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This Viewpoint discusses reports from China after its zero COVID-19 policy ended in December 2022.

This Viewpoint examines the increase in “mega-journals” (prolific publishers of medical articles) and both the opportunities and threats to scientific research they present.

ImportanceDuring the COVID-19 pandemic, children and adolescents were massively infected worldwide. In 2022, reinfections became a main feature of the endemic phase of SARS-CoV-2, so it is important to understand the epidemiology and clinical impact of reinfections.ObjectiveTo assess the incidence, risk, and severity of pediatric SARS-CoV-2 reinfection.Design, Setting, and ParticipantsThis retrospective cohort study used epidemiologic data of documented SARS-CoV-2 infections from the surveillance database of the Institute for Public Health of Vojvodina. A total of 32 524 children and adolescents from Vojvodina, Serbia, with laboratory-confirmed SARS-CoV-2 infection between March 6, 2020, and April 30, 2022, were followed up for reinfection until July 31, 2022.Main Outcomes and MeasuresIncidence rates of documented SARS-CoV-2 reinfection per 1000 person-months, estimated risk of documented reinfection 90 days or more after laboratory confirmation of primary infection, reinfection severity, hospitalizations, and deaths.ResultsThe study cohort included 32 524 children and adolescents with COVID-19 (mean [SD] age, 11.2 [4.9] years; 15 953 [49.1%] male), including 964 children (3.0%) who experienced documented reinfection. The incidence rate of documented reinfections was 3.2 (95% CI, 3.0-3.4) cases per 1000 person-months and was highest in adolescents aged 12 to 17 years (3.4; 95% CI, 3.2-3.7). Most reinfections (905 [93.9%]) were recorded in 2022. The cumulative reinfection risk was 1.3% at 6 months, 1.9% at 9 months, 4.0% at 12 months, 6.7% at 15 months, 7.2% at 18 months, and 7.9% after 21 months. Pediatric COVID-19 cases were generally mild. The proportion of severe clinical forms decreased from 14 (1.4%) in initial episodes to 3 (0.3%) in reinfections. Reinfected children were approximately 5 times less likely to have severe disease during reinfection compared with initial infection (McNemar odds ratio, 0.2; 95% CI, 0.0-0.8). Pediatric reinfections rarely led to hospitalization (0.5% vs 1.3% during primary infections), and none resulted in death.Conclusions and RelevanceThis cohort study found that the SARS-CoV-2 reinfection risk remained substantially lower for children and adolescents compared with adults as of July 2022. Pediatric infections were mild, and reinfections were even milder than primary infections.

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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.

This survey analysis of randomized clinical trials registered within 100 days of the first reported case of coronavirus disease 2019 assessed recruitment and results reporting.

