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Regulatory agencies are increasingly considering real‐world evidence (RWE) to support label expansions of approved medicines. We conducted a comparative effectiveness study to emulate a proposed randomized trial of romiplostim vs. standard‐of‐care (SOC) therapy among patients with recently diagnosed (≤12 months) immune thrombocytopenia (ITP), that could support expansion of the romiplostim label. We discuss challenges that we encountered and solutions that were developed to address those challenges. Study size was a primary concern, particularly for romiplostim initiators, given the rarity of ITP and the stringent trial eligibility criteria. For this reason, we leveraged multiple data sources (Nordic Country Patient Registry for Romiplostim; chart review study of romiplostim initiators in Europe; Flatiron Health EMR linked with MarketScan claims). Additionally, unlike the strictly controlled clinical trial setting, platelet counts were not measured at regular intervals in the observational data sources, and therefore the end point of durable platelet response often used in trials could not be reliably measured. Instead, the median platelet count was chosen as the primary end point. Ultimately, while we observed a slightly higher median platelet count in the romiplostim group vs. SOC, precision was limited because of small study size (median difference was 11 × 109/L (95% CI: −59, 81)). We underscore the importance of conducting comprehensive feasibility assessments to identify fit‐for‐purpose data sources with sufficient sample size, data elements, and follow‐up. Beyond technical challenges, we also discuss approaches to increase the credibility of RWE, including systematic incorporation of clinical expertise into study design decisions, and separation between decision makers and the data. [ABSTRACT FROM AUTHOR]

Purpose In contrast to randomized clinical trials, comparative safety and effectiveness assessments of osteoporosis medications in clinical practice may be subject to confounding by indication. We used negative control outcomes to detect residual confounding when comparing osteoporosis medications. Methods: Using MarketScan Commercial and Supplemental claims, we identified women aged ≥55 years who initiated an oral bisphosphonate (BP) (risedronate, alendronate, or ibandronate), denosumab (an injected biologic), or intravenous zoledronic acid (ZA) from October 1, 2010 to September 30, 2015. Women with Paget's disease or cancer were excluded. We compared individual oral BPs to each other, denosumab to ZA, denosumab to oral BPs, and ZA to oral BPs, with respect to 11 negative control outcomes identified by subject matter experts. We estimated the 12‐month cumulative risk difference (RD) using inverse probability of treatment and censoring weights. Results: Among 148 587 women, most initiated alendronate (57%), followed by ibandronate (12%), ZA (11%), risedronate (10%), and denosumab (10%). Compared with denosumab, patients initiating ZA had similar risks of all negative control outcomes. Compared with oral BPs, patients initiating denosumab had a higher risk of a wellness visit (RD = 1.2%, 95% CI: 0.4, 1.9) and a lower risk of receiving herpes zoster vaccine (RD = −0.6%, 95% CI: −1.1, −0.2). Comparing ZA with oral BP initiators resulted in two outcomes with positive associations. Conclusions: Caution is warranted when comparing injectable vs oral osteoporosis medications, given the potential for unmeasured confounding. Evaluating negative control outcomes could be a standard validity check prior to conducting comparative studies. [ABSTRACT FROM AUTHOR]

The United States Environmental Protection Agency considers nutrient pollution in stream ecosystems one of the United States' most pressing environmental challenges. But limited independent replicates, lack of experimental randomization, and space- and time-varying confounding handicap causal inference on effects of nutrient pollution. In this article, the causal g-methods are extended to allow for exposures to vary in time and space in order to assess the effects of nutrient pollution on chlorophyll a—a proxy for algal production. Publicly available data from North Carolina's Cape Fear River and a simulation study are used to show how causal effects of upstream nutrient concentrations on downstream chlorophyll a levels may be estimated from typical water quality monitoring data. Estimates obtained from the parametric g-formula, a marginal structural model, and a structural nested model indicate that chlorophyll a concentrations at Lock and Dam 1 were influenced by nitrate concentrations measured 86 to 109 km upstream, an area where four major industrial and municipal point sources discharge wastewater. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Interference occurs when the treatment (or exposure) of one individual affects the outcomes of others. In some settings, it may be reasonable to assume that individuals can be partitioned into clusters such that there is no interference between individuals in different clusters, that is, there is partial interference. In observational studies with partial interference, inverse probability weighted (IPW) estimators have been something else different possible treatment effects. However, the validity of IPW estimators depends on the propensity score being known or correctly modelled. Alternatively, one can estimate the treatment effect using an outcome regression model. In this paper, we propose doubly robust (DR) estimators that utilize both models and are consistent and asymptotically normal if either model, but not necessarily both, is correctly specified. Empirical results are presented to demonstrate the DR property of the proposed estimators and the efficiency gain of DR over IPW estimators when both models are correctly specified. The different estimators are illustrated using data from a study examining the effects of cholera vaccination in Bangladesh. [ABSTRACT FROM AUTHOR]

