The Impact of Intermittent Fasting on Patients With Suspected Gastroesophageal Reflux Disease.

GOAL: The aim was to investigate the short-term impact of time restricted feeding on patients with suspected gastroesophageal reflux disease (GERD). BACKGROUND: Lifestyle modifications are often suggested, but the role of diet in GERD is unclear. Intermittent fasting is popular in the media and has demonstrated potential benefits with weight loss and inflammatory conditions as well as alterations in gastrointestinal hormones. STUDY: Patients who were referred for 96-hour ambulatory wireless pH monitoring off proton pump inhibitor to investigate GERD symptoms were screened for eligibility. Patients were instructed to maintain their baseline diet for the first 2 days of pH monitoring and switch to an intermittent fasting regimen (16 consecutive hour fast and 8 h eating window) for the second 2 days. Objective measures of reflux and GERD symptom severity were collected and analyzed. RESULTS: A total of 25 participants were analyzed. 9/25 (36%) fully adhered to the intermittent fasting regimen, with 21/25 (84%) demonstrating at least partial compliance. Mean acid exposure time on fasting days was 3.5% versus 4.3% on nonfasting days. Intermittent fasting was associated with a 0.64 reduction in acid exposure time (95% CI: -2.32, 1.05). There was a reduction in GERD symptom scores of heartburn and regurgitation during periods of intermittent fasting (14.3 vs. 9.9; difference of -4.46, 95% CI: -7.6,-1.32). CONCLUSIONS: Initial adherence to time restricted eating may be difficult for patients. There is weak statistical evidence to suggest that intermittent fasting mildly reduces acid exposure. Our data show that short-term intermittent fasting improves symptoms of both regurgitation and heartburn.

How large should the next study be? Predictive power and sample size requirements for replication studies.

We use information derived from over 40K trials in the Cochrane Collaboration database of systematic reviews (CDSR) to compute the replication probability, or predictive power of an experiment given its observed (two-sided) P$$ P $$ -value. We find that an exact replication of a marginally significant result with P=.05$$ P=.05 $$ has less than 30% chance of again reaching significance. Moreover, the replication of a result with P=.005$$ P=.005 $$ still has only 50% chance of significance. We also compute the probability that the direction (sign) of the estimated effect is correct, which is closely related to the type S error of Gelman and Tuerlinckx. We find that if an estimated effect has P=.05$$ P=.05 $$ , there is a 93% probability that its sign is correct. If P=.005$$ P=.005 $$ , then that probability is 99%. Finally, we compute the required sample size for a replication study to achieve some specified power conditional on the p$$ p $$ -value of the original study. We find that the replication of a result with P=.05$$ P=.05 $$ requires a sample size more than 16 times larger than the original study to achieve 80% power, while P=.005$$ P=.005 $$ requires at least 3.5 times larger sample size. These findings confirm that failure to replicate the statistical significance of a trial does not necessarily indicate that the original result was a fluke.

U.S. Food and Drug Administration Reasoning in Approval Decisions When Efficacy Evidence Is Borderline, 2013-2018.

BACKGROUND: The U.S. Food and Drug Administration (FDA) has substantial flexibility in its approval criteria in the context of life-threatening disease and unmet therapeutic need. OBJECTIVE: To understand the FDA's evidentiary standards when flexible criteria are employed. DESIGN: Case series. SETTING: Applications submitted between 2013 and 2018 that went through multiple review cycles because the evidence for clinical efficacy was initially deemed insufficient. MEASUREMENTS: Information was obtained from the approval package (available on Drugs@FDA), including advisory committee minutes, FDA reviews, and complete response letters. RESULTS: Of 912 applications reviewed, 117 went through multiple review cycles; only 22 of these faced additional review primarily because of issues related to clinical efficacy. Concerns about the end point, the clinical meaningfulness of the observed effect, and inconsistent results were common bases for initial rejection. In 7 of the 22 cases, the approval did not require new evidence but rather new interpretations of the original evidence. No FDA decisions cited reasoning used in previous decisions. LIMITATION: The conclusions rely on the authors' interpretation of the FDA statements and on a series of "close calls." CONCLUSION: The FDA has no mechanism to find or tradition to cite similar cases when weighing evidence for approvals, resulting in standalone, bespoke decisions. These decisions show highly variable criteria for "substantial evidence" when flexible evidential criteria are used, highlighted by the recent approval of aducanumab. A precedential tradition and suitable information system are required for the FDA to improve institutional memory and build upon past decisions. These would increase the FDA's decisional transparency, consistency, and predictability, which are critical to preserving the FDA's most valuable asset, the public's trust. PRIMARY FUNDING SOURCE: U.S. Food and Drug Administration.

Clinical Trial Participants' Views of the Risks and Benefits of Data Sharing.

Background Sharing of participant-level clinical trial data has potential benefits, but concerns about potential harms to research participants have led some pharmaceutical sponsors and investigators to urge caution. Little is known about clinical trial participants' perceptions of the risks of data sharing. Methods We conducted a structured survey of 771 current and recent participants from a diverse sample of clinical trials at three academic medical centers in the United States. Surveys were distributed by mail (350 completed surveys) and in clinic waiting rooms (421 completed surveys) (overall response rate, 79%). Results Less than 8% of respondents felt that the potential negative consequences of data sharing outweighed the benefits. A total of 93% were very or somewhat likely to allow their own data to be shared with university scientists, and 82% were very or somewhat likely to share with scientists in for-profit companies. Willingness to share data did not vary appreciably with the purpose for which the data would be used, with the exception that fewer participants were willing to share their data for use in litigation. The respondents' greatest concerns were that data sharing might make others less willing to enroll in clinical trials (37% very or somewhat concerned), that data would be used for marketing purposes (34%), or that data could be stolen (30%). Less concern was expressed about discrimination (22%) and exploitation of data for profit (20%). Conclusions In our study, few clinical trial participants had strong concerns about the risks of data sharing. Provided that adequate security safeguards were in place, most participants were willing to share their data for a wide range of uses. (Funded by the Greenwall Foundation.).

Assessing scientists for hiring, promotion, and tenure.

Assessment of researchers is necessary for decisions of hiring, promotion, and tenure. A burgeoning number of scientific leaders believe the current system of faculty incentives and rewards is misaligned with the needs of society and disconnected from the evidence about the causes of the reproducibility crisis and suboptimal quality of the scientific publication record. To address this issue, particularly for the clinical and life sciences, we convened a 22-member expert panel workshop in Washington, DC, in January 2017. Twenty-two academic leaders, funders, and scientists participated in the meeting. As background for the meeting, we completed a selective literature review of 22 key documents critiquing the current incentive system. From each document, we extracted how the authors perceived the problems of assessing science and scientists, the unintended consequences of maintaining the status quo for assessing scientists, and details of their proposed solutions. The resulting table was used as a seed for participant discussion. This resulted in six principles for assessing scientists and associated research and policy implications. We hope the content of this paper will serve as a basis for establishing best practices and redesigning the current approaches to assessing scientists by the many players involved in that process.

Association of Convalescent Plasma Treatment With Clinical Outcomes in Patients With COVID-19: A Systematic Review and Meta-analysis.

Importance: Convalescent plasma is a proposed treatment for COVID-19. Objective: To assess clinical outcomes with convalescent plasma treatment vs placebo or standard of care in peer-reviewed and preprint publications or press releases of randomized clinical trials (RCTs). Data Sources: PubMed, the Cochrane COVID-19 trial registry, and the Living Overview of Evidence platform were searched until January 29, 2021. Study Selection: The RCTs selected compared any type of convalescent plasma vs placebo or standard of care for patients with confirmed or suspected COVID-19 in any treatment setting. Data Extraction and Synthesis: Two reviewers independently extracted data on relevant clinical outcomes, trial characteristics, and patient characteristics and used the Cochrane Risk of Bias Assessment Tool. The primary analysis included peer-reviewed publications of RCTs only, whereas the secondary analysis included all publicly available RCT data (peer-reviewed publications, preprints, and press releases). Inverse variance-weighted meta-analyses were conducted to summarize the treatment effects. The certainty of the evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation. Main Outcomes and Measures: All-cause mortality, length of hospital stay, clinical improvement, clinical deterioration, mechanical ventilation use, and serious adverse events. Results: A total of 1060 patients from 4 peer-reviewed RCTs and 10 722 patients from 6 other publicly available RCTs were included. The summary risk ratio (RR) for all-cause mortality with convalescent plasma in the 4 peer-reviewed RCTs was 0.93 (95% CI, 0.63 to 1.38), the absolute risk difference was -1.21% (95% CI, -5.29% to 2.88%), and there was low certainty of the evidence due to imprecision. Across all 10 RCTs, the summary RR was 1.02 (95% CI, 0.92 to 1.12) and there was moderate certainty of the evidence due to inclusion of unpublished data. Among the peer-reviewed RCTs, the summary hazard ratio was 1.17 (95% CI, 0.07 to 20.34) for length of hospital stay, the summary RR was 0.76 (95% CI, 0.20 to 2.87) for mechanical ventilation use (the absolute risk difference for mechanical ventilation use was -2.56% [95% CI, -13.16% to 8.05%]), and there was low certainty of the evidence due to imprecision for both outcomes. Limited data on clinical improvement, clinical deterioration, and serious adverse events showed no significant differences. Conclusions and Relevance: Treatment with convalescent plasma compared with placebo or standard of care was not significantly associated with a decrease in all-cause mortality or with any benefit for other clinical outcomes. The certainty of the evidence was low to moderate for all-cause mortality and low for other outcomes.

