We evaluated neural tube defect (NTD) risk associated with prescription opioid analgesic use during early pregnancy. We conducted a cohort study of liveborn singletons during 2001-2014 among 9 U.S. health plans. Risks of medical chart-confirmed primary NTD (anencephaly and select spina bifida) and any NTD (primary and other NTD) were compared among singletons with maternal new use of opioids during 18-56 days after last menstrual period to those with no maternal use during this period or pre-pregnancy. In a sensitivity analysis, singletons exposed during the second or third trimesters only ("negative control exposure") were compared to those never exposed. The main analysis adjusted odds ratio (OR) associated with exposure was 3.0 (95% CI, 0.7-12.7) for primary NTD and 2.3 (95% CI, 0.8-6.7) for any NTD. The sensitivity analysis ORs were similar 2.4 and 1.6, respectively. Our results do not support or refute the hypothesis that prescription opioid analgesic exposure in early pregnancy increases risk of NTD. Although ORs were increased in the main analysis, the sensitivity analysis suggested the presence of residual confounding. Future studies that are sufficiently powered and adjust for confounders beyond those in the present study are warranted but likely challenging.

Importance High-quality evidence guiding opioid prescribing decisions for acute pain across common diagnoses is lacking. Objective To describe pain trajectories and patterns of opioid and nonopioid treatment use among individuals who were offered opioids for the treatment of acute pain. Design, Setting, and Participants This was a prospective cohort study of patients recruited from 5 US health systems between September 2020 and March 2023 in emergency departments (EDs), primary care clinics, dental practices, or after cesarean delivery or knee replacement. Eligible patients were opioid-naive adults aged 18 years or older at all study sites, as well as adolescents aged 15 to17 years undergoing impacted molar extraction at 1 site, who were offered an opioid prescription for acute pain. Analysis was conducted April 2023 through February 2026. Exposure Offer of a prescription for an opioid analgesic. Main Outcomes and Measures Time to pain resolution (3 consecutive reports of no pain), patterns of opioid and nonopioid treatment use, and opioid-related adverse effects, ascertained from digital questionnaires. Results Among 1708 enrolled patients (median age, 38 years [IQR, 28-52 years]; 615 [36.0%] reporting race or ethnicity underrepresented in studies of acute pain management) followed up for 180 days, 915 (53.6%) were recruited in EDs, 307 (18.0%) in primary or urgent care, 263 (15.4%) in dental settings, and 223 (13.1%) in inpatient settings. Pain sources included dental (302 patients [17.7%]), trauma or injury (302 [17.7%]), obstetric (176 [10.3%]), musculoskeletal (131 [7.7%]), and low back (100 [5.9%]). Among 1502 patients reporting pain level at least once, median time to pain resolution irrespective of the treatment approach was 20 days (IQR, 8-88 days), with longer durations for surgical pain (74 days [IQR, 30 days to not reached]) and low back pain (69 days [IQR, 18 days to not reached]). The median time to opioid discontinuation among 1189 patients (69.6%) who reported any opioid use was 7 days (IQR, 2-31 days); an estimated 10.0% (95% CI, 7.7%-12.7%) of patients used opioids for at least 90 days, with higher rates in people reporting frequent pain before enrollment. Of 1482 patients (86.8%) completing at least 1 survey during the first 2 weeks of follow-up, 1153 (77.8%) reported using any opioids and 1287 (86.8%) reported using acetaminophen or ibuprofen. Among 619 (52.1%) patients with any opioid use who reported the dose of opioids taken in the first 15 days, daily doses were low (median, 10 [IQR, 5-15] morphine milligram equivalents). Most respondents reported leftover opioids (657 of 982 responding [66.9%]). Conclusions and Relevance In this cohort study of opioid-naive patients with acute pain, opioid use was generally low dose and of short duration, although some patients reported prolonged opioid use; most reported achieving pain resolution within 3 weeks, with longer times for surgical and low back pain. The findings suggest current guidelines for multimodal treatment and for short-duration opioid prescriptions if needed will serve many but not all patients, and treatment should be tailored to address individual patients’ needs.

