there were 32 recurrences in 3163 person-years of follow-up among statin-exposed patients, and 612 recurrences in 45,655 person-years among unexposed patients (incidence rate per 1000 person-years: 10.12 [95% CI 6.92-14.28] and 13.40 [95% CI 12.36-14.51], respectively). In multivariable models, any statin exposure was associated with a reduced rate of 5-year breast cancer recurrence (adjusted HR 0.72 [95% CI 0.50-1.04]). Considering only lipophilic statins as exposure the results were similar (adjusted HR 0.70 [95% CI 0.48-1.02]). CONCLUSIONS: Statin use was associated with a reduced risk of breast cancer recurrence among postmenopausal patients diagnosed with early stage breast cancer who received adjuvant AI therapy.

Adaptive Validation Design: A Bayesian Approach to Validation Substudy Design With Prospective Data Collection.

An internal validation substudy compares an imperfect measurement of a variable with a gold-standard measurement in a subset of the study population. Validation data permit calculation of a bias-adjusted estimate, which has the same expected value as the association that would have been observed had the gold-standard measurement been available for the entire study population. Existing guidance on optimal sampling for validation substudies assumes complete enrollment and follow-up of the target cohort. No guidance exists for validation substudy design while cohort data are actively being collected. In this article, we use the framework of Bayesian monitoring methods to develop an adaptive approach to validation study design. This method monitors whether sufficient validation data have been collected to meet predefined criteria for estimation of the positive and negative predictive values. We demonstrate the utility of this method using the Study of Transition, Outcomes and Gender-a cohort study of transgender and gender nonconforming people. We demonstrate the method's ability to determine efficacy (when sufficient validation data have accumulated to obtain estimates of the predictive values that fall above a threshold value) and futility (when sufficient validation data have accumulated to conclude the mismeasured variable is an untenable substitute for the gold-standard measurement). This proposed method can be applied within the context of any parent epidemiologic study design and modified to meet alternative criteria given specific study or validation study objectives. Our method provides a novel approach to effective and efficient estimation of classification parameters as validation data accrue.