Cytoplasmic synthesis of endogenous Alu complementary DNA via reverse transcription and implications in age-related macular degeneration.

Alu retroelements propagate via retrotransposition by hijacking long interspersed nuclear element-1 (L1) reverse transcriptase (RT) and endonuclease activities. Reverse transcription of Alu RNA into complementary DNA (cDNA) is presumed to occur exclusively in the nucleus at the genomic integration site. Whether Alu cDNA is synthesized independently of genomic integration is unknown. Alu RNA promotes retinal pigmented epithelium (RPE) death in geographic atrophy, an untreatable type of age-related macular degeneration. We report that Alu RNA-induced RPE degeneration is mediated via cytoplasmic L1-reverse-transcribed Alu cDNA independently of retrotransposition. Alu RNA did not induce cDNA production or RPE degeneration in L1-inhibited animals or human cells. Alu reverse transcription can be initiated in the cytoplasm via self-priming of Alu RNA. In four health insurance databases, use of nucleoside RT inhibitors was associated with reduced risk of developing atrophic macular degeneration (pooled adjusted hazard ratio, 0.616; 95% confidence interval, 0.493-0.770), thus identifying inhibitors of this Alu replication cycle shunt as potential therapies for a major cause of blindness.

Summarizing empirical information on between-study heterogeneity for Bayesian random-effects meta-analysis.

In Bayesian meta-analysis, the specification of prior probabilities for the between-study heterogeneity is commonly required, and is of particular benefit in situations where only few studies are included. Among the considerations in the set-up of such prior distributions, the consultation of available empirical data on a set of relevant past analyses sometimes plays a role. How exactly to summarize historical data sensibly is not immediately obvious; in particular, the investigation of an empirical collection of heterogeneity estimates will not target the actual problem and will usually only be of limited use. The commonly used normal-normal hierarchical model for random-effects meta-analysis is extended to infer a heterogeneity prior. Using an example data set, we demonstrate how to fit a distribution to empirically observed heterogeneity data from a set of meta-analyses. Considerations also include the choice of a parametric distribution family. Here, we focus on simple and readily applicable approaches to then translate these into (prior) probability distributions.

A straightforward meta-analysis approach for oncology phase I dose-finding studies.

Phase I early-phase clinical studies aim at investigating the safety and the underlying dose-toxicity relationship of a drug or combination. While little may still be known about the compound's properties, it is crucial to consider quantitative information available from any studies that may have been conducted previously on the same drug. A meta-analytic approach has the advantages of being able to properly account for between-study heterogeneity, and it may be readily extended to prediction or shrinkage applications. Here we propose a simple and robust two-stage approach for the estimation of maximum tolerated dose(s) utilizing penalized logistic regression and Bayesian random-effects meta-analysis methodology. Implementation is facilitated using standard R packages. The properties of the proposed methods are investigated in Monte Carlo simulations. The investigations are motivated and illustrated by two examples from oncology.

Bounds for the weight of external data in shrinkage estimation.

Shrinkage estimation in a meta-analysis framework may be used to facilitate dynamical borrowing of information. This framework might be used to analyze a new study in the light of previous data, which might differ in their design (e.g., a randomized controlled trial and a clinical registry). We show how the common study weights arise in effect and shrinkage estimation, and how these may be generalized to the case of Bayesian meta-analysis. Next we develop simple ways to compute bounds on the weights, so that the contribution of the external evidence may be assessed a priori. These considerations are illustrated and discussed using numerical examples, including applications in the treatment of Creutzfeldt-Jakob disease and in fetal monitoring to prevent the occurrence of metabolic acidosis. The target study's contribution to the resulting estimate is shown to be bounded below. Therefore, concerns of evidence being easily overwhelmed by external data are largely unwarranted.

Neutrophil Gelatinase-Associated Lipocalin Measured on Clinical Laboratory Platforms for the Prediction of Acute Kidney Injury and the Associated Need for Dialysis Therapy: A Systematic Review and Meta-analysis.

