Bayesian Sample Size Calculations for SMART Studies Article Swipe
Armando Turchetta
,
Erica E. M. Moodie
,
David A. Stephens
,
Sylvie Lambert
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2108.01041
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2108.01041
In the management of most chronic conditions characterized by the lack of universally effective treatments, adaptive treatment strategies (ATSs) have been growing in popularity as they offer a more individualized approach, and sequential multiple assignment randomized trials (SMARTs) have gained attention as the most suitable clinical trial design to formalize the study of these strategies. While the number of SMARTs has increased in recent years, their design has remained limited to the frequentist setting, which may not fully or appropriately account for uncertainty in design parameters and hence not yield appropriate sample size recommendations. Specifically, standard frequentist formulae rely on several assumptions that can be easily misspecified. The Bayesian framework offers a straightforward path to alleviate some of these concerns. In this paper, we provide calculations in a Bayesian setting to allow more realistic and robust estimates that account for uncertainty in inputs through the `two priors' approach. Additionally, compared to the standard formulae, this methodology allows us to rely on fewer assumptions, integrate pre-trial knowledge, and switch the focus from the standardized effect size to the minimal detectable difference. The proposed methodology is evaluated in a thorough simulation study and is implemented to estimate the sample size for a full-scale SMART of an Internet-Based Adaptive Stress Management intervention based on a pilot SMART conducted on cardiovascular disease patients from two Canadian provinces.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2108.01041
- https://arxiv.org/pdf/2108.01041
- OA Status
- green
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3190657895
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3190657895Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2108.01041Digital Object Identifier
- Title
-
Bayesian Sample Size Calculations for SMART StudiesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-02Full publication date if available
- Authors
-
Armando Turchetta, Erica E. M. Moodie, David A. Stephens, Sylvie LambertList of authors in order
- Landing page
-
https://arxiv.org/abs/2108.01041Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2108.01041Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2108.01041Direct OA link when available
- Concepts
-
Bayesian probability, Sample size determination, Sample (material), