OBJECTIVE: To reanalyze data that were used in a linear model to predict that mean sperm counts have been reduced globally by approximately 50% in the last 50 years. DESIGN: The mean sperm counts and their temporal distribution were reanalyzed via several different statistical models (quadratic, spline fit, and stairstep). CONCLUSION: There are several reasons why a published linear regression model is inappropriate to infer a 50% reduction in mean sperm counts in the last 50 years. These include [1] the potential selection biases that may have occurred with the 61 assembled studies such that they are not representative of their underlying populations; [2] the likely variability in collection methods, in particular, the lack of adherence to a minimum prescribed abstinence period, as has been stated for the largest study, which contained 29.7% of all the subjects included in the analysis; [3] the paucity of data in the first 30 years of the 50-year trend analysis; [4] the fact that if the last 20 years of data are examined, which contains 78.7% of all the studies and 88.1% of the total number of subjects, there is no decrease in sperm counts, in fact, sperm counts were observed to have increased; [5] the conflicting data from a large individual laboratory, which was not prone to the collection variability that likely occurred between the 61 studies, that did not suggest a decline in mean sperm count or seminal volume during a comparable time period, even though this laboratory published the data that were largely responsible for the high historical values in the linear model; and, most importantly, [6] the variety of other mathematical models that perform statistically better at describing the recent data than the linear model and thus offer substantially different hypotheses. The data are only robust during the last 20 years of the analysis, in which all the models, except the linear model, suggest constant or slightly increasing sperm counts.
OBJECTIVE: To reanalyze data that were used in a linear model to predict that mean sperm counts have been reduced globally by approximately 50% in the last 50 years. DESIGN: The mean sperm counts and their temporal distribution were reanalyzed via several different statistical models (quadratic, spline fit, and stairstep). CONCLUSION: There are several reasons why a published linear regression model is inappropriate to infer a 50% reduction in mean sperm counts in the last 50 years. These include [1] the potential selection biases that may have occurred with the 61 assembled studies such that they are not representative of their underlying populations; [2] the likely variability in collection methods, in particular, the lack of adherence to a minimum prescribed abstinence period, as has been stated for the largest study, which contained 29.7% of all the subjects included in the analysis; [3] the paucity of data in the first 30 years of the 50-year trend analysis; [4] the fact that if the last 20 years of data are examined, which contains 78.7% of all the studies and 88.1% of the total number of subjects, there is no decrease in sperm counts, in fact, sperm counts were observed to have increased; [5] the conflicting data from a large individual laboratory, which was not prone to the collection variability that likely occurred between the 61 studies, that did not suggest a decline in mean sperm count or seminal volume during a comparable time period, even though this laboratory published the data that were largely responsible for the high historical values in the linear model; and, most importantly, [6] the variety of other mathematical models that perform statistically better at describing the recent data than the linear model and thus offer substantially different hypotheses. The data are only robust during the last 20 years of the analysis, in which all the models, except the linear model, suggest constant or slightly increasing sperm counts.
Authors: W Himmel; E Ittner; M M Kochen; H W Michelmann; B Hinney; M Reuter; M Kallerhoff; R H Ringert Journal: Br J Gen Pract Date: 1997-02 Impact factor: 5.386
Authors: Michael F Costello; Peter Sjoblom; Youala Haddad; Stephen J Steigrad; Edward G Bosch Journal: J Assist Reprod Genet Date: 2002-06 Impact factor: 3.412