Analysis of subgroup results in a clinical trial is surprisingly unreliable, even in a large trial. This is the result of a combination of reduced statistical power, increased variance and the play of chance. Reliance on such analyses is likely to be more erroneous, and hence harmful, than application of the overall proportional (or relative) result in the whole trial to the estimate of absolute risk in that subgroup. Plausible explanations can usually be found for effects that are, in reality, simply due to the play of chance. When clinicians believe such subgroup analyses, there is a real danger of harm to the individual patient.

In one of the biggest dilemmas facing cardiovascular clinical research, clinical trials are increasingly being required to show benefits on clinical end-points rather than surrogate end-points, while at the same time the incremental benefits of newer treatments are getting smaller. These two factors have a huge impact on sample size, which has led some investigators to design trials to show that the new treatment has an effect similar to that of the standard, rather than outright superiority. Recent examples of fibrinolytic trials that have demonstrated similar effects of two drugs are ASSENT (Assessment of the Safety and Efficacy of a New Thrombolytic)-2, GUSTO (Global Use of Strategies to Open Occluded Coronary Arteries)-III, and COBALT (Continuous Infusion Versus Double-Bolus Administration of Alteplase) [1,2,3,4]. However, as discussed by several authors [5,6,7,8], there are issues with trials of this type that make them considerably less credible than superiority trials.

Some problems with the quality of controlled clinical trials can be addressed by following these procedures: registering all trials at inception; using systematic reviews to inform the design of new studies; posting and obtaining feedback on preprints; reporting all well conducted trials, regardless of their results; reducing biased and inefficient assessment of reports submitted for publication; publishing sufficiently detailed reports; linking trial reports to relevant external information; providing readier access to reports; and reviewing and amending reports after initial publication. The launch of a new range of electronic journals by Current Controlled Trials offers an opportunity to contribute to progress in these ways.

Surrogate outcomes are frequently used in cardiovascular disease research. A concern is that changes in surrogate markers may not reflect changes in disease outcomes. Two recent clinical trials (Heart and Estrogen/Progestin Replacement Study [HERS], and the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial [ALLHAT]) underscore this problem since their results contradicted what was expected based on the surrogate outcomes. The current regulatory policy to allow new therapies to be introduced onto the market based solely on surrogate outcomes may need to be reviewed.

Randomized trials and observational studies, such as case-control studies, are often seen as opposing approaches. However, in many instances results obtained by different designs may complement each other. For instance, case-control studies on aetiology of disease may help to give the direction of future trials. In this commentary, the author discusses the purpose of randomization and observation, and under which conditions one design may be preferred to another. Randomization is useful to combat 'confounding by indication', and is therefore the design of choice for most therapeutic trials. When this confounding is not an issue, as in studies of genetic risk factors or side-effects, then case-control studies are preferred.

The Losartan Heart Failure Survival Study (ELITE II) and the Valsartan Heart Failure Trial (Val-HeFT) both evaluated the efficacy and tolerability of a selective angiotensin II receptor antagonist on morbidity and mortality in patients with symptomatic heart failure. The trials differed, however, in terms of their primary hypothesis, study design, and treatment regimens, and this must be taken into consideration when comparing and interpreting the data from these studies. The data are in many ways complementary, and add to our understanding of the optimal treatment of symptomatic heart failure. Additional studies are needed, however, to fully define the role of angiotensin II receptor antagonists in the management of this very heterogeneous group of patients.

Background Many randomized trials involve measuring a continuous outcome - such as pain, body weight or blood pressure - at baseline and after treatment. In this paper, I compare four possibilities for how such trials can be analyzed: post-treatment; change between baseline and post-treatment; percentage change between baseline and post-treatment and analysis of covariance (ANCOVA) with baseline score as a covariate. The statistical power of each method was determined for a hypothetical randomized trial under a range of correlations between baseline and post-treatment scores. Results ANCOVA has the highest statistical power. Change from baseline has acceptable power when correlation between baseline and post-treatment scores is high;when correlation is low, analyzing only post-treatment scores has reasonable power. Percentage change from baseline has the lowest statistical power and was highly sensitive to changes in variance. Theoretical considerations suggest that percentage change from baseline will also fail to protect from bias in the case of baseline imbalance and will lead to an excess of trials with non-normally distributed outcome data. Conclusions Percentage change from baseline should not be used in statistical analysis. Trialists wishing to report this statistic should use another method, such as ANCOVA, and convert the results to a percentage change by using mean baseline scores.

The publication of the results of the Swedish Trial in Old Patients with Hypertension-2 (STOP-2) and the termination of the doxazocin arm of the Antihypertensive and Lipid Lowering Treatment to Prevent Heart Attack (ALLHAT) study again raise the question of whether all antihypertensives deliver equal cardiovascular outcome benefits. Data from research on congestive heart failure and from the Heart Outcomes Prevention Evaluation (HOPE) trial illuminate the roles and possible mechanisms of humoral mediators of vascular damage, suggesting, first, that some antihypertensives (thiazides, beta-blockers, and angiotensin-converting enzyme inhibitors) can deliver more improvement in outcomes than other agents and, second, that decisions on whom to treat are best made based on risk appraisal, not merely pressures.