Key Points Question What are the available data on cancer treatment outcomes for new cancer therapies approved by the US Food and Drug Administration? Findings In this comparative effectiveness study of 92 novel cancer therapies approved for 100 indications over 17 years, 44% of drug approvals were based on data from nonrandomized clinical trials. Randomized clinical trials typically reported that these drugs were associated with substantial tumor responses and delays in the time to progression or death, but the median absolute increase in overall survival was only 2 months. Meaning This study’s findings indicate that, at the time of drug approval, limited supporting data are available to decision-makers, and the increase in overall survival associated with new cancer drugs is typically small., Importance Clinical trial evidence used to support drug approval is typically the only information on benefits and harms that patients and clinicians can use for decision-making when novel cancer therapies become available. Various evaluations have raised concern about the uncertainty surrounding these data, and a systematic investigation of the available information on treatment outcomes for cancer drugs approved by the US Food and Drug Administration (FDA) is warranted. Objective To describe the clinical trial data available on treatment outcomes at the time of FDA approval of all novel cancer drugs approved for the first time between 2000 and 2016. Design, Setting, and Participants This comparative effectiveness study analyzed randomized clinical trials and single-arm clinical trials of novel drugs approved for the first time to treat any type of cancer. Approval packages were obtained from drugs@FDA, a publicly available database containing information on drug and biologic products approved for human use in the US. Data from January 2000 to December 2016 were included in this study. Main Outcomes and Measures Regulatory and clinical trial characteristics were described. For randomized clinical trials, summary treatment outcomes for overall survival, progression-free survival, and tumor response across all therapies were calculated, and median absolute survival increases were estimated. Tumor types and regulatory characteristics were assessed separately. Results Between 2000 and 2016, 92 novel cancer drugs were approved by the FDA for 100 indications based on data from 127 clinical trials. The 127 clinical trials included a median of 191 participants (interquartile range [IQR], 106-448 participants). Overall, 65 clinical trials (51.2%) were randomized, and 95 clinical trials (74.8%) were open label. Of 100 indications, 44 indications underwent accelerated approval, 42 indications were for hematological cancers, and 58 indications were for solid tumors. Novel drugs had mean hazard ratios of 0.77 (95% CI, 0.73-0.81; I2 = 46%) for overall survival and 0.52 (95% CI, 0.47-0.57; I2 = 88%) for progression-free survival. The median tumor response, expressed as relative risk, was 2.37 (95% CI, 2.00-2.80; I2 = 91%). The median absolute survival benefit was 2.40 months (IQR, 1.25-3.89 months). Conclusions and Relevance In this study, data available at the time of FDA drug approval indicated that novel cancer therapies were associated with substantial tumor responses but with prolonging median overall survival by only 2.40 months. Approval data from 17 years of clinical trials suggested that patients and clinicians typically had limited information available regarding the benefits of novel cancer treatments at market entry., This comparative effectiveness research examines clinical trial data on treatment outcomes used to support US Food and Drug Administration approval of novel cancer therapies that were approved for the first time between 2000 and 2016.

Importance Atrial fibrillation (AF) affects more than 6 million people in the United States; however, much AF remains undiagnosed. Given that more than 265 million people in the United States own smartphones (>80% of the population), smartphone applications have been proposed for detecting AF, but the accuracy of these applications remains unclear. Objective To determine the accuracy of smartphone camera applications that diagnose AF. Data Sources and Study Selection MEDLINE and Embase were searched until January 2019 for studies that assessed the accuracy of any smartphone applications that use the smartphone’s camera to measure the amplitude and frequency of the user’s fingertip pulse to diagnose AF. Data Extraction and Synthesis Bivariate random-effects meta-analyses were constructed to synthesize data. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) of Diagnostic Test Accuracy Studies reporting guideline. Main Outcomes and Measures Sensitivity and specificity were measured with bivariate random-effects meta-analysis. To simulate the use of these applications as a screening tool, the positive predictive value (PPV) and negative predictive value (NPV) for different population groups (ie, age ≥65 years and age ≥65 years with hypertension) were modeled. Lastly, the association of methodological limitations with outcomes were analyzed with sensitivity analyses and metaregressions. Results A total of 10 primary diagnostic accuracy studies, with 3852 participants and 4 applications, were included. The oldest studies were published in 2016 (2 studies [20.0%]), while most studies (4 [40.0%]) were published in 2018. The applications analyzed the pulsewave signal for a mean (range) of 2 (1-5) minutes. The meta-analyzed sensitivity and specificity for all applications combined were 94.2% (95% CI, 92.2%-95.7%) and 95.8% (95% CI, 92.4%-97.7%), respectively. The PPV for smartphone camera applications detecting AF in an asymptomatic population aged 65 years and older was between 19.3% (95% CI, 19.2%-19.4%) and 37.5% (95% CI, 37.4%-37.6%), and the NPV was between 99.8% (95% CI, 99.83%-99.84%) and 99.9% (95% CI, 99.94%-99.95%). The PPV and NPV increased for individuals aged 65 years and older with hypertension (PPV, 20.5% [95% CI, 20.4%-20.6%] to 39.2% [95% CI, 39.1%-39.3%]; NPV, 99.8% [95% CI, 99.8%-99.8%] to 99.9% [95% CI, 99.9%-99.9%]). There were methodological limitations in a number of studies that did not appear to be associated with diagnostic performance, but this could not be definitively excluded given the sparsity of the data. Conclusions and Relevance In this study, all smartphone camera applications had relatively high sensitivity and specificity. The modeled NPV was high for all analyses, but the PPV was modest, suggesting that using these applications in an asymptomatic population may generate a higher number of false-positive than true-positive results. Future research should address the accuracy of these applications when screening other high-risk population groups, their ability to help monitor chronic AF, and, ultimately, their associations with patient-important outcomes.

