Efficacy of experimental treatments compared with standard treatments in non-inferiority trials: a meta-analysis of randomized controlled trials

Background There is concern that non-inferiority trials might be deliberately designed to conceal that a new treatment is less effective than a standard treatment. In order to test this hypothesis we performed a meta-analysis of non-inferiority trials to assess the average effect of experimental treatments compared with standard treatments. Methods One hundred and seventy non-inferiority treatment trials published in 121 core clinical journals were included. The trials were identified through a search of PubMed (1991 to 20 February 2009). Combined relative risk (RR) from meta-analysis comparing experimental with standard treatments was the main outcome measure. Results The 170 trials contributed a total of 175 independent comparisons of experimental with standard treatments. The combined RR for all 175 comparisons was 0.994 [95% confidence interval (CI) 0.978–1.010] using a random-effects model and 1.002 (95% CI 0.996–1.008) using a fixed-effects model. Of the 175 comparisons, experimental treatment was considered to be non-inferior in 130 (74%). The combined RR for these 130 comparisons was 0.995 (95% CI 0.983–1.006) and the point estimate favoured the experimental treatment in 58% (n = 76) and standard treatment in 42% (n = 54). The median non-inferiority margin (RR) pre-specified by trialists was 1.31 [inter-quartile range (IQR) 1.18–1.59]. Conclusion In this meta-analysis of non-inferiority trials the average RR comparing experimental with standard treatments was close to 1. The experimental treatments that gain a verdict of non-inferiority in published trials do not appear to be systematically less effective than the standard treatments. Importantly, publication bias and bias in the design and reporting of the studies cannot be ruled out and may have skewed the study results in favour of the experimental treatments. Further studies are required to examine the importance of such bias.

Effects of glucosamine, chondroitin, or placebo in patients with osteoarthritis of hip or knee: network meta-analysis

OBJECTIVE: To determine the effect of glucosamine, chondroitin, or the two in combination on joint pain and on radiological progression of disease in osteoarthritis of the hip or knee. Design Network meta-analysis. Direct comparisons within trials were combined with indirect evidence from other trials by using a Bayesian model that allowed the synthesis of multiple time points. MAIN OUTCOME MEASURE: Pain intensity. Secondary outcome was change in minimal width of joint space. The minimal clinically important difference between preparations and placebo was prespecified at -0.9 cm on a 10 cm visual analogue scale. DATA SOURCES: Electronic databases and conference proceedings from inception to June 2009, expert contact, relevant websites. Eligibility criteria for selecting studies Large scale randomised controlled trials in more than 200 patients with osteoarthritis of the knee or hip that compared glucosamine, chondroitin, or their combination with placebo or head to head. Results 10 trials in 3803 patients were included. On a 10 cm visual analogue scale the overall difference in pain intensity compared with placebo was -0.4 cm (95% credible interval -0.7 to -0.1 cm) for glucosamine, -0.3 cm (-0.7 to 0.0 cm) for chondroitin, and -0.5 cm (-0.9 to 0.0 cm) for the combination. For none of the estimates did the 95% credible intervals cross the boundary of the minimal clinically important difference. Industry independent trials showed smaller effects than commercially funded trials (P=0.02 for interaction). The differences in changes in minimal width of joint space were all minute, with 95% credible intervals overlapping zero. Conclusions Compared with placebo, glucosamine, chondroitin, and their combination do not reduce joint pain or have an impact on narrowing of joint space. Health authorities and health insurers should not cover the costs of these preparations, and new prescriptions to patients who have not received treatment should be discouraged.

