Choosing Appropriate Estimands in Clinical Trials

Lack of adherence to study protocol and missing data are often unavoidable in clinical trials, and both increase the need to differentiate between the ideal treatment effect if the medication is taken as directed and the treatment effect in presence of the actual adherence pattern. In this regard, estimands have become the focus of attention. An estimand is simply that which is being estimated. In the context of treatment benefit, an estimand may address either efficacy or effectiveness aspects. Defining the estimand of interest is an essential step to take before deciding on trial design and primary analysis. The choice of estimand has consequences for various other factors to be considered during any clinical trial’s planning phase. This study presents a process chart including all aspects to consider during planning. After deciding on the primary estimand, the trial design should be specified, followed by the primary analysis. Both should appropriately address the chosen estimand. Finally, sensitivity analyses should be taken into account. Provided are suggestions for all the planning steps involved, especially on choosing between efficacy and effectiveness, and relevant examples from clinical practice to illustrate them. It is recommended that one bear in mind the process chart during planning of any clinical trial and give reasonable justification for each decision in the study protocol.

[1]  Roderick J. A. Little,et al.  Statistical Analysis with Missing Data , 1988 .

[2]  Michael G. Kenward,et al.  Multiple Imputation and its Application , 2013 .

[3]  R Little,et al.  Intent-to-treat analysis for longitudinal studies with drop-outs. , 1996, Biometrics.

[4]  Geert Molenberghs,et al.  Missing Data in Clinical Studies , 2007 .

[5]  Geert Molenberghs,et al.  The effect of correlation structure on treatment contrasts estimated from incomplete clinical trial data with likelihood-based repeated measures compared with last observation carried forward ANOVA , 2004, Clinical trials.

[6]  Craig Mallinckrodt,et al.  Preventing and Treating Missing Data in Longitudinal Clinical Trials: A Practical Guide , 2013 .

[7]  R T O'Neill,et al.  The Prevention and Treatment of Missing Data in Clinical Trials: An FDA Perspective on the Importance of Dealing With It , 2012, Clinical pharmacology and therapeutics.

[8]  Geert Molenberghs,et al.  Assessing and interpreting treatment effects in longitudinal clinical trials with missing data , 2003, Biological Psychiatry.

[9]  T. Fleming Addressing Missing Data in Clinical Trials , 2011, Annals of Internal Medicine.

[10]  Geert Molenberghs,et al.  Incomplete Data in Clinical Studies: Analysis, Sensitivity, and Sensitivity Analysis , 2009 .

[11]  S D Imber,et al.  Some conceptual and statistical issues in analysis of longitudinal psychiatric data. Application to the NIMH treatment of Depression Collaborative Research Program dataset. , 1993, Archives of general psychiatry.

[12]  D. Rubin,et al.  Statistical Analysis with Missing Data , 1988 .

[13]  James R Carpenter,et al.  Analysis of Longitudinal Trials with Protocol Deviation: A Framework for Relevant, Accessible Assumptions, and Inference via Multiple Imputation , 2013, Journal of biopharmaceutical statistics.

[14]  Michael G. Kenward,et al.  Multiple Imputation and its Application: Carpenter/Multiple Imputation and its Application , 2013 .

[15]  Geert Molenberghs,et al.  Assessing Response Profiles from Incomplete Longitudinal Clinical Trial Data Under Regulatory Considerations , 2003, Journal of biopharmaceutical statistics.

[16]  Michael G. Kenward,et al.  The handling of missing data in clinical trials , 2013 .

[17]  Craig H. Mallinckrodt,et al.  Type I Error Rates from Mixed Effects Model Repeated Measures Versus Fixed Effects Anova with Missing Values Imputed Via Last Observation Carried Forward , 2001 .

[18]  C H Mallinckrodt,et al.  A structured approach to choosing estimands and estimators in longitudinal clinical trials , 2012, Pharmaceutical statistics.

[19]  Geert Molenberghs,et al.  A Multiple-Imputation-Based Approach to Sensitivity Analyses and Effectiveness Assessments in Longitudinal Clinical Trials , 2014, Journal of biopharmaceutical statistics.

[20]  B Ratitch,et al.  Recent Developments in the Prevention and Treatment of Missing Data , 2014, Therapeutic innovation & regulatory science.

[21]  M. Neuhäuser,et al.  Estimation of the treatment effect in the presence of non‐compliance and missing data , 2014, Statistics in medicine.

[22]  Bohdana Ratitch,et al.  Missing data in clinical trials: from clinical assumptions to statistical analysis using pattern mixture models , 2013, Pharmaceutical statistics.

[23]  R. Little,et al.  The prevention and treatment of missing data in clinical trials. , 2012, The New England journal of medicine.

[24]  Tim Friede,et al.  Aspects of Modernizing Drug Development Using Clinical Scenario Planning and Evaluation , 2010 .

[25]  Geert Molenberghs,et al.  Analyzing incomplete longitudinal clinical trial data. , 2004, Biostatistics.

[26]  Michael G. Kenward,et al.  Conceptual Considerations regarding Endpoints, Hypotheses, and Analyses for Incomplete Longitudinal Clinical Trial Data , 2009 .

[27]  A structured framework for assessing sensitivity to missing data assumptions in longitudinal clinical trials , 2013, Pharmaceutical statistics.

[28]  Yahong Peng,et al.  Recommendations for the Primary Analysis of Continuous Endpoints in Longitudinal Clinical Trials , 2008 .