Screening for prostate cancer: systematic review and meta-analysis of randomised controlled trials

Objective To examine the evidence on the benefits and harms of screening for prostate cancer

Choosing a control intervention for a randomised clinical trial

BACKGROUND: Randomised controlled clinical trials are performed to resolve uncertainty concerning comparator interventions. Appropriate acknowledgment of uncertainty enables the concurrent achievement of two goals : the acquisition of valuable scientific knowledge and an optimum treatment choice for the patient-participant. The ethical recruitment of patients requires the presence of clinical equipoise. This involves the appropriate choice of a control intervention, particularly when unapproved drugs or innovative interventions are being evaluated. DISCUSSION: We argue that the choice of a control intervention should be supported by a systematic review of the relevant literature and, where necessary, solicitation of the informed beliefs of clinical experts through formal surveys and publication of the proposed trial's protocol. SUMMARY: When clinical equipoise is present, physicians may confidently propose trial enrollment to their eligible patients as an act of therapeutic beneficence

Quality and methods of developing practice guidelines

BACKGROUND: It is not known whether there are differences in the quality and recommendations between evidence-based (EB) and consensus-based (CB) guidelines. We used breast cancer guidelines as a case study to assess for these differences. METHODS: Five different instruments to evaluate the quality of guidelines were identified by a literature search. We also searched MEDLINE and the Internet to locate 8 breast cancer guidelines. These guidelines were classified in three categories: evidence based, consensus based and consensus based with no explicit consideration of evidence (CB-EB). Each guideline was evaluated by three of the authors using each of the instruments. For each guideline we assessed the agreement among 14 decision points which were selected from the NCCN (National Cancer Comprehensive Network) guidelines algorithm. For each decision point we recorded the level of the quality of the information used to support it. A regression analysis was performed to assess if the percentage of high quality evidence used in the guidelines development was related to the overall quality of the guidelines. RESULTS: Three guidelines were classified as EB, three as CB-EB and two as CB. The EB guidelines scored better than CB, with the CB-EB scoring in the middle among all instruments for guidelines quality assessment. No major disagreement in recommendations was detected among the guidelines regardless of the method used for development, but the EB guidelines had a better agreement with the benchmark guideline for any decision point. When the source of evidence used to support decision were of high quality, we found a higher level of full agreement among the guidelines' recommendations. Up to 94% of variation in the quality score among guidelines could be explained by the quality of evidence used for guidelines development. CONCLUSION: EB guidelines have a better quality than CB guidelines and CB-EB guidelines. Explicit use of high quality evidence can lead to a better agreement among recommendations. However, no major disagreement among guidelines was noted regardless of the method for their development

Estimating the mean and variance from the median, range, and the size of a sample

BACKGROUND: Usually the researchers performing meta-analysis of continuous outcomes from clinical trials need their mean value and the variance (or standard deviation) in order to pool data. However, sometimes the published reports of clinical trials only report the median, range and the size of the trial. METHODS: In this article we use simple and elementary inequalities and approximations in order to estimate the mean and the variance for such trials. Our estimation is distribution-free, i.e., it makes no assumption on the distribution of the underlying data. RESULTS: We found two simple formulas that estimate the mean using the values of the median (m), low and high end of the range (a and b, respectively), and n (the sample size). Using simulations, we show that median can be used to estimate mean when the sample size is larger than 25. For smaller samples our new formula, devised in this paper, should be used. We also estimated the variance of an unknown sample using the median, low and high end of the range, and the sample size. Our estimate is performing as the best estimate in our simulations for very small samples (n ≤ 15). For moderately sized samples (15 <n ≤ 70), our simulations show that the formula range/4 is the best estimator for the standard deviation (variance). For large samples (n > 70), the formula range/6 gives the best estimator for the standard deviation (variance). We also include an illustrative example of the potential value of our method using reports from the Cochrane review on the role of erythropoietin in anemia due to malignancy. CONCLUSION: Using these formulas, we hope to help meta-analysts use clinical trials in their analysis even when not all of the information is available and/or reported

Allogeneic stem cell transplantation for acute myeloid leukemia in first complete remission: systematic review and meta-analysis of prospective clinical trials.

