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A P value, or the magnitude or direction of results can influence decisions about whether, when, and how research findings are disseminated. Regardless of whether an entire study or a particular study result is unavailable because investigators considered the results to be unfavorable, bias in a meta‐analysis may occur when available results differ systematically from missing results. In this article, we summarize the empirical evidence for various reporting biases that lead to study results being unavailable for inclusion in systematic reviews, with a focus on health research. These biases include publication bias and selective nonreporting bias. We describe processes that systematic reviewers can use to minimize the risk of bias due to missing results in meta‐analyses of health research, such as comprehensive searches and prospective approaches to meta‐analysis. We also outline methods that have been designed for assessing risk of bias due to missing results in meta‐analyses of health research, including using tools to assess selective nonreporting of results, ascertaining qualitative signals that suggest not all studies were identified, and generating funnel plots to identify small‐study effects, one cause of which is reporting bias. Highlights: Bias in a meta‐analysis may occur when available results differ systematically from missing results.Several different tools, plots, and statistical methods have been designed for assessing risk of bias due to missing results in meta‐analyses. These include comparison of prespecified analysis plans with completed reports to detect selective nonreporting of results, consideration of qualitative signals that suggest not all studies were identified, and the use of funnel plots to identify small‐study effects, for which reporting bias is one of several causes.Information from approaches such as funnel plots and selection models is more difficult to interpret than from less subjective approaches such as detection of incompletely reported results in studies for which prespecified analysis plans were available. [ABSTRACT FROM AUTHOR]

Importance: Effective therapies for patients with coronavirus disease 2019 (COVID-19) are needed, and clinical trial data have demonstrated that low-dose dexamethasone reduced mortality in hospitalized patients with COVID-19 who required respiratory support.Objective: To estimate the association between administration of corticosteroids compared with usual care or placebo and 28-day all-cause mortality.Design, Setting, and Participants: Prospective meta-analysis that pooled data from 7 randomized clinical trials that evaluated the efficacy of corticosteroids in 1703 critically ill patients with COVID-19. The trials were conducted in 12 countries from February 26, 2020, to June 9, 2020, and the date of final follow-up was July 6, 2020. Pooled data were aggregated from the individual trials, overall, and in predefined subgroups. Risk of bias was assessed using the Cochrane Risk of Bias Assessment Tool. Inconsistency among trial results was assessed using the I2 statistic. The primary analysis was an inverse variance-weighted fixed-effect meta-analysis of overall mortality, with the association between the intervention and mortality quantified using odds ratios (ORs). Random-effects meta-analyses also were conducted (with the Paule-Mandel estimate of heterogeneity and the Hartung-Knapp adjustment) and an inverse variance-weighted fixed-effect analysis using risk ratios.Exposures: Patients had been randomized to receive systemic dexamethasone, hydrocortisone, or methylprednisolone (678 patients) or to receive usual care or placebo (1025 patients).Main Outcomes and Measures: The primary outcome measure was all-cause mortality at 28 days after randomization. A secondary outcome was investigator-defined serious adverse events.Results: A total of 1703 patients (median age, 60 years [interquartile range, 52-68 years]; 488 [29%] women) were included in the analysis. Risk of bias was assessed as "low" for 6 of the 7 mortality results and as "some concerns" in 1 trial because of the randomization method. Five trials reported mortality at 28 days, 1 trial at 21 days, and 1 trial at 30 days. There were 222 deaths among the 678 patients randomized to corticosteroids and 425 deaths among the 1025 patients randomized to usual care or placebo (summary OR, 0.66 [95% CI, 0.53-0.82]; P < .001 based on a fixed-effect meta-analysis). There was little inconsistency between the trial results (I2 = 15.6%; P = .31 for heterogeneity) and the summary OR was 0.70 (95% CI, 0.48-1.01; P = .053) based on the random-effects meta-analysis. The fixed-effect summary OR for the association with mortality was 0.64 (95% CI, 0.50-0.82; P < .001) for dexamethasone compared with usual care or placebo (3 trials, 1282 patients, and 527 deaths), the OR was 0.69 (95% CI, 0.43-1.12; P = .13) for hydrocortisone (3 trials, 374 patients, and 94 deaths), and the OR was 0.91 (95% CI, 0.29-2.87; P = .87) for methylprednisolone (1 trial, 47 patients, and 26 deaths). Among the 6 trials that reported serious adverse events, 64 events occurred among 354 patients randomized to corticosteroids and 80 events occurred among 342 patients randomized to usual care or placebo.Conclusions and Relevance: In this prospective meta-analysis of clinical trials of critically ill patients with COVID-19, administration of systemic corticosteroids, compared with usual care or placebo, was associated with lower 28-day all-cause mortality. [ABSTRACT FROM AUTHOR]