This study describes views, downloads, Altmetric scores, and citations of articles published as preprints and differences in Altmetric scores and citations of published articles by prior preprint status.

The evidence for nonreproducibility in basic and preclinical biomedical research is compelling. Accumulating data from diverse subdisciplines and types of experimentation suggest numerous problems that can create a fertile ground for nonreproducibility.1 For example, most raw data and protocols are often not available for in-depth scrutiny and use by other scientists. The current incentive system rewards selective reporting of success stories.

Importance The use and misuse of P values has generated extensive debates. Objective To evaluate in large scale the P values reported in the abstracts and full text of biomedical research articles over the past 25 years and determine how frequently statistical information is presented in ways other than P values. Design Automated text-mining analysis was performed to extract data on P values reported in 12 821 790 MEDLINE abstracts and in 843 884 abstracts and full-text articles in PubMed Central (PMC) from 1990 to 2015. Reporting of P values in 151 English-language core clinical journals and specific article types as classified by PubMed also was evaluated. A random sample of 1000 MEDLINE abstracts was manually assessed for reporting of P values and other types of statistical information; of those abstracts reporting empirical data, 100 articles were also assessed in full text. Main Outcomes and Measures P values reported. Results Text mining identified 4 572 043 P values in 1 608 736 MEDLINE abstracts and 3 438 299 P values in 385 393 PMC full-text articles. Reporting of P values in abstracts increased from 7.3% in 1990 to 15.6% in 2014. In 2014, P values were reported in 33.0% of abstracts from the 151 core clinical journals (n = 29 725 abstracts), 35.7% of meta-analyses (n = 5620), 38.9% of clinical trials (n = 4624), 54.8% of randomized controlled trials (n = 13 544), and 2.4% of reviews (n = 71 529). The distribution of reported P values in abstracts and in full text showed strong clustering at P values of .05 and of .001 or smaller. Over time, the “best” (most statistically significant) reported P values were modestly smaller and the “worst” (least statistically significant) reported P values became modestly less significant. Among the MEDLINE abstracts and PMC full-text articles with P values, 96% reported at least 1 P value of .05 or lower, with the proportion remaining steady over time in PMC full-text articles. In 1000 abstracts that were manually reviewed, 796 were from articles reporting empirical data; P values were reported in 15.7% (125/796 [95% CI, 13.2%-18.4%]) of abstracts, confidence intervals in 2.3% (18/796 [95% CI, 1.3%-3.6%]), Bayes factors in 0% (0/796 [95% CI, 0%-0.5%]), effect sizes in 13.9% (111/796 [95% CI, 11.6%-16.5%]), other information that could lead to estimation of P values in 12.4% (99/796 [95% CI, 10.2%-14.9%]), and qualitative statements about significance in 18.1% (181/1000 [95% CI, 15.8%-20.6%]); only 1.8% (14/796 [95% CI, 1.0%-2.9%]) of abstracts reported at least 1 effect size and at least 1 confidence interval. Among 99 manually extracted full-text articles with data, 55 reported P values, 4 presented confidence intervals for all reported effect sizes, none used Bayesian methods, 1 used false-discovery rates, 3 used sample size/power calculations, and 5 specified the primary outcome. Conclusions and Relevance In this analysis of P values reported in MEDLINE abstracts and in PMC articles from 1990-2015, more MEDLINE abstracts and articles reported P values over time, almost all abstracts and articles with P values reported statistically significant results, and, in a subgroup analysis, few articles included confidence intervals, Bayes factors, or effect sizes. Rather than reporting isolated P values, articles should include effect sizes and uncertainty metrics.