Introduction Opioid analgesics are often used to treat moderate-to-severe acute non-cancer pain; however, there is little high-quality evidence to guide clinician prescribing. An essential element to developing evidence-based guidelines is a better understanding of pain management and pain control among individuals experiencing acute pain for various common diagnoses. Methods and analysis This multicentre prospective observational study will recruit 1550 opioid-naïve participants with acute pain seen in diverse clinical settings including primary/urgent care, emergency departments and dental clinics. Participants will be followed for 6 months with the aid of a patient-centred health data aggregating platform that consolidates data from study questionnaires, electronic health record data on healthcare services received, prescription fill data from pharmacies, and activity and sleep data from a Fitbit activity tracker. Participants will be enrolled to represent diverse races and ethnicities and pain conditions, as well as geographical diversity. Data analysis will focus on assessing patients’ patterns of pain and opioid analgesic use, along with other pain treatments; associations between patient and condition characteristics and patient-centred outcomes including resolution of pain, satisfaction with care and long-term use of opioid analgesics; and descriptive analyses of patient management of leftover opioids. Ethics and dissemination This study has received approval from IRBs at each site. Results will be made available to participants, funders, the research community and the public. Trial registration number NCT04509115 .

We present a Bayesian framework for sequential monitoring that allows for use of external data, and that can be applied in a wide range of clinical trial applications. The basis for this framework is the idea that, in many cases, specification of priors used for sequential monitoring and the stopping criteria can be semi-algorithmic byproducts of the trial hypotheses and relevant external data, simplifying the process of prior elicitation. Monitoring priors are defined using the family of generalized normal distributions, which comprise a flexible class of priors, naturally allowing one to construct a prior that is peaked or flat about the parameter values thought to be most likely. External data are incorporated into the monitoring process through mixing an a priori skeptical prior with an enthusiastic prior using a weight that can be fixed or adaptively estimated. In particular, we introduce an adaptive monitoring prior for efficacy evaluation that dynamically weighs skeptical and enthusiastic prior components based on the degree to which observed data are consistent with an enthusiastic perspective. The proposed approach allows for prospective and pre-specified use of external data in the monitoring procedure. We illustrate the method for both single-arm and two-arm randomized controlled trials. For the latter case, we also include a retrospective analysis of actual trial data using the proposed adaptive sequential monitoring procedure. Both examples are motivated by completed pediatric trials, and the designs incorporate information from adult trials to varying degrees. Preposterior analysis and frequentist operating characteristics of each trial design are discussed.

OBJECTIVE To assess whether initiation of insulin glargine (glargine), compared with initiation of NPH or insulin detemir (detemir), was associated with an increased risk of breast cancer in women with diabetes. RESEARCH DESIGN AND METHODS This was a retrospective new-user cohort study of female Medicare beneficiaries aged ≥65 years initiating glargine (203,159), detemir (67,012), or NPH (47,388) from September 2006 to September 2015, with follow-up through May 2017. Weighted Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% CIs for incidence of breast cancer according to ever use, cumulative duration of use, cumulative dose of insulin, length of follow-up time, and a combination of dose and length of follow-up time. RESULTS Ever use of glargine was not associated with an increased risk of breast cancer compared with NPH (HR 0.97; 95% CI 0.88–1.06) or detemir (HR 0.98; 95% CI 0.92–1.05). No increased risk was seen with glargine use compared with either NPH or detemir by duration of insulin use, length of follow-up, or cumulative dose of insulin. No increased risk of breast cancer was observed in medium- or high-dose glargine users compared with low-dose users. CONCLUSIONS Overall, glargine use was not associated with an increased risk of breast cancer compared with NPH or detemir in female Medicare beneficiaries.

In this paper, we develop the fixed‐borrowing adaptive design, a Bayesian adaptive design which facilitates information borrowing from a historical trial using subject‐level control data while assuring a reasonable upper bound on the maximum type I error rate and lower bound on the minimum power. First, one constructs an informative power prior from the historical data to be used for design and analysis of the new trial. At an interim analysis opportunity, one evaluates the degree of prior‐data conflict. If there is too much conflict between the new trial data and the historical control data, the prior information is discarded and the study proceeds to the final analysis opportunity at which time a noninformative prior is used for analysis. Otherwise, the trial is stopped early and the informative power prior is used for analysis. Simulation studies are used to calibrate the early stopping rule. The proposed design methodology seamlessly accommodates covariates in the statistical model, which the authors argue is necessary to justify borrowing information from historical controls. Implementation of the proposed methodology is straightforward for many common data models, including linear regression models, generalized linear regression models, and proportional hazards models. We demonstrate the methodology to design a cardiovascular outcomes trial for a hypothetical new therapy for treatment of type 2 diabetes mellitus and borrow information from the SAVOR trial, one of the earliest cardiovascular outcomes trials designed to assess cardiovascular risk in antidiabetic therapies.

