Impact of covariate misclassification on the power and type I error in clinical trials using covariate-adaptive randomization

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Impact of covariate misclassification on the power and type I error in clinical trials using covariate-adaptive randomization L I Q I O N G F A N S H A R O N D. Y E A T T S W E N L E Z H A O M E D I C A L U N I V E R S I T Y O F S O U T H C A R O L I N A D E P A R T M E N T O F P U B L I C H E A L T H S C I E N C E D A T A C O O R D I N A T I O N U N I T L. Fan, SD. Yeatts, W. Zhao; MUSC 1 / 16

Background Properties of covariate-adaptive randomization Well balance important prognostic covariate More generalizable and convincing result Increase power for subgroup analysis Require correct specification of analysis model for hypothesis testing Shao J, Yu X. Validity of tests under covariate-adaptive biased coin randomization and generalized linear models. Biometrics. 2013;69:960-9. L. Fan, SD. Yeatts, W. Zhao; MUSC 2 / 16

Background Covariate adjustment during analysis Type of analysis: model based Type of outcome: Dichotomized Covariate adjustment in logistic regression Precision Efficiency Robinson LD, Jewell NP. Some Surprising Results About Covariate Adjustment In Logistic Regression Models. International Statistical Review. 1991;58:227-40. L. Fan, SD. Yeatts, W. Zhao; MUSC 3 / 16

Background Motivation Interventional Management of Stroke III (IMS III) Stratified biased-coin randomization: Stroke severity Misclassification Rate: 2%, non-differential Primary analysis: True severity VS. Randomized severity High-Dose Deferoxamine in Intracerebral Hemorrhage (Hi-DEF) Stratified biased-coin randomization: time from symptom onset to treatment Misclassification Rate: 27.3% and 6.3%, differential Primary analysis: Anticipated time window VS. Actual time window L. Fan, SD. Yeatts, W. Zhao; MUSC 4 / 16

Issues? Impact of adjusting with misclassified covariates under covariate adaptive randomization. IMS III Hi-DEF NIHSS STRATUM (ACTUAL) TIME WINDOW (ACTUAL) NIHSS STRATUM (RANDOMIZED) NIHSS 19 OR LOWER N % Randomized NIHSS 20 OR HIGHER N % Randomized TIME WINDOW (RANDOMIZED) Actual <= 12 hours N % Anticipated Actual > 12 hours N % Anticipated NIHSS 19 OR LOWER 448 98.03% 9 1.97% Anticipated <= 12 hour 8 72.7% 3 27.3% NIHSS 20 OR HIGHER 4 2.01% 195 97.99% Anticipated > 12 hours 1 6.3% 15 93.8% L. Fan, SD. Yeatts, W. Zhao; MUSC 5 / 16

Simulation Method Regression based analysis: Logistic regression 1 prognostic covariate: subject to misclassification 1 perfect measured variable: treatment assignment No interaction Dichotomized outcome Randomization schemes Simple randomization Covariate adaptive randomization: Stratified Block Randomization Stratified Biased-coin Randomization L. Fan, SD. Yeatts, W. Zhao; MUSC 6 / 16

Method Simulation Scenarios Fixed control group rate (40%) and treatment group rate (50%) Varied covariate effect: -3 to +3 (OR: 0.064-23.2) Varied misclassification rate: 0% to 40% Type of misclassification: non-differential L. Fan, SD. Yeatts, W. Zhao; MUSC 7 / 16

Method Simulation Models compared Unadjusted model Model adjusted with misclassified covariate Model adjusted with corrected covariate Operating characteristics evaluated Bias Power Type I error rate L. Fan, SD. Yeatts, W. Zhao; MUSC 8 / 16

Result-Bias True model Naive model Unadjusted model Simple Randomization Covariate adaptive Randomization L. Fan, SD. Yeatts, W. Zhao; MUSC 9 / 16

Result-Power True model Naive model Unadjusted model Simple Randomization Covariate adaptive Randomization L. Fan, SD. Yeatts, W. Zhao; MUSC 10 / 16

Result-Type I error Rate True model Naive model Unadjusted model Simple Randomization Covariate adaptive Randomization L. Fan, SD. Yeatts, W. Zhao; MUSC 11 / 16

Conclusion Impact on the estimate of treatment effect Direction: Bias towards Null Same direction of bias caused by unadjusting covariate Adjusted estimate VS. unadjusted estimate Magnitude Misclassification rate: about 30% misclassification is almost the same as not adjusting the covariate. The covariate effect on the outcome given the treatment effect Greenland S. The effect of misclassification in the presence of covariates. American journal of epidemiology. 1980;112:564-9. John MNN, P. Jewell. A geometric approach to assess bias due to omitted covariates in generalized linear models. Biometrika. 1993;80:807-15. L. Fan, SD. Yeatts, W. Zhao; MUSC 12 / 16

Conclusion Impact on power for detecting treatment effect Randomization schemes do not have effect on power Adjusting with corrected covariate minimize the power loss due to study design The amount of power loss Misclassification rate Covariate effect on the outcome given the treatment L. Fan, SD. Yeatts, W. Zhao; MUSC 13 / 16

Conclusion Impact on type I error rate Simple randomization: maintained Covariate-adaptive randomization Adjusting with corrected covariate: maintained Adjusting with misclassified covariate: maintained Without adjusting covariate: conservative L. Fan, SD. Yeatts, W. Zhao; MUSC 14 / 16

Discussion Under covariate adaptive randomization, adjusting randomized covariate is always recommended for final analysis Randomization scheme does not have an add-on effect on bias and power loss caused by covariate misclassification Attention should be drawn to correct the bias in estimating the effect of treatment and sample size reassessment with the presence of covariate misclassification L. Fan, SD. Yeatts, W. Zhao; MUSC 15 / 16

Questions? L. Fan, SD. Yeatts, W. Zhao; MUSC 16 / 16

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