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Hilden J.,Copenhagen University | Boutron I.,French Cochrane Center | Ravau P.,French Cochrane Center | Brorson S.,Herlev University Hospital
CMAJ | Year: 2013

Background: Clinical trials are commonly done without blinded outcome assessors despite the risk of bias. We wanted to evaluate the effect of nonblinded outcome assessment on estimated effects in randomized clinical trials with outcomes that involved subjective measurement scales. Methods: We conducted a systematic review of randomized clinical trials with both blinded and nonblinded assessment of the same measurement scale outcome. We searched PubMed, EMBASE, PsycINFO, CINAHL, Cochrane Central Register of Controlled Trials, HighWire Press and Google Scholar for relevant studies. Two investigators agreed on the inclusion of trials and the outcome scale. For each trial, we calculated the difference in effect size (i.e., standardized mean difference between nonblinded and blinded assessments). A difference in effect size of less than 0 suggested that nonblinded assessors generated more optimistic estimates of effect. We pooled the differences in effect size using inverse variance random-effects meta-analysis and used metaregression to identify potential reasons for variation. Results: We included 24 trials in our review. The main meta-analysis included 16 trials (involving 2854 patients) with subjective outcomes. The estimated treatment effect was more beneficial when based on nonblinded assessors (pooled difference in effect size -0.23 [95% confidence interval (CI) -0.40 to -0.06]). In relative terms, nonblinded assessors exaggerated the pooled effect size by 68% (95% CI 14% to 230%). Heterogeneity was moderate (I2 = 46%, p = 0.02) and unexplained by metaregression. Interpretation: We provide empirical evidence for observer bias in randomized clinical trials with subjective measurement scale outcomes. A failure to blind assessors of outcomes in such trials results in a high risk of substantial bias. © 2013 Canadian Medical Association or its licensors.


Escalas C.,French Institute of Health and Medical Research | Dalichampt M.,Assistance Publique Hopitaux de Paris | Combe B.,Montpellier University Hospital Center | Fautrel B.,University Pierre and Marie Curie | And 5 more authors.
Annals of the Rheumatic Diseases | Year: 2012

Objective: To assess the association of adherence to the 2007 recommendations of the European League Against Rheumatism (EULAR) for managing early arthritis and radiographic progression and disability in patients Methods: The authors conducted a prospective population-based cohort study. The ESPOIR cohort was a French cohort of 813 patients with early arthritis not receiving disease-modifying antirheumatic drugs (DMARDs). Adherence to the 2007 EULAR recommendations was defined by measuring adherence to three of the recommendations concerning the initiation and early adjustment of DMARDs. The study endpoints were radiographic progression, defined as the presence of at least one new erosion between baseline and 1 year, and disability as a heath assessment questionnaire score ≥1 at 2 years. A propensity score of being treated according to the recommendations was developed. Results: After adjustment for propensity score, treatment centre and the main confounding factors, patients without recommendation adherence were at increased risk of radiographic progression at 1 year, and of functional impairment at 2 years (OR 1.98, (95% CI: 1.08 to 3.62 and OR: 2.36, (95% CI: 1.17 to 4.67), respectively). Conclusions: Early arthritis patients whose treatment adhered to the 2007 EULAR recommendations seemed to benefit from such treatment in terms of risk of clinical and radiographic progression. Using a propensity score of being treated according to recommendations in observational studies may be useful in assessing the potential impact of these recommendations on outcome. Copyright Article author (or their employer) 2012.


Dechartres A.,French Institute of Health and Medical Research | Dechartres A.,Center dEpidemiologie Clinique | Dechartres A.,University of Paris Descartes | Altman D.G.,Center for Statistics in Medicine | And 11 more authors.
JAMA - Journal of the American Medical Association | Year: 2014