Meta‐analyses based on systematic literature reviews are commonly used to obtain a quantitative summary of the available evidence on a given topic. However, the reliability of any meta‐analysis is constrained by that of its constituent studies. One major limitation is the possibility of small‐study effects, when estimates from smaller and larger studies differ systematically. Small‐study effects may result from reporting biases (ie, publication bias), from inadequacies of the included studies that are related to study size, or from reasons unrelated to bias. We propose two estimators based on the median and mode to increase the reliability of findings in a meta‐analysis by mitigating the influence of small‐study effects. By re‐examining data from published meta‐analyses and by conducting a simulation study, we show that these estimators offer robustness to a range of plausible bias mechanisms, without making explicit modelling assumptions. They are also robust to outlying studies without explicitly removing such studies from the analysis. When meta‐analyses are suspected to be at risk of bias because of small‐study effects, we recommend reporting the mean, median and modal pooled estimates. [ABSTRACT FROM AUTHOR]

Purpose: We conducted a systematic review and individual patient data (IPD) meta-analysis to examine the utility of cystatin C for evaluation of glomerular function in children with cancer.Methods: Eligible studies evaluated the accuracy of cystatin C for detecting poor renal function in children undergoing chemotherapy. Study quality was assessed using QUADAS-2. Authors of four studies shared IPD. We calculated the correlation between log cystatin C and GFR stratified by study and measure of cystatin C. We dichotomized the reference standard at GFR 80 ml/min/1.73m2 and stratified cystatin C at 1 mg/l, to calculate sensitivity and specificity in each study and according to age group (0-4, 5-12, and ≥ 13 years). In sensitivity analyses, we investigated different GFR and cystatin C cut points. We used logistic regression to estimate the association of impaired renal function with log cystatin C and quantified diagnostic accuracy using the area under the ROC curve (AUC).Results: Six studies, which used different test and reference standard thresholds, suggested that cystatin C has the potential to monitor renal function in children undergoing chemotherapy for malignancy. IPD data (504 samples, 209 children) showed that cystatin C has poor sensitivity (63%) and moderate specificity (89%), although use of a GFR cut point of < 60 ml/min/1.73m2 (data only available from two of the studies) estimated sensitivity to be 92% and specificity 81.3%. The AUC for the combined data set was 0.890 (95% CI 0.826, 0.951). Diagnostic accuracy appeared to decrease with age.Conclusions: Cystatin C has better diagnostic accuracy than creatinine as a test for glomerular dysfunction in young people undergoing treatment for cancer. Diagnostic accuracy is not sufficient for it to replace current reference standards for predicting clinically relevant impairments that may alter dosing of important nephrotoxic agents. [ABSTRACT FROM AUTHOR]

