Abstract
Meta-analysis is a statistical method to summarize quantitative results from a set of published studies. Recently, a frequentist estimator was proposed for individual studies’ means in terms of the pretest (preliminary test) estimator. However, the confidence interval has not been considered yet for the pretest estimator for meta-analysis. In this paper, a novel approach to construct a CI is considered based on the pretest estimator for meta-analysis. By pivoting the cumulative distribution function of the pretest estimator, we define an explicit formula of the CI. Furthermore, we show that the coverage probability of the CI controls the nominal confidence level both theoretically and numerically. To facilitate the proposed estimator and CI, we have implemented the computational tools in the R package “meta.shrinkage”. Finally, three datasets are analyzed to illustrate the proposed CI in real meta-analyses. The R code to produce all the numerical results of the paper is given in Supplementary Materials.
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Acknowledgements
The authors would like to thank the special feature editor and two reviewers for their insightful comments on our paper. Emura T was supported financially by JSPS KAKENHI (22K11948; 20H04147) and Konno Y was supported financially JSPS KAKENHI (19K11867). An initial draft of this manuscript was presented at a Workshop in Japan Statistics Research Institute (Hosei University) hosted by Toshihiro Abe. Comments from the audience helped improve the article.
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42081_2023_221_MOESM1_ESM.r
Simulation results for the CP (Table 1) (R 2 KB)
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Plot of CP of the Wald CI and the pivot CI (Fig. 2) (R 35 KB)
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The CP of the proposed CI under estimation error (Fig. 3) (R 9 KB)
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Data analysis (Section 5) (R 2 KB)
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Graph of the CDF of the PT estimator (Fig. 1) (R 3 KB)
Appendices
Appendices
1.1 Appendix A: The proof of Lemma 1
We let \({c}_{{\lambda }_{i}}=P\left({\mu }_{i}\in \left[{\delta }_{i}^{\mathrm{PT}}-{z}_{\gamma /2}{\sigma }_{i}, {\delta }_{i}^{\mathrm{PT}}+{z}_{\gamma /2}{\sigma }_{i}\right]\right)\).
We first consider the case of \(\alpha \ge \gamma\) (Case 1). Then
Putting \({Z}_{i}=\left({Y}_{i}-{\mu }_{i}\right)/{\sigma }_{i}\), we have
Since \(\alpha \ge \gamma\) is equivalent to \({z}_{\alpha /2}\le {z}_{\gamma /2}\), one has
This completes the proof for Case 1 (\(\alpha \ge \gamma\)).
Case 2 (\(\alpha <\gamma\) and \({z}_{\alpha /2}\le 2{z}_{\gamma /2}\)) and Case 3 (\(\alpha <\gamma\) and \({z}_{\alpha /2}>2{z}_{\gamma /2}\)) are similar to Case 1 (\(\alpha \ge \gamma\)). In Cases 2 and 3, the formula of \({c}_{{\lambda }_{i}}\) is computed from Eq. (A1), as well. Figure 4 shows two different cases (\({z}_{\alpha /2}\le 2{z}_{\gamma /2}\) and \({z}_{\alpha /2}>2{z}_{\gamma /2}\)) for the ranges of \({\lambda }_{i}\) defined by \(-{z}_{\alpha /2}+{z}_{\gamma /2}\le {\lambda }_{i}\le {z}_{\alpha /2}-{z}_{\gamma /2}\). For the first case, \({c}_{{\lambda }_{i}}\) is calculated only using the first term in Eq. (A1). For the second case, \({c}_{{\lambda }_{i}}\) on \(-{z}_{\alpha /2}+{z}_{\gamma /2}\le {\lambda }_{i}<{z}_{\gamma /2}\) or \({z}_{\gamma /2}<{\lambda }_{i}\le {z}_{\alpha /2}-{z}_{\gamma /2}\) is calculated using the first term, whereas \({c}_{{\lambda }_{i}}\) on \(-{z}_{\gamma /2}\le {\lambda }_{i}\le {z}_{\gamma /2}\) is calculated using the first term and the second term in Eq. (A1). Thus, the proof is completed. \(\hfill\square\)
1.2 Appendix B: The proof of Corollary 1
We now derive the confidence coefficient for Case 2 (\(\alpha <\gamma\) and \({z}_{\alpha /2}\le 2{z}_{\gamma /2}\)). Then
Below, we will compute all the infimums.
