Critical reading of systematic review articles.

A systematic review provides an overview of primary studies investigating a given research question, e.g., the effect of a certain treatment. Individual study results are sometimes synthesised in a meta-analysis. A critical reader should consider whether the systematic review is relevant and reliable, e.g., does it follow a protocol, address the risk of bias, and consider potential heterogeneity. PRISMA 2020 guideline recommends a minimum set of items that should be reported in a systematic review article, and AMSTAR 2 and ROBIS are tools for critical appraisal of systematic reviews.

Commercial funding and estimated intervention effects in randomized clinical trials: Systematic review of meta-epidemiological studies.

We investigated to which degree commercial funding is associated with estimated intervention effects in randomized trials. We included meta-epidemiological studies with published data on the association between commercial funding and results or conclusions of randomized trials. We searched five databases and other sources. We selected one result per meta-epidemiological study, preferably unadjusted ratio of odds ratios (ROR), for example, odds ratio(commercial funding)/odds ratio(noncommercial funding). We pooled RORs in random-effects meta-analyses (ROR <1 indicated exaggerated intervention effects in commercially funded trials), subgrouped (preplanned) by study aim: commercial funding per se versus risk of commercial funder influence. We included eight meta-epidemiological studies (264 meta-analyses, 2725 trials). The summary ROR was 0.95 (95% confidence interval 0.85-1.06). Subgroup analysis revealed a difference (p = 0.02) between studies of commercial funding per se, ROR 1.06 (0.95-1.17) and studies of risk of commercial funder influence, ROR 0.88 (0.79-0.97). In conclusion, we found no statistically significant association between commercial funding and estimated intervention effects when combining studies of commercial funding per se and studies of risk of commercial funder influence. A preplanned subgroup analysis indicated that trials with high risk of commercial funder influence exaggerated intervention effects by 12% (21%-3%), on average. Our results differ from previous theoretical considerations and findings from methodological studies and therefore call for confirmation. We suggest it is prudent to interpret results from commercially funded trials with caution, especially when there is a risk that the funder had direct influence on trial design, conduct, analysis, or reporting.

Combining meta-epidemiological study datasets on commercial funding of randomised clinical trials: Database, methods, and descriptive results of the COMFIT study.

Randomised trials are often funded by commercial companies and methodological studies support a widely held suspicion that commercial funding may influence trial results and conclusions. However, these studies often have a risk of confounding and reporting bias. The risk of confounding is markedly reduced in meta-epidemiological studies that compare fairly similar trials within meta-analyses, and risk of reporting bias is reduced with access to unpublished data. Therefore, we initiated the COMmercial Funding In Trials (COMFIT) study aimed at investigating the impact of commercial funding on estimated intervention effects in randomised clinical trials based on a consortium of researchers who agreed to share meta-epidemiological study datasets with information on meta-analyses and trials included in meta-epidemiological studies. Here, we describe the COMFIT study, its database, and descriptive results. We included meta-epidemiological studies with published or unpublished data on trial funding source and results or conclusions. We searched five bibliographic databases and other sources. We invited authors of eligible meta-epidemiological studies to join the COMFIT consortium and to share data. The final construction of the COMFIT database involves checking data quality, identifying trial references, harmonising variable categories, and removing non-informative meta-analyses as well as correlated meta-analyses and trial results. We included data from 17 meta-epidemiological studies, covering 728 meta-analyses and 6841 trials. Seven studies (405 meta-analyses, 3272 trials) had not published analyses on the impact of commercial funding, but shared unpublished data on funding source. On this basis, we initiated the construction of a combined database. Once completed, the database will enable comprehensive analyses of the impact of commercial funding on trial results and conclusions with increased statistical power and a markedly reduced risk of confounding and reporting bias.

Randomized clinical trials with run-in periods: frequency, characteristics and reporting.

BACKGROUND: Run-in periods are occasionally used in randomized clinical trials to exclude patients after inclusion, but before randomization. In theory, run-in periods increase the probability of detecting a potential treatment effect, at the cost of possibly affecting external and internal validity. Adequate reporting of exclusions during the run-in period is a prerequisite for judging the risk of compromised validity. Our study aims were to assess the proportion of randomized clinical trials with run-in periods, to characterize such trials and the types of run-in periods and to assess their reporting. MATERIALS AND METHODS: This was an observational study of 470 PubMed-indexed randomized controlled trial publications from 2014. We compared trials with and without run-in periods, described the types of run-in periods and evaluated the completeness of their reporting by noting whether publications stated the number of excluded patients, reasons for exclusion and baseline characteristics of the excluded patients. RESULTS: Twenty-five trials reported a run-in period (5%). These were larger than other trials (median number of randomized patients 217 vs 90, P=0.01) and more commonly industry trials (11% vs 3%, P<0.01). The run-in procedures varied in design and purpose. In 23 out of 25 trials (88%), the run-in period was incompletely reported, mostly due to missing baseline characteristics. CONCLUSION: Approximately 1 in 20 trials used run-in periods, though much more frequently in industry trials. Reporting of the run-in period was often incomplete, precluding a meaningful assessment of the impact of the run-in period on the validity of trial results. We suggest that current trials with run-in periods are interpreted with caution and that updates of reporting guidelines for randomized trials address the issue.