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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: It is unknown whether posttraumatic stress disorder (PTSD) is associated with incident infections. This study's objectives were to examine (1) the association between PTSD diagnosis and 28 types of infections and (2) the interaction between PTSD diagnosis and sex on the rate of infections.Methods: The study population consisted of a longitudinal nationwide cohort of all residents of Denmark who received a PTSD diagnosis between 1995 and 2011, and an age- and sex-matched general population comparison cohort. We fit Cox proportional hazards regression models to examine associations between PTSD diagnosis and infections. To account for multiple estimation, we adjusted the hazard ratios (HRs) using semi-Bayes shrinkage. We calculated interaction contrasts to assess the presence of interaction between PTSD diagnosis and sex.Results: After semi-Bayes shrinkage, the HR for any type of infection was 1.8 (95% confidence interval: 1.6, 2.0), adjusting for marital status, non-psychiatric comorbidity, and diagnoses of substance abuse, substance dependence, and depression. The association between PTSD diagnosis and some infections (e.g., urinary tract infections) were stronger among women, whereas other associations were stronger among men (e.g., skin infections).Conclusions: This study's findings suggest that PTSD diagnosis is a risk factor for numerous infection types and that the associations between PTSD diagnosis and infections are modified by sex. [ABSTRACT FROM AUTHOR]

Background: Aspirin, nonsteroidal anti-inflammatory drugs (NSAIDs), and selective COX-2 inhibitors may improve outcomes in breast cancer patients. We investigated the association of aspirin, NSAIDs, and use of selective COX-2 inhibitors with breast cancer recurrence.Methods: We identified incident stage I-III Danish breast cancer patients in the Danish Breast Cancer Cooperative Group registry, who were diagnosed during 1996-2008. Prescriptions for aspirin (>99% low-dose aspirin), NSAIDs, and selective COX-2 inhibitors were ascertained from the National Prescription Registry. Follow-up began on the date of breast cancer primary surgery and continued until the first of recurrence, death, emigration, or 1 January 2013. We used Cox regression models to compute hazard ratios (HR) and corresponding 95% confidence intervals (95% CI) associating prescriptions with recurrence, adjusting for confounders.Results: We identified 34,188 breast cancer patients with 233,130 person-years of follow-up. Median follow-up was 7.1 years; 5,325 patients developed recurrent disease. Use of aspirin, NSAIDs, or selective COX-2 inhibitors was not associated with the rate of recurrence (HRadjusted aspirin = 1.0, 95% CI = 0.90, 1.1; NSAIDs = 0.99, 95% CI = 0.92, 1.1; selective COX-2 inhibitors = 1.1, 95% CI = 0.98, 1.2), relative to nonuse. Prediagnostic use of the exposure drugs was associated with reduced recurrence rates (HRaspirin = 0.92, 95% CI = 0.82, 1.0; HRNSAIDs = 0.86, 95% CI = 0.81, 0.91; HRsCOX-2inhibitors = 0.88, 95% CI = 0.83, 0.95).Conclusions: This prospective cohort study suggests that post diagnostic prescriptions for aspirin, NSAIDs, and selective COX-2 inhibitors have little or no association with the rate of breast cancer recurrence. Prediagnostic use of the drugs was, however, associated with a reduced rate of breast cancer recurrence. [ABSTRACT FROM AUTHOR]

To account for misclassification of dichotomous variables using probabilistic bias analysis, beta distributions are often assigned to bias parameters (e.g., PPV and NPV) based on data from an internal validation substudy. Due to the small sample size of validation substudies, zero cell frequencies can occur. In these scenarios, it may be helpful to assign prior distributions or apply continuity corrections to the predictive value estimates. We simulated cohort studies of varying sizes, with a binary exposure and outcome and a true risk ratio (RR) = 2.0, as well as internal validation substudies to account for exposure misclassification. We conducted bias adjustment under five approaches assigning prior distributions to the NPV and PPV parameters: (1) conventional method (i.e., no prior), (2) uniform prior beta (α = 1, β = 1), (3) Jeffreys prior beta (α = 0.5, β = 0.5), (4) using Jeffreys prior as a continuity correction only when zero cells occurred, and (5) using the uniform prior as a continuity correction only when zero cells occurred. We evaluated performance by measuring coverage probability, bias, and mean squared error. For sparse validation data, methods (2)-(5) all had better coverage and lower MSE than the conventional method, with the uniform prior (2) yielding the best performance. However, little difference between methods was observed when the validation substudy did not contain zero cells. If sparse data are expected in a validation substudy, using a uniform prior for the beta distribution of bias parameters can improve the validity of bias-adjusted measures., Competing Interests: Conflict of Interest Statement: Timothy L. Lash is a member of the Amgen Epidemiology Methods Advisory Council, for which he receives consulting fees and travel support. Qi Zhang’s doctoral training is supported by a grant from Amgen Inc. to Emory University. Lindsay J. Collin and Qi Zhang report personal fees from Epidemiologic Research & Methods, LLC outside of the submitted work., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: A 2013 meta-analysis observed a protective association between overweight BMI (versus normal BMI) and all-cause mortality that was particularly strong in people aged ≥65. Estimates informing this meta-analysis were highly heterogeneous, and critics raised insufficient or inappropriate confounder adjustment in many studies as an explanation for the protective summary association. Using this topic as an example, we demonstrate a novel approach for external adjustment of individual studies for a uniform and sufficient confounder set before meta-analysis., Methods: We abstracted summary data on the 33 associations comprising the age ≥65 stratum of the 2013 meta-analysis. Using an external dataset (NHANES III), we derived covariates used in each study's multivariable model of the overweight-mortality association. We then calculated a bias factor to quantify the direction and magnitude of displacement of the ratio measure of association after changing from the original adjustment set to a sufficient adjustment set. After applying bias factors to adjust original associations, we compared summary results from random effects meta-analyses with and without such adjustment., Results: We reproduced the original meta-analysis of overweight-mortality estimates among older participants and found a protective association similar to that reported in 2013 (summary RR=0.88, 95% CI: 0.84, 0.92, I 2 =38.4%). After we simulated uniform adjustment of all 33 associations for a minimally sufficient confounder set (age, sex, and smoking status), the meta-analysis showed a similar summary association (summary RR=0.90, 95% CI: 0.86, 0.94), but with reduced heterogeneity (I 2 =34.6%)., Conclusion: Simulated uniform adjustment for a sufficient confounder set may improve rigor and promote consensus in meta-analysis., Competing Interests: Conflict of interest: No author declares a conflict of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)