Effectively Selecting a Target Population for a Future Comparative Study

@article{Zhao2013EffectivelySA,
  title={Effectively Selecting a Target Population for a Future Comparative Study},
  author={Lihui Zhao and L. Tian and T. Cai and B. Claggett and L. Wei},
  journal={Journal of the American Statistical Association},
  year={2013},
  volume={108},
  pages={527 - 539}
}
  • Lihui Zhao, L. Tian, +2 authors L. Wei
  • Published 2013
  • Computer Science, Medicine
  • Journal of the American Statistical Association
When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this article, we show a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, using the data from a current study involving similar comparator treatments… Expand

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References

SHOWING 1-10 OF 42 REFERENCES
Analysis of randomized comparative clinical trial data for personalized treatment selections.
TLDR
This article proposes a systematic, 2-stage estimation procedure for the subject-level treatment differences for future patient's disease management and treatment selections using a parametric or semiparametric method and a nonparametric function estimation method. Expand
Patterns of treatment effects in subsets of patients in clinical trials.
TLDR
This work focuses on the case in which patient subgroups are defined to contain patients having increasingly larger values of one particular covariate of interest, with the intent of exploring the possible interaction between treatment effect and that covariate. Expand
The calibration of treatment effects from clinical trials to target populations
TLDR
The case is made for why calibration requires both clinical knowledge from observational studies, and new statistical insights, and why RCT results are useful only if they can calibrate their results to predict treatment efficacy in the target population of interest. Expand
Generalizing evidence from randomized clinical trials to target populations: The ACTG 320 trial.
TLDR
The authors illustrate a model-based method to standardize observed trial results to a specified target population using a seminal human immunodeficiency virus (HIV) treatment trial, and they provide Monte Carlo simulation evidence supporting the method. Expand
Evaluating markers for selecting a patient's treatment.
TLDR
A graphical display, the selection impact (SI) curve that shows the population response rate as a function of treatment selection criteria based on the marker, is proposed and nonparametric and parametric estimates of the SI curve are proposed. Expand
Evaluating markers for treatment selection based on survival time.
TLDR
The covariate-specific SI curve to incorporate covariate information in treatment selection is proposed and nonparametric and semiparametric estimators are developed accordingly that are consistent and asymptotically normal. Expand
A graphical method to assess treatment-covariate interactions using the Cox model on subsets of the data.
We introduce the subpopulation treatment effect pattern plot (STEPP) method, designed to facilitate the interpretation of estimates of treatment effect derived from different but potentiallyExpand
Measuring the Performance of Markers for Guiding Treatment Decisions
Treatment selection markers, sometimes called predictive markers, are factors that help clinicians select therapies that maximize good outcomes and minimize adverse outcomes for patients. ExistingExpand
External validity of randomised controlled trials: “To whom do the results of this trial apply?”
TLDR
A review of the determinants of external validity in trial publications and systematic reviews is usually inadequate, a checklist for clinicians is presented, and recommendations for greater consideration of External validity in the design and reporting of RCTs are made. Expand
Estimating average regression effect under non-proportional hazards.
TLDR
An estimator of average regression effect under a non-proportional hazards model, where the regression effect of the covariates on the log hazard ratio changes with time, and an approximation of the population average effect as integral beta(t)dF(t). Expand
...
1
2
3
4
5
...