Disability reduction following a lumbar stabilization exercise program for low back pain: large vs. small improvement subgroup analyses of physical and psychological variables

BACKGROUND

Little is known about why patients with low back pain (LBP) respond differently to treatment, and more specifically, to a lumbar stabilization exercise program. As a first step toward answering this question, the present study evaluates how subgroups of patients who demonstrate large and small clinical improvements differ in terms of physical and psychological changes during treatment.

METHODS

Participants (n = 110) performed the exercise program (clinical sessions and home exercises) over eight weeks, with 100 retained at six-month follow-up. Physical measures (lumbar segmental instability, motor control impairments, range of motion, trunk muscle endurance and physical performance tests) were collected twice (baseline, end of treatment), while psychological measures (fear-avoidance beliefs, pain catastrophizing, psychological distress, illness perceptions, outcome expectations) were collected at four time points (baseline, mid-treatment, end of treatment, follow-up). The participants were divided into three subgroups (large, moderate and small clinical improvements) based on the change of perceived disability scores. ANOVA for repeated measure compared well-contrasted subgroups (large vs. small improvement) at different times to test for SUBGROUP × TIME interactions.

RESULTS

Statistically significant interactions were observed for several physical and psychological measures. In all these interactions, the large- and small-improvement subgroups were equivalent at baseline, but the large-improvement subgroup showed more improvements over time compared to the small-improvement subgroup. For psychological measures only (fear-avoidance beliefs, pain catastrophizing, illness perceptions), between-group differences reached moderate to strong effect sizes, at the end of treatment and follow-up.

CONCLUSIONS

The large-improvement subgroup showed more improvement than the small-improvement subgroup with regard to physical factors typically targeted by this specific exercise program as well as for psychological factors that are known to influence clinical outcomes.

Severer air pollution, poorer cognitive function: Findings from 176,345 elders in Northwestern China

BACKGROUND

Limited evidence exists regarding the link between air pollution exposure and cognitive function in developing countries, particularly in areas with abundant natural sources of particulate matter.

OBJECTIVES

To investigate this association in a large representative sample of the elderly in northwestern China.

METHODS

We performed a cross-sectional study among 176,345 participants aged 60-100 years in northwestern China in 2020. A satellite-based spatiotemporal model was applied to assess three-year annual averages of particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM2.5), ≤ 10 µm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) at residential address. Poor cognitive function was assessed using the Mini-Mental State Examination (MMSE). Generalized linear mixed models were used to assess associations.

RESULTS

Compared with participants with the lowest quartiles of PM2.5, PM10, and O3 levels, those with the second, third, and highest quartiles of air pollutants consistently showed increased odds of poor cognitive function and decreased MMSE scores. The odds ratios of poor cognitive function associated with a 10 μg/m3 increment in PM2.5, PM10, and O3 were 1.26 (95 % confidence interval [CI]: 1.17, 1.36), 1.06 (95 %CI: 1.04, 1.08), and 2.76 (95 %CI: 2.11, 3.62), respectively. Subgroup analyses suggested stronger associations between air pollution exposures and poor cognitive function among participants who were younger, were non-Uyghur and were physically active.

CONCLUSION

Long-term exposures to PM2.5, PM10 and O3 were associated with poor cognitive function in elders. Our results suggest that reducing air pollution may alleviate the burden of poor cognitive function in the elderly.

Associations between urinary glyphosate and diabetes mellitus in the US general adult: a cross-sectional study from NHANES 2013-2016

Glyphosate-based herbicides (GBHs) are used extensively around the world and have become the leading agrochemicals. However, study about the association between glyphosate exposure and the risk of diabetes mellitus (DM) is scarce. This study used 4 years of NHANES data (2013-2016) to further investigate the association. A total of 2535 participants were enrolled in this cross-sectional study. The baseline information and urinary glyphosate levels in diabetic and non-diabetic groups were compared. Using multivariable logistic regression mode, we explored the association between both the continuous and categorical forms of urinary glyphosate and DM risk. Further subgroup analyses based on categorical covariates were also conducted. Urinary glyphosate levels were 0.42 ng/ml in participants with diabetes and 0.34 ng/ml in participants without diabetes (P < 0.05). As a continuous variable, ln-transformed urinary glyphosate was significantly associated with an increased risk of DM in the most adjusted model (OR 1.28, 95% CI 1.03-1.57). However, the association was not significant in the most adjusted categorical model (P > 0.05).In further subgroup analyses, the associations remained significant in several subgroups. This study provides new evidence that glyphosate exposure was associated with a higher risk of diabetes in the American general adult population.

Considerations for Subgroup Analyses in Cluster-Randomized Trials Based on Aggregated Individual-Level Predictors

In research assessing the effect of an intervention or exposure, a key secondary objective often involves assessing differential effects of this intervention or exposure in subgroups of interest; this is often referred to as assessing effect modification or heterogeneity of treatment effects (HTE). Observed HTE can have important implications for policy, including intervention strategies (e.g., will some patients benefit more from intervention than others?) and prioritizing resources (e.g., to reduce observed health disparities). Analysis of HTE is well understood in studies where the independent unit is an individual. In contrast, in studies where the independent unit is a cluster (e.g., a hospital or school) and a cluster-level outcome is used in the analysis, it is less well understood how to proceed if the HTE analysis of interest involves an individual-level characteristic (e.g., self-reported race) that must be aggregated at the cluster level. Through simulations, we show that only individual-level models have power to detect HTE by individual-level variables; if outcomes must be defined at the cluster level, then there is often low power to detect HTE by the corresponding aggregated variables. We illustrate the challenges inherent to this type of analysis in a study assessing the effect of an intervention on increasing COVID-19 booster vaccination rates at long-term care centers.

Finding the (biomarker-defined) subgroup of patients who benefit from a novel therapy: No time for a game of hide and seek

An important element of precision medicine is the ability to identify, for a specific therapy, those patients for whom benefits of that therapy meaningfully exceed the risks. To achieve this goal, treatment effect usually is examined across subgroups defined by a variety of factors, including demographic, clinical, or pathologic characteristics or by molecular attributes of patients or their disease. Frequently such subgroups are defined by the measurement of biomarkers. Even though such examination is necessary when pursuing this goal, the evaluation of treatment effect across a variety of subgroups is statistically fraught due to both the danger of inflated false-positive error rate from multiple testing and the inherent insensitivity to how treatment effects differ across subgroups.Pre-specification of subgroup analyses with appropriate control of false-positive (i.e. type I) error is recommended when possible. However, when subgroups are specified by biomarkers, which could be measured by different assays and might lack established interpretation criteria, such as cut-offs, it might not be possible to fully specify those subgroups at the time a new therapy is ready for definitive evaluation in a Phase 3 trial. In these situations, further refinement and evaluation of treatment effect in biomarker-defined subgroups might have to take place within the trial. A common scenario is that evidence suggests that treatment effect is a monotone function of a biomarker value, but optimal cut-offs for therapy decisions are not known. In this setting, hierarchical testing strategies are widely used, where testing is first conducted in a particular biomarker-positive subgroup and then is conducted in the expanded pool of biomarker-positive and biomarker-negative patients, with control for multiple testing. A serious limitation of this approach is the logical inconsistency of excluding the biomarker-negatives when evaluating effects in the biomarker-positives, yet allowing the biomarker-positives to drive the assessment of whether a conclusion of benefit could be extrapolated to the biomarker-negative subgroup.Examples from oncology and cardiology are described to illustrate the challenges and pitfalls. Recommendations are provided for statistically valid and logically consistent subgroup testing in these scenarios as alternatives to reliance on hierarchical testing alone, and approaches for exploratory assessment of continuous biomarkers as treatment effect modifiers are discussed.

LPP polymorphisms are risk factors for allergic rhinitis in the Chinese Han population

BACKGROUND

Lipoma preferred partner (LPP) polymorphisms are related to immune diseases, but the role of LPP gene in the pathogenesis of allergic rhinitis (AR) is unclear. The current study aimed to explore the contribution of LPP variants to AR susceptibility in the Chinese Han population.

METHODS

A total of 992 healthy controls and 992 patients with AR were recruited. Agena MassARRAY system was applied for genotyping. Odds ratios (OR) and 95% confidence intervals (CI) adjusted by age, sex, and body mass index (BMI) were calculated to conduct the risk assessment of LPP variants in people with a predisposition to AR. Additionally, multifactor dimensionality reduction (MDR) was applied to identify high-order interaction models for AR risk.

RESULTS

We found that rs2030519-G (p = 0.027, OR: 1.15, 95% CI: 1.02-1.31), rs6780858-G (p = 0.019, OR: 1.16, 95% CI: 1.03-1.32), and rs60946162-T (p = 0.014, OR: 1.18, 95% CI: 1.03-1.34) were associated with increased susceptibility to AR. Subgroup analyses indicated the interaction of LPP polymorphisms in terms of age, gender, and BMI with AR susceptibility (p < 0.05, OR > 1). MDR analysis revealed that rs60946162 had the information gain (0.40%) of individual attribute regarding AR.

CONCLUSION

Our results first determined that rs2030519, rs6780858, and rs60946162 were correlated with increased susceptibility to AR in the Chinese Han population, which add to our understanding of the impact of LPP gene variants on AR development.

Neoadjuvant versus definitive radiochemotherapy of locoregionally advanced oesophageal cancer-who benefits?

PURPOSE

For years, there have been discussions on whether neoadjuvant radiochemotherapy followed by surgery (nRCT-S) is superior to definitive radiochemotherapy (dRCT) as the standard of care for locoregionally advanced oesophageal cancer (OC). This retrospective study aimed to evaluate our patient cohort regarding differences in survival and recurrence between nRCT‑S and dRCT.

METHODS

Data from 68 patients with dRCT and 33 patients with nRCT‑S treated from 2010 to 2018 were analysed. Comorbidities were recorded using the Charlson Comorbidity Index (CCI). Recurrence patterns were recorded as in-field or out-field. Kaplan-Meier analyses were used to compare survival data (overall survival [OS], progression-free survival [PFS], and locoregional control [LRC]).

RESULTS

Patients with nRCT‑S showed significantly lower CCI values than those with dRCT (p = 0.001). The median follow-up was 47 months. The median OS times were 31 months for nRCT‑S and 12 months for dRCT (p = 0.009), the median PFS times were 11 and 9 months, respectively (p = 0.057), and the median LRC times were not reached and 23 months, respectively (p = 0.037). The only further factor with a significant impact on OS was the CCI (p = 0.016). In subgroup analyses for comorbidities regarding differences in OS, the superiority of the nRCT‑S remained almost significant for CCI values 2-6 (p = 0.061).

CONCLUSION

Our study showed significantly longer OS and LRC for patients with nRCT‑S than for those with dRCT. Due to different comorbidities in the groups, it can be deduced from the subgroup analysis that patients with few comorbidities seem to especially profit from nRCT‑S.

Update on SARS-CoV-2 seroprevalence: regional and worldwide

BACKGROUND

With limited vaccine supplies, an informed position on the status of SARS-CoV-2 infection in people can assist the prioritization of vaccine deployment.

OBJECTIVES

We performed a systematic review and meta-analysis to estimate the global and regional SARS-CoV-2 seroprevalences around the world.

DATA SOURCES

We systematically searched peer-reviewed databases (PubMed, Embase and Scopus), and preprint servers (medRxiv, bioRxiv and SSRN) for articles published between 1 January 2020 and 30 March 2021.

STUDY ELIGIBILITY CRITERIA

Population-based studies reporting the SARS-CoV-2 seroprevalence in the general population were included.

PARTICIPANTS

People of different age groups, occupations, educational levels, ethnic backgrounds and socio-economic status from the general population.

INTERVENTIONS

There were no interventions.

METHODS

We used the random-effects meta-analyses and empirical Bayesian method to estimate the pooled seroprevalence and conducted subgroup and meta-regression analyses to explore potential sources of heterogeneity as well as the relationship between seroprevalence and socio-demographics.

RESULTS

We identified 241 eligible studies involving 6.3 million individuals from 60 countries. The global pooled seroprevalence was 9.47% (95% CI 8.99-9.95%), although the heterogeneity among studies was significant (I² = 99.9%). We estimated that ∼738 million people had been infected with SARS-CoV-2 (as of December 2020). Highest and lowest seroprevalences were recorded in Central and Southern Asia (22.91%, 19.11-26.72%) and Eastern and South-eastern Asia (1.62%, 1.31-1.95%), respectively. Seroprevalence estimates were higher in males, persons aged 20-50 years, in minority ethnic groups living in countries or regions with low income and human development indices.

CONCLUSIONS

The present study indicates that the majority of the world's human population was still highly susceptible to SARS-CoV-2 infection in mid-2021, emphasizing the need for vaccine deployment to vulnerable groups of people, particularly in developing countries, and for the implementation of enhanced preventive measures until 'herd immunity' to SARS-CoV-2 has developed.

