Agreement of treatment effects from observational studies and randomized controlled trials evaluating hydroxychloroquine, lopinavir-ritonavir, or dexamethasone for covid-19: meta-epidemiological study

Clinical evaluation of the BioFire Respiratory Pathogen Panel for the guidance of empirical antimicrobial therapy in critically ill patients with hospital-acquired pneumonia: A multicenter, quality improvement project

BACKGROUND

We aimed to determine whether implementing antimicrobial stewardship based on multiplex bacterial PCR examination of respiratory fluid can enhance outcomes of critically ill patients with hospital-acquired pneumonia (HAP).

METHODS

We conducted a quality improvement study in two hospitals in France. Adult patients requiring invasive mechanical ventilation with a diagnosis of HAP were included. In the pre-intervention period (August 2019 to April 2020), antimicrobial therapy followed European guidelines. In the «intervention» phase (June 2020 to October 2021), treatment followed a multiplex PCR-guided protocol. The primary endpoint was a composite endpoint made of mortality on day 28, clinical cure between days 7 and 10, and duration of invasive mechanical ventilation on day 28. The primary outcome was analyzed with a DOOR strategy.

RESULTS

A total of 443 patients were included in 3 ICUs from 2 hospitals (220 pre-intervention; 223 intervention). No difference in the ranking of the primary composite outcome was found (DOOR: 50.3%; 95%CI, 49.9%-50.8%). The number of invasive mechanical ventilation-free days at day 28 was 10.0 [0.0; 19.0] in the baseline period and 9.0 [0.0; 20.0] days during the intervention period (p = 0.95). The time-to-efficient antimicrobial treatment was 0.43 ± 1.29 days before versus 0.55 ± 1.13 days after the intervention (p = 0.56).

CONCLUSION

Implementation of Rapid Multiplex PCR to guide empirical antimicrobial therapy for critically ill patients with HAP was not associated with better outcomes. However, adherence to stewardship was low, and the study may have had limited power to detect a clinically important difference.

Rapid review: A review of methods and recommendations based on current evidence

Rapid review (RR) could accelerate the traditional systematic review (SR) process by simplifying or omitting steps using various shortcuts. With the increasing popularity of RR, numerous shortcuts had emerged, but there was no consensus on how to choose the most appropriate ones. This study conducted a literature search in PubMed from inception to December 21, 2023, using terms such as "rapid review" "rapid assessment" "rapid systematic review" and "rapid evaluation". We also scanned the reference lists and performed citation tracking of included impact studies to obtain more included studies. We conducted a narrative synthesis of all RR approaches, shortcuts and studies assessing their effectiveness at each stage of RRs. Based on the current evidence, we provided recommendations on utilizing certain shortcuts in RRs. Ultimately, we identified 185 studies focusing on summarizing RR approaches and shortcuts, or evaluating their impact. There was relatively sufficient evidence to support the use of the following shortcuts in RRs: limiting studies to those published in English-language; conducting abbreviated database searches (e.g., only searching PubMed/MEDLINE, Embase, and CENTRAL); omitting retrieval of grey literature; restricting the search timeframe to the recent 20 years for medical intervention and the recent 15 years for reviewing diagnostic test accuracy; conducting a single screening by an experienced screener. To some extent, the above shortcuts were also applicable to SRs. This study provided a reference for future RR researchers in selecting shortcuts, and it also presented a potential research topic for methodologists.

Design differences and variation in results between randomised trials and non-randomised emulations: meta-analysis of RCT-DUPLICATE data

Objective

To explore how design emulation and population differences relate to variation in results between randomised controlled trials (RCT) and non-randomised real world evidence (RWE) studies, based on the RCT-DUPLICATE initiative (Randomised, Controlled Trials Duplicated Using Prospective Longitudinal Insurance Claims: Applying Techniques of Epidemiology).

Design

Meta-analysis of RCT-DUPLICATE data.

Data sources

Trials included in RCT-DUPLICATE, a demonstration project that emulated 32 randomised controlled trials using three real world data sources: Optum Clinformatics Data Mart, 2004-19; IBM MarketScan, 2003-17; and subsets of Medicare parts A, B, and D, 2009-17.

