Responder analysis without dichotomization

@article{Zhang2016ResponderAW,
  title={Responder analysis without dichotomization},
  author={Zhiwei Zhang and Jianxiong Chu and Dewi Rahardja and Hui Zhang and Li Tang},
  journal={Journal of Biopharmaceutical Statistics},
  year={2016},
  volume={26},
  pages={1125 - 1135}
}
ABSTRACT In clinical trials, it is common practice to categorize subjects as responders and non-responders on the basis of one or more clinical measurements under pre-specified rules. Such a responder analysis is often criticized for the loss of information in dichotomizing one or more continuous or ordinal variables. It is worth noting that a responder analysis can be performed without dichotomization, because the proportion of responders for each treatment can be derived from a model for the… 
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References

SHOWING 1-10 OF 20 REFERENCES
Responder Analyses—A PhRMA Position Paper
TLDR
It is found that the well-known loss of statistical power associated with a responder analysis outweighs any real or perceived benefits of this approach, and between-group comparisons of the percentages of “responders” can play a role in the assessment and reporting of the clinical meaningfulness of the treatment effect.
Disappointing dichotomies
A regrettably common means of judging the effect of treatments is by ‘responder analysis’: at the end of the trial every patient is classified according to a binary variable with ‘responded’ and ‘did…
Consequences of dichotomization.
TLDR
Only in certain cases, for instance, in estimating a value of the cumulative distribution function and when the assumed model is very different from the true model, can the use of dichotomized outcomes be considered a reasonable approach.
The cost of dichotomising continuous variables
TLDR
The impact of converting continuous data to two groups (dichotomising) is considered, as this is the most common approach in clinical research.
Responder analyses and the assessment of a clinically relevant treatment effect
TLDR
Weaknesses with a responder analysis, in which a continuous primary efficacy measure is dichotomized into "responders" and "non-responders", are discussed.
Categorical Data Analysis
TLDR
Modeling and inferential procedures (estimation and precision assessment) are discussed for a single proportion, thereafter in the context of cross-classified data, i.e., contingency tables, is discussed, with particular emphasis on testing the null hypothesis of no association (independence) between the row and column classification.
Semiparametric Theory and Missing Data
This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of…
Committee for Medicinal Products for Human Use (CHMP) guideline on the choice of the non-inferiority margin.
TLDR
With regard to intended therapeutic indications or procedure scopes listed against products, it must be noted that these may not reflect the full wording proposed by applicants and may also vary during the course of the review.
Maximum Likelihood Estimation of Misspecified Models
This paper examines the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference. The quasi-maximum likelihood estimator (QMLE)…
INFERENCE AND MISSING DATA
Two results are presented concerning inference when data may be missing. First, ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the…
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