SARS-CoV-2 seroprevalence worldwide: a systematic review and meta-analysis

OBJECTIVES

COVID-19 has been arguably the most important public health concern worldwide in 2020, and efforts are now escalating to suppress or eliminate its spread. In this study we undertook a meta-analysis to estimate the global and regional seroprevalence rates in humans of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and to assess whether seroprevalence is associated with geographical, climatic and/or sociodemographic factors.

METHODS

We systematically reviewed PubMed, Scopus, Embase, medRxiv and bioRxiv databases for preprints or peer-reviewed articles (up to 14 August 2020). Study eligibility criteria were population-based studies describing the prevalence of anti-SARS-CoV-2 (IgG and/or IgM) serum antibodies. Participants were people from different socioeconomic and ethnic backgrounds (from the general population), whose prior COVID-19 status was unknown and who were tested for the presence of anti-SARS-CoV-2 serum antibodies. We used a random-effects model to estimate pooled seroprevalence, and then extrapolated the findings to the global population (for 2020). Subgroup and meta-regression analyses explored potential sources of heterogeneity in the data, and relationships between seroprevalence and sociodemographic, geographical and/or climatic factors.

RESULTS

In total, 47 studies involving 399 265 people from 23 countries met the inclusion criteria. Heterogeneity (I2 = 99.4%, p < 0.001) was seen among studies; SARS-CoV-2 seroprevalence in the general population varied from 0.37% to 22.1%, with a pooled estimate of 3.38% (95%CI 3.05-3.72%; 15 879/399 265). On a regional level, seroprevalence varied from 1.45% (0.95-1.94%, South America) to 5.27% (3.97-6.57%, Northern Europe), although some variation appeared to relate to the serological assay used. The findings suggested an association of seroprevalence with income levels, human development indices, geographic latitudes and/or climate. Extrapolating to the 2020 world population, we estimated that 263.5 million individuals had been exposed or infected at the time of this study.

CONCLUSIONS

This study showed that SARS-CoV-2 seroprevalence varied markedly among geographic regions, as might be expected early in a pandemic. Longitudinal surveys to continually monitor seroprevalence around the globe will be critical to support prevention and control efforts, and might indicate levels of endemic stability or instability in particular countries and regions.

Key concepts in clinical epidemiology: detecting and dealing with heterogeneity in meta-analyses

In a meta-analysis, a question always arises. Is it worthwhile to combine estimates from studies of different populations using various formulations of an intervention, evaluating outcomes measured differently? Sometimes even study designs differ. Differences are expected in a meta-analysis. These may be negligible, and a pooled estimate of effect can guide the clinical decision. However, when the differences are large, this estimate may mislead. Effect estimates from study to study differ because of real differences (between-study variability) and because of chance (within-study variability). To combine estimates when there is heterogeneity (between-study differences are large) may not be sensible. Two complementary methods may be used to detect heterogeneity: visual inspection of the forest plot and calculating numerical measures of heterogeneity (I² and Q). Visual inspection can show effects that are different from the rest. A large I² (proportion of overall variability attributed to between-study variation) or a small P-value associated with Q may suggest heterogeneity. Large P-values, however, do not mean the absence of heterogeneity. It is more informative to report the confidence interval of the I². If there is no heterogeneity, a pooled estimate of the true effect may be generated using only within-study variation (fixed-effect model). If there is substantial heterogeneity, reasons should be sought. Subgroup analysis or meta-regression using study-level characteristics may be done. Although more involved and potentially challenging, individual-level data (Individual Participant Data, IPD) may also be used. In the case of unexplained heterogeneity, both within- and between-study variation should be used to generate a pooled estimate (random-effects model). This estimate does not estimate a single true effect but estimates the average of a range of effects of the intervention on populations represented by the studies. If precise enough (narrow confidence interval), this estimate, together with the prediction interval (a measure of uncertainty in the effect one might see in a particular context), can guide clinical and policy decisions.

A systematic review of subgroup analyses in randomised clinical trials in cardiovascular disease

Background:

Subgroup analyses are frequently used to assess heterogeneity of treatment effects in randomised clinical trials. Inconsistent, improper and incomplete implementation, reporting and interpretation have been identified as ongoing challenges. Further, subgroup analyses were frequently criticised because of unreliable or potentially misleading results. More recently, recommendations and guidelines have been provided to improve the reporting of data in this regard.

