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.

Clinical trialists and statisticians are very wary of subgroup analysis, for good reasons. Clinicians have to deal with situations in which subgroups of patients differ widely from one another in their prognosis and response to treatment. Few trials are large enough to demonstrate convincingly these differences in outcome, but often provide suggestive evidence. Should we ignore this and treat all patients as the same, or should we allow dubious statistical evidence to buttress biological plausibility in making clinical decisions?

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 Readers may question the interpretation of findings in clinical trials when multiple outcome measures are used without adjustment of the p-value. This question arises because of the increased risk of Type I errors (findings of false "significance") when multiple simultaneous hypotheses are tested at set p-values. The primary aim of this study was to estimate the need to make appropriate p-value adjustments in clinical trials to compensate for a possible increased risk in committing Type I errors when multiple outcome measures are used. Discussion The classicists believe that the chance of finding at least one test statistically significant due to chance and incorrectly declaring a difference increases as the number of comparisons increases. The rationalists have the following objections to that theory: 1) P-value adjustments are calculated based on how many tests are to be considered, and that number has been defined arbitrarily and variably; 2) P-value adjustments reduce the chance of making type I errors, but they increase the chance of making type II errors or needing to increase the sample size. Summary Readers should balance a study's statistical significance with the magnitude of effect, the quality of the study and with findings from other studies. Researchers facing multiple outcome measures might want to either select a primary outcome measure or use a global assessment measure, rather than adjusting the p-value.

Background There is debate concerning methods for calculating numbers needed to treat (NNT) from results of systematic reviews. Methods We investigate the susceptibility to bias for alternative methods for calculating NNTs through illustrative examples and mathematical theory. Results Two competing methods have been recommended: one method involves calculating the NNT from meta-analytical estimates, the other by treating the data as if it all arose from a single trial. The 'treat-as-one-trial' method was found to be susceptible to bias when there were imbalances between groups within one or more trials in the meta-analysis (Simpson's paradox). Calculation of NNTs from meta-analytical estimates is not prone to the same bias. The method of calculating the NNT from a meta-analysis depends on the treatment effect used. When relative measures of treatment effect are used the estimates of NNTs can be tailored to the level of baseline risk. Conclusions The treat-as-one-trial method of calculating numbers needed to treat should not be used as it is prone to bias. Analysts should always report the method they use to compute estimates to enable readers to judge whether it is appropriate.

Background Calculation of numbers needed to treat (NNT) is more complex from meta-analysis than from single trials. Treating the data as if it all came from one trial may lead to misleading results when the trial arms are imbalanced. Discussion An example is shown from a published Cochrane review in which the benefit of nursing intervention for smoking cessation is shown by formal meta-analysis of the individual trial results. However if these patients were added together as if they all came from one trial the direction of the effect appears to be reversed (due to Simpson's paradox). Whilst NNT from meta-analysis can be calculated from pooled Risk Differences, this is unlikely to be a stable method unless the event rates in the control groups are very similar. Since in practice event rates vary considerably, the use a relative measure, such as Odds Ratio or Relative Risk is advocated. These can be applied to different levels of baseline risk to generate a risk specific NNT for the treatment. Summary The method used to calculate NNT from meta-analysis should be clearly stated, and adding the patients from separate trials as if they all came from one trial should be avoided.

The evidence base for drug treatment of hypertension is strong. Early trials using thiazide diuretics suggested a shortfall in prevention of coronary heart disease. The superiority of newer drugs has been widely advocated but trial evidence does not support an advantage of beta-blockers, angiotensin converting enzyme inhibitors, calcium channel blockers or alpha-blockers for this outcome. Even meta-analyses have failed to clarify matters. If this issue is to be settled, bigger and better trials of longer duration in high-risk patients are needed. Meanwhile, the importance of rigorous blood pressure control using multiple drugs has been established. This should be the focus of our attention rather than agonising over differences in cause-specific outcomes that may not be generalisable to all patient populations.