High prevalence of potential biases threatens the interpretation of trials in patients with chronic disease
Daniela Vollenweider , Cynthia M Boyd and Milo A Puhan
BMC Medicine 2011, 9:73doi:10.1186/1741-7015-9-73
Published: 13 June 2011
Abstract (provisional)
Background
The complexity of chronic diseases challenges investigators conducting randomized trials. The cause for this is the often more difficult control for confounding, the selection of outcomes from many potentially important outcomes, the risk for missing data with long follow-up or 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 II diabetes mellitus, stroke and heart failure that were included in current Cochrane reviews. We assessed whether these trials defined a single or several primary outcomes, statistically compared baseline characteristics to assess comparability of treatment groups, reported on between-group comparisons and we 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 while 33% reported one or more primary outcomes. Two out of the 51 trials that tested for statistical significance of baseline characteristics adjusted the comparison for a characteristic that was statistically significantly different. 10% of trials 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) did a complete case analysis, 13% (n=4) used a fixed value imputation and 10% (n=3) used more advanced methods. 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 statement (2001 or later) and 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 naive methods to handle missing data, if at all. As a consequence, biases are likely to be introduced in many trials on widely prescribed treatments in patients with chronic disease.
