R.L. ANDERSON COLLOQUIUM

 

PRESENTED BY: STATISTICS AND BIOSTATISTICS

 

Analysis of Covariance: Model-based and Nonparametric

 

Dr. Gary Koch

 

Biostatistics Department, University of North Carolina, Chapel Hill

 

April 24, 2013

 

3:00-5:00 PM

 

Refreshments and Talk: Hillary Boone Center

 

Rose & Columbia Room

 

For randomized clinical trials with at least moderate sample size, adjustment of comparisons between treatments for baseline covariables can be helpful for two reasons. One is enhancement of power, and the other is the removal of the influence of baseline imbalances for the covariables. Adjustment for baseline covariables can either be through generalized linear (or semi-parametric) models or through a nonparametric extension of Mantel-Haenszel methods. The former has the limitation of assumptions that may be debatable or unrealistic, although it can have the advantage of fully describing the relationship of an endpoint to both treatments and covariables in a general population. The latter has the advantage of no external assumptions (beyond its intrinsic assumptions of valid randomization and valid data), although it only enables inference for the comparison between treatments for the randomized population. The nonparametric method has invocation by constraining differences between treatments for means of covariables to 0 in a multivariate vector that additionally includes the unadjusted treatment effect sizes for the endpoints under assessment. Such nonparametric randomization based analysis of covariance (RBANCOVA) is applicable to differences between means for continuous measurements (or their ranks), differences between proportions, log hazard ratios for time to event data, log incidence density ratios for counted event data, and rank measures of association for ordinal data. Also, extensions to account for stratification factors in the randomization are available as well. Several examples which illustrate RBANCOVA and model based counterparts have discussion.