Randomized controlled trials of treatments for hematologic malignancies: study characteristics and outcomes

Prognostic significance of osteopontin in acute myeloid leukemia: A meta-analysis

Osteopontin (OPN) has been investigated in the field of tumor research for several years. However, the prognostic role of OPN overexpression in acute myeloid leukemia (AML) remains controversial. A meta-analysis of four studies, including a total of 492 patients, was performed to determine the association of OPN with overall survival (OS) in AML patients. The random-effects model of Der Simonian and Laird was used to synthesize data; hazard ratio (HR) with its 95% confidence interval (CI) was used as the effect size estimate. It was observed that serum-based OPN was inversely correlated with OS and the difference was statistically significant (HR=1.83; 95% CI: 1.43-2.35; P<0.001). Experimental findings indicate that OPN overexpression is associated with a poor prognosis in AML and may be of prognostic value for AML stage and metastasis.

Factors associated with publication of randomized phase iii cancer trials in journals with a high impact factor

BACKGROUND

Impact factor (if) is often used as a measure of journal quality. The purpose of the present study was to determine whether trials with positive outcomes are more likely to be published in journals with higher ifs.

METHODS

We reviewed 476 randomized phase iii cancer trials published in 13 journals between 1995 and 2005. Multivariate logistic regression models were used to investigate predictors of publication in journals with high ifs (compared with low and medium ifs).

RESULTS

A positive outcome had the strongest association with publication in high-if journals [odds ratio (or): 4.13; 95% confidence interval (ci): 2.67 to 6.37; p < 0.001]. Other associated factors were a larger sample size (or: 1.06; 95% ci: 1.02 to 1.10; p = 0.001), intention-to-treat analysis (or: 2.53; 95% ci: 1.56 to 4.10; p < 0.001), North American authors (or for European authors: 0.36; 95% ci: 0.23 to 0.58; or for international authors: 0.41; 95% ci: 0.20 to 0.82; p < 0.001), adjuvant therapy trial (or: 2.58; 95% ci: 1.61 to 4.15; p < 0.001), shorter time to publication (or: 0.84; 95% ci: 0.77 to 0.92; p < 0.001), uncommon tumour type (or: 1.39; 95% ci: 0.90 to 2.13; p = 0.012), and hematologic malignancy (or: 3.15; 95% ci: 1.41 to 7.03; p = 0.012).

CONCLUSIONS

Cancer trials with positive outcomes are more likely to be published in journals with high ifs. Readers of medical literature should be aware of this "impact factor bias," and investigators should be encouraged to submit reports of trials of high methodologic quality to journals with high ifs regardless of study outcomes.

New treatments compared to established treatments in randomized trials

BACKGROUND

The proportion of proposed new treatments that are 'successful' is of ethical, scientific, and public importance. We investigated how often new, experimental treatments evaluated in randomized controlled trials (RCTs) are superior to established treatments.

OBJECTIVES

Our main question was: "On average how often are new treatments more effective, equally effective or less effective than established treatments?" Additionally, we wanted to explain the observed results, i.e. whether the observed distribution of outcomes is consistent with the 'uncertainty requirement' for enrollment in RCTs. We also investigated the effect of choice of comparator (active versus no treatment/placebo) on the observed results.

SEARCH METHODS

We searched the Cochrane Methodology Register (CMR) 2010, Issue 1 in The Cochrane Library (searched 31 March 2010); MEDLINE Ovid 1950 to March Week 2 2010 (searched 24 March 2010); and EMBASE Ovid 1980 to 2010 Week 11 (searched 24 March 2010).

SELECTION CRITERIA

Cohorts of studies were eligible for the analysis if they met all of the following criteria: (i) consecutive series of RCTs, (ii) registered at or before study onset, and (iii) compared new against established treatments in humans.

DATA COLLECTION AND ANALYSIS

RCTs from four cohorts of RCTs met all inclusion criteria and provided data from 743 RCTs involving 297,744 patients. All four cohorts consisted of publicly funded trials. Two cohorts involved evaluations of new treatments in cancer, one in neurological disorders, and one for mixed types of diseases. We employed kernel density estimation, meta-analysis and meta-regression to assess the probability of new treatments being superior to established treatments in their effect on primary outcomes and overall survival.

MAIN RESULTS

The distribution of effects seen was generally symmetrical in the size of difference between new versus established treatments. Meta-analytic pooling indicated that, on average, new treatments were slightly more favorable both in terms of their effect on reducing the primary outcomes (hazard ratio (HR)/odds ratio (OR) 0.91, 99% confidence interval (CI) 0.88 to 0.95) and improving overall survival (HR 0.95, 99% CI 0.92 to 0.98). No heterogeneity was observed in the analysis based on primary outcomes or overall survival (I(2) = 0%). Kernel density analysis was consistent with the meta-analysis, but showed a fairly symmetrical distribution of new versus established treatments indicating unpredictability in the results. This was consistent with the interpretation that new treatments are only slightly superior to established treatments when tested in RCTs. Additionally, meta-regression demonstrated that results have remained stable over time and that the success rate of new treatments has not changed over the last half century of clinical trials. The results were not significantly affected by the choice of comparator (active versus placebo/no therapy).

AUTHORS' CONCLUSIONS

Society can expect that slightly more than half of new experimental treatments will prove to be better than established treatments when tested in RCTs, but few will be substantially better. This is an important finding for patients (as they contemplate participation in RCTs), researchers (as they plan design of the new trials), and funders (as they assess the 'return on investment'). Although we provide the current best evidence on the question of expected 'success rate' of new versus established treatments consistent with a priori theoretical predictions reflective of 'uncertainty or equipoise hypothesis', it should be noted that our sample represents less than 1% of all available randomized trials; therefore, one should exercise the appropriate caution in interpretation of our findings. In addition, our conclusion applies to publicly funded trials only, as we did not include studies funded by commercial sponsors in our analysis.