Abstract / Description of output
Correctable weaknesses in the design, conduct, and analysis of biomedical and public health research studies can produce misleading results and waste valuable resources. Small effects can be difficult to distinguish from bias introduced by study design and analyses. An absence of detailed written protocols and poor documentation of research is common. Information obtained might not be useful or important, and statistical precision or power is often too low or used in a misleading way. Insufficient consideration might be given to both previous and continuing studies. Arbitrary choice of analyses and an overemphasis on random extremes might affect the reported findings. Several problems relate to the research workforce, including failure to involve experienced statisticians and methodologists, failure to train clinical researchers and laboratory scientists in research methods and design, and the involvement of stakeholders with conflicts of interest. Inadequate emphasis is placed on recording of research decisions and on reproducibility of research. Finally, reward systems incentivise quantity more than quality, and novelty more than reliability. We propose potential solutions for these problems, including improvements in protocols and documentation, consideration of evidence from studies in progress, standardisation of research efforts, optimisation and training of an experienced and non-conflicted scientific workforce, and reconsideration of scientific reward systems.
Original language | English |
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Pages (from-to) | 166-175 |
Number of pages | 10 |
Journal | The Lancet |
Volume | 383 |
Issue number | 9912 |
Early online date | 8 Jan 2014 |
DOIs | |
Publication status | Published - 11 Jan 2014 |
Keywords / Materials (for Non-textual outputs)
- Reproducibility of Results
- Research Design
- Research Personnel
- Reward
- Statistics as Topic
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Malcolm MacLeod
- Deanery of Clinical Sciences - Personal Chair of Neurology and Translational Neuroscience
- Centre for Clinical Brain Sciences
- Edinburgh Neuroscience
- Edinburgh Imaging
- Collaborative Approach to Meta Analysis and Review of Animal Data from Experimental Studies
- Cerebrovascular Research Group
Person: Academic: Research Active