Matching methods to create paired survival data based on an exposure occurring over time: a simulation study with application to breast cancer

Drug Shortages Prior to and During the COVID-19 Pandemic

Importance

Drug shortages are a chronic and worsening issue that compromises patient safety. Despite the destabilizing impact of the COVID-19 pandemic on pharmaceutical production, it remains unclear whether issues affecting the drug supply chain were more likely to result in meaningful shortages during the pandemic.

Objective

To estimate the proportion of supply chain issue reports associated with drug shortages overall and with the COVID-19 pandemic.

Design, Setting, and Participants

This longitudinal cross-sectional study used data from the IQVIA Multinational Integrated Data Analysis database, comprising more than 85% of drug purchases by US pharmacies from wholesalers and manufacturers, from 2017 to 2021. Data were analyzed from January to May 2023.

Exposure

Presence of a supply chain issue report to the US Food and Drug Administration or the American Society of Health-Systems Pharmacists (ASHP).

Main Outcomes and Measures

The main outcome was drug shortage, defined as at least 33% decrease in units purchased within 6 months of a supply chain issue report. Random-effects logistic regression models compared the marginal odds of shortages for drugs with vs without reports. Interaction terms assessed heterogeneity prior to vs during the COVID-19 pandemic and by drug characteristics (formulation, age, essential medicine status, clinician- vs self-administered, sales volume, and number of manufacturers).

Results

A total of 571 drugs exposed to 731 supply chain issue reports were matched to 7296 comparison medications with no reports. After adjusting for drug characteristics, 13.7% (95% CI, 10.4%-17.8%) of supply chain issue reports were associated with subsequent drug shortages vs 4.1% (95% CI, 3.6%-4.8%) of comparators (marginal odds ratio [mOR], 3.7 [95% CI, 2.6-5.1]). Shortages increased among both drugs with and without reports in February to April 2020 (34.2% of drugs with supply chain issue reports and 9.5% of comparison drugs; mOR, 4.9 [95% CI, 2.1-11.6]), and then decreased after May 2020 (9.8% of drugs with reports and 3.6% of comparison drugs; mOR, 2.9 [95% CI, 1.6-5.3]). Significant associations were identified by formulation (parenteral mOR, 1.9 [95% CI, 1.1-3.2] vs oral mOR, 5.4 [95% CI, 3.3-8.8]; P for interaction = .008), WHO essential medicine status (essential mOR, 2.2 [95% CI, 1.3-5.2] vs nonessential mOR, 4.6 [95% CI, 3.2-6.7]; P = .02), and for brand-name vs generic status (brand-name mOR, 8.1 [95% CI, 4.0-16.0] vs generic mOR, 2.4 [95% CI, 1.7-3.6]; P = .002).

Conclusions and Relevance

In this national cross-sectional study, supply chain issues associated with drug shortages increased at the beginning of the COVID-19 pandemic. Ongoing policy work is needed to protect US drug supplies from future shocks and to prioritize clinically valuable drugs at greatest shortage risk.

Risk of Esophageal Cancer in Achalasia: A Matched Cohort Study Using the Nationwide Veterans Affairs Achalasia Cohort

INTRODUCTION

Achalasia is a postulated risk factor of esophageal cancer (EC); however, EC-associated risk in achalasia is understudied. We aimed to evaluate EC risk among individuals within the nationwide Veterans Affairs Achalasia Cohort.

METHODS

We conducted a matched cohort study among US veterans aged 18 years or older from 1999 to 2019. Individuals with achalasia were age matched and sex matched 1:4 to individuals without achalasia. Follow-up continued from study entry until diagnosis with incident/fatal EC (primary outcome), death from non-EC-related causes, or end of the study follow-up (December 31, 2019). Association between achalasia and EC risk was examined using Cox regression models.

RESULTS

We included 9,315 individuals in the analytic cohort (median age 55 years; 92% male): 1,863 with achalasia matched to 7,452 without achalasia. During a median 5.5 years of follow-up, 17 EC occurred (3 esophageal adenocarcinoma, 12 squamous cell carcinoma, and 2 unknown type) among individuals with achalasia, compared with 15 EC (11 esophageal adenocarcinoma, 1 squamous cell carcinoma, and 3 unknown type) among those without achalasia. EC incidence for those with achalasia was 1.4 per 1,000 person-years, and the median time from achalasia diagnosis to EC development was 3.0 years (Q1-Q3: 1.3-9.1). Individuals with achalasia had higher cumulative EC incidence at 5, 10, and 15 years of follow-up compared with individuals without achalasia, and EC risk was 5-fold higher (hazard ratio 4.6, 95% confidence interval: 2.3-9.2).

DISCUSSION

Based on substantial EC risk, individuals with achalasia may benefit from a high index of suspicion and endoscopic surveillance for EC.

