Reporting of sample size and power in negative clinical trials published in Indian medical journals

Abstract

Background and Aim: It is observed that negative clinical trials published in medical journals are poor in reporting of sample size calculation, various components of calculation of sample size. It is also observed that they are underpowered to detect the actual difference between the treatment outcomes. Because of scarcity of these data for Indian medical journals we designed this study to critically analyze the Indian medical journals for reporting of sample size, components of sample size and power. We calculated post hoc power for 30% and 50% difference between the treatment outcomes. Materials and Methods: All the negative clinical trials published in five Indian medical journals (Indian Journal of Pharmacology (IJP), Indian Pediatrics (IP), Indian Journal of Dermatology (IJD), Indian Journal of Dermatology, Vanereology and Leprology (IJDVL), and Journal of Postgraduate Medicine (JPGM)), between 2001 and 2008, were analyzed by each author for reporting of the sample size and components of the sample size. Post hoc power for 30% and 50% differences between the outcome was calculated by G Power software. All data were expressed as frequency, percentages, and 95% confidence interval around the percentages with the help of SPSS ver. 17. Results: The median sample size was observed to be 33 (range 9-85). Power was calculated in 28 (41.1%, 95% CI 30.2% to 53%) trials. The sample size was not calculated in 34 (50%, 95% CI 38.4% to 61.5%) trials. The full sample size was calculated in only 2 (2.9%, 95% CI 0.8% to 10.1%) trials. Post hoc power above 80% for 30% of difference was reported in 3 (4.4%, 95% CI 1.5% to 12%) trials and for 50% difference in outcome it was reported in 42 (61.7%, 95% CI 49.8% to 72.3) trials. Conclusion: Negative clinical trials published in five Indian journals are poor in reporting of sample size calculation. It is also observed that most of trials are underpowered to see the 30% and 50% difference between the outcomes. There is a need to generate more awareness regarding the sample size and power calculation