Importance Reanalyses of randomized clinical trial (RCT) data may help the scientific community assess the validity of reported trial results. Objectives To identify published reanalyses of RCT data, to characterize methodological and other differences between the original trial and reanalysis, to evaluate the independence of authors performing the reanalyses, and to assess whether the reanalysis changed interpretations from the original article about the types or numbers of patients who should be treated. Design We completed an electronic search of MEDLINE from inception to March 9, 2014, to identify all published studies that completed a reanalysis of individual patient data from previously published RCTs addressing the same hypothesis as the original RCT. Four data extractors independently screened articles and extracted data. Main Outcomes and Measures Changes in direction and magnitude of treatment effect, statistical significance, and interpretation about the types or numbers of patients who should be treated. Results We identified 37 eligible reanalyses in 36 published articles, 5 of which were performed by entirely independent authors (2 based on publicly available data and 2 on data that were provided on request; data availability was unclear for 1). Reanalyses differed most commonly in statistical or analytical approaches (n = 18) and in definitions or measurements of the outcome of interest (n = 12). Four reanalyses changed the direction and 2 changed the magnitude of treatment effect, whereas 4 led to changes in statistical significance of findings. Thirteen reanalyses (35%) led to interpretations different from that of the original article, 3 (8%) showing that different patients should be treated; 1 (3%), that fewer patients should be treated; and 9 (24%), that more patients should be treated. Conclusions and Relevance A small number of reanalyses of RCTs have been published to date. Only a few were conducted by entirely independent authors. Thirty-five percent of published reanalyses led to changes in findings that implied conclusions different from those of the original article about the types and number of patients who should be treated.

It is possible that more than 50% of complex disease risk is attributed to differences in an individual’s environment.1 Air pollution, smoking, and diet are documented environmental factors affecting health, yet these factors are but a fraction of the “exposome,” the totality of the exposure load occurring throughout a person’s lifetime.1 Investigating one or a handful of exposures at a time has led to a highly fragmented literature of epidemiologic associations. Much of that literature is not reproducible, and selective reporting may be a major reason for the lack of reproducibility. A new model is required to discover environmental exposures associated with disease while mitigating possibilities of selective reporting.

Importance Whole-genome sequencing (WGS) is increasingly applied in clinical medicine and is expected to uncover clinically significant findings regardless of sequencing indication. Objectives To examine coverage and concordance of clinically relevant genetic variation provided by WGS technologies; to quantitate inherited disease risk and pharmacogenomic findings in WGS data and resources required for their discovery and interpretation; and to evaluate clinical action prompted by WGS findings. Design, Setting, and Participants An exploratory study of 12 adult participants recruited at Stanford University Medical Center who underwent WGS between November 2011 and March 2012. A multidisciplinary team reviewed all potentially reportable genetic findings. Five physicians proposed initial clinical follow-up based on the genetic findings. Main Outcomes and Measures Genome coverage and sequencing platform concordance in different categories of genetic disease risk, person-hours spent curating candidate disease-risk variants, interpretation agreement between trained curators and disease genetics databases, burden of inherited disease risk and pharmacogenomic findings, and burden and interrater agreement of proposed clinical follow-up. Results Depending on sequencing platform, 10% to 19% of inherited disease genes were not covered to accepted standards for single nucleotide variant discovery. Genotype concordance was high for previously described single nucleotide genetic variants (99%-100%) but low for small insertion/deletion variants (53%-59%). Curation of 90 to 127 genetic variants in each participant required a median of 54 minutes (range, 5-223 minutes) per genetic variant, resulted in moderate classification agreement between professionals (Gross κ, 0.52; 95% CI, 0.40-0.64), and reclassified 69% of genetic variants cataloged as disease causing in mutation databases to variants of uncertain or lesser significance. Two to 6 personal disease-risk findings were discovered in each participant, including 1 frameshift deletion in the BRCA1 gene implicated in hereditary breast and ovarian cancer. Physician review of sequencing findings prompted consideration of a median of 1 to 3 initial diagnostic tests and referrals per participant, with fair interrater agreement about the suitability of WGS findings for clinical follow-up (Fleiss κ, 0.24; P Conclusions and Relevance In this exploratory study of 12 volunteer adults, the use of WGS was associated with incomplete coverage of inherited disease genes, low reproducibility of detection of genetic variation with the highest potential clinical effects, and uncertainty about clinically reportable findings. In certain cases, WGS will identify clinically actionable genetic variants warranting early medical intervention. These issues should be considered when determining the role of WGS in clinical medicine.

