Response to Dr Deeg: Analysis paralysis: Models and data revisited

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Letters to the Editors

RESPONSE TO DR DEEG: ANALYSIS PARALYSIS: MODELS AND DATA REVISITED When the editors inquired whether I wanted to comment on Dr Deeg’s letter, I asked to see Dr Wilcosky’s reply and they complied. Though this analysis of data from the Lipid Research Clinics Program [l] is of interest to obesity researchers, it has methodological issues that are generic to epidemiologic studies as was discussed in the commentary following the paper [2,3]. It is these issues that I am addressing in this response. The term “analysis paralysis” is used in psychology to describe a person who constantly analyzes a problem without coming to a decision. This word couplet not only has a nice ring to it but it can be used to describe a characteristic of researchers developing statistical models to explain biomedical phenomena. In this context it refers to the intense focus on a single analytical model to explain everything: it may be likened to a paralysis of the extraocular and intrinsic musculature of the eye. Analysis of data from large databases requires a shotgun, not a rifle. The trial and error approach to the use of different models and the presentation of the basic data allows the reader to determine when the target has been hit. The target of course is the knowledge that is concealed in the data. In the context of Dr Wilcosky’s paper it is pointed out by Dr Deeg (above) that an adequate explanation was not given why the Cox model was used. Also there was no reassurance that the underlying assumption of proportional hazards was satisfied. Furthermore, Dr Deeg requests critical raw data concerning the followup that was overlooked in the paper: this oversight is symptomatic of ophthalmoplegia. In Dr Wilcosky’s response to my original requests concerning the revealing of basic rates, he supplied only some of them in his Table 1, p. 756 [3]. Though denominators were not supplied it was possible to derive estimates of the mortality rates for the five BMI groups. These

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Fig. 1. Seven year cumulative mortality rates and predicted curve from Cox regression models.

are plotted in Fig. 1 using the BMI equal to 2.6 to “seat” the graph. Also shown is the predicted curve from the Cox model from his paper, p. 748 [l]. This visual comparison between the model and the data suggests that the model is not a good fit, and because there is curvature, why the quadratic term was significant. Dr Wilcosky states that the data I requested, as plotted in Fig. 1 “. . . do not support anything.” Since the adjustment for age does not account for the departure between the predicted curve and the data, it appears that the opposite is true: the model “does not support anything.” These five data points are quite revealing and it is unfortunate we were not allowed to see the other mortality rates in this study. ALFRED A. RIMM Division of BiostatisticslEpidemiology Medical College of Wisconsin Milwaukee, WI U.S.A.

REFERENCES Wilcosky T et al. Obesity and mortality in the Lipid Research Clinics Program Follow-up Study. J Clin Epidendol 1990; 43: 143-152. Rimm AA. A reveal-conceal test for manuscript review: its application in the obesity mortality study. J Clin Epidemiol 1990; 43: 753-754. Wilcosky T. Analysis of sparse data. J Clin Epidemiol 1990; 43: X-756.

RESPONSE TO DR RIMM: REVIEWER Dr Rimm’s above letter eventually restates criticisms from his earlier Dissent [l]: we not present data that he wanted to see, our mortality analysis was inappropriate.

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suggested in a recent editorial [2], manuscript authors should exclude needless material, and our decision to exclude extensive schedules of crude mortality rates reflects this viewpoint.