IMPORTANCE: A persistent dilemma when performing meta-analyses is whether all available trials should be included in the meta-analysis. OBJECTIVES: To compare treatment outcomes estimated by meta-analysis of all trials and several alternative analytic strategies: single most precise trial (ie, trial with the narrowest confidence interval), meta-analysis restricted to the 25% largest trials, limit meta-analysis (a meta-analysis model adjusted for small-study effect), and meta-analysis restricted to trials at low overall risk of bias. DATA SOURCES: One hundred sixty-three meta-analyses published between 2008 and 2010 in high-impact-factor journals and between 2011 and 2013 in the Cochrane Database of Systematic Reviews: 92 (705 randomized clinical trials [RCTs]) with subjective outcomes and 71 (535 RCTs) with objective outcomes. DATA SYNTHESIS: For each meta-analysis, the difference in treatment outcomes between meta-analysis of all trials and each alternative strategy, expressed as a ratio of odds ratios (ROR), was assessed considering the dependency between strategies. A difference greater than 30% was considered substantial. RORs were combined by random-effects meta-analysis models to obtain an average difference across the sample. An ROR greater than 1 indicates larger treatment outcomes with meta-analysis of all trials. Subjective and objective outcomes were analyzed separately. RESULTS: Treatment outcomes were larger in the meta-analysis of all trials than in the single most precise trial (combined ROR, 1.13 [95%CI, 1.07-1.19]) for subjective outcomes and 1.03 (95%CI, 1.01-1.05) for objective outcomes). The difference in treatment outcomes between these strategies was substantial in 47 of 92 (51%) meta-analyses of subjective outcomes (meta-analysis of all trials showing larger outcomes in 40/47) and in 28 of 71 (39%) meta-analyses of objective outcomes (meta-analysis of all trials showing larger outcomes in 21/28). The combined ROR for subjective and objective outcomes was, respectively, 1.08 (95%CI, 1.04-1.13) and 1.03 (95%CI, 1.00-1.06) when comparing meta-analysis of all trials and meta-analysis of the 25% largest trials, 1.17 (95%CI, 1.11-1.22) and 1.13 (95%CI, 0.82-1.55) when comparing meta-analysis of all trials and limit meta-analysis, and 0.94 (95%CI, 0.86-1.04) and 1.03 (95%CI, 1.00-1.06) when comparing meta-analysis of all trials and meta-analysis restricted to trials at low risk of bias. CONCLUSIONS AND RELEVANCE: Estimation of treatment outcomes in meta-analyses differs depending on the strategy used. This instability in findings can result in major alterations in the conclusions derived from the analysis and underlines the need for systematic sensitivity analyses.


Lonjon G.,French Institute of Health and Medical Research | Boutron I.,French Institute of Health and Medical Research | Boutron I.,Center dEpidemiologie Clinique | Boutron I.,University of Paris Descartes | And 14 more authors.
Annals of Surgery | Year: 2014

OBJECTIVE: We aimed to compare treatment effect estimates from NRSs with PS analysis and RCTs of surgery. BACKGROUND: Evaluating a surgical procedure in randomized controlled trials (RCTs) is challenging. Nonrandomized studies (NRSs) involving use of propensity score (PS) analysis to limit bias are of increasing interest. DESIGN: Meta-epidemiological study. METHODS: We systematically searched MEDLINE via PubMed for all prospective NRSs with PS analysis evaluating a surgical procedure. Related RCTs, addressing the same clinical questions, were systematically retrieved. Our primary outcome of interest was all-cause mortality. We also selected 1 subjective outcome. We calculated the summary odds ratios (OR) for each study design, the ratio of OR (ROR) between the designs and the summary ROR across clinical questions. An ROR < 1 indicated that the experimental intervention is more favorable in NRSs with PS analysis than RCTs. RESULTS: We retrieved 70 reports of NRSs with PS analysis and 94 related RCTs evaluating 31 clinical questions, of which 22 assessed all-cause mortality and 26 a subjective outcome. The combined ROR for all-cause mortality was 0.83 (95% confidence interval: 0.65-1.04). For subjective outcomes, the combined ROR was 1.07 (0.87-1.33). CONCLUSIONS: There was no statistically significant difference in treatment effect between NRSs with PS analysis and RCTs. Prospective NRSs with suitable and careful PS analysis can be relied upon as evidence when RCTs are not possible. Copyright © 2013 by Lippincott Williams & Wilkins.


Nguyen Y.-L.,Cochin Hospital | Nguyen Y.-L.,French Institute of Health and Medical Research | Wallace D.J.,University of Pittsburgh | Yordanov Y.,French Institute of Health and Medical Research | And 11 more authors.
Chest | Year: 2015

OBJECTIVE: The purpose of this study was to systematically review the research on volume and outcome relationships in critical care. METHODS: From January 1, 2001, to April 30, 2014, MEDLINE and EMBASE were searched for studies assessing the relationship between admission volume and clinical outcomes in critical illness. Bibliographies were reviewed to identify other articles of interest, and experts were contacted about missing or unpublished studies. Of 127 studies reviewed, 46 met inclusion criteria, covering seven clinical conditions. Two investigators independently reviewed each article using a standardized form to abstract information on key study characteristics and results. RESULTS: Overall, 29 of the studies (63%) reported a statistically significant association between higher admission volume and improved outcomes. The magnitude of the association (mortality OR between the lowest vs highest stratum of volume centers), as well as the thresholds used to characterize high volume, varied across clinical conditions. Critically ill patients with cardiovascular (n = 7, OR = 1.49 [1.11-2.00]), respiratory (n = 12, OR = 1.20 [1.04-1.38]), severe sepsis (n = 4, OR = 1.17 [1.03-1.33]), hepato-GI (n = 3, OR = 1.30 [1.08-1.78]), neurologic (n = 3, OR = 1.38 [1.22-1.57]), and postoperative admission diagnoses (n = 3, OR = 2.95 [1.05-8.30]) were more likely to benefit from admission to higher-volume centers compared with lower-volume centers. Studies that controlled for ICU or hospital organizational factors were less likely to find a significant volume-outcome relationship than studies that did not control for these factors. CONCLUSIONS: Critically ill patients generally benefit from care in high-volume centers, with more substantial benefits in selected high-risk conditions. This relationship may in part be mediated by specific ICU and hospital organizational factors. © 2015 AMERICAN COLLEGE OF CHEST PHYSICIANS.


Riveros C.,French Institute of Health and Medical Research | Riveros C.,University of Paris Descartes | Riveros C.,Center dEpidemiologie Clinique | Dechartres A.,French Institute of Health and Medical Research | And 15 more authors.
PLoS Medicine | Year: 2013

Background:The US Food and Drug Administration Amendments Act requires results from clinical trials of Food and Drug Administration-approved drugs to be posted at ClinicalTrials.gov within 1 y after trial completion. We compared the timing and completeness of results of drug trials posted at ClinicalTrials.gov and published in journals.Methods and Findings:We searched ClinicalTrials.gov on March 27, 2012, for randomized controlled trials of drugs with posted results. For a random sample of these trials, we searched PubMed for corresponding publications. Data were extracted independently from ClinicalTrials.gov and from the published articles for trials with results both posted and published. We assessed the time to first public posting or publishing of results and compared the completeness of results posted at ClinicalTrials.gov versus published in journal articles. Completeness was defined as the reporting of all key elements, according to three experts, for the flow of participants, efficacy results, adverse events, and serious adverse events (e.g., for adverse events, reporting of the number of adverse events per arm, without restriction to statistically significant differences between arms for all randomized patients or for those who received at least one treatment dose).From the 600 trials with results posted at ClinicalTrials.gov, we randomly sampled 50% (n = 297) had no corresponding published article. For trials with both posted and published results (n = 202), the median time between primary completion date and first results publicly posted was 19 mo (first quartile = 14, third quartile = 30 mo), and the median time between primary completion date and journal publication was 21 mo (first quartile = 14, third quartile = 28 mo). Reporting was significantly more complete at ClinicalTrials.gov than in the published article for the flow of participants (64% versus 48% of trials, p<0.001), efficacy results (79% versus 69%, p = 0.02), adverse events (73% versus 45%, p<0.001), and serious adverse events (99% versus 63%, p<0.001).The main study limitation was that we considered only the publication describing the results for the primary outcomes.Conclusions:Our results highlight the need to search ClinicalTrials.gov for both unpublished and published trials. Trial results, especially serious adverse events, are more completely reported at ClinicalTrials.gov than in the published article.Please see later in the article for the Editors' Summary. © 2013 Riveros et al.