Background: Epidemiologic evidence, reinforced by clinical and laboratory studies, shows that the rich Western diet is the major underlying cause of death and disability (e.g, from cardiovascular disease and type 2 diabetes) in Western industrialized societies. The objective of this study is to document the effects that eating a low-fat (≤10% of calories), high-carbohydrate (~80% of calories), moderate-sodium, purely plant-based diet ad libitum for 7 days can have on the biomarkers of cardiovascular disease and type 2 diabetes. Methods: Retrospective analysis of measurements of weight, blood pressure, blood sugar, and blood lipids and estimation of cardiovascular disease risk at baseline and day 7 from 1615 participants in a 10-day residential dietary intervention program from 2002 to 2011. Wilcoxon’s signed-rank test was used for testing the significance of changes from baseline. Results: The median (interquartile range, IQR) weight loss was 1.4 (1.8) kg (p < .001). The median (IQR) decrease in total cholesterol was 22 (29) mg/dL (p < .001). Even though most antihypertensive and antihyperglycemic medications were reduced or discontinued at baseline, systolic blood pressure decreased by a median (IQR) of 8 (18) mm Hg (p < .001), diastolic blood pressure by a median (IQR) of 4 (10) mm Hg (p < .001), and blood glucose by a median (IQR) of 3 (11) mg/dL (p < .001). For patients whose risk of a cardiovascular event within 10 years was >7.5% at baseline, the risk dropped to 5.5% (>27%) at day 7 (p < .001). Conclusions: A low-fat, starch-based, vegan diet eaten ad libitum for 7 days results in significant favorable changes in commonly tested biomarkers that are used to predict future risks for cardiovascular disease and metabolic diseases. [ABSTRACT FROM AUTHOR]

A correction to the article "Effects of 7 days on an ad libitum low-fat vegan diet: the McDougall Program cohort" by John McDougall and colleagues is presented.

The overconsumption of calorie-dense foods high in added salt, sugar, and fat is a major contributor to current rates of obesity, and methods to reduce consumption are needed. Prolonged water-only fasting followed by an exclusively whole-plant-food diet free of added salt, oil, and sugar may reduce the consumption of these hyper-palatable foods, but such effects have not been quantified. Therefore, we conducted a preliminary study to estimate the effects of this intervention on salty and sweet taste detection and recognition thresholds and perceived taste intensity after at least five days of fasting and at refeed day three. We also assessed the effects on sweet, salty, and fatty food preference and overall dietary consumption 30 days after the day three refeed visit. Based on this data, we estimated that 10 days after the start of the fasting, salty taste recognition, sweet taste detection, and sweet taste recognition thresholds decreased significantly, salty taste intensity ratings increased significantly, and sweet taste intensity ratings decreased significantly. We also have preliminary data that prolonged water-only fasting followed by refeeding on an exclusively whole-food-plant diet may reduce salty/fatty and sweet/fatty food liking, reduce sugar intake, and increase vegetable intake. These results support further research into the effects of fasting and diet on taste function and food likability and consumption., Competing Interests: The authors have declared financial relationships, which are detailed in the next section., (Copyright © 2022, Myers et al.)

M-estimation, or estimating equation, methods are widely applicable for point estimation and asymptotic inference. In this paper, we present an R package that can find roots and compute the empirical sandwich variance estimator for any set of user-specified, unbiased estimating equations. Examples from the M-estimation primer by Stefanski and Boos (2002) demonstrate use of the software. The package also includes a framework for finite sample, heteroscedastic, and autocorrelation variance corrections, and a website with an extensive collection of tutorials.

