Literature DB >> 30234179

The Kaplan-Meier Method for Estimating and Comparing Proportions in a Randomized Controlled Trial with Dropouts.

Jarcy Zee1, Sharon X Xie2.   

Abstract

We propose a method for estimating and comparing proportions of study participants who reached an event of interest during a randomized controlled trial. Standard methods for estimating this proportion include the intent-to-treat method, which counts the number who reached the event of interest divided by the total number of participants, and the completers-only method, which counts the number who reached the event only among those who completed the entire study. When participants drop out of the study early, however, these methods will either be biased or inefficient. We propose to use the Kaplan-Meier method from survival analysis to estimate the proportion of interest in this non-survival setting. We show through extensive simulation studies that the Kaplan-Meier method has less bias and is more efficient than the standard methods. We demonstrate the performance of all methods for estimating proportions in one sample and for comparing proportions across two samples. Finally, we apply the proposed method to a data set for estimating and comparing proportions of patients who achieved treatment response during a Parkinson's disease trial for the treatment of impulse control disorders.

Entities:  

Keywords:  Kaplan-Meier; clinical trial; dropout; proportion; survival

Year:  2017        PMID: 30234179      PMCID: PMC6141203          DOI: 10.1080/24709360.2017.1407866

Source DB:  PubMed          Journal:  Biostat Epidemiol        ISSN: 2470-9360


  4 in total

1.  Handling drop-out in longitudinal clinical trials: a comparison of the LOCF and MMRM approaches.

Authors:  Peter Lane
Journal:  Pharm Stat       Date:  2008 Apr-Jun       Impact factor: 1.894

Review 2.  An overview of practical approaches for handling missing data in clinical trials.

Authors:  Cynthia M DeSouza; Anna T R Legedza; Abdul J Sankoh
Journal:  J Biopharm Stat       Date:  2009-11       Impact factor: 1.051

3.  The prevention and treatment of missing data in clinical trials.

Authors:  Roderick J Little; Ralph D'Agostino; Michael L Cohen; Kay Dickersin; Scott S Emerson; John T Farrar; Constantine Frangakis; Joseph W Hogan; Geert Molenberghs; Susan A Murphy; James D Neaton; Andrea Rotnitzky; Daniel Scharfstein; Weichung J Shih; Jay P Siegel; Hal Stern
Journal:  N Engl J Med       Date:  2012-10-04       Impact factor: 91.245

4.  Naltrexone for impulse control disorders in Parkinson disease: a placebo-controlled study.

Authors:  Kimberly Papay; Sharon X Xie; Matthew Stern; Howard Hurtig; Andrew Siderowf; John E Duda; James Minger; Daniel Weintraub
Journal:  Neurology       Date:  2014-07-18       Impact factor: 9.910

  4 in total
  2 in total

1.  The "1-year-death number needed to treat" for comparing the impact of distinct interventions on patient outcomes.

Authors:  Brett N Hryciw; Finlay A McAlister; Meltem Tuna; Carl van Walraven
Journal:  CMAJ       Date:  2019-11-11       Impact factor: 8.262

2.  Probability of receiving a high cumulative radiation dose and primary clinical indication of CT examinations: a 5-year observational cohort study.

Authors:  Cécile R L P N Jeukens; Hub Boere; Bart A J M Wagemans; Patty J Nelemans; Estelle C Nijssen; Rebecca Smith-Bindman; Joachim E Wildberger; Anna M Sailer
Journal:  BMJ Open       Date:  2021-01-17       Impact factor: 2.692

  2 in total

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