Page 102 - 20dynamics of cancer
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PROGRESSION DYNAMICS                                         87


                                        THE IMPORTANCE OF COMPARATIVE HYPOTHESES
                                A mathematical analysis for the age of cancer onset depends on sev-
                              eral parameters. Those parameters might include the number of stages
                              in progression, the somatic mutation rate that moves a tissue from one
                              stage to the next, the number of cells in the tissue, and the precancerous
                              rate of cell division. Given values for those parameters, the mathemati-
                              cal model generates an age-specific incidence curve.
                                A mathematical model may be used in two different ways: fit or com-
                              parison.
                                A fit chooses values for all parameters that minimize the distance be-
                              tween the predicted and observed age-specific incidence curves. A good
                              fit provides a close match between prediction and observation. A good
                              fit also uses realistic values for parameters such as rates of mutation
                              and cell division.
                                A comparison sets an explicit hypothesis: as a parameter changes,
                              the model predicts a particular direction of change for the age-specific
                              incidence curve. For example, an inherited mutation may reduce by one
                              the number of stages that must be passed during progression. Mathe-
                              matical models predict that fewer stages cause the incidence curve to
                              have a lower slope and to shift to earlier ages (higher intercept). I will
                              show data that support this comparative prediction.

                              FITTING
                                One can fit theory to observation, but the match usually arises be-
                              cause a model with several parameters creates a flexible manifold that
                              conforms to the data. Even when one constrains parameter estimates
                              to realistic values, an incorrect model with several parameters often has
                              great flexibility to conform to the shape of the data. A fit is achieved so
                              easily that such a model, fitting widely and well, actually explains very
                              little. As Dyson (2004) tells it:
                                 In desperation I asked Fermi whether he was not impressed by
                                the agreement between our calculated numbers and his measured
                                numbers. He replied, “How many arbitrary parameters did you
                                use for your calculations?” I thought for a moment about our cut-
                                off procedures and said, “Four.” He said, “I remember my friend
                                Johnny von Neumann used to say, with four parameters I can fit
                                an elephant, and with five I can make him wiggle his trunk.” With
                                that, the conversation was over.
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