Data on a new biomarker for kidney transplant recipients: The number of FoxP3 regulatory T cells in the circulation

This article presents unrevealed details of the systematic review process of the article “The number of FoxP3 regulatory T cells in the circulation may be a predictive biomarker for kidney transplant recipients: A multistage systematic review” (Herrera-Gómez et al., 2018). Eligibility criteria guiding searches and study selection, the risk of bias assessment, the assessment of medicine-test codependency (evaluation of the body of evidence), and meta-analytic calculations are provided. The data allows other researchers, particularly those involved in experiments on Translational Epidemiology applied to Pharmacology, to corroborate and extend our assessments.

codependency (evaluation of the body of evidence), and metaanalytic calculations are provided. The data allows other researchers, particularly those involved in experiments on Translational Epidemiology applied to Pharmacology, to corroborate and extend our assessments.
& 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Subject area
Biology More specific subject area Translational pharmacology Type of data Text, tables, and figures.

How data was acquired
Definition of eligibility criteria and search strategy for study selection, risk of bias assessment, assessment of codependent health technologies, and meta-analytic assessment.

Data Format
Raw and analyzed.

Experimental factors
Systematic review protocol registration, study selection process (against eligibility criteria), and data extraction.

Experimental features
Inclusion and exclusion criteria, full search strategy, risk of bias assessment, assessment of medicine-test codependency, and continuous data meta-analysis.

Data source location
Valladolid, Spain, 41.654444°, À 4.7175°D ata accessibility Data is with this article. Value of the data In the field of Translational Pharmacology, sharing systematic review process details is very important.
This data allows other researchers to corroborate and extend our assessments. The main aim of sharing this data is to improve the qualification of potential predictive biomarkers.

Data
In addition to links to the four systematic review protocols registered in the International Prospective Register of Systematic Reviews (PROSPERO) (Appendix A. Supplementary material. Text S1), this article presents the inclusion and exclusion criteria ( Tables 1 and 2), the entire search strategy for the systematic reviews performed (Appendix A. Supplementary material. Text S2), the risk of bias (quality) assessment details (Tables 3-5), the assessment of medicine-test codependency (Table 6), and the meta-analyses (Figs. 1-3) were not included in the article of Herrera-Gómez et al. [1]. c,e,f Less AR/AAD events.

Table 3
Operationalization of the QUIPS tool bias items for assessing risk of bias in prognostic studies.
Potential bias Items to be considered for assessment potential opportunities of bias Study participationThe study sample adequately represents the population of interest.
There is adequate participation in the study by eligible individuals (kidney recipients).
The source population or population of interest is adequately described (demographic and transplantation details).
The sampling frame and recruitment, period of recruitment, and place of recruitment (setting and geographic location) are adequately described.
Inclusion and exclusion criteria are adequately described.
Study attritionThe study data available (i.e., participants not lost to follow-up) adequately represents the study sample.
Response rate (i.e., proportion of study sample completing the study and providing outcome data) is adequate.
Attempts to collect information on participants who dropped out of the study are described, and reasons for loss to follow-up are provided.
Participants lost to follow-up are adequately described

Prognostic factor measurement
A clear definition or description of the prognostic factor measured (i.e., the changes in the immune phenotype associated with operational tolerance) is provided.
Continuous variables are reported and appropriate (i.e., not data-dependent) cut-points are used.
The prognostic factor measurement and methods are adequately valid and reliable.
An adequate proportion of the study sample has complete data for the prognostic factor.
The method and setting of measurement are the same for all study participants.
The prognostic factor of interest is measured similarly for all participants.

Outcome measurement
A clear definition of the outcome of interest (i.e., clinical operational tolerance after kidney transplantation) is provided.
The outcome measures and methods used are adequately valid and reliable (and may include characteristics, such as blind measurement and confirmation of outcome with a valid and reliable test).
The method and setting of measurement are the same for all study participants.
The outcome of interest is measured similarly for all participants.

Experimental design, materials and methods
For study selection, definition of inclusion and exclusion criteria and the full search strategy were based on the PICOS elements (participants/population, intervention(s)/exposure(s), comparators, outcomes and study design) [1]. The operationalization of the Quality in Prognosis Studies (QUIPS) tool was necessary (Table 3) [2,3]. Nevertheless, for the risk of bias assessment, the QUIPS tool and the Cochrane Collaboration tool [4] were used when appropriate. For the assessment of medicine-test

Potential bias
Items to be considered for assessment potential opportunities of bias

Confounding measurement and account
All confounders, including treatments, are measured. Clear definitions of the important confounders measured are provided (e.g., including dose, level, and duration of exposures).
The measurement of all important confounders is adequately valid and reliable.
The method and setting of confounding measurement is the same for all study participants.
Appropriate methods are used if imputation is used for missing confounder data.
Important potential confounders are accounted for in the study design (e.g., matching for key variables, stratification, and initial assembly of comparable groups).
Important potential confounders are accounted for in the analysis (e.g., appropriate adjustment).
Important potential confounding factors are appropriately accounted for

Statistical analysis and reporting
There is sufficient presentation of data to assess the adequacy of the analysis.
The strategy for model building (i.e., inclusion of variables) is appropriate and is based on a conceptual framework or model.
The selected model is adequate for the design of the study. There is no selective reporting of results.
The statistical analysis is appropriate, and all primary outcomes are reported Table 4 Assessing risk of bias in eligible prognostic studies eligible using the QUIPS tool.   codependency, an adaptation of Merlin's tool included in the guidelines for preparing a submission to the Pharmaceutical Benefits Advisory Committee (PBAC) from the Department of health of Australia was used [5,6]. Finally, meta-analytic calculations on continuous outcomes (standardized meandifference effect sizes obtained under inverse variance random-effects model) were performed.