Methods:

This systematic review was based on a literature search within the digital archives of three selected medical journals, The New England Journal of Medicine, The Lancet and Circulation. We reviewed articles of randomised clinical trials in the domain of cardiovascular disease which were published in 2015 and 2016. We screened and evaluated the selected articles for the mode of implementation and reporting of subgroup analyses.

Results:

We were able to identify a total of 130 eligible publications of randomised clinical trials. In 89/130 (68%) articles, results of at least one subgroup analysis were presented. This was dependent on the considered journal (p < 0.001), the number of included patients (p < 0.001) and the lack of statistical significance of a trial’s primary analysis (p < 0.001). The number of reported subgroup analyses ranged from 1 to 101 (median = 13). We were able to comprehend the specification time of reported subgroup analyses for 71/89 (80%) articles, with 55/89 (62%) articles presenting exclusively pre-specified analyses. This information was not always traceable on the basis of provided trial protocols and often did not include the pre-definition of cut-off values for the categorization of subgroups. The use of interaction tests was reported in 84/89 (94%) articles, with 36/89 (40%) articles reporting heterogeneity of the treatment effect for at least one primary or secondary trial outcome. Subgroup analyses were reported more frequently for larger randomised clinical trials, and if primary analyses did not reach statistical significance. Information about the implementation of subgroup analyses was reported most consistently for articles from The New England Journal of Medicine, since it was also traceable on the basis of provided trial protocols. We were able to comprehend whether subgroup analyses were pre-specified in a majority of the reviewed publications. Even though results of multiple subgroup analyses were reported for most published trials, a corresponding adjustment for multiple testing was rarely considered.

Conclusion:

Compared to previous reviews in this context, we observed improvements in the reporting of subgroup analyses of cardiovascular randomised clinical trials. Nonetheless, critical shortcomings, such as inconsistent reporting of the implementation and insufficient pre-specification, persist.

Integrating expert opinions with clinical trial data to analyse low-powered subgroup analyses: a Bayesian analysis of the VeRDiCT trial

BACKGROUND

Typically, subgroup analyses in clinical trials are conducted by comparing the intervention effect in each subgroup by means of an interaction test. However, trials are rarely, if ever, adequately powered for interaction tests, so clinically important interactions may go undetected. We discuss the application of Bayesian methods by using expert opinions alongside the trial data. We applied this methodology to the VeRDiCT trial investigating the effect of preoperative volume replacement therapy (VRT) versus no VRT (usual care) in diabetic patients undergoing cardiac surgery. Two subgroup effects were of clinical interest, a) preoperative renal failure and b) preoperative type of antidiabetic medication.

METHODS

Clinical experts were identified within the VeRDiCT trial centre in the UK. A questionnaire was designed to elicit opinions on the impact of VRT on the primary outcome of time from surgery until medically fit for hospital discharge, in the different subgroups. Prior beliefs of the subgroup effect of VRT were elicited face-to-face using two unconditional and one conditional questions per subgroup analysis. The robustness of results to the 'community of priors' was assessed. The community of priors was built using the expert priors for the mean average treatment effect, the interaction effect or both in a Bayesian Cox proportional hazards model implemented in the STAN software in R.

RESULTS

Expert opinions were obtained from 7 clinicians (6 cardiac surgeons and 1 cardiac anaesthetist). Participating experts believed VRT could reduce the length of recovery compared to usual care and the greatest benefit was expected in the subgroups with the more severe comorbidity. The Bayesian posterior estimates were more precise compared to the frequentist maximum likelihood estimate and were shifted toward the overall mean treatment effect.

CONCLUSIONS

In the VeRDiCT trial, the Bayesian analysis did not provide evidence of a difference in treatment effect across subgroups. However, this approach increased the precision of the estimated subgroup effects and produced more stable treatment effect point estimates than the frequentist approach. Trial methodologists are encouraged to prospectively consider Bayesian subgroup analyses when low-powered interaction tests are planned.

TRIAL REGISTRATION

ISRCTN, ISRCTN02159606 . Registered 29th October 2008.

Determinants of response to inhaled extrafine triple therapy in asthma: analyses of TRIMARAN and TRIGGER

Background

A number of single-inhaler triple therapies are being developed for asthma, including the extrafine formulation of beclometasone dipropionate (BDP), formoterol fumarate (FF), and glycopyrronium (G). Given asthma is a heterogenous disease, we investigated whether the clinical response to the addition of the long-acting muscarinic antagonist component within inhaled triple therapy was impacted by a range of clinical characteristics.