Does land-based exercise reduce pain and disability associated with hip osteoarthritis? A meta-analysis of randomized controlled trials

Objective To determine if clinical guidelines recommending therapeutic exercise for people with hip osteoarthritis (OA) are supported by rigorous scientific evidence. Methods A meta-analysis of randomized controlled trials (RCTs) recruiting people with hip OA and comparing some form of land-based exercise program (as opposed to exercises conducted in the water) with a non-exercise group in terms of hip pain and/or self-reported physical function. Results Thirty-two RCTs were identified, but only five met the inclusion criteria. Only one of the five included RCTs restricted recruitment to people with hip OA, the other four RCTs also recruiting participants with knee OA. The five included studies provided data on 204 and 187 hip OA participants for pain and physical function, respectively. Combining the results of the five included RCTs using a fixed-effects model demonstrated a small treatment effect for pain (standardized mean difference (SMD) −0.38; 95% confidence interval (CI) −0.67 to −0.09). No significant benefit in terms of improved self-reported physical function was detected (SMD −0.02; 95% CI −0.31 to 0.28). Conclusion Currently there is only silver level evidence (one small RCT) supporting the benefit of land-based therapeutic exercise for people with symptomatic hip OA in terms of reduced pain and improved physical function. The limited number and small sample size of the included RCTs restricts the confidence that can be attributed to these results.

Run-Time Evolution through Explicit Meta-Objects

Software must be constantly adapted due to evolving domain knowledge and unanticipated requirements changes. To adapt a system at run-time we need to reflect on its structure and its behavior. Object-oriented languages introduced reflection to deal with this issue, however, no reflective approach up to now has tried to provide a unified solution to both structural and behavioral reflection. This paper describes Albedo, a unified approach to structural and behavioral reflection. Albedo is a model of fined-grained unanticipated dynamic structural and behavioral adaptation. Instead of providing reflective capabilities as an external mechanism we integrate them deeply in the environment. We show how explicit meta-objects allow us to provide a range of reflective features and thereby evolve both application models and environments at run-time.

Role of hepatitis C virus genotype 3 in liver fibrosis progression - a systematic review and meta-analysis

The progression of liver fibrosis in chronic hepatitis C has long been considered to be independent from viral genotypes. However, recent studies suggest an association between Hepatitis C virus (HCV) genotype 3 and accelerated liver disease progression. We completed a systematic review and meta-analysis of studies evaluating the association between HCV genotypes and fibrosis progression. PubMed, Embase and ISI Web of Knowledge databases were searched for cohort, cross-sectional and case-control studies on treatment-naïve HCV-infected adults in which liver fibrosis progression rate (FPR) was assessed by the ratio of fibrosis stage in one single biopsy to the duration of infection (single-biopsy studies) or from the change in fibrosis stage between two biopsies (paired biopsies studies). A random effect model was used to derive FPR among different HCV genotypes. Eight single-biopsy studies (3182 patients, mean/median duration of infection ranging from 9 to 21 years) and eight paired biopsies studies (mean interval between biopsies 2-12 years) met the selection criteria. The odds ratio for the association of genotype 3 with accelerated fibrosis progression was 1.52 (95% CI 1.12-2.07, P = 0.007) in single-biopsy studies and 1.37 (95% CI 0.87-2.17, P = 0.17) in paired biopsy studies. In conclusion, viral genotype 3 was associated with faster fibrosis progression in single-biopsy studies. This observation may have important consequences on the clinical management of genotype 3-infected patients. The association was not significant in paired biopsies studies, although the latter may be limited by important indication bias, short observation time and small sample size.

Growth hormone receptor polymorphism and growth hormone therapy response in children: a bayesian meta-analysis