Eligibility criteria for selecting studies

Trials where the primary analysis resulted in a hazard ratio; 29 RCT-RWE study pairs from RCT-DUPLICATE.

Results

Differences and variation in effect sizes between the results from randomised controlled trials and real world evidence studies were investigated. Most of the heterogeneity in effect estimates between the RCT-RWE study pairs in this sample could be explained by three emulation differences in the meta-regression model: treatment started in hospital (which does not appear in health insurance claims data), discontinuation of some baseline treatments at randomisation (which would have been an unusual care decision in clinical practice), and delayed onset of drug effects (which would be under-reported in real world clinical practice because of the relatively short persistence of the treatment). Adding the three emulation differences to the meta-regression reduced heterogeneity from 1.9 to almost 1 (absence of heterogeneity).

Conclusions

This analysis suggests that a substantial proportion of the observed variation between results from randomised controlled trials and real world evidence studies can be attributed to differences in design emulation.

Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials: a meta-epidemiological study

BACKGROUND

Researchers and decision-makers often use evidence from randomised controlled trials (RCTs) to determine the efficacy or effectiveness of a treatment or intervention. Studies with observational designs are often used to measure the effectiveness of an intervention in 'real world' scenarios. Numerous study designs and their modifications (including both randomised and observational designs) are used for comparative effectiveness research in an attempt to give an unbiased estimate of whether one treatment is more effective or safer than another for a particular population. An up-to-date systematic analysis is needed to identify differences in effect estimates from RCTs and observational studies. This updated review summarises the results of methodological reviews that compared the effect estimates of observational studies with RCTs from evidence syntheses that addressed the same health research question.

OBJECTIVES

To assess and compare synthesised effect estimates by study type, contrasting RCTs with observational studies. To explore factors that might explain differences in synthesised effect estimates from RCTs versus observational studies (e.g. heterogeneity, type of observational study design, type of intervention, and use of propensity score adjustment). To identify gaps in the existing research comparing effect estimates across different study types.

SEARCH METHODS

We searched MEDLINE, the Cochrane Database of Systematic Reviews, Web of Science databases, and Epistemonikos to May 2022. We checked references, conducted citation searches, and contacted review authors to identify additional reviews.

SELECTION CRITERIA

We included systematic methodological reviews that compared quantitative effect estimates measuring the efficacy or effectiveness of interventions tested in RCTs versus in observational studies. The included reviews compared RCTs to observational studies (including retrospective and prospective cohort, case-control and cross-sectional designs). Reviews were not eligible if they compared RCTs with studies that had used some form of concurrent allocation.

DATA COLLECTION AND ANALYSIS

Using results from observational studies as the reference group, we examined the relative summary effect estimates (risk ratios (RRs), odds ratios (ORs), hazard ratios (HRs), mean differences (MDs), and standardised mean differences (SMDs)) to evaluate whether there was a relatively larger or smaller effect in the ratio of odds ratios (ROR) or ratio of risk ratios (RRR), ratio of hazard ratios (RHR), and difference in (standardised) mean differences (D(S)MD). If an included review did not provide an estimate comparing results from RCTs with observational studies, we generated one by pooling the estimates for observational studies and RCTs, respectively. Across all reviews, we synthesised these ratios to produce a pooled ratio of ratios comparing effect estimates from RCTs with those from observational studies. In overviews of reviews, we estimated the ROR or RRR for each overview using observational studies as the reference category. We appraised the risk of bias in the included reviews (using nine criteria in total). To receive an overall low risk of bias rating, an included review needed: explicit criteria for study selection, a complete sample of studies, and to have controlled for study methodological differences and study heterogeneity. We assessed reviews/overviews not meeting these four criteria as having an overall high risk of bias. We assessed the certainty of the evidence, consisting of multiple evidence syntheses, with the GRADE approach.