Methods

These were pre-specified and post-hoc sub-group analyses of TRIMARAN and TRIGGER, which were double-blind, 52-week studies comparing medium-strength (100/6/10 µg; TRIMARAN) and high-strength (200/6/10 µg; TRIGGER) BDP/FF/G with the respective BDP/FF strengths in adults with uncontrolled asthma and a history of ≥ 1 exacerbation. Co-primary endpoints were pre-dose forced expiratory volume in 1 s (FEV₁) at Week 26 and the rate of moderate-to-severe exacerbations over 52 weeks. Key secondary endpoints: peak FEV₁ at Week 26 and average morning peak expiratory flow over the first 26 weeks in each study, and severe exacerbation rate over 52 weeks (pooled data).

Results

Baseline clinical characteristics (pre-specified analyses) had no consistent effect on the lung function improvements with BDP/FF/G. For the exacerbation endpoints, sub-groups with higher reversibility gained greatest relative benefit from BDP/FF/G versus BDP/FF. In post-hoc analyses with patients sub-grouped by screening blood eosinophil values, in TRIMARAN the greatest relative effect of BDP/FF/G versus BDP/FF on the lung function endpoints was in the ≤ 300 cells/µL group; in TRIGGER, eosinophil levels did not markedly influence the relative efficacy of BDP/FF/G versus BDP/FF. Eosinophil levels did not influence relative efficacy on moderate-to-severe or severe exacerbations.

Conclusion

Overall, the relative efficacy of extrafine BDP/FF/G versus BDP/FF was not influenced by a range of clinical characteristics. However, some patient sub-groups gained additional benefit from BDP/FF/G for certain endpoints. In particular, for exacerbations the relative efficacy of BDP/FF/G was greater in more reversible patients.

Trial registration ClinicalTrials.gov: TRIMARAN, NCT02676076 (registered February 8, 2016, https://clinicaltrials.gov/ct2/show/NCT02676076?term=NCT02676076&draw=2&rank=1,); TRIGGER, NCT02676089 (registered February 8, 2016, https://clinicaltrials.gov/ct2/show/NCT02676089?term=NCT02676089&draw=2&rank=1)

A meta-epidemiological study of subgroup analyses in cochrane systematic reviews of atrial fibrillation

BACKGROUND

Information on subgroup assessments in systematic reviews (SR) of atrial fibrillation (AF) is limited. This review aims to describe subgroup analyses in AF SRs to inform the design of SRs and randomized trials as well as clinical practice.

METHODS

We conducted a cross sectional meta-epidemiological study of Cochrane AF reviews by searching AF (including variants) in the title, abstract, or keyword field without date or language restrictions (Issue 9; September 2018). Two reviewers independently extracted study characteristics to summarize frequency of subgroups pre-specified and conducted and report credibility of subgroup effects claimed.

RESULTS

Of 39 Cochrane reviews identified, 17 met inclusion criteria (including 168 reports of 127 randomized trials) and the majority (16; 94.1%) conducted meta-analysis of outcomes. Most (13; 76.5%) planned pre-specified subgroup analyses; 7 of which (41.2%) conducted subgroups. In these 7 reviews, 56 subgroups were planned, 17 (30.4%) conducted and 6 (10.7%) yielded subgroup effects. Variables such as co-morbid disease, stroke risk factors, prior stroke/transient ischemic attack, age, race, and sex represented 44% (24 subgroups) of all planned subgroups (8 conducted; 14.3%); however, information on covariate selection was lacking. Overall, more subgroups were planned than conducted (mean difference (95% CI) 2.3 (1.2-3.5, p < 0.001)). Of all subgroups conducted, anticoagulant characteristics comprised a third of all subgroup effects (n = 5, 35.7%). The credibility of subgroups identified (n = 14) was assessed and less than half (43%) represented one of a small number of pre-specified hypothesis and rarely were effects seen within studies (7%). Of 5 reviews that reported subgroup effects, only 3 discussed subgroup effects as part of the overall conclusions; none discussed credibility of subgroup effects.