RATIONALE & OBJECTIVE: The usefulness of measures of neutrophil gelatinase-associated lipocalin (NGAL) in urine or plasma obtained on clinical laboratory platforms for predicting acute kidney injury (AKI) and AKI requiring dialysis (AKI-D) has not been fully evaluated. We sought to quantitatively summarize published data to evaluate the value of urinary and plasma NGAL for kidney risk prediction. STUDY DESIGN: Literature-based meta-analysis and individual-study-data meta-analysis of diagnostic studies following PRISMA-IPD guidelines. SETTING & STUDY POPULATIONS: Studies of adults investigating AKI, severe AKI, and AKI-D in the setting of cardiac surgery, intensive care, or emergency department care using either urinary or plasma NGAL measured on clinical laboratory platforms. SELECTION CRITERIA FOR STUDIES: PubMed, Web of Science, Cochrane Library, Scopus, and congress abstracts ever published through February 2020 reporting diagnostic test studies of NGAL measured on clinical laboratory platforms to predict AKI. DATA EXTRACTION: Individual-study-data meta-analysis was accomplished by giving authors data specifications tailored to their studies and requesting standardized patient-level data analysis. ANALYTICAL APPROACH: Individual-study-data meta-analysis used a bivariate time-to-event model for interval-censored data from which discriminative ability (AUC) was characterized. NGAL cutoff concentrations at 95% sensitivity, 95% specificity, and optimal sensitivity and specificity were also estimated. Models incorporated as confounders the clinical setting and use versus nonuse of urine output as a criterion for AKI. A literature-based meta-analysis was also performed for all published studies including those for which the authors were unable to provide individual-study data analyses. RESULTS: We included 52 observational studies involving 13,040 patients. We analyzed 30 data sets for the individual-study-data meta-analysis. For AKI, severe AKI, and AKI-D, numbers of events were 837, 304, and 103 for analyses of urinary NGAL, respectively; these values were 705, 271, and 178 for analyses of plasma NGAL. Discriminative performance was similar in both meta-analyses. Individual-study-data meta-analysis AUCs for urinary NGAL were 0.75 (95% CI, 0.73-0.76) and 0.80 (95% CI, 0.79-0.81) for severe AKI and AKI-D, respectively; for plasma NGAL, the corresponding AUCs were 0.80 (95% CI, 0.79-0.81) and 0.86 (95% CI, 0.84-0.86). Cutoff concentrations at 95% specificity for urinary NGAL were>580ng/mL with 27% sensitivity for severe AKI and>589ng/mL with 24% sensitivity for AKI-D. Corresponding cutoffs for plasma NGAL were>364ng/mL with 44% sensitivity and>546ng/mL with 26% sensitivity, respectively. LIMITATIONS: Practice variability in initiation of dialysis. Imperfect harmonization of data across studies. CONCLUSIONS: Urinary and plasma NGAL concentrations may identify patients at high risk for AKI in clinical research and practice. The cutoff concentrations reported in this study require prospective evaluation.

Repeating noninvasive risk stratification improves prediction of outcome in ICD patients.

BACKGROUND: Noninvasive risk stratification aims to detect abnormalities in the pathophysiological mechanisms underlying ventricular arrhythmias. We studied the predictive value of repeating risk stratification in patients with an implantable cardioverter-defibrillator (ICD). METHODS: The EUTrigTreat clinical study was a prospective multicenter trial including ischemic and nonischemic cardiomyopathies and arrhythmogenic heart disease. Left ventricular ejection fraction ≤40% (LVEF), premature ventricular complexes >400/24 hr (PVC), non-negative microvolt T-wave alternans (MTWA), and abnormal heart rate turbulence (HRT) were considered high risk. Tests were repeated within 12 months after inclusion. Adjusted Cox regression analysis was performed for mortality and appropriate ICD shocks. RESULTS: In total, 635 patients had analyzable baseline data with a median follow-up of 4.4 years. Worsening of LVEF was associated with increased mortality (HR 3.59, 95% CI 1.17-11.04), as was consistent abnormal HRT (HR 8.34, 95%CI 1.06-65.54). HRT improvement was associated with improved survival when compared to consistent abnormal HRT (HR 0.10, 95%CI 0.01-0.82). For appropriate ICD shocks, a non-negative MTWA test or high PVC count at any moment was associated with increased arrhythmic risk independent of the evolution of test results (worsening: HR 3.76 (95%CI 1.43-9.88) and HR 2.50 (95%CI 1.15-5.46); improvement: HR 2.80 (95%CI 1.03-7.61) and HR 2.45 (95%CI 1.07-5.62); consistent: HR 2.47 (95%CI 0.95-6.45) and HR 2.40 (95%CI 1.33-4.33), respectively). LVEF improvement was associated with a lower arrhythmic risk (HR 0.34, 95%CI 0.12-0.94). CONCLUSIONS: Repeating LVEF and HRT improved the prediction of mortality, whereas stratification of ventricular arrhythmias may be improved by repeating LVEF measurements, MTWA and ECG Holter monitoring.

