Ben French
A volume-outcome study is typically used to evaluate whether patients
treated by high-volume health care providers (e.g. surgeons or hospitals)
have better post-treatment outcomes than those treated by low-volume
providers. Previous methodological literature does not provide definitive
guidance on appropriate methods for a volume-outcome analysis. To provide a unified framework I explore a recurrent marked point process and examine the use of existing longitudinal analysis methods in the context of disaggregate volume-outcome data. Results from a simulation study indicate that generalized estimating equations and linear mixed models may provide a biased estimate of the volume-outcome association. However, an independence
estimating equation provides an unbiased estimate with nominal confidence
interval coverage. In this talk I will review the analysis of typical
longitudinal data. I will then describe the recurrent marked point process
setting and discuss implications for a volume-outcome analysis.
