Errors in statistical analysis and questionable randomization lead to unreliable conclusions.

Dear Editor, We read with interest the paper, “The effect of food service system modifications on staff body mass index in an industrial organization”[1]. We noticed several substantial issues with data and calculations, calling into question the randomized nature of the study and validity of analyses.The distribution of baseline weight was significantly differentbetween groups (p-value = “0.00”). We replicated the test using reported means and standard deviations (SDs) andobtained a p-value of approximately 1.9*10 -17 . It is extraordinarily unlikely that any variable would be that different between two groups if allocation was truly random. Even it was truly random, the stated method of “the samples were randomly divided into two groups”[1] does not describe the “method used to generate the random allocation sequence” and the “type of randomization; details of any restriction (such as blocking and block size)” details specified by Consolidated Standards of Reporting Trials (CONSORT)[2]. Given the large difference in baseline weights, it is unusual that the difference in baseline body mass index (BMI) between groups is not more significant (p=0.032), raising the question of what the groups’ distributions of height were. Both groups have 30 males (58.8%), so sex differences are unlikely to explain this discrepancy. Height was not explicitly reported, but it was possible to estimate height utilizing geometric means from body weight and BMI[3,4]. We calculated the baseline control group geometric mean as 2.04 cm taller than the test group. These calculations also suggest the control group shrunk by 1.26 cm while the test group grew by 1.52 cm over the study. Neither change is explained by rounding error nor seems plausible for adult subjects over 40 days.Because there were no SDs of the change scores reported, we were unable to replicate the reported p-value (0.318) for the between-group test of weight change exactly. However, we were able to consider the pre and post-intervention SDs and calculate possible SDs of within-group change scores for a range of pre-post correlations. The largest p-value possible was 0.1282, calculated when each group had perfect negative pre-post correlation (correlation=-1), which is unlikely. If there was no or a positive correlation, the p-value would be much smaller (p=0.0449 when correlation=0 for each group) and plausibly indicates a significant difference between groups. Therefore, although the published results are impossible the correct analysis could make the intervention appear more effective than reported.The results section describes an initial sample size of 116 with 14 dropping out (p. 115). The tables report the remaining sample size to be 102, but the body of the text reports 101 subjects remained until study completion. It is unclear which values were correct; this lack of clarity also fails CONSORT guidelines[2]. Considering that the reported findings are essentially impossible given the stated study design, we encourage the authors to explain the treatment allocationand make the raw data available, or the journal to act according to the Committee on Publication Ethics[5] in situations where findings are unreliable