Decline of COPD exacerbations in clinical trials over two decades - a systematic review and meta-regression.

BACKGROUND: An important goal of chronic obstructive pulmonary disease (COPD) treatment is to reduce the frequency of exacerbations. Some observations suggest a decline in exacerbation rates in clinical trials over time. A more systematic understanding would help to improve the design and interpretation of COPD trials. METHODS: We performed a systematic review and meta-regression of the placebo groups in published randomized controlled trials reporting exacerbations as an outcome. A Bayesian negative binomial model was developed to accommodate results that are reported in different formats; results are reported with credible intervals (CI) and posterior tail probabilities (pB). RESULTS: Of 1114 studies identified by our search, 55 were ultimately included. Exacerbation rates decreased by 6.7% (95% CI (4.4, 9.0); pB <  0.001) per year, or 50% (95% CI (36, 61)) per decade. Adjusting for available study and baseline characteristics such as forced expiratory volume in 1 s (FEV1) did not alter the observed trend considerably. Two subsets of studies, one using a true placebo group and the other allowing inhaled corticosteroids in the "placebo" group, also yielded consistent results. CONCLUSIONS: In conclusion, this meta-regression indicates that the rate of COPD exacerbations decreased over the past two decades to a clinically relevant extent independent of important prognostic factors. This suggests that care is needed in the design of new trials or when comparing results from older trials with more recent ones. Also a considerable effect of adjunct therapy on COPD exacerbations can be assumed. REGISTRATION: PROSPERO 2018 CRD4218118823.

Model averaging for robust extrapolation in evidence synthesis.

Extrapolation from a source to a target, eg, from adults to children, is a promising approach to utilize external information when data are sparse. In the context of meta-analyses, one is commonly faced with a small number of studies, whereas potentially relevant additional information may also be available. Here, we describe a simple extrapolation strategy using heavy-tailed mixture priors for effect estimation in meta-analysis, which effectively results in a model-averaging technique. The described method is robust in the sense that a potential prior-data conflict, ie, a discrepancy between source and target data, is explicitly anticipated. The aim of this paper is to develop a solution for this particular application to showcase the ease of implementation by providing R code, and to demonstrate the robustness of the general approach in simulations.

Likelihood-based random-effects meta-analysis with few studies: empirical and simulation studies.

BACKGROUND: Standard random-effects meta-analysis methods perform poorly when applied to few studies only. Such settings however are commonly encountered in practice. It is unclear, whether or to what extent small-sample-size behaviour can be improved by more sophisticated modeling. METHODS: We consider likelihood-based methods, the DerSimonian-Laird approach, Empirical Bayes, several adjustment methods and a fully Bayesian approach. Confidence intervals are based on a normal approximation, or on adjustments based on the Student-t-distribution. In addition, a linear mixed model and two generalized linear mixed models (GLMMs) assuming binomial or Poisson distributed numbers of events per study arm are considered for pairwise binary meta-analyses. We extract an empirical data set of 40 meta-analyses from recent reviews published by the German Institute for Quality and Efficiency in Health Care (IQWiG). Methods are then compared empirically as well as in a simulation study, based on few studies, imbalanced study sizes, and considering odds-ratio (OR) and risk ratio (RR) effect sizes. Coverage probabilities and interval widths for the combined effect estimate are evaluated to compare the different approaches. RESULTS: Empirically, a majority of the identified meta-analyses include only 2 studies. Variation of methods or effect measures affects the estimation results. In the simulation study, coverage probability is, in the presence of heterogeneity and few studies, mostly below the nominal level for all frequentist methods based on normal approximation, in particular when sizes in meta-analyses are not balanced, but improve when confidence intervals are adjusted. Bayesian methods result in better coverage than the frequentist methods with normal approximation in all scenarios, except for some cases of very large heterogeneity where the coverage is slightly lower. Credible intervals are empirically and in the simulation study wider than unadjusted confidence intervals, but considerably narrower than adjusted ones, with some exceptions when considering RRs and small numbers of patients per trial-arm. Confidence intervals based on the GLMMs are, in general, slightly narrower than those from other frequentist methods. Some methods turned out impractical due to frequent numerical problems. CONCLUSIONS: In the presence of between-study heterogeneity, especially with unbalanced study sizes, caution is needed in applying meta-analytical methods to few studies, as either coverage probabilities might be compromised, or intervals are inconclusively wide. Bayesian estimation with a sensibly chosen prior for between-trial heterogeneity may offer a promising compromise.