MAIN RESULTS

We included 39 systematic reviews and eight overviews of reviews, for a total of 47. Thirty-four of these contributed data to our primary analysis. Based on the available data, we found that the reviews/overviews included 2869 RCTs involving 3,882,115 participants, and 3924 observational studies with 19,499,970 participants. We rated 11 reviews/overviews as having an overall low risk of bias, and 36 as having an unclear or high risk of bias. Our main concerns with the included reviews/overviews were that some did not assess the quality of their included studies, and some failed to account appropriately for differences between study designs - for example, they conducted aggregate analyses of all observational studies rather than separate analyses of cohort and case-control studies. When pooling RORs and RRRs, the ratio of ratios indicated no difference or a very small difference between the effect estimates from RCTs versus from observational studies (ratio of ratios 1.08, 95% confidence interval (CI) 1.01 to 1.15). We rated the certainty of the evidence as low. Twenty-three of 34 reviews reported effect estimates of RCTs and observational studies that were on average in agreement. In a number of subgroup analyses, small differences in the effect estimates were detected: - pharmaceutical interventions only (ratio of ratios 1.12, 95% CI 1.04 to 1.21); - RCTs and observational studies with substantial or high heterogeneity; that is, I2 ≥ 50% (ratio of ratios 1.11, 95% CI 1.04 to 1.18); - no use (ratio of ratios 1.07, 95% CI 1.03 to 1.11) or unclear use (ratio of ratios 1.13, 95% CI 1.03 to 1.25) of propensity score adjustment in observational studies; and - observational studies without further specification of the study design (ratio of ratios 1.06, 95% CI 0.96 to 1.18). We detected no clear difference in other subgroup analyses.

AUTHORS' CONCLUSIONS

We found no difference or a very small difference between effect estimates from RCTs and observational studies. These findings are largely consistent with findings from recently published research. Factors other than study design need to be considered when exploring reasons for a lack of agreement between results of RCTs and observational studies, such as differences in the population, intervention, comparator, and outcomes investigated in the respective studies. Our results underscore that it is important for review authors to consider not only study design, but the level of heterogeneity in meta-analyses of RCTs or observational studies. A better understanding is needed of how these factors might yield estimates reflective of true effectiveness.

Totality of evidence of the effectiveness of repurposed therapies for COVID-19: Can we use real-world studies alongside randomized controlled trials?

Rapid and robust strategies to evaluate the efficacy and effectiveness of novel and existing pharmacotherapeutic interventions (repurposed treatments) in future pandemics are required. Observational "real-world studies" (RWS) can report more quickly than randomized controlled trials (RCTs) and would have value were they to yield reliable results. Both RCTs and RWS were deployed during the coronavirus disease 2019 (COVID-19) pandemic. Comparing results between them offers a unique opportunity to determine the potential value and contribution of each. A learning review of these parallel evidence channels in COVID-19, based on quantitative modeling, can help improve speed and reliability in the evaluation of repurposed therapeutics in a future pandemic. Analysis of all-cause mortality data from 249 observational RWS and RCTs across eight treatment regimens for COVID-19 showed that RWS yield more heterogeneous results, and generally overestimate the effect size subsequently seen in RCTs. This is explained in part by a few study factors: the presence of RWS that are imbalanced for age, gender, and disease severity, and those reporting mortality at 2 weeks or less. Smaller studies of either type contributed negligibly. Analysis of evidence generated sequentially during the pandemic indicated that larger RCTs drive our ability to make conclusive decisions regarding clinical benefit of each treatment, with limited inference drawn from RWS. These results suggest that when evaluating therapies in future pandemics, (1) large RCTs, especially platform studies, be deployed early; (2) any RWS should be large and should have adequate matching of known confounders and long follow-up; (3) reporting standards and data standards for primary endpoints, explanatory factors, and key subgroups should be improved; in addition, (4) appropriate incentives should be in place to enable access to patient-level data; and (5) an overall aggregate view of all available results should be available at any given time.

Evaluating the impact of including non-randomised studies of interventions in meta-analysis of randomised controlled trials: a protocol for a meta-epidemiological study

INTRODUCTION

Although interest in including non-randomised studies of interventions (NRSIs) in meta-analysis of randomised controlled trials (RCTs) is growing, estimates of effectiveness obtained from NRSIs are vulnerable to greater bias than RCTs. The objectives of this study are to: (1) explore how NRSIs can be integrated into a meta-analysis of RCTs; (2) assess concordance of the evidence from non-randomised and randomised trials and explore factors associated with agreement; and (3) investigate the impact on estimates of pooled bodies of evidence when NRSIs are included.