Flaws in the design of randomized trials may bias intervention effect estimates and increase between-trial heterogeneity. Empirical evidence suggests that these problems are greatest for subjectively assessed outcomes. For the Risk of Bias in Evidence Synthesis (ROBES) Study, we extracted risk-of-bias judgements (for sequence generation, allocation concealment, blinding, and incomplete data) from a large collection of meta-analyses published in the Cochrane Library (issue 4; April 2011). We categorized outcome measures as mortality, other objective outcome, or subjective outcome, and we estimated associations of bias judgements with intervention effect estimates using Bayesian hierarchical models. Among 2,443 randomized trials in 228 meta-analyses, intervention effect estimates were, on average, exaggerated in trials with high or unclear (versus low) risk-of-bias judgements for sequence generation (ratio of odds ratios (ROR) = 0.91, 95% credible interval (CrI): 0.86, 0.98), allocation concealment (ROR = 0.92, 95% CrI: 0.86,0.98), and blinding (ROR = 0.87, 95% CrI: 0.80,0.93). In contrast to previous work, we did not observe consistently different bias for subjective outcomes compared with mortality. However, we found an increase in between-trial heterogeneity associated with lack of blinding in meta-analyses with subjective outcomes. Inconsistency in criteria for risk-of-bias judgements applied by individual reviewers is a likely limitation of routinely collected bias assessments. Inadequate randomization and lack of blinding may lead to exaggeration of intervention effect estimates in randomized trials. [ABSTRACT FROM AUTHOR]

Objective: To synthesise evidence on the average bias and heterogeneity associated with reported methodological features of randomized trials. Design: Systematic review of meta-epidemiological studies. Methods: We retrieved eligible studies included in a recent AHRQ-EPC review on this topic (latest search September 2012), and searched Ovid MEDLINE and Ovid EMBASE for studies indexed from Jan 2012-May 2015. Data were extracted by one author and verified by another. We combined estimates of average bias (e.g. ratio of odds ratios (ROR) or difference in standardised mean differences (dSMD)) in meta-analyses using the random-effects model. Analyses were stratified by type of outcome (“mortality” versus “other objective” versus “subjective”). Direction of effect was standardised so that ROR < 1 and dSMD < 0 denotes a larger intervention effect estimate in trials with an inadequate or unclear (versus adequate) characteristic. Results: We included 24 studies. The available evidence suggests that intervention effect estimates may be exaggerated in trials with inadequate/unclear (versus adequate) sequence generation (ROR 0.93, 95% CI 0.86 to 0.99; 7 studies) and allocation concealment (ROR 0.90, 95% CI 0.84 to 0.97; 7 studies). For these characteristics, the average bias appeared to be larger in trials of subjective outcomes compared with other objective outcomes. Also, intervention effects for subjective outcomes appear to be exaggerated in trials with lack of/unclear blinding of participants (versus blinding) (dSMD -0.37, 95% CI -0.77 to 0.04; 2 studies), lack of/unclear blinding of outcome assessors (ROR 0.64, 95% CI 0.43 to 0.96; 1 study) and lack of/unclear double blinding (ROR 0.77, 95% CI 0.61 to 0.93; 1 study). The influence of other characteristics (e.g. unblinded trial personnel, attrition) is unclear. Conclusions: Certain characteristics of randomized trials may exaggerate intervention effect estimates. The average bias appears to be greatest in trials of subjective outcomes. More research on several characteristics, particularly attrition and selective reporting, is needed. [ABSTRACT FROM AUTHOR]

The article focuses on a meta-analysis of several randomized clinical trials to investigate the effectiveness of exercise in adults with clinical depression. The results of the meta-analysis suggest that exercise has a short-live effect on depression. There is also little evidence that exercise has long-term beneficial effects in clinically depressed adults.