The optimal treatment of acute myeloid leukemia (AML) in first complete remission (CR1) is uncertain. Current consensus, based on cytogenetic risk, recommends myeloablative allogeneic stem cell transplantation (SCT) for poor-risk but not for good-risk AML. Allogeneic SCT, autologous transplantation, and consolidation chemotherapy are considered of equivalent benefit for intermediate-risk AML

Erythropoietin, uncertainty principle and cancer related anaemia

BACKGROUND: This study was designed to evaluate if erythropoietin (EPO) is effective in the treatment of cancer related anemia, and if its effect remains unchanged when data are analyzed according to various clinical and methodological characteristics of the studies. We also wanted to demonstrate that cumulative meta-analysis (CMA) can be used to resolve uncertainty regarding clinical questions. METHODS: Systematic Review (SR) of the published literature on the role of EPO in cancer-related anemia. A cumulative meta-analysis (CMA) using a conservative approach was performed to determine the point in time when uncertainty about the effect of EPO on transfusion-related outcomes could be considered resolved. Participants: Patients included in randomized studies that compared EPO versus no therapy or placebo. Main outcome measures: Number of patients requiring transfusions. RESULTS: Nineteen trials were included. The pooled results indicated a significant effect of EPO in reducing the number of patients requiring transfusions [odds ratio (OR) = 0.41; 95%CI: 0.33 to 0.5; p < 0.00001;relative risk (RR) = 0.61; 95% CI: 0.54 to 0.68]. The results remain unchanged after the sensitivity analyses were performed according to the various clinical and methodological characteristics of the studies. The heterogeneity was less pronounced when OR was used instead of RR as the measure of the summary point estimate. Analysis according to OR was not heterogeneous, but the pooled RR was highly heterogeneous. A stepwise metaregression analysis did point to the possibility that treatment effect could have been exaggerated by inadequacy in allocation concealment and that larger treatment effects are seen at hb level > 11.5 g/dl. We identified 1995 as the point in time when a statistically significant effect of EPO was demonstrated and after which we considered that uncertainty about EPO efficacy was resolved. CONCLUSION: EPO is effective in the treatment of anemia in cancer patients. This could have already been known in 1995 if a CMA had been performed at that time

Mortality Risk Prediction by an Insurance Company and Long-Term Follow-Up of 62,000 Men

Background: Insurance companies use medical information to classify the mortality risk of applicants. Adding genetic tests to this assessment is currently being debated. This debate would be more meaningful, if results of present-day risk prediction were known. Therefore, we compared the predicted with the observed mortality of men who applied for life insurance, and determined the prognostic value of the risk assessment. Methods: Long-term follow-up was available for 62,334 male applicants whose mortality risk was predicted with medical evaluation and they were assigned to five groups with increasing risk from 1 to 5. We calculated all cause standardized mortality ratios relative to the Dutch population and compared groups with Cox's regression. We compared the discriminative ability of risk assessments as indicated by a concordance index (c). Results: In 844,815 person years we observed 3,433 deaths. The standardized mortality relative to the Dutch male population was 0.76 (95 percent confidence interval, 0.73 to 0.78). The standardized mortality ratios ranged from 0.54 in risk group 1 to 2.37 in group 5. A large number of risk factors and diseases were significantly associated with increased mortality. The algorithm of prediction was significantly, but only slightly better than summation of the number of disorders and risk factors (c-index, 0.64 versus 0.60, P,0.001). Conclusions: Men applying for insurance clearly had better survival relative to the general population. Readily available medical evaluation enabled accurate prediction of the mortality risk of large groups, but the deceased men could not have been identified with the applied prediction method

Instrumental variable meta-analysis of individual patient data: application to adjust for treatment non-compliance

<p>Abstract</p> <p>Background</p> <p>Intention-to-treat (ITT) is the standard data analysis method which includes all patients regardless of receiving treatment. Although the aim of ITT analysis is to prevent bias due to prognostic dissimilarity, it is also a counter-intuitive type of analysis as it counts patients who did not receive treatment, and may lead to "bias toward the null." As treated (AT) method analyzes patients according to the treatment actually received rather than intended, but is affected by the selection bias. Both ITT and AT analyses can produce biased estimates of treatment effect, so instrumental variable (IV) analysis has been proposed as a technique to control for bias when using AT data. Our objective is to correct for bias in non-experimental data from previously published individual patient data meta-analysis by applying IV methods</p> <p>Methods</p> <p>Center prescribing preference was used as an IV to assess the effects of methotrexate (MTX) in preventing debilitating complications of chronic graft-versus-host-disease (cGVHD) in patients who received peripheral blood stem cell (PBSCT) or bone marrow transplant (BMT) in nine randomized controlled trials (1107 patients). IV methods are applied using 2-stage logistic, 2-stage probit and generalized method of moments models.</p> <p>Results</p> <p>ITT analysis showed a statistically significant detrimental effect with the use of day 11 MTX, resulting in cGVHD odds ratio (OR) of 1.34 (95% CI 1.02-1.76). AT results showed no difference in the odds of cGVHD with the use of MTX [OR 1.31 (95%CI 0.99-1.73)]. IV analysis further corrected the results toward no difference in the odds of cGVHD between PBSCT vs. BMT, allowing for a possibility of beneficial effects of MTX in preventing cGVHD in PBSCT recipients (OR 1.14; 95%CI 0.83-1.56).</p> <p>Conclusion</p> <p>All instrumental variable models produce similar results. IV estimates correct for bias and do not exclude the possibility that MTX may be beneficial, contradicting the ITT analysis.</p