Twin studies as an innovative approach to address research questions in cancer care within primary care settings

This paper proposes the utilisation of twin studies as a novel and powerful methodological approach to investigate critical research questions pertaining to cancer prevention, screening, diagnosis, treatment and survivorship within primary care contexts. The inherent genetic similarity between monozygotic (MZ) (identical) twins provides a unique opportunity to disentangle genetic and environmental influences on cancer-related outcomes. MZ twins share virtually identical genetic makeup, offering a unique opportunity to discern the relative contributions of genetic and environmental factors to cancer-related outcomes. In contrast, dizygotic (DZ) twins, also known as fraternal twins, develop from two separate eggs fertilised by two different sperm and share on average 50% of their genetic material, the same level of genetic similarity found in non-twin siblings. Comparisons between MZ and DZ twins enable researchers to disentangle hereditary factors from shared environmental influences. This methodology has the potential to advance our understanding of the multifaceted interplay between genetic predisposition, lifestyle factors and healthcare interventions in the context of cancer care. This paper outlines the rationale, design considerations and potential applications of twin studies in primary care-based cancer research.

Pregnancy After Breast Cancer - Prognostic Safety and Pregnancy Outcomes According to Oestrogen Receptor Status: A Systematic Review

PURPOSE

Breast cancer is the primary cause of cancer-related death in women. Women diagnosed with estrogen receptor (ER)-positive breast cancer have prolonged treatment durations. Owing to the paucity of research and lack of consensus regarding conception planning and pregnancy for patients with ER-positive breast cancer, we aimed to assess pregnancy and survival outcomes in women with ER-positive breast cancer during and after treatment.

METHODS

We conducted a systematic review of the available studies on pregnancy after ER-positive breast cancer. The assessed outcomes included overall survival (OS), disease-free survival (DFS), hormonal therapy duration, and pregnancy outcomes.

RESULTS

Ultimately, 2,669 patients from five studies were included in this study. When all breast cancer receptor subtypes were included in the analysis, pregnancy after breast cancer was associated with a time-dependent protective effect on both DFS and OS. This protective effect was not evident when examining ER-positive patients with subsequent pregnancies, and no significant differences in DFS were observed. ER-positive patients who became pregnant received significantly lower rates of hormonal therapy. Hormonal treatment at the time of pregnancy was correlated with increased rates of termination owing to concerns about teratogenic effects.

CONCLUSIONS

Pregnancy after breast cancer did not significantly affect DFS in ER-positive patients over a follow-up period of 5-10 years from diagnosis, although did significantly affect hormonal treatment duration in the reviewed studies. Further analysis and in-depth studies are required to assess the effects of altered hormonal treatment times, as well as patient management related to pregnancy planning after breast cancer.

Weighted U-statistics for likelihood-ratio ordering of bivariate data

Characterisation of marginal distribution and density functions is of interest where data on a pair of random variables (X, Y) are observed. Stochastic orderings between (X, Y) have been studied in statistics and economics. Likelihood-ratio ordering is useful in understanding the behaviour of the random variables. In this article, tests based on U-statistics are proposed to test for equality of marginal density functions against the alternative of likelihood-ratio ordered when (X, Y) are dependent. The tests can be used when the data are either completely observed or subjected to independent univariate right censoring. The asymptotic variances of these tests are complicated and hence, are estimated using jackknife variance estimators. Validity of the jackknife variance estimators in statistical inference based on the proposed tests is demonstrated using simulation studies. The test for uncensored setting has desired size and good power for small sample. The performance of the tests for censored case depends on the sample size, proportion of censoring and the measure of dependence between X and Y. The tests are illustrated on three real data sets chosen in order to bring out various aspects of the tests.

Exposure density sampling: Dynamic matching with respect to a time-dependent exposure

Estimating the potential risk associated with an exposure occurring over time requires complex statistical techniques, since ignoring the time from study entry until the exposure leads to potentially seriously biased effect estimates. A prominent example is estimating the effect of hospital-acquired infections on adverse outcomes in patients admitted to the intensive care unit. Exposure density sampling has been proposed as an approach to dynamic matching with respect to a time-dependent exposure. Firstly, exposure density sampling can be useful to reduce the workload of study follow up, as it includes all exposed but only a subset of the not yet exposed individuals. Secondly, it can help to obtain a comparable control group by including propensity score matching. In the present article, we provide the theoretical justification that data obtained by exposure density sampling can be analyzed as a left-truncated cohort. It is shown that exposure density sampling allows estimation of the effect of a time-dependent exposure as well as further baseline covariates on a subsequent event, with only minor loss in precision as compared with a full cohort analysis. The sampling is applied to a real data example (hospital-acquired infections in intensive care units) and in a simulation study. We also provide an estimate of the loss in precision in terms of an increased standard error in the reduced data set after exposure density sampling as compared with the full cohort.