There has been extensive discussion of problems of reproducibility of research across a range of scientific disciplines (Ioannidis, Reference Ioannidis2005). A number of factors have been identified that may contribute to this, such as data fabrication (Simonsohn, Reference Simonsohn2013), publication bias (Smulders, Reference Smulders2013), peer review methods (Park et al. Reference Park, Peacey and Munafò2013) and low statistical power (Button et al. Reference Button, Ioannidis, Mokrysz, Nosek, Flint, Robinson and Munafò2013). For the most part these are not new concerns; however, one factor that may have changed over recent years is the scope for flexible data analysis, given the increasing automation of statistical analyses, and the ease with which multiple outcomes can be tested in the same dataset.

The impact of flexible analytical procedures has recently been described by Simmons et al. (Reference Simmons, Nelson and Simonsohn2011), who concluded that it is ‘unacceptably easy to accumulate (and report) statistically significant evidence for a false hypothesis’. This problem is not confined to behavioural experiments in psychology – Carp (Reference Carp2012) recently reviewed 241 functional magnetic resonance imaging (MRI) studies and showed that there were almost as many unique analytical pipelines reported as there were individual studies, with many studies not reporting critical methodological details. However, it is not clear whether analytical flexibility has increased over time.

We therefore investigated whether analytical flexibility in structural imaging studies of bipolar disorder and major depression has increased over time, using the number of measures collected as a proxy index of analytical flexibility. With more measures available, there is greater scope for conducting multiple statistical tests, and selecting those that provide the clearest results for reporting or highlighting. We also investigated whether the number of participants tested has increased over time.

Data were taken from the Bipolar Disorder Neuroimaging Database (bipolardatabase.org) (Kempton et al. Reference Kempton, Geddes, Ettinger, Williams and Grasby2008) and the Major Depression Neuroimaging Database (depressiondatabase.org) (Kempton et al. Reference Kempton, Salvador, Munafò, Geddes, Simmons, Frangou and Williams2011). These online databases include peer-reviewed computerized tomography and structural MRI studies that compare patients with bipolar disorder or patients with major depression, diagnosed using standard diagnostic criteria, with a healthy control group. From studies within these two databases, the total number of participants (patients and controls) and total number of different brain measures recorded per study were extracted for the present analyses. Brain measures were defined as the measurement of a brain region (e.g. left hippocampus volume) or the measurement of a cerebral abnormality (e.g. the presence of periventricular hyperintensities).

We used linear regression to explore the relationship between year of publication, number of measures and number of participants. For studies of bipolar disorder (k = 141), year of publication was not associated with number of participants [B = −0.01, 95% confidence interval (CI) −0.02 to 0.01, R ² = 0.01, p = 0.23] but was positively associated with number of measures (B = 0.04, 95% CI 0.02–0.06, R ² = 0.07, p = 0.001). However, for studies of major depression (k = 225), year of publication was positively associated with number of participants (B = 0.02, 95% CI 0.01–0.03, R ² = 0.04, p = 0.001) but not with number of measures (B = 0.01, 95% CI −0.01 to 0.03, R ² = 0.01, p = 0.21). A Z test indicated that these estimates differed, with strong evidence for number of participants (p = 0.004) and weaker evidence for number of measures (p = 0.080). These results are shown in Fig. 1.

Fig. 1.

Association of year of publication with number of participants and number of measures in structural imaging studies of major depression and bipolar disorder. In studies of bipolar disorder (a), year of publication is not associated with number of participants (R ² = 0.01, p = 0.23) but is associated with number of measures collected (R ² = 0.07, p = 0.001). However, in studies of major depression (b), year of publication is associated with number of participants (R ² = 0.04, p = 0.001) but not with number of measures collected (R ² = 0.01, p = 0.21).

Our results partly support the possibility that analytical flexibility has increased over time. Among structural imaging studies of bipolar disorder, the number of measures taken per study (assumed here to be a proxy index of analytical flexibility) has increased, while the average sample size has not. However, among structural imaging studies of major depression we observed the opposite pattern, with no increase in the number of measures taken per study but an increase in average sample size. The reasons for this discrepancy are not clear. We restricted our analysis to structural MRI region-of-interest studies because relevant analysis techniques are well established, and therefore consistent across studies. While strength of the MRI scanner and slice thickness may influence results, we previously found no evidence that these factors influenced measures of six key brain regions (Kempton et al. Reference Kempton, Salvador, Munafò, Geddes, Simmons, Frangou and Williams2011). It is possible that our results represent chance findings, but the statistical evidence is sufficiently strong that this explanation is unlikely. The results also do not appear to be driven by a small number of outliers.

One possibility is that there are in fact fewer true effects in bipolar disorder compared with major depression (or the effects are considerably smaller). If it is harder to detect effects this may lead to increased pressure to collect multiple measures to increase the likelihood of finding something. The addition of future study databases recording analytical flexibility may clarify the apparent discrepancy between the major depression and bipolar disorder literatures. More generally, there is growing interest in methods to interrogate published literature. Our approach, which uses number of measures as a metric of analytical flexibility, may be useful as a scalable tool for analysing all available studies across a published literature.