Mega-trials for blockbusters are readily feasible. There would be no cost for the government or health care system. Drug production cost to industry (as opposed to sales cost) is negligible. The proposed mandate could easily stipulate that blockbuster manufacturers donate drugs and placebos for such trials. Total cost to the industry would be approximately 1% of their cumulative sales for the product.

Administrative data offer impressive amounts of information that can be readily analyzed. However, how credible are the results derived from these behemoth data sets? Moreover, how prudent is it to translate those results into policy actions, and, if this is done, what should that translation path involve?

THECONDUCTOFVERYLARGE,SIMPLETRIALS(MEGA-TRIALS) isuncommonand faces several challenges, inparticularcostanddifficulty inpatient recruitment.However, thesechallengescanbeovercomewhen interventions are widely used by hundreds of thousands of individuals and when there is a potential profit to accommodate the trial cost. Accordingly,every licensed interventionwithannualsales that exceed $1 billion, ie, a blockbuster, should have at least 1 trial performed with at least 10 000 patients randomized to the intervention of interest and as many randomized either to placebo (if deemed to be a reasonable choice) or to another active intervention that is the least expensive effective intervention available. The comparison drug can be a generic drug with a well-established effectiveness and safety profile. Adata-intensivemega-trialof20 000randomizedparticipants with 4 years of follow-up costs an estimated $420 million, but streamlining the design, monitoring, data collection, and outcomes could save 90% of that cost. For a blockbuster with $2 billion annual sales, 1-month sales ($167 million) would suffice to conduct a mega-trial of 80 000 participants. The one outcome that should routinely be collected is death. For 80% power to detect a 10% relative risk reduction, sufficient follow-up is needed to achieve a 13.2% death rate in the control group with 10 000 participants per study group, or 3.3% with 40 000 participants per group. Furthermore, information also could be collected on major clinical end points that have not been adequately studied in previous trials of the blockbuster. How many interventions would be affected if such a rule were to be applied? In 2011, sixty different drug products had sales exceeding $1 billion in the United States alone; of those, 24 exceeded $2 billion in sales. More than 100 drugs have exceeded $1 billion in US annual sales at least once. The number is substantially larger if global markets are considered. Sales of the top 100 blockbusters globally accounted for $285 billion in 2009, exceeding 35% of the global pharmaceutical market. Because blockbusters typically maintain top sales for almost a decade, less than 1% of profits could fund an efficient mega-trials agenda. Blockbusterdrugsareeventuallyusedbymillionsofpatients. Typically there is evidence from randomized trials suggesting that thesedrugsareeffective—at least for someendpoints (not necessarilythemostseriousones), forsomefollow-up(notnecessarily longenough),and insomespecificcircumstances(not necessarily representing what happens in real life). The supportingrandomizedtrials typically includeonlya fewhundred participants (as in the case of testosterone [AndroGel] or modafinil [Provigil]) or, in the best case, a few thousand participants,oftenwithrelativelyshort-termfollow-upandareconducted among populations selected to avoid patients with comorbid conditions and those who take some other drugs. Of the 24 top blockbusters of 2011, only clopidogrel has had a randomized trial with more than 10 000 participants allocated to the experimental group—COMMIT (Clopidogrel and Metoprolol in Myocardial Infarction Trial) randomized 22 961 participants to clopidogrel and 22 891 to placebo. Rosuvastatin also came close to this threshold but not quite— JUPITER (Justification for the Use of Statins in Primary Prevention: An Intervention Trial Evaluating Rosuvastatin) randomized 8901 patients to rosuvastatin and 8901 to placebo. A few other blockbuster drugs have been studied in trials of 5000 to 10 000 patients (eg, tiotropium in the UPLIFT [Understanding Long-term Impacts on Function With Tiotropium] trial [n=5993] and fluticasone salmeterol in the 4-group TORCH [Towards a Revolution in COPD Health] trial [n=6112]), but such sample sizes coupled with modest follow-up cannot offer definitive evidence for risk of death. Fewof therandomizedtrials supporting theevidence legacy ofblockbustershave includeddeathasaprimaryoutcome.Evidenceofother seriousclinical events is alsooften limited, even though the conditions targeted for these therapies are serious, and for most of them death is the most important outcome for decision-making. Both COMMIT and JUPITER documented asignificantreductioninmortality(relativerisks,0.93[P=.03] and0.80[P=.02],respectively).Perhapsthisdocumentedbenefithascontributedtotheeventualmarketsuccessoftheseproducts. The UPLIFT and TORCH trials came close, but did not passnominalstatisticalsignificanceforreducedmortality(relativerisks,0.89[P=.08]and0.83[P=.052],respectively).Conversely, for some popular agents, such as erythropoietin, significantexcesses inmortalityhavebeendocumented inspe