Background: Lack of evidence-based outcomes data leads to uncertainty in developing treatment regimens in children who are newly diagnosed with ulcerative colitis. We hypothesised that pretreatment clinical, transcriptomic, and microbial factors predict disease course., Methods: In this inception cohort study, we recruited paediatric patients aged 4-17 years with newly diagnosed ulcerative colitis from 29 centres in the USA and Canada. Patients initially received standardised mesalazine or corticosteroids, with pre-established criteria for escalation to immunomodulators (ie, thiopurines) or anti-tumor necrosis factor-α (TNFα) therapy. We used RNA sequencing to define rectal gene expression before treatment, and 16S sequencing to characterise rectal and faecal microbiota. The primary outcome was week 52 corticosteroid-free remission with no therapy beyond mesalazine. We assessed factors associated with the primary outcome using logistic regression models of the per-protocol population. This study is registered with ClinicalTrials.gov, number NCT01536535., Findings: Between July 10, 2012, and April 21, 2015, of 467 patients recruited, 428 started medical therapy, of whom 400 (93%) were evaluable at 52 weeks and 386 (90%) completed the study period with no protocol violations. 150 (38%) of 400 participants achieved week 52 corticosteroid-free remission, of whom 147 (98%) were taking mesalazine and three (2%) were taking no medication. 74 (19%) of 400 were escalated to immunomodulators alone, 123 (31%) anti-TNFα therapy, and 25 (6%) colectomy. Low baseline clinical severity, high baseline haemoglobin, and week 4 clinical remission were associated with achieving week 52 corticosteroid-free remission (n=386, logistic model area under the curve [AUC] 0·70, 95% CI 0·65-0·75; specificity 77%, 95% CI 71-82). Baseline severity and remission by week 4 were validated in an independent cohort of 274 paediatric patients with newly diagnosed ulcerative colitis. After adjusting for clinical predictors, an antimicrobial peptide gene signature (odds ratio [OR] 0·57, 95% CI 0·39-0·81; p=0·002) and abundance of Ruminococcaceae (OR 1·43, 1·02-2·00; p=0·04), and Sutterella (OR 0·81, 0·65-1·00; p=0·05) were independently associated with week 52 corticosteroid-free remission., Interpretation: Our findings support the utility of initial clinical activity and treatment response by 4 weeks to predict week 52 corticosteroid-free remission with mesalazine alone in children who are newly diagnosed with ulcerative colitis. The development of personalised clinical and biological signatures holds the promise of informing ulcerative colitis therapeutic decisions., Funding: US National Institutes of Health., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Interference occurs when the treatment (or exposure) of one individual affects the outcomes of others. In some settings it may be reasonable to assume individuals can be partitioned into clusters such that there is no interference between individuals in different clusters, i.e., there is partial interference. In observational studies with partial interference, inverse probability weighted (IPW) estimators have been proposed of different possible treatment effects. However, the validity of IPW estimators depends on the propensity score being known or correctly modeled. Alternatively, one can estimate the treatment effect using an outcome regression model. In this paper, we propose doubly robust (DR) estimators which utilize both models and are consistent and asymptotically normal if either model, but not necessarily both, is correctly specified. Empirical results are presented to demonstrate the DR property of the proposed estimators, as well as the efficiency gain of DR over IPW estimators when both models are correctly specified. The different estimators are illustrated using data from a study examining the effects of cholera vaccination in Bangladesh.

In causal inference, interference occurs when the treatment of one subject affects the outcome of other subjects. Interference can distort research conclusions about causal effects when not accounted for properly. In the absence of interference, inverse probability weighted (IPW) estimators are commonly used to estimate causal effects from observational data. Recently, IPW estimators have been extended to handle interference. Tchetgen Tchetgen and VanderWeele (2012) proposed IPW methods to estimate direct and indirect (or spillover) effects that allow for interference between individuals within groups. In this paper, we present inferference , an R package that computes these IPW causal effect estimates when interference may be present within groups. We illustrate use of the package with examples from political science and infectious disease.