Research Article: Current Controlled Trials in Cardiovascular Medicine

Background Physicians reading the medical literature attempt to determine whether research studies are valid. However, articles with negative results may not provide sufficient information to allow physicians to properly assess validity. Methods We analyzed all original research articles with negative results published in 1997 in the weekly journals BMJ, JAMA, Lancet, and New England Journal of Medicine as well as those published in the 1997 and 1998 issues of the bimonthly Annals of Internal Medicine (N = 234). Our primary objective was to quantify the proportion of studies with negative results that comment on power and present confidence intervals. Secondary outcomes were to quantify the proportion of these studies with a specified effect size and a defined primary outcome. Stratified analyses by study design were also performed. Results Only 30% of the articles with negative results comment on power. The reporting of power (range: 15%-52%) and confidence intervals (range: 55–81%) varied significantly among journals. Observational studies of etiology/risk factors addressed power less frequently (15%, 95% CI, 8–21%) than did clinical trials (56%, 95% CI, 46–67%, p < 0.001). While 87% of articles with power calculations specified an effect size the authors sought to detect, a minority gave a rationale for the effect size. Only half of the studies with negative results clearly defined a primary outcome. Conclusion Prominent medical journals often provide insufficient information to assess the validity of studies with negative results.

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.

A common problem in clinical trials is the missing data that occurs when patients do not complete the study and drop out without further measurements. Missing data cause the usual statistical analysis of complete or all available data to be subject to bias. There are no universally applicable methods for handling missing data. We recommend the following: (1) Report reasons for dropouts and proportions for each treatment group; (2) Conduct sensitivity analyses to encompass different scenarios of assumptions and discuss consistency or discrepancy among them; (3) Pay attention to minimize the chance of dropouts at the design stage and during trial monitoring; (4) Collect post-dropout data on the primary endpoints, if at all possible; and (5) Consider the dropout event itself an important endpoint in studies with many.

Background Decisions on treatment are guided, not only by the potential for benefit, but also by the nature and severity of adverse drug reactions. However, some researchers have found numerous deficiencies in trial reports of adverse effects. We sought to confirm these findings by evaluating trials of drug therapy published in seven eminent medical journals in 1997. Methods Literature review to determine whether the definition, recording and reporting of adverse drug reactions in clinical trials were in accordance with published recommendations on structured reporting. Results Of the 185 trials reviewed, 25 (14%) made no mention of adverse drug reactions. Data in a further 60 (32%) could not be fully evaluated, either because numbers were not given for each treatment arm (31 trials), or because a generic statement was made without full details (29 trials). When adverse drug reactions such as clinical events or patient symptoms were mentioned in the reports, details on how they had been recorded were given in only 14/95 (15%) and 18/104 (17%) trials respectively. Of the 86 trials that mentioned severity of adverse drug reactions, only 42 (49%) stated how severity had been defined. The median amount of space used for safety data in the Results and Discussion sections was 5.8%. Conclusions Trial reports often failed to provide details on how adverse drug reactions were defined or recorded. The absence of such methodological information makes comparative evaluation of adverse reaction rates potentially unreliable. Authors and journals should adopt recommendations on the structured reporting of adverse effects.

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.

Background Guidelines published in major medical journals are very influential in determining clinical practice. It would be essential to evaluate whether conflicts of interests are disclosed in these publications. We evaluated the reporting of conflicts of interest and the factors that may affect such disclosure in a sample of 191 guidelines on therapeutic and/or preventive measures published in 6 major clinical journals (Annals of Internal Medicine, BMJ, JAMA, Lancet, New England Journal of Medicine, Pediatrics) in 1979, 1984, 1989, 1994 and 1999. Results Only 7 guidelines (3.7%) mentioned conflicts of interest and all were published in 1999 (17.5% (7/40) of guidelines published in 1999 alone). Reporting of conflicts of interest differed significantly by journal (p=0.026), availability of disclosure policy by the journal (p=0.043), source of funding (p < 0.001) and number of authors (p=0.004). In the entire database of 191 guidelines, a mere 18 authors disclosed a total of 24 potential conflicts of interest and most pertained to minor issues. Conclusions Despite some recent improvement, reporting of conflicts of interest in clinical guidelines published in influential journals is largely neglected.

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.

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?