Recombinant human growth hormone (rhGH) therapy is used in the long-term treatment of children with growth disorders, but there is considerable treatment response variability. The exon 3-deleted growth hormone receptor polymorphism (GHR(d3)) may account for some of this variability. The authors performed a systematic review (to April 2011), including investigator-only data, to quantify the effects of the GHR(fl-d3) and GHR(d3-d3) genotypes on rhGH therapy response and used a recently established Bayesian inheritance model-free approach to meta-analyze the data. The primary outcome was the 1-year change-in-height standard-deviation score for the 2 genotypes. Eighteen data sets from 12 studies (1,527 children) were included. After several prior assumptions were tested, the most appropriate inheritance model was codominant (posterior probability = 0.93). Compared with noncarriers, carriers had median differences in 1-year change-in-height standard-deviation score of 0.09 (95% credible interval (CrI): 0.01, 0.17) for GHR(fl-d3) and of 0.14 (95% CrI: 0.02, 0.26) for GHR(d3-d3). However, the between-study standard deviation of 0.18 (95% CrI: 0.10, 0.33) was considerable. The authors tested by meta-regression for potential modifiers and found no substantial influence. They conclude that 1) the GHR(d3) polymorphism inheritance is codominant, contrasting with previous reports; 2) GHR(d3) genotypes account for modest increases in rhGH effects in children; and 3) considerable unexplained variability in responsiveness remains.

Bivariate meta-analysis of predictive values of diagnostic tests can be an alternative to bivariate meta-analysis of sensitivity and specificity

Meta-analysis of predictive values is usually discouraged because these values are directly affected by disease prevalence, but sensitivity and specificity sometimes show substantial heterogeneity as well. We propose a bivariate random-effects logitnormal model for the meta-analysis of the positive predictive value (PPV) and negative predictive value (NPV) of diagnostic tests.

Using Smalltalk as a Reflective Executable Meta-Language

Object-oriented meta-languages such as MOF or EMOF are often used to specify domain specific languages. However, these meta-languages lack the ability to describe behavior or operational semantics. Several approaches used a subset of Java mixed with OCL as executable meta-languages. In this paper, we report our experience of using Smalltalk as an executable and integrated meta-language. We validated this approach in incrementally building over the last decade, Moose, a meta-described reengineering environment. The reflective capabilities of Smalltalk support a uniform way of letting the base developer focus on his tasks while at the same time allowing him to meta-describe his domain model. The advantage of our this approach is that the developer uses the same tools and environment

Ozone and Mortality: A Meta-Analysis of Time-Series Studies and Comparison to a Multi-City Study (The National Morbidity, Mortality, and Air Pollution Study)

While many time-series studies of ozone and daily mortality identified positive associations,others yielded null or inconclusive results. We performed a meta-analysis of 144 effect estimates from 39 time-series studies, and estimated pooled effects by lags, age groups,cause-specific mortality, and concentration metrics. We compared results to estimates from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), a time-series study of 95 large U.S. cities from 1987 to 2000. Both meta-analysis and NMMAPS results provided strong evidence of a short-term association between ozone and mortality, with larger effects for cardiovascular and respiratory mortality, the elderly, and current day ozone exposure as compared to other single day lags. In both analyses, results were not sensitive to adjustment for particulate matter and model specifications. In the meta-analysis we found that a 10 ppb increase in daily ozone is associated with a 0.83 (95% confidence interval: 0.53, 1.12%) increase in total mortality, whereas the corresponding NMMAPS estimate is 0.25%(0.12, 0.39%). Meta-analysis results were consistently larger than those from NMMAPS,indicating publication bias. Additional publication bias is evident regarding the choice of lags in time-series studies, and the larger heterogeneity in posterior city-specific estimates in the meta-analysis, as compared with NMAMPS.

RANDOM EFFECTS MODELS IN A META-ANALYSIS OF THE ACCURACY OF DIAGNOSTIC TESTS WITHIN A GOLD STANDARD IN THE PRESENCE OF MISSING DATA

