Compliance Subsampling Designs for Comparative Research: Estimation and Optimal Planning

For studies with treatment noncompliance, analyses have been developed recently to better estimate treatment efficacy. However, the advantage and cost of measuring compliance data have implications on the study design that have not been as systematically explored. In order to estimate better treatment efficacy with lower cost, we propose a new class of compliance subsampling (CSS) designs where, after subjects are assigned treatment, compliance behavior is measured for only subgroups of subjects. The sizes of the subsamples are allowed to relate to the treatment assignment, the assignment probability, the total sample size, the anticipated distributions of outcome and compliance, and the cost parameters of the study. The CSS design methods relate to prior work (i) on two-phase designs in which a covariate is subsampled and (ii) on causal inference because the subsampled postrandomization compliance behavior is not the true covariate of interest. For each CSS design, we develop efficient estimation of treatment efficacy under binary outcome and all-or-none observed compliance. Then we derive a minimal cost CSS design that achieves a required precision for estimating treatment efficacy. We compare the properties of the CSS design to those of conventional protocols in a study of patient choices for medical care at the end of life.

[1]  S. Miles,et al.  Advance End-of-Life Treatment Planning , 1996 .

[2]  D. Rubin.  Assignment to Treatment Group on the Basis of a Covariate , 1976 .

[3]  Geert Molenberghs,et al.  Causal Inference in a Placebo-Controlled Clinical Trial with Binary Outcome and Ordered Compliance , 1996 .

[4]  J. Neyman.  Contribution to the Theory of Sampling Human Populations , 1938 .

[5]  Stuart G. Baker,et al.  Analyzing a Randomized Cancer Prevention Trial with a Missing Binary Outcome, an Auxiliary Variable, and All-or-None Compliance , 2000 .

[6]  Donald B. Rubin,et al.  Addressing an Idiosyncrasy in Estimating Survival Curves Using Double Sampling in the Presence of Self‐Selected Right Censoring , 2001 .

[7]  Donald B. Rubin,et al.  Bayesian Inference for Causal Effects: The Role of Randomization , 1978 .

[8]  M. Zelen.  A new design for randomized clinical trials. , 1979, The New England journal of medicine.

[9]  S G Baker,et al.  Regression analysis of grouped survival data: informative censoring and double sampling. , 1993, Biometrics.

[10]  D. Rubin.  ASSIGNMENT TO TREATMENT GROUP ON THE BASIS OF A COVARIATE , 1976 .

[11]  E. Emanuel,et al.  Advance directives for medical care--a case for greater use. , 1991, The New England journal of medicine.

[12]  Jerald F. Lawless,et al.  Semiparametric methods for response‐selective and missing data problems in regression , 1999 .

[13]  D B Rubin,et al.  More powerful randomization-based p-values in double-blind trials with non-compliance. , 1998, Statistics in medicine.

[14]  J. Pearl,et al.  Bounds on Treatment Effects from Studies with Imperfect Compliance , 1997 .

[15]  J. Robins,et al.  Estimation of Regression Coefficients When Some Regressors are not Always Observed , 1994 .

[16]  Nilanjan Chatterjee,et al.  Design and analysis of two‐phase studies with binary outcome applied to Wilms tumour prognosis , 1999 .

[17]  J M Robins,et al.  Correction for non-compliance in equivalence trials. , 1998, Statistics in medicine.

[18]  D. Rubin.  Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[19]  S G Baker,et al.  Rethinking historical controls. , 2001, Biostatistics.

[20]  W M Tierney,et al.  Effectiveness of Computer-Generated Reminders for Increasing Discussions about Advance Directives and Completion of Advance Directive Forms , 1998, Annals of Internal Medicine.

[21]  Raymond J. Carroll,et al.  Semiparametric Estimation in Logistic Measurement Error Models , 1989 .

[22]  Stuart G. Baker,et al.  Analysis of Survival Data from a Randomized Trial with All-or-None Compliance: Estimating the Cost-Effectiveness of a Cancer Screening Program , 1998 .

[23]  Joshua D. Angrist,et al.  Identification of Causal Effects Using Instrumental Variables , 1993 .

[24]  S. Zeger,et al.  On estimating efficacy from clinical trials. , 1991, Statistics in medicine.

[25]  S G Baker,et al.  The partial testing design: a less costly way to test equivalence for sensitivity and specificity. , 1998, Statistics in medicine.

[26]  R. L. Prentice,et al.  A case-cohort design for epidemiologic cohort studies and disease prevention trials , 1986 .

[27]  K. Mcintyre,et al.  Implementation of advance directives. For physicians, a legal dilemma becomes an ethical imperative. , 1992, Archives of internal medicine.

[28]  Boutain Dm,et al.  A research review: involving the African American family in anti-hypertensive medication management. , 1997, Journal of National Black Nurses' Association : JNBNA.

[29]  S G Baker,et al.  The paired availability design: a proposal for evaluating epidural analgesia during labor. , 1994, Statistics in medicine.

[30]  D. Rubin,et al.  Addressing complications of intention-to-treat analysis in the combined presence of all-or-none treatment-noncompliance and subsequent missing outcomes , 1999 .

[31]  A. Winsor.  Sampling techniques. , 2000, Nursing times.

[32]  D. Rubin,et al.  School Choice in NY City: A Bayesian Analysis of an Imperfect Randomized Experiment , 2001 .

[33]  J. Robins,et al.  Adjusting for differential rates of prophylaxis therapy for PCP in high- versus low-dose AZT treatment arms in an AIDS randomized trial , 1994 .

[34]  S. Miles,et al.  Advance end-of-life treatment planning. A research review. , 1996, Archives of internal medicine.