Urodynamic factors associated with the large capacity bladder and incomplete emptying after prolapse repair (2009-2015).

AIMS: To identify the clinical and urodynamic factors associated with the large capacity bladder and incomplete bladder emptying after prolapse repair. METHODS: We identified 592 women who underwent anterior and/or apical prolapse repair at our institution from 2009 to 2015. Women were stratified by urodynamic capacity. The primary outcome was incomplete emptying at the longest follow-up (postvoid residual [PVR] > 200 mL). Data were analyzed in the Statistical Analysis System software. RESULTS: Two hundred and sixty-six women (mean age, 61 years) had preoperative urodynamic tracings available for review. After surgery, there were 519 PVRs in 239 women recorded at up to 2949 days (mean, 396) and nine time points (median, 2; IQR, 1-3). The receiver operator curve for predicted probability of longest follow-up PVR greater than 200 mL (area under curve = 0.67) identified the 600 mL cutpoint which defined large capacity bladder. Large capacity bladders (capacity, >600 mL [n=79] vs ≤600 mL, [n=160]) had a mean: detrusor pressure at maximum flow (21 vs 22 cm H2 O; P = 0.717), maximum flow rate (19 vs 17 mL/s; P = 0.148), significantly elevated PVR (202 vs 73 mL; P < 0.001), and significantly lower voiding efficiency (VE) (74 vs 82%, P < 0.05). Following prolapse repair, elevated PVR was associated with large capacity (PVR 101 vs 49 mL, P < 0.05). Large bladders had a two- to three-fold risk of longest follow-up PVR greater than 200 mL (14.3%-20.3% [capacity, >600 mL] vs 4.1%-7.0% [capacity, ≤600 mL]). VE was similar after surgery regardless of the capacity (87% vs 88%, P = 0.772). CONCLUSIONS: The decision to pursue prolapse repair should be individualized and take into account, the bladder capacity and goals for PVR improvement after surgery.

Meta-research: Evaluation and Improvement of Research Methods and Practices.

As the scientific enterprise has grown in size and diversity, we need empirical evidence on the research process to test and apply interventions that make it more efficient and its results more reliable. Meta-research is an evolving scientific discipline that aims to evaluate and improve research practices. It includes thematic areas of methods, reporting, reproducibility, evaluation, and incentives (how to do, report, verify, correct, and reward science). Much work is already done in this growing field, but efforts to-date are fragmented. We provide a map of ongoing efforts and discuss plans for connecting the multiple meta-research efforts across science worldwide.

Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations.

Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so-and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting.

Quantifying over-estimation in early stopped clinical trials and the "freezing effect" on subsequent research.

BACKGROUND: Despite the wide use of the design with statistical stopping guidelines to stop a randomized clinical trial early for efficacy, there are unsettled debates of potential harmful consequences of such designs. These concerns include the possible over-estimation of treatment effects in early stopped trials and a newer argument of a "freezing effect" that will halt future randomized clinical trials on the same comparison since an early stopped trial represents an effective declaration that randomization to the unfavored arm is unethical. The purpose of this study is to determine the degree of bias in designs that allow for early stopping and to assess the impact on estimation if indeed future experimentation is "frozen" by an early stopped trial. METHODS: We perform simulations to study the effect of early stopping. We simulate a collection of trials and contrast the treatment-effect estimates (risk differences and ratios) with the simulation truth. Simulations consider various scenarios of between-study variation, including an empirically derived distribution of effects from the clinical literature. RESULTS: Across the trials whose true effects are sampled from a uniform distribution, estimates from trials that stop early for efficacy deviate minimally from the simulation truth (median bias of the estimate of risk difference is 0.005). Over-estimation becomes appreciable only when the true effect is close to the null value 0 (median bias of the risk difference estimate is 0.04) or when stopping happens with 40% information or less; however, stopping under these situations is rare. We also find slight reverse bias of the estimated treatment effect (median bias of the risk difference estimate is -0.002) among trials that do not cross the early stopping boundaries but continue to the final analysis. Similar results occur with relative risk estimates. In contrast, Bayesian estimation of the treatment effect shrinks the estimate from trials stopping early and pulls back under-estimation from completed trials, largely rectifying any over-estimation among trials that terminate early. Regarding the so-called freezing effect, the pooled effects from meta-analyses that include truncated randomized clinical trials show an unimportant deviation from the true value, even when no subsequent trials are conducted after a truncated randomized clinical trial. CONCLUSION: Group sequential designs with stopping rules seek to minimize exposure of patients to a disfavored therapy and speed dissemination of results, and such designs do not lead to materially biased estimates. The likelihood and magnitude of a "freezing effect" is minimal. Superiority demonstrated in a randomized clinical trial stopping early and designed with appropriate statistical stopping rules is likely a valid inference, even if the estimate may be slightly inflated.