Dealing With Change: Using the Conditional Change Model for Clinical Research

Virtually all clinical medicine is about change. The criteria for deciding whether a therapy has been successful nearly always include consideration of the degree to which the patient's initial condition has improved or to which a deteriorating condition has been stabilized. Both criteria depend on change. In the first case it is a rise in some measurement of benefit or drop in some measurement of burden, whereas in the second it is that a downward change has been prevented. In clinical research, therefore, one of the most frequently used approaches is to compare changes in a treated group with corresponding changes in a control group. Perhaps the most notable pedagogic failing of statistics courses and textbooks is that they do not present the appropriate way to analyze data coming from this design, which explains why published analyses are so often suboptimal, if not actually incorrect. The purposes of this article are to explain what should be the default method of analyzing change data and to indicate how to compute and display the results graphically.