Present criteria for prophylactic ICD implantation: Insights from the EU-CERT-ICD (Comparative Effectiveness Research to Assess the Use of Primary ProphylacTic Implantable Cardioverter Defibrillators in EUrope) project.

BACKGROUND: The clinical effectiveness of primary prevention implantable cardioverter defibrillator (ICD) therapy is under debate. It is urgently needed to better identify patients who benefit from prophylactic ICD therapy. The EUropean Comparative Effectiveness Research to Assess the Use of Primary ProphylacTic Implantable Cardioverter Defibrillators (EU-CERT-ICD) completed in 2019 will assess this issue. SUMMARY: The EU-CERT-ICD is a prospective investigator-initiated non-randomized, controlled, multicenter observational cohort study done in 44 centers across 15 European countries. A total of 2327 patients with heart failure due to ischemic heart disease or dilated cardiomyopathy indicated for primary prophylactic ICD implantation were recruited between 2014 and 2018 (>1500 patients at first ICD implantation, >750 patients non-randomized non-ICD control group). The primary endpoint was all-cause mortality, and first appropriate shock was co-primary endpoint. At baseline, all patients underwent 12­lead ECG and Holter-ECG analysis using multiple advanced methods for risk stratification as well as documentation of clinical characteristics and laboratory values. The EU-CERT-ICD data will provide much needed information on the survival benefit of preventive ICD therapy and expand on previous prospective risk stratification studies which showed very good applicability of clinical parameters and advanced risk stratifiers in order to define patient subgroups with above or below average ICD benefit. CONCLUSION: The EU-CERT-ICD study will provide new and current data about effectiveness of primary prophylactic ICD implantation. The study also aims for improved risk stratification and patient selection using clinical risk markers in general, and advanced ECG risk markers in particular.

Using phase II data for the analysis of phase III studies: An application in rare diseases.

BACKGROUND: Clinical research and drug development in orphan diseases are challenging, since large-scale randomized studies are difficult to conduct. Formally synthesizing the evidence is therefore of great value, yet this is rarely done in the drug-approval process. Phase III designs that make better use of phase II data can facilitate drug development in orphan diseases. METHODS: A Bayesian meta-analytic approach is used to inform the phase III study with phase II data. It is particularly attractive, since uncertainty of between-trial heterogeneity can be dealt with probabilistically, which is critical if the number of studies is small. Furthermore, it allows quantifying and discounting the phase II data through the predictive distribution relevant for phase III. A phase III design is proposed which uses the phase II data and considers approval based on a phase III interim analysis. The design is illustrated with a non-inferiority case study from a Food and Drug Administration approval in herpetic keratitis (an orphan disease). Design operating characteristics are compared to those of a traditional design, which ignores the phase II data. RESULTS: An analysis of the phase II data reveals good but insufficient evidence for non-inferiority, highlighting the need for a phase III study. For the phase III study supported by phase II data, the interim analysis is based on half of the patients. For this design, the meta-analytic interim results are conclusive and would justify approval. In contrast, based on the phase III data only, interim results are inconclusive and require further evidence. CONCLUSION: To accelerate drug development for orphan diseases, innovative study designs and appropriate methodology are needed. Taking advantage of randomized phase II data when analyzing phase III studies looks promising because the evidence from phase II supports informed decision-making. The implementation of the Bayesian design is straightforward with public software such as R.

Meta-analysis of two studies in the presence of heterogeneity with applications in rare diseases.

Random-effects meta-analyses are used to combine evidence of treatment effects from multiple studies. Since treatment effects may vary across trials due to differences in study characteristics, heterogeneity in treatment effects between studies must be accounted for to achieve valid inference. The standard model for random-effects meta-analysis assumes approximately normal effect estimates and a normal random-effects model. However, standard methods based on this model ignore the uncertainty in estimating the between-trial heterogeneity. In the special setting of only two studies and in the presence of heterogeneity, we investigate here alternatives such as the Hartung-Knapp-Sidik-Jonkman method (HKSJ), the modified Knapp-Hartung method (mKH, a variation of the HKSJ method) and Bayesian random-effects meta-analyses with priors covering plausible heterogeneity values; R code to reproduce the examples is presented in an appendix. The properties of these methods are assessed by applying them to five examples from various rare diseases and by a simulation study. Whereas the standard method based on normal quantiles has poor coverage, the HKSJ and mKH generally lead to very long, and therefore inconclusive, confidence intervals. The Bayesian intervals on the whole show satisfying properties and offer a reasonable compromise between these two extremes.