Bafeta A.,French Institute of Health and Medical Research | Trinquart L.,French Institute of Health and Medical Research | Trinquart L.,University of Paris Descartes | Trinquart L.,French Cochrane Center | And 7 more authors.
BMJ (Online) | Year: 2014

Objective: To examine how the results of network meta-analyses are reported. Design: Methodological systematic review of published reports of network meta-analyses. Data sources: Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Medline, and Embase, searched from inception to 12 July 2012. Study selection: All network meta-analyses comparing the clinical efficacy of three or more interventions in randomised controlled trials were included, excluding meta-analyses with an open loop network of three interventions. Data extraction and synthesis: The reporting of the network and results was assessed. A composite outcome included the description of the network (number of interventions, direct comparisons, and randomised controlled trials and patients for each comparison) and the reporting of effect sizes derived from direct evidence, indirect evidence, and the network meta-analysis. Results: 121 network meta-analyses (55 published in general journals; 48 funded by at least one private source) were included. The network and its geometry (network graph) were not reported in 100 (83%) articles. The effect sizes derived from direct evidence, indirect evidence, and the network meta-analysis were not reported in 48 (40%), 108 (89%), and 43 (36%) articles, respectively. In 52 reports that ranked interventions, 43 did not report the uncertainty in ranking. Overall, 119 (98%) reports of network meta-analyses did not give a description of the network or effect sizes from direct evidence, indirect evidence, and the network meta-analysis. This finding did not differ by journal type or funding source. Conclusions: The results of network meta-analyses are heterogeneously reported. Development of reporting guidelines to assist authors in writing and readers in critically appraising reports of network meta-analyses is timely.


Gillaizeau F.,French Cochrane Center
The Cochrane database of systematic reviews | Year: 2013

Maintaining therapeutic concentrations of drugs with a narrow therapeutic window is a complex task. Several computer systems have been to identify specific therapeutic areas where the computerized advice on drug dosage was beneficial compared with routine care:1. it increased target peak serum concentrations (standardized mean difference (SMD) 0.79, 95% CI 0.46 to 1.13) and the proportion of people with plasma drug concentrations within the therapeutic range after two days (pooled risk ratio (RR) 4.44, 95% CI 1.94 to 10.13) for aminoglycoside antibiotics;2. it led to a physiological parameter more often within the desired range for oral anticoagulants (SMD for percentage of time spent in target international normalized ratio +0.19, 95% CI 0.06 to 0.33) and insulin (SMD for percentage of time in target glucose range: +1.27, 95% CI 0.56 to 1.98);3. it decreased the time to achieve stabilization for oral anticoagulants (SMD -0.56, 95% CI -1.07 to -0.04);4. it decreased the thromboembolism events (rate ratio 0.68, 95% CI 0.49 to 0.94) and tended to decrease bleeding events for anticoagulants although the difference was not significant (rate ratio 0.81, 95% CI 0.60 to 1.08). It tended to decrease unwanted effects for aminoglycoside antibiotics (nephrotoxicity: RR 0.67, 95% CI 0.42 to 1.06) and anti-rejection drugs (cytomegalovirus infections: RR 0.90, 95% CI 0.58 to 1.40);5. it tended to reduce the length of time spent in the hospital although the difference was not significant (SMD -0.15, 95% CI -0.33 to 0.02) and to achieve comparable or better cost-effectiveness ratios than usual care;6. there was no evidence of differences in mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, anti-rejection drugs and antidepressants.For all outcomes, statistical heterogeneity quantified by I(2) statistics was moderate to high. This review update suggests that computerized advice for drug dosage has some benefits: it increases the serum concentrations for aminoglycoside antibiotics and improves the proportion of people for which the plasma drug is within the therapeutic range for aminoglycoside antibiotics.It leads to a physiological parameter more often within the desired range for oral anticoagulants and insulin. It decreases the time to achieve stabilization for oral anticoagulants. It tends to decrease unwanted effects for aminoglycoside antibiotics and anti-rejection drugs, and it significantly decreases thromboembolism events for anticoagulants. It tends to reduce the length of hospital stay compared with routine care while comparable or better cost-effectiveness ratios were achieved.However, there was no evidence that decision support had an effect on mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, anti-rejection drugs and antidepressants. In addition, there was no evidence to suggest that some decision support technical features (such as its integration into a computer physician order entry system) or aspects of organization of care (such as the setting) could optimize the effect of computerized advice.Taking into account the high risk of bias of, and high heterogeneity between, studies, these results must be interpreted with caution.


