<?xml version="1.0" encoding="utf-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.0 20120330//EN" "JATS-journalpublishing1.dtd">
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">JDS</journal-id>
      <journal-title-group>
        <journal-title>Journal of Data Science</journal-title>
      </journal-title-group>
      <issn pub-type="epub">1680-743X</issn>
      <issn pub-type="ppub">1680-743X</issn>
      <publisher>
        <publisher-name>SOSRUC</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">130107</article-id>
      <article-id pub-id-type="doi">10.6339/JDS.201501_13(1).0007</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>A Type of Sample Size Planning for Mean Comparison in Clinical Trials</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Liu</surname>
            <given-names>Junfeng</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">GCE Solutions, Inc</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Dey</surname>
            <given-names>Dipak K.</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">Department of Statistics, University of Connecticut</aff>
      </contrib-group>
      <volume>13</volume>
      <issue>1</issue>
      <fpage>115</fpage>
      <lpage>126</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>Abstract: Early phase clinical trials may not have a known variation (σ) for the response variable. In the light of applying t-test statistics, several procedures were proposed to use the information gained from stage-I (pilot study) to adaptively re estimate the sample size for managing the overall hypothesis test. We are interested in choosing a reasonable stage-I sample size (m) towards achieving an accountable overall sample size (stage-I and later). Conditional on any specified m, this paper replaces σ by the estimated σ (from stage-I with sample size m) to use the conventional formula under normal distribution assumption to re-estimate an overall sample size. The estimated σ, re-estimated overall sample size and the collective information (stage-I and later) would be incorporated into a surrogate normal variable which undergoes hypothesis test based on standard normal distribution. We plot the actual type I&amp;II error rates and the expected sample size against m in order to choose a good universal stage-I sample size (𝑚∗ ) to start</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>Hypothesis test</kwd>
        <kwd>normal test, surrogate</kwd>
        <kwd>Type-I(II) error</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