In evaluating the accuracy of diagnosis tests, it is common to apply two imperfect tests jointly or sequentially to a study population. In a recent meta-analysis of the accuracy of microsatellite instability testing (MSI) and traditional mutation analysis (MUT) in predicting germline mutations of the mismatch repair (MMR) genes, a Bayesian approach (Chen, Watson, and Parmigiani 2005) was proposed to handle missing data resulting from partial testing and the lack of a gold standard. In this paper, we demonstrate an improved estimation of the sensitivities and specificities of MSI and MUT by using a nonlinear mixed model and a Bayesian hierarchical model, both of which account for the heterogeneity across studies through study-specific random effects. The methods can be used to estimate the accuracy of two imperfect diagnostic tests in other meta-analyses when the prevalence of disease, the sensitivities and/or the specificities of diagnostic tests are heterogeneous among studies. Furthermore, simulation studies have demonstrated the importance of carefully selecting appropriate random effects on the estimation of diagnostic accuracy measurements in this scenario.

A unification of models for meta-analysis of diagnostic accuracy studies

Studies of diagnostic accuracy require more sophisticated methods for their meta-analysis than studies of therapeutic interventions. A number of different, and apparently divergent, methods for meta-analysis of diagnostic studies have been proposed, including two alternative approaches that are statistically rigorous and allow for between-study variability: the hierarchical summary receiver operating characteristic (ROC) model (Rutter and Gatsonis, 2001) and bivariate random-effects meta-analysis (van Houwelingen and others, 1993), (van Houwelingen and others, 2002), (Reitsma and others, 2005). We show that these two models are very closely related, and define the circumstances in which they are identical. We discuss the different forms of summary model output suggested by the two approaches, including summary ROC curves, summary points, confidence regions, and prediction regions.

Effect of initial treatment strategy on survival of patients with advanced-stage Hodgkin's lymphoma: a systematic review and network meta-analysis

BACKGROUND Several treatment strategies are available for adults with advanced-stage Hodgkin's lymphoma, but studies assessing two alternative standards of care-increased dose bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisone (BEACOPPescalated), and doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD)-were not powered to test differences in overall survival. To guide treatment decisions in this population of patients, we did a systematic review and network meta-analysis to identify the best initial treatment strategy. METHODS We searched the Cochrane Library, Medline, and conference proceedings for randomised controlled trials published between January, 1980, and June, 2013, that assessed overall survival in patients with advanced-stage Hodgkin's lymphoma given BEACOPPbaseline, BEACOPPescalated, BEACOPP variants, ABVD, cyclophosphamide (mechlorethamine), vincristine, procarbazine, and prednisone (C[M]OPP), hybrid or alternating chemotherapy regimens with ABVD as the backbone (eg, COPP/ABVD, MOPP/ABVD), or doxorubicin, vinblastine, mechlorethamine, vincristine, bleomycin, etoposide, and prednisone combined with radiation therapy (the Stanford V regimen). We assessed studies for eligibility, extracted data, and assessed their quality. We then pooled the data and used a Bayesian random-effects model to combine direct comparisons with indirect evidence. We also reconstructed individual patient survival data from published Kaplan-Meier curves and did standard random-effects Poisson regression. Results are reported relative to ABVD. The primary outcome was overall survival. FINDINGS We screened 2055 records and identified 75 papers covering 14 eligible trials that assessed 11 different regimens in 9993 patients, providing 59 651 patient-years of follow-up. 1189 patients died, and the median follow-up was 5·9 years (IQR 4·9-6·7). Included studies were of high methodological quality, and between-trial heterogeneity was negligible (τ(2)=0·01). Overall survival was highest in patients who received six cycles of BEACOPPescalated (HR 0·38, 95% credibility interval [CrI] 0·20-0·75). Compared with a 5 year survival of 88% for ABVD, the survival benefit for six cycles of BEACOPPescalated is 7% (95% CrI 3-10)-ie, a 5 year survival of 95%. Reconstructed individual survival data showed that, at 5 years, BEACOPPescalated has a 10% (95% CI 3-15) advantage over ABVD in overall survival. INTERPRETATION Six cycles of BEACOPPescalated significantly improves overall survival compared with ABVD and other regimens, and thus we recommend this treatment strategy as standard of care for patients with access to the appropriate supportive care.