Background: The complexity of chronic diseases is a challenge for investigators conducting randomized trials. The causes for this include the often difficult control for confounding, the selection of outcomes from many potentially important outcomes, the risk of missing data with long follow-up and the detection of heterogeneity of treatment effects. Our aim was to assess such aspects of trial design and analysis for four prevalent chronic diseases.
Methods: We included 161 randomized trials on drug and non-drug treatments for chronic obstructive pulmonary disease, type 2 diabetes mellitus, stroke and heart failure, which were included in current Cochrane reviews. We assessed whether these trials defined a single outcome or several primary outcomes, statistically compared baseline characteristics to assess comparability of treatment groups, reported on between-group comparisons, and we also assessed how they handled missing data and whether appropriate methods for subgroups effects were used.
Results: We found that only 21% of all chronic disease trials had a single primary outcome, whereas 33% reported one or more primary outcomes. Two of the fifty-one trials that tested for statistical significance of baseline characteristics adjusted the comparison for a characteristic that was significantly different. Of the 161 trials, 10% reported a within-group comparison only; 17% (n = 28) of trials reported how missing data were handled (50% (n = 14) carried forward last values, 27% (n = 8) performed a complete case analysis, 13% (n = 4) used a fixed value imputation and 10% (n = 3) used more advanced methods); and 27% of trials performed a subgroup analysis but only 23% of them (n = 10) reported an interaction test. Drug trials, trials published after wide adoption of the CONSORT (CONsolidated Standards of Reporting Trials) statement (2001 or later) and trials in journals with higher impact factors were more likely to report on some of these aspects of trial design and analysis.
Conclusion: Our survey showed that an alarmingly large proportion of chronic disease trials do not define a primary outcome, do not use appropriate methods for subgroup analyses, or use naïve methods to handle missing data, if at all. As a consequence, biases are likely to be introduced in many trials on widely prescribed treatments for patients with chronic disease.