(i) For \({\lambda }_{i}<-{z}_{\alpha /2}-{z}_{\gamma /2}\), \({c}_{{\lambda }_{i}}=1-\gamma\). Obviously, we have
(ii) For \(-{z}_{\alpha /2}-{z}_{\gamma /2}\le {\lambda }_{i}<-{z}_{\gamma /2}\), \({c}_{{\lambda }_{i}}\) is the decreasing function of \({\lambda }_{i}\). Since \({c}_{{\lambda }_{i}}\) is not continuous at \({\lambda }_{i}=-{z}_{\gamma /2}\), we take the limit \({\lambda }_{i}\uparrow -{z}_{\gamma /2}\). Therefore, we have
(iii) For \(-{z}_{\gamma /2}\le {\lambda }_{i}<-{z}_{\alpha /2}+{z}_{\gamma /2}\), \({c}_{{\lambda }_{i}}\) is the decreasing function of \({\lambda }_{i}\). We have
(iv) For \(-{z}_{\alpha /2}+{z}_{\gamma /2}\le {\lambda }_{i}\le {z}_{\alpha /2}-{z}_{\gamma /2}\), \({c}_{{\lambda }_{i}}=\Phi \left(-{\lambda }_{i}+{z}_{\alpha /2}\right)-\Phi \left(-{\lambda }_{i}-{z}_{\alpha /2}\right)\). Then, we have
From this equation, it holds that \(\partial {c}_{{\lambda }_{i}}/\partial {\lambda }_{i}=0\) for \({\lambda }_{i}=0\), \(\partial {c}_{{\lambda }_{i}}/\partial {\lambda }_{i}>0\) for \(-{z}_{\alpha /2}+{z}_{\gamma /2}\le {\lambda }_{i}<0\) and \(\partial {c}_{{\lambda }_{i}}/\partial {\lambda }_{i}<0\) for \(0<{\lambda }_{i}\le {z}_{\alpha /2}-{z}_{\gamma /2}\). That is, \({c}_{{\lambda }_{i}}\) takes minimum at \({\lambda }_{i}=-{z}_{\alpha /2}+{z}_{\gamma /2}\) or \({\lambda }_{i}={z}_{\alpha /2}-{z}_{\gamma /2}\). For \({\lambda }_{i}=-{z}_{\alpha /2}+{z}_{\gamma /2}\) and \({\lambda }_{i}={z}_{\alpha /2}-{z}_{\gamma /2}\), we have \({c}_{{\lambda }_{i}}=\Phi \left(2{z}_{\alpha /2}-{z}_{\gamma /2}\right)-\gamma /2\),
(v) For \({z}_{\alpha /2}-{z}_{\gamma /2}<{\lambda }_{i}\le {z}_{\gamma /2}\), \({c}_{{\lambda }_{i}}\) is the increasing function of \({\lambda }_{i}\). Therefore, we have
(vi) For \({z}_{\gamma /2}<{\lambda }_{i}\le {z}_{\alpha /2}+{z}_{\gamma /2}\), \({c}_{{\lambda }_{i}}\) is the increasing function of \({\lambda }_{i}\). Since \({c}_{{\lambda }_{i}}\) is not continuous at \({\lambda }_{i}={z}_{\gamma /2}\), we take limit \({\lambda }_{i}\downarrow {z}_{\gamma /2}\).
(vii) For \({\lambda }_{i}>{z}_{\alpha /2}+{z}_{\gamma /2}\), \({c}_{{\lambda }_{i}}=1-\gamma\). We trivially have
Using (i)–(vii), and \(\alpha <\gamma\) (equivalent to \({z}_{\alpha /2}>{z}_{\gamma /2}\)), we have
Since \({z}_{\gamma /2}-{z}_{\alpha /2}<{z}_{\gamma /2}\) is equivalent to \(\Phi \left({z}_{\gamma /2}-{z}_{\alpha /2}\right)<\Phi \left({z}_{\gamma /2}\right)\), we have
This inequality holds for all \(\gamma\) in Case 2 (\(\alpha <\gamma\) and \({z}_{\alpha /2}\le 2{z}_{\gamma /2}\)). For the proofs for Case 1 (\(\alpha \ge \gamma\)) and Case 3 (\(\alpha <\gamma\) and \({z}_{\alpha /2}>2{z}_{\gamma /2}\)), similar calculations can be performed.\(\hfill\square\)
1.3 Appendix C: The proof of Theorem 1
We derive the CDF of \({\delta }_{i}^{\mathrm{PT}}\) as follows:
Putting \({Z}_{i}=\left({Y}_{i}-{\mu }_{i}\right)/{\sigma }_{i}\), we have
Since \({Z}_{i}\) is a continuous random variable, \(P\left({Z}_{i}<z\right)=P\left({Z}_{i}\le z\right)\) for \(z\in (-\infty ,\infty )\). Therefore
The proof is complete. \(\hfill\square\)
1.4 Appendix D: The derivation of the pivot CI
We first review the pivoting method in a general setting. The following lemma is quoted from Theorem 9.2.14 of Casella and Berger (2002):
Lemma 3 (Pivoting a CDF; Casella & Berger, 2002) Let \({\delta }_{i}\) be a discrete statistic with CDF \({F}_{{\delta }_{i}}\left(d|{\mu }_{i}\right)=P\left({\delta }_{i}\le d|{\mu }_{i}\right)\). Let \(\mathcal{D}\) be the sample space of \({\delta }_{i}\). Let \({\gamma }_{1}+{\gamma }_{2}=\gamma\) with \(0<\gamma <1\) be fixed values. Suppose that for each \(d\in \mathcal{D}\), \({\mu }_{i\mathrm{L}}\left(d\right)\) and \({\mu }_{i\mathrm{U}}\left(d\right)\) can be defined as follows.