Background: Dietary modifications and supplements are used widely by patients with cancer and preinvasive lesions as an adjunct to standard treatment. Given the widespread use of nutritional modifications and supplements by such patients and concerns about the lack of benefit and possible harm, we conducted a systematic review of randomized controlled trials to examine the effect of nutritional interventions on patients with cancer or preinvasive lesions. Methods: We searched electronic databases and reference lists to locate all eligible trials and analyzed trial quality. Outcome measures were all-cause and cancer mortality, disease-free survival, cancer recurrence, second primary cancer, recurrence of a preinvasive lesion, or progression to cancer. Results of individual trials were combined by use of random-effects meta-analyses. Results: We identified 59 eligible trials, 25 in patients with cancer and 34 in patients with preinvasive lesions, respectively. Trial quality was generally low; only three trials (two of cancer and one of preinvasive lesions) had adequate methods for generating the allocation sequence, allocation concealment, and masking both outcome assessors and participants. The combined odds ratio (OR) for the effect of a healthy die—given alone or with dietary supplements, weight loss, or exercise—on all-cause mortality was 0.90 (95% confidence interval [CI] = 0.46 to 1.77). There was no evidence of an association between the use of antioxidant (OR = 1.01, 95% CI = 0.88 to 1.15) or retinol (OR = 0.97, 95% CI = 0.83 to 1.13) supplements and all-cause mortality. Meta-analyses of all other outcomes did not show clear evidence of benefit or harm. Conclusions: The impact of most nutritional interventions cannot be reliably estimated because of the limited number of trials, many of which were of low quality. There is no evidence that dietary modification by cancer patients improves survival and benefits disease prognosis. [ABSTRACT FROM AUTHOR]

Objectives: Primary objective: To estimate the effect of corticosteroids compared with usual care or placebo on mortality up to 28 days after randomization. Secondary objectives: To examine whether the effect of corticosteroids compared with usual care or placebo on mortality up to 28 days after randomization varies between subgroups related to treatment characteristics, disease severity at the time of randomization, patient characteristics, or risk of bias. To examine the effect of corticosteroids compared with usual care or placebo on serious adverse events.Study Design: Prospective meta-analysis of randomized controlled trials. Both placebo-controlled and open-label trials are eligible.Participants: Hospitalised, critically ill patients with suspected or confirmed COVID-19.Intervention and Comparator: Intervention groups will have received therapeutic doses of a steroid (dexamethasone, hydrocortisone or methylprednisolone) with IV or oral administration immediately after randomization. The comparator groups will have received standard of care or usual care or placebo.Main Outcome: All-cause mortality up to 28 days after randomization.Search Methods: Systematic searching of clinicaltrials.gov , EudraCT, the WHO ISRCTN registry, and the Chinese clinical trials registry. Additionally, research and WHO networks will be asked for relevant trials.Risk Of Bias Assessments: These will be based on the Cochrane RoB 2 tool, and will use structured information provided by the trial investigators on a form designed for this prospective meta-analysis. We will use GRADE to assess the certainty of the evidence.Statistical Analyses: Trial investigators will provide data on the numbers of participants who did and did not experience each outcome according to intervention group, overall and in specified subgroups. We will conduct fixed-effect (primary analysis) and random-effects (Paule-Mandel estimate of heterogeneity and Hartung-Knapp adjustment) meta-analyses. We will quantify inconsistency in effects between trials using I2 statistics. Evidence for subgroup effects will be quantified by ratios of odds ratios comparing effects in the subgroups, and corresponding interaction p-values. Comparisons between subgroups defined by trial characteristics will be made using random-effects meta-regression. Comparisons between subgroups defined by patient characteristics will be made by estimating trial-specific ratios of odds ratios comparing intervention effects between subgroups then combining these using random-effects meta-analysis. Steroid interventions will be classified as high or low dose according to whether the dose is greater or less than or equal to 400 mg hydrocortisone per day or equivalent. We will use network meta-analysis methods to make comparisons between the effects of high and low dose steroid interventions (because one trial randomized participants to both low and high dose steroid arms).Prospero Registration Number: CRD42020197242 FULL PROTOCOL: The full protocol for this prospective meta-analysis is attached as an additional file, accessible from the Trials website (Additional file 1). To expedite dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol for the systematic review. [ABSTRACT FROM AUTHOR]

The article focuses on the study of whether treatment effects of randomised controlled trials in surrogate primary outcomes are different from final patient relevant primary outcomes or not. It is mentioned that advantage of surrogate outcomes are its short duration and size which reduces the cost of trials. It is concluded that surrogate primary outcomes trial are more likely to report larger treatment effects than trials reporting final patient relevant outcomes.