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SUMMARY
Certain important nonlinear regression models lead to biased estimates of treatment effect, even in randomized experiments, if needed covariates are omitted. The asymptotic bias is determined both for estimates based on the method of moments and for maximum likelihood estimates. The asymptotic bias from omitting covariates is shown to be zero if the regression of the response variable on treatment and covariates is linearor exponential, and, in regular cases, this is a necessary condition for zero bias. Many commonly used models do have such exponential regressions; thus randomization ensures unbiased treatment estimates in a large number of important nonlinear models. For moderately censored exponential survival data, analysis with the exponential survival model yields less biased estimates of treatment effect than analysis with the proportional hazards model of Cox, if needed covariates are omitted. Simulations confirm that calculations of asymptotic bias are in excellent agreement with the bias observed in experiments of modest size. Some key words: Bias; Clinical trial; Nonlinear model; Omitted covariate; Randomization. J O E{H(y I T, X) exp (oc*T)} d where E is the usual expectation operator over T, X and Y. In principle, (4-15) may be solved for oc* for each fixed (oc, /B) to obtain the exact bias. We illustrate this calculation for exponential data with uniform entry on [0, Y0] and analysis at Y0. Without loss of generality, AO(w) = I because, from (41), A(w I X, T) = A0 exp (j) exp (ocT + ?X). For fixed T, X, H(yI|T, X) = pr [Y > yI|T, X] = exp {-exp (C~)y} (1-y/Y0). This content downloaded from 91.229.248.104 on Wed