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i. If \({F}_{{\delta }_{i}}\left(d|{\mu }_{i}\right)\) is a decreasing function of \({\mu }_{i}\) for each \(d\), define \({\mu }_{i\mathrm{L}}\left(d\right)\) and \({\mu }_{i\mathrm{U}}\left(d\right)\) by \(P\left({\delta }_{i}\le d|{\mu }_{i\mathrm{U}}\left(d\right)\right)={\gamma }_{1}, P\left({\delta }_{i}\ge d|{\mu }_{i\mathrm{L}}\left(d\right)\right)={\gamma }_{2}.\)
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ii. If \({F}_{{\delta }_{i}}\left(d|{\mu }_{i}\right)\) is an increasing function of \({\mu }_{i}\) for each \(d\), define \({\mu }_{i\mathrm{L}}\left(d\right)\) and \({\mu }_{i\mathrm{U}}\left(d\right)\) by \(P\left({\delta }_{i}\ge d|{\mu }_{i\mathrm{U}}\left(d\right)\right)={\gamma }_{1}, P\left({\delta }_{i}\le d|{\mu }_{i\mathrm{L}}\left(d\right)\right)={\gamma }_{2}.\)
Then, the random interval \(\left[{\mu }_{i\mathrm{L}}\left({\delta }_{i}\right), {\mu }_{i\mathrm{U}}\left({\delta }_{i}\right)\right]\) is a \(100\left(1-\gamma \right)\mathrm{\%}\) CI for \({\mu }_{i}\).
Let \({\gamma }_{1}+{\gamma }_{2}=\gamma\) with \(0<\gamma <1\) being a fixed value. From Eq. (1), the sample space of \({\delta }_{i}^{\mathrm{PT}}\) is \({\delta }_{i}^{\mathrm{PT}}<-{z}_{\alpha /2}{\sigma }_{i}\) or \({\delta }_{i}^{\mathrm{PT}}=0\) or \({\delta }_{i}^{\mathrm{PT}}>{z}_{\alpha /2}{\sigma }_{i}\). We derive \(\left[{L}_{i}^{\mathrm{PT}}, {U}_{i}^{\mathrm{PT}}\right]=\left[{\mu }_{i\mathrm{L}}\left({\delta }_{i}^{\mathrm{PT}}\right), {\mu }_{i\mathrm{U}}\left({\delta }_{i}^{\mathrm{PT}}\right)\right]\) for each of three cases.
For each \({d}_{0}<-{z}_{\alpha /2}{\sigma }_{i}\), \({F}_{{\delta }_{i}^{\mathrm{PT}}}\left({d}_{0}|{\mu }_{i}\right)=\Phi \left\{\left({d}_{0}-{\mu }_{i}\right)/{\sigma }_{i}\right\}\) is the decreasing function of \({\mu }_{i}\). We define \({\mu }_{i\mathrm{L}}\left({d}_{0}\right)\) and \({\mu }_{i\mathrm{U}}\left({d}_{0}\right)\) by
These are equivalent to
or equivalently
Therefore, we have \(\left[{\mu }_{i\mathrm{L}}\left({d}_{0}\right), {\mu }_{i\mathrm{U}}\left({d}_{0}\right)\right]=\left[{d}_{0}-{z}_{{\gamma }_{2}}{\sigma }_{i},{d}_{0}+{z}_{{\gamma }_{1}}{\sigma }_{i}\right]\).