CONCLUSIONS

This meta-epidemiological review of a subset of Cochrane AF reviews suggests that planning and reporting of subgroup analyses in AF reviews can be improved to better inform clinical management. Most pre-specified subgroup analyses were not performed, important variables (such as stroke, bleeding risk, and other comorbidities) were rarely examined and credibility of subgroup effects claimed was low. Future reviews should aim to identify important subgroups in their protocols and use recommended approaches to test subgroup effects in order to better support clinical decision-making.

Age-treatment subgroup analyses in Cochrane intervention reviews: a meta-epidemiological study

BACKGROUND

There is growing interest in evaluating differences in healthcare interventions across routinely collected demographic characteristics. However, individual subgroup analyses in randomized controlled trials are often not prespecified, adjusted for multiple testing, or conducted using the appropriate statistical test for interaction, and therefore frequently lack credibility. Meta-analyses can be used to examine the validity of potential subgroup differences by collating evidence across trials. Here, we characterize the conduct and clinical translation of age-treatment subgroup analyses in Cochrane reviews.

METHODS

For a random sample of 928 Cochrane intervention reviews of randomized trials, we determined how often subgroup analyses of age are reported, how often these analyses have a P < 0.05 from formal interaction testing, how frequently subgroup differences first observed in an individual trial are later corroborated by other trials in the same meta-analysis, and how often statistically significant results are included in commonly used clinical management resources (BMJ Best Practice, UpToDate, Cochrane Clinical Answers, Google Scholar, and Google search).

RESULTS

Among 928 Cochrane intervention reviews, 189 (20.4%) included plans to conduct age-treatment subgroup analyses. The vast majority (162 of 189, 85.7%) of the planned analyses were not conducted, commonly because of insufficient trial data. There were 22 reviews that conducted their planned age-treatment subgroup analyses, and another 3 reviews appeared to perform unplanned age-treatment subgroup analyses. These 25 (25 of 928, 2.7%) reviews conducted a total of 97 age-treatment subgroup analyses, of which 65 analyses (in 20 reviews) had non-overlapping subgroup levels. Among the 65 age-treatment subgroup analyses, 14 (21.5%) did not report any formal interaction testing. Seven (10.8%) reported P < 0.05 from formal age-treatment interaction testing; however, none of these seven analyses were in reviews that discussed the potential biological rationale or clinical significance of the subgroup findings or had results that were included in common clinical practice resources.

CONCLUSION

Age-treatment subgroup analyses in Cochrane intervention reviews were frequently planned but rarely conducted, and implications of detected interactions were not discussed in the reviews or mentioned in common clinical resources. When subgroup analyses are performed, authors should report the findings, compare the results to previous studies, and outline any potential impact on clinical care.

Gabapentin in procedure-specific postoperative pain management - preplanned subgroup analyses from a systematic review with meta-analyses and trial sequential analyses

Background

It has been argued that postoperative pain treatment should be “procedure-specific”, since different analgesics may have specific effects dependent on the surgical procedure. The aim of the present subgroup analysis was to compare the beneficial and harmful effects of perioperative gabapentin treatment in different surgical procedures.

Methods

Relevant databases were searched for randomized clinical trials (RCTs) comparing gabapentin versus placebo. Two authors independently screened titles and abstracts, extracted data and assessed risk of bias. The primary outcomes were differences in 24-h morphine consumption, and serious adverse events (SAE) between surgical procedures. These subgroup analyses were predefined in a PRISMA compliant systematic review registered at PROSPERO (ID: CRD42013006538). It was predefined that conclusions should primarily be based on trials classified as overall low risk of bias.

Results

Seventy-four RCTs with 5645 patients were included, assessing benefit and harm in cholecystectomy, hysterectomy, mastectomy, and arthroplasty surgery, spinal surgery, and thoracic surgery.

Only eight of 74 trials were classified as overall low risk of bias limiting our ability to conclude on the estimates in most meta-analyses. The differences between surgical procedures in these trials were not statistically significant when tested for subgroup differences. Fifteen trials with 1377 patients reported a total of 59 SAEs, most of which were observed in the thoracic surgery group.

Conclusion

Both beneficial and harmful effects in these subgroup analyses were influenced by bias and insufficient data, limiting conclusions. With these limitations, we could not adequately test for differences in beneficial or harmful outcomes between six surgical subgroups undergoing perioperative gabapentin treatment.

Electronic supplementary material

The online version of this article (doi:10.1186/s12871-017-0373-8) contains supplementary material, which is available to authorized users.