Dechartres A.,French Institute of Health and Medical Research | Dechartres A.,Center pidemiologie Clinique | Dechartres A.,University of Paris Descartes | Trinquart L.,French Institute of Health and Medical Research | And 10 more authors.
BMJ (Online) | Year: 2013

Objective To assess the influence of trial sample size on treatment effect estimates within meta-analyses. Design Meta-epidemiological study. Data sources 93 meta-analyses (735 randomised controlled trials) assessing therapeutic interventions with binary outcomes, published in the 10 leading journals of each medical subject category of the Journal Citation Reports or in the Cochrane Database of Systematic Reviews. Data extraction Sample size, outcome data, and risk of bias extracted from each trial. Data synthesis Trials within each meta-analysis were sorted by their sample size: using quarters within each meta-analysis (from quarter 1 with 25% of the smallest trials, to quarter 4 with 25% of the largest trials), and using size groups across meta-analyses (ranging from <50 to ≥1000 patients). Treatment effects were compared within each meta-analysis between quarters or between size groups by average ratios of odds ratios (where a ratio of odds ratios less than 1 indicates larger effects in smaller trials). Results Treatment effect estimates were significantly larger in smaller trials, regardless of sample size. Compared with quarter 4 (which included the largest trials), treatment effects were, on average, 32% larger in trials in quarter 1 (which included the smallest trials; ratio of odds ratios 0.68, 95% confidence interval 0.57 to 0.82), 17% larger in trials in quarter 2 (0.83, 0.75 to 0.91), and 12% larger in trials in quarter 3 (0.88, 0.82 to 0.95). Similar results were obtained when comparing treatment effect estimates between different size groups. Compared with trials of 1000 patients or more, treatment effects were, on average, 48% larger in trials with fewer than 50 patients (0.52, 0.41 to 0.66) and 10% larger in trials with 500-999 patients (0.90, 0.82 to 1.00). Conclusions Treatment effect estimates differed within meta-analyses solely based on trial sample size, with stronger effect estimates seen in small to moderately sized trials than in the largest trials.


Sidorkiewicz S.,University of Paris Descartes | Sidorkiewicz S.,University of Paris Pantheon Sorbonne | Tran V.-T.,University of Paris Pantheon Sorbonne | Tran V.-T.,University Paris Diderot | And 5 more authors.
Annals of Family Medicine | Year: 2016

PURPOSE Among patients on long-term medical therapy, we compared (1) patient and physician assessments of drug adherence and of drug importance and (2) drug adherence reported by patients with drug importance as assessed by their physicians. METHODS We recruited to the study patients receiving at least 1 long-term drug treatment from both hospital and ambulatory settings in France. We compared drug adherence reported by patients and drug importance assessed by physicians using Spearman correlation coefficients. Reasons for nonadherence were collected with open-ended questions and classified as intentional or unintentional. RESULTS Between April and August 2014, we recruited 128 patients taking 498 drugs. Patients and physicians showed only weak agreement in their assessments of drug adherence (r = -0.25; 95% CI, -0.37 to -0.11) and drug importance (r = 0.07; 95% CI, 0.00 to 0.13). We did not find any correlation between physician-assessed drug importance and patient-reported drug adherence (r = -0.04; 95% CI, -0.14 to 0.06). In all, 94 (18.9%) of the drugs that physicians considered important were not correctly taken by patients. Patients intentionally did not adhere to 26 (48.1%) of the drugs for which they reported reasons for nonadherence. CONCLUSIONS We found substantial discordance between patient and physician evaluations of drug adherence and drug importance. Nearly 20% of drugs considered important by physicians were not correctly taken by patients. These findings highlight the need for better patient-physician collaboration in drug treatment. © 2016, Annals of Family Medicine, Inc. All rights reserved.

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