Hirsch index and truth survival in clinical research

Factors associated with the survival of truth of clinical conclusions in the medical literature are unknown. We hypothesized that publications with a first author having a higher Hirsch' index value (h-I), which quantifies and predicts an individual's scientific research output, should have a longer half-life.

474 original articles concerning cirrhosis or hepatitis published from 1945 to 1999 were selected. The survivals of the main conclusions were updated in 2009. The truth survival was assessed by time-dependent methods (Kaplan Meier method and Cox). A conclusion was considered to be true, obsolete or false when three or more observers out of the six stated it to be so. 284 out of 474 conclusions (60%) were still considered true, 90 (19%) were considered obsolete and 100 (21%) false. The median of the h-I was=24 (range 1-85). Authors with true conclusions had significantly higher h-I (median=28) than those with obsolete (h-I=19; P=0.002) or false conclusions (h-I=19; P=0.01). The factors associated (P<0.0001) with h-I were: scientific life (h-I=33 for>30 years vs. 16 for<30 years), -methodological quality score (h-I=36 for high vs. 20 for low scores), and -positive predictive value combining power, ratio of true to not-true relationships and bias (h-I=33 for high vs. 20 for low values). In multivariate analysis, the risk ratio of h-I was 1.003 (95%CI, 0.994-1.011), and was not significant (P=0.56). In a subgroup restricted to 111 articles with a negative conclusion, we observed a significant independent prognostic value of h-I (risk ratio=1.033; 95%CI, 1.008-1.059; P=0.009). Using an extrapolation of h-I at the time of article publication there was a significant and independent prognostic value of baseline h-I (risk ratio=0.027; P=0.0001).

The present study failed to clearly demonstrate that the h-index of authors was a prognostic factor for truth survival. However the h-index was associated with true conclusions, methodological quality of trials and positive predictive values.