Extensive acute axonal damage in pediatric multiple sclerosis lesions.

OBJECTIVE: Axonal damage occurs early in multiple sclerosis (MS) and contributes to the degree of clinical disability. Children with MS more often show disabling and polyfocal neurological symptoms at disease onset than adults with MS. Thus, axonal damage may differ between pediatric and adult MS patients. METHODS: We analyzed axonal pathology in archival brain biopsy and autopsy samples from 19 children with early MS. Lesions were classified according to demyelinating activity and presence of remyelination. Axonal density and extent of acute axonal damage were assessed using Bielschowsky silver impregnation and immunohistochemistry for amyloid precursor protein (APP), respectively. Axonal injury was correlated with the inflammatory infiltrate as well as clinical characteristics. Results were compared with data from adult MS patients. RESULTS: Acute axonal damage was most extensive in early active demyelinating (EA) lesions of pediatric patients and correlated positively with the Expanded Disability Status Scale at attack leading to biopsy/autopsy. Comparison with 12 adult patients showed a 50% increase in the extent of acute axonal damage in EA lesions from children compared to adults, with the highest number of APP-positive spheroids found prior to puberty. The extent of acute axonal damage correlated positively with the number of lesional macrophages. Axonal density was reduced in pediatric lesions irrespective of the demyelinating activity or the presence of remyelination. Axonal reduction was similar between children and adults. INTERPRETATION: Our results provide evidence for more pronounced acute axonal damage in inflammatory demyelinating lesions from children compared to adults.

Evidence synthesis for count distributions based on heterogeneous and incomplete aggregated data.

The analysis of count data is commonly done using Poisson models. Negative binomial models are a straightforward and readily motivated generalization for the case of overdispersed data, that is, when the observed variance is greater than expected under a Poissonian model. Rate and overdispersion parameters then need to be considered jointly, which in general is not trivial. Here, we are concerned with evidence synthesis in the case where the reporting of data is rather heterogeneous, that is, events are reported either in terms of mean event counts, the proportion of event-free patients, or rate estimates and standard errors. Either figure carries some information about the relevant parameters, and it is the joint modeling that allows for coherent inference on the parameters of interest. The methods are motivated and illustrated by a systematic review in chronic obstructive pulmonary disease.

Hartung-Knapp-Sidik-Jonkman approach and its modification for random-effects meta-analysis with few studies.

BACKGROUND: Random-effects meta-analysis is commonly performed by first deriving an estimate of the between-study variation, the heterogeneity, and subsequently using this as the basis for combining results, i.e., for estimating the effect, the figure of primary interest. The heterogeneity variance estimate however is commonly associated with substantial uncertainty, especially in contexts where there are only few studies available, such as in small populations and rare diseases. METHODS: Confidence intervals and tests for the effect may be constructed via a simple normal approximation, or via a Student-t distribution, using the Hartung-Knapp-Sidik-Jonkman (HKSJ) approach, which additionally uses a refined estimator of variance of the effect estimator. The modified Knapp-Hartung method (mKH) applies an ad hoc correction and has been proposed to prevent counterintuitive effects and to yield more conservative inference. We performed a simulation study to investigate the behaviour of the standard HKSJ and modified mKH procedures in a range of circumstances, with a focus on the common case of meta-analysis based on only a few studies. RESULTS: The standard HKSJ procedure works well when the treatment effect estimates to be combined are of comparable precision, but nominal error levels are exceeded when standard errors vary considerably between studies (e.g. due to variations in study size). Application of the modification on the other hand yields more conservative results with error rates closer to the nominal level. Differences are most pronounced in the common case of few studies of varying size or precision. CONCLUSIONS: Use of the modified mKH procedure is recommended, especially when only a few studies contribute to the meta-analysis and the involved studies' precisions (standard errors) vary.