For \({d}_{0}=0\), \({\delta }_{i}^{\mathrm{PT}}\left({d}_{0}|{\mu }_{i}\right)=\Phi \left(-{\mu }_{i}/{\sigma }_{i}+{z}_{\alpha /2}\right)\) is the decreasing function of \({\mu }_{i}\). We define \({\mu }_{i\mathrm{L}}\left({d}_{0}\right)\) and \({\mu }_{i\mathrm{U}}\left({d}_{0}\right)\) by
Expanding these equations, we have
They are equivalent to
Therefore, we have \(\left[{\mu }_{i\mathrm{L}}\left(d\right), {\mu }_{i\mathrm{U}}\left(d\right)\right]=\left[-\left({z}_{\alpha /2}+{z}_{{\gamma }_{2}}\right){\sigma }_{i},\left({z}_{\alpha /2}+{z}_{{\gamma }_{1}}\right){\sigma }_{i}\right]\) for \({\delta }_{i}^{\mathrm{PT}}=0\).
For \({d}_{0}>{z}_{\alpha /2}{\sigma }_{i}\), we can derive \(\left[{\mu }_{i\mathrm{L}}\left({d}_{0}\right), {\mu }_{i\mathrm{U}}\left({d}_{0}\right)\right]=\left[{d}_{0}-{z}_{{\gamma }_{2}}{\sigma }_{i},{d}_{0}+{z}_{{\gamma }_{1}}{\sigma }_{i}\right]\) as well as for \({d}_{0}<-{z}_{\alpha /2}{\sigma }_{i}\). In conclusion, we have
Thus, the derivation is complete. \(\hfill\square\)
1.5 Appendix E: The proof of Theorem 2
We derive the CP of the pivot CI for each of three cases. We have
Each term in the above equation can be expressed as follows. For the first term, we have
and for the second term, we have
and for the third term, we have
Summing up the three terms, we obtain the following CP. For \({z}_{\alpha /2}-{z}_{{\gamma }_{2}}<-{z}_{\alpha /2}+{z}_{{\gamma }_{1}}\)
for \({z}_{\alpha /2}-{z}_{{\gamma }_{2}}=-{z}_{\alpha /2}+{z}_{{\gamma }_{1}}\),
for \({z}_{\alpha /2}-{z}_{{\gamma }_{2}}>-{z}_{\alpha /2}+{z}_{{\gamma }_{1}}\),
\(\hfill\square\)
1.6 Appendix F: The proof of Theorem 3
We recall that the CP takes three cases (Cases 1–3 in Theorem 2). The first and second cases are expressed as \({z}_{\alpha /2}-{z}_{{\gamma }_{2}}<-{z}_{\alpha /2}+{z}_{{\gamma }_{1}}\) and \({z}_{\alpha /2}-{z}_{{\gamma }_{2}}=-{z}_{\alpha /2}+{z}_{{\gamma }_{1}}\), respectively. In these cases, the CP of Theorem 2 immediately yields
Now, we consider the last case expressed as \(-{z}_{\alpha /2}+{z}_{{\gamma }_{1}}<{z}_{\alpha /2}-{z}_{{\gamma }_{2}}\). Note that \(-{\lambda }_{i}+{z}_{\alpha /2}>{z}_{{\gamma }_{2}}\) is equivalent to \(\Phi \left(-{\lambda }_{i}+{z}_{\alpha /2}\right)>1-{\gamma }_{2},\) and that \(-{\lambda }_{i}-{z}_{\alpha /2}<-{z}_{{\gamma }_{1}}\) is equivalent to \(-\Phi \left(-{\lambda }_{i}-{z}_{\alpha /2}\right)>-{\gamma }_{1}\). From these inequalities, for \(-{z}_{\alpha /2}+{z}_{{\gamma }_{1}}<{\lambda }_{i}<{z}_{\alpha /2}-{z}_{{\gamma }_{2}}\), we have
For \({\lambda }_{i}\le -{z}_{\alpha /2}+{z}_{{\gamma }_{1}}\) and \({\lambda }_{i}\ge {z}_{\alpha /2}-{z}_{{\gamma }_{2}}\), we have
Therefore
The proof is complete. \(\hfill\square\)
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Taketomi, N., Chang, YT., Konno, Y. et al. Confidence interval for normal means in meta-analysis based on a pretest estimator. Jpn J Stat Data Sci 7, 537–568 (2024). https://doi.org/10.1007/s42081-023-00221-2
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DOI: https://doi.org/10.1007/s42081-023-00221-2