METHODS AND ANALYSIS

We will conduct a systematic survey of 210 systematic reviews that include both RCTs and NRSIs, published from 2017 to 2022. We will randomly select reviews, stratified in a 1:1 ratio by Core vs non-Core clinical journals, as defined by the National Library of Medicine. Teams of paired reviewers will independently determine eligibility and abstract data using standardised, pilot-tested forms. The concordance of the evidence will be assessed by exploring agreement in the relative effect reported by NRSIs and RCT addressing the same clinical question, defined as similarity of the population, intervention/exposure, control and outcomes. We will conduct univariable and multivariable logistic regression analyses to examine the association of prespecified study characteristics with agreement in the estimates between NRSIs and RCTs. We will calculate the ratio of the relative effect estimate from NRSIs over that from RCTs, along with the corresponding 95% CI. We will use a bias-corrected meta-analysis model to investigate the influence on pooled estimates when NRSIs are included in the evidence synthesis.

ETHICS AND DISSEMINATION

Ethics approval is not required. The findings of this study will be disseminated through peer-reviewed publications, conference presentations and condensed summaries for clinicians, health policymakers and guideline developers regarding the design, conduct, analysis, and interpretation of meta-analysis that integrate RCTs and NRSIs.

DESIGN DIFFERENCES EXPLAIN VARIATION IN RESULTS BETWEEN RANDOMIZED TRIALS AND THEIR NON-RANDOMIZED EMULATIONS

Objectives

While randomized controlled trials (RCTs) are considered a standard for evidence on the efficacy of medical treatments, non-randomized real-world evidence (RWE) studies using data from health insurance claims or electronic health records can provide important complementary evidence. The use of RWE to inform decision-making has been questioned because of concerns regarding confounding in non-randomized studies and the use of secondary data. RCT-DUPLICATE was a demonstration project that emulated the design of 32 RCTs with non-randomized RWE studies. We sought to explore how emulation differences relate to variation in results between the RCT-RWE study pairs.

Methods

We include all RCT-RWE study pairs from RCT-DUPLICATE where the measure of effect was a hazard ratio and use exploratory meta-regression methods to explain differences and variation in the effect sizes between the results from the RCT and the RWE study. The considered explanatory variables are related to design and population differences.

Results

Most of the observed variation in effect estimates between RCT-RWE study pairs in this sample could be explained by three emulation differences in the meta-regression model: (i) in-hospital start of treatment (not observed in claims data), (ii) discontinuation of certain baseline therapies at randomization (not part of clinical practice), (iii) delayed onset of drug effects (missed by short medication persistence in clinical practice).

Conclusions

This analysis suggests that a substantial proportion of the observed variation between results from RCTs and RWE studies can be attributed to design emulation differences. (238 words).

Inclusion of nonrandomized studies of interventions in systematic reviews of interventions: updated guidance from the Agency for Health Care Research and Quality Effective Health Care program

OBJECTIVES

We developed guidance to inform decisions regarding the inclusion of nonrandomized studies of interventions (NRSIs) in systematic reviews (SRs) of the effects of interventions.

STUDY DESIGN AND SETTING

The guidance workgroup comprised SR experts and used an informal consensus generation method.

RESULTS

Instead of recommending NRSI inclusion only if randomized controlled trials (RCTs) are insufficient to address the SR key question, different topics may require different decisions regarding NRSI inclusion. We identified important considerations to inform such decisions from topic refinement through protocol development. During topic scoping and refinement, considerations were related to the clinical decisional dilemma, adequacy of RCTs to address the key questions, risk of bias in NRSIs, and the extent to which NRSIs are likely to complement RCTs. When NRSIs are included, during SR team formation, familiarity with topic-specific data sources and advanced analytic methods for NRSIs should be considered. During protocol development, the decision regarding NRSI inclusion or exclusion should be justified, and potential implications explained. When NRSIs are included, the protocol should describe the processes for synthesizing evidence from RCTs and NRSIs and determining the overall strength of evidence.

CONCLUSION

We identified specific considerations for decisions regarding NRSI inclusion in SRs and highlight the importance of flexibility and transparency.