Have you noticed when you browse a book, journal, study report, or product label how your eye is drawn to figures more than to words and tables? Statistical graphs are powerful ways to transparently and succinctly communicate the key points of medical research. Furthermore, the graphic design itself adds to the clarity of the messages in the data. The goal of this paper is to provide a mechanism for selecting the appropriate graph to thoughtfully construct quality deliverables using good graphic design principles. Examples are motivated by the efforts of a Safety Graphics Working Group that consisted of scientists from the pharmaceutical industry, Food and Drug Administration, and academic institutions. Copyright © 2015 John Wiley & Sons, Ltd.

Evaluation of safety is a critical component of drug review at the US Food and Drug Administration (FDA). Statisticians are playing an increasingly visible role in quantitative safety evaluation and regulatory decision-making. This article reviews the history and the recent events relating to quantitative drug safety evaluation at the FDA. The article then focuses on five active areas of quantitative drug safety evaluation and the role Division of Biometrics VII (DBVII) plays in these areas, namely meta-analysis for safety evaluation, large safety outcome trials, post-marketing requirements (PMRs), the Sentinel Initiative, and the evaluation of risk from extended/long-acting opioids. This article will focus chiefly on developments related to quantitative drug safety evaluation and not on the many additional developments in drug safety in general.

BACKGROUND:Several studies have reported an association between abacavir (ABC) exposure and increased risk of myocardial infarction (MI) among HIV-infected individuals. Randomized controlled trials (RCTs) and a pooled analysis by GlaxoSmithKline, however, do not support this association. To better estimate the effect of ABC use on risk of MI, the US Food and Drug Administration (FDA) conducted a trial-level meta-analysis of RCTs in which ABC use was randomized as part of a combined antiretroviral regimen.METHODS:From a literature search conducted among 4 databases, 26 RCTs were selected that met the following criteria: conducted in adults, sample size more than 50 subjects, status completed, not a pharmacokinetic trial, and not conducted in Africa. The Mantel-Haenszel method, with risk difference and 95% confidence interval, was used for the primary analysis, along with additional alternative analyses, based on FDA-requested adverse event reports of MI provided by each investigator.RESULTS:The 26 RCTs were conducted from 1996 to 2010, and included 9868 subjects (5028 ABC and 4840 non-ABC). Mean follow-up was 1.43 person-years in the ABC group and 1.49 person-years in the non-ABC group. Forty-six (0.47%) MI events were reported [24 (0.48%) ABC and 22 (0.46%) non-ABC], with no significant difference noted between the 2 groups (risk difference of 0.008% with 95% confidence interval: -0.26% to 0.27%).CONCLUSIONS:To the best of our knowledge, our study represents the largest trial-level meta-analysis to date of clinical trials in which ABC use was randomized. Our analysis found no association between ABC use and MI risk.

Clinicians need to evaluate the quality of individual clinical studies and synthesize the information from multiple clinical studies to provide insights in selecting appropriate therapies for patients. Understanding the key statistical principles that underlie a clinical trial and how they may be implemented can help clinicians properly interpret the efficacy and safety findings of clinical trials. Several factors should be considered when evaluating clinical studies reported in the literature, as important differences might exist among reported studies, thereby impacting the reliability of their findings. Studies vary in terms of study design, conduct, analysis, and presentation of findings. The key features to consider when evaluating clinical trials are inferential intent (exploratory versus confirmatory), choice of control group, randomization, extent of blinding, prespecification of analyses, appropriate handling of missing data, and multiple end points. Making comparisons across studies is extremely difficult and rarely statistically justified. However, this article will point out issues to keep in mind when evaluating multiple studies, such as variations in design and study populations.