BMC medical research methodology · 2014
A test for reporting bias in trial networks: simulation and case studies.
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Trinquart Ludovic, Ioannidis John P A, Chatellier Gilles, Ravaud Philippe
Аннотация
BACKGROUND: Networks of trials assessing several treatment options available for the same condition are increasingly considered. Randomized trial evidence may be missing because of reporting bias. We propose a test for reporting bias in trial networks.
METHODS: We test whether there is an excess of trials with statistically significant results across a network of trials. The observed number of trials with nominally statistically significant p-values across the network is compared with the expected number. The performance of the test (type I error rate and power) was assessed using simulation studies under different scenarios of selective reporting bias. Examples are provided for networks of antidepressant and antipsychotic trials, where reporting biases have been previously demonstrated by comparing published to Food and Drug Administration (FDA) data.
RESULTS: In simulations, the test maintained the type I error rate and was moderately powerful after adjustment for type I error rate, except when the between-trial variance was substantial. In all, a positive test result increased moderately or markedly the probability of reporting bias being present, while a negative test result was not very informative. In the two examples, the test gave a signal for an excess of statistically significant results in the network of published data but not in the network of FDA data.
CONCLUSION: The test could be useful to document an excess of significant findings in trial networks, providing a signal for potential publication bias or other selective analysis and outcome reporting biases.
METHODS: We test whether there is an excess of trials with statistically significant results across a network of trials. The observed number of trials with nominally statistically significant p-values across the network is compared with the expected number. The performance of the test (type I error rate and power) was assessed using simulation studies under different scenarios of selective reporting bias. Examples are provided for networks of antidepressant and antipsychotic trials, where reporting biases have been previously demonstrated by comparing published to Food and Drug Administration (FDA) data.
RESULTS: In simulations, the test maintained the type I error rate and was moderately powerful after adjustment for type I error rate, except when the between-trial variance was substantial. In all, a positive test result increased moderately or markedly the probability of reporting bias being present, while a negative test result was not very informative. In the two examples, the test gave a signal for an excess of statistically significant results in the network of published data but not in the network of FDA data.
CONCLUSION: The test could be useful to document an excess of significant findings in trial networks, providing a signal for potential publication bias or other selective analysis and outcome reporting biases.