Context Most medical interventions have modest effects, but occasionally some clinical trials may find very large effects for benefits or harms. Objective To evaluate the frequency and features of very large effects in medicine. Data Sources Cochrane Database of Systematic Reviews (CDSR, 2010, issue 7). Study Selection We separated all binary-outcome CDSR forest plots with comparisons of interventions according to whether the first published trial, a subsequent trial (not the first), or no trial had a nominally statistically significant (P Data Extraction We assessed the types of treatments and outcomes in trials with very large effects, examined how often large-effect trials were followed up by other trials on the same topic, and how these effects compared against the effects of the respective meta-analyses. Results Among 85 002 forest plots (from 3082 reviews), 8239 (9.7%) had a significant very large effect in the first published trial, 5158 (6.1%) only after the first published trial, and 71 605 (84.2%) had no trials with significant very large effects. Nominally significant very large effects typically appeared in small trials with median number of events: 18 in first trials and 15 in subsequent trials. Topics with very large effects were less likely than other topics to address mortality (3.6% in first trials, 3.2% in subsequent trials, and 11.6% in no trials with significant very large effects) and were more likely to address laboratory-defined efficacy (10% in first trials,10.8% in subsequent, and 3.2% in no trials with significant very large effects). First trials with very large effects were as likely as trials with no very large effects to have subsequent published trials. Ninety percent and 98% of the very large effects observed in first and subsequently published trials, respectively, became smaller in meta-analyses that included other trials; the median odds ratio decreased from 11.88 to 4.20 for first trials, and from 10.02 to 2.60 for subsequent trials. For 46 of the 500 selected topics (9.2%; first and subsequent trials) with a very large-effect trial, the meta-analysis maintained very large effects with P Conclusions Most large treatment effects emerge from small studies, and when additional trials are performed, the effect sizes become typically much smaller. Well-validated large effects are uncommon and pertain to nonfatal outcomes.