Interleukin-2 receptor antagonists for pediatric liver transplant recipients: a systematic review and meta-analysis of controlled studies.

IL-2RA are frequently used as induction therapy in liver transplant recipients to decrease the risk of AR while allowing the reduction of concomitant immunosuppression. The exact association with the use of IL-2RA, however, is uncertain. We performed a systematic literature search for relevant studies. Random effects models were used to assess the incidence of AR, steroid-resistant rejection, graft loss, patient death, and adverse drug reaction, with or without IL-2RA. Six studies (two randomized and four non-randomized) met the eligibility criteria. Acute rejection at six months or later favored the use of IL-2RA significantly (RR 0.38; 95% CI 0.22-0.66, p = 0.0005). Although not statistically significant, IL-2RA showed a substantial reduction of the risk of steroid-resistant rejection (RR 0.32; CI 0.19-1.03, p = 0.0594). Graft loss and patient death showed a reductive tendency through the use of IL-2RA. The use of IL-2RA is safe and is associated with a statistically significantly lower incidence of AR after transplantation and substantial reduction of steroid-resistant rejection, graft loss, and patient death.

How trace plots help interpret meta-analysis results.

The trace plot is seldom used in meta-analysis, yet it is a very informative plot. In this article, we define and illustrate what the trace plot is, and discuss why it is important. The Bayesian version of the plot combines the posterior density of τ , the between-study standard deviation, and the shrunken estimates of the study effects as a function of τ . With a small or moderate number of studies, τ is not estimated with much precision, and parameter estimates and shrunken study effect estimates can vary widely depending on the correct value of τ . The trace plot allows visualization of the sensitivity to τ along with a plot that shows which values of τ are plausible and which are implausible. A comparable frequentist or empirical Bayes version provides similar results. The concepts are illustrated using examples in meta-analysis and meta-regression; implementation in R is facilitated in a Bayesian or frequentist framework using the bayesmeta and metafor packages, respectively.

Bayesian random-effects meta-analysis with empirical heterogeneity priors for application in health technology assessment with very few studies.

In Bayesian random-effects meta-analysis, the use of weakly informative prior distributions is of particular benefit in cases where only a few studies are included, a situation often encountered in health technology assessment (HTA). Suggestions for empirical prior distributions are available in the literature but it is unknown whether these are adequate in the context of HTA. Therefore, a database of all relevant meta-analyses conducted by the Institute for Quality and Efficiency in Health Care (IQWiG, Germany) was constructed to derive empirical prior distributions for the heterogeneity parameter suitable for HTA. Previously, an extension to the normal-normal hierarchical model had been suggested for this purpose. For different effect measures, this extended model was applied on the database to conservatively derive a prior distribution for the heterogeneity parameter. Comparison of a Bayesian approach using the derived priors with IQWiG's current standard approach for evidence synthesis shows favorable properties. Therefore, these prior distributions are recommended for future meta-analyses in HTA settings and could be embedded into the IQWiG evidence synthesis approach in the case of very few studies.

Explaining temporal trends in annualised relapse rates in placebo groups of randomised controlled trials in relapsing multiple sclerosis: systematic review and meta-regression.

BACKGROUND: Recent studies have shown a decrease in annualised relapse rates (ARRs) in placebo groups of randomised controlled trials (RCTs) in relapsing multiple sclerosis (RMS). METHODS: We conducted a systematic literature search of RCTs in RMS. Data on eligibility criteria and baseline characteristics were extracted and tested for significant trends over time. A meta-regression was conducted to estimate their contribution to the decrease of trial ARRs over time. RESULTS: We identified 56 studies. Patient age at baseline (p < 0.001), mean duration of multiple sclerosis (MS) at baseline (p = 0.048), size of treatment groups (p = 0.003), Oxford Quality Scale scores (p = 0.021), and the number of eligibility criteria (p<0.001) increased significantly, whereas pre-trial ARR (p = 0.001), the time span over which pre-trial ARR was calculated (p < 0.001), and the duration of placebo-controlled follow-up (p = 0.006) decreased significantly over time. In meta-regression of trial placebo ARR, the temporal trend was found to be insignificant, with major factors explaining the variation: pre-trial ARR, the number of years used to calculate pre-trial ARR and study duration. CONCLUSION: The observed decline in trial ARRs may result from decreasing pre-trial ARRs and a shorter time period over which pre-trial ARRs were calculated. Increasing patient age and duration of illness may also contribute.