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PC program for assessing the effect of a treatment when subjects are growing: comparative studies

We describe, illustrate, and make available a menu-driven PC program which can be used to assess the effect of a treatment on growth when random allocation of subjects to the treatment and control groups is not feasible. Three different estimators of, and confidence intervals for, this effect are co...

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Published in:International journal of bio-medical computing 1995-03, Vol.38 (3), p.217-224
Main Authors: Kowalskia, Charles J., Schneiderman, Emet D., Willis, Stephen M.
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Language:English
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cited_by cdi_FETCH-LOGICAL-c389t-59edfa1c8385434d6c19fb84442008ea752b4b16923c6973711bf464faac55273
cites cdi_FETCH-LOGICAL-c389t-59edfa1c8385434d6c19fb84442008ea752b4b16923c6973711bf464faac55273
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container_title International journal of bio-medical computing
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creator Kowalskia, Charles J.
Schneiderman, Emet D.
Willis, Stephen M.
description We describe, illustrate, and make available a menu-driven PC program which can be used to assess the effect of a treatment on growth when random allocation of subjects to the treatment and control groups is not feasible. Three different estimators of, and confidence intervals for, this effect are computed, namely, the simple gains, standardized gains, and covariance adjusted estimators. It is shown by means of several examples that these estimators can differ substantially, and some guidelines for choosing between them in specific circumstances are provided.
doi_str_mv 10.1016/S0020-7101(05)80003-0
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subjects Analysis of Variance
Animals
Bias reduction
Biological and medical sciences
Body Weight - drug effects
Child
Computer Simulation
Computerized, statistical medical data processing and models in biomedicine
Confidence Intervals
Growth
Growth - drug effects
Humans
Jackknife
Linear Models
Mathematical Computing
Medical sciences
Medical statistics
Microcomputers
Rats
Regression to mean
Software
Thyroxine - pharmacology
Treatment effects
Treatment Outcome
User-Computer Interface
title PC program for assessing the effect of a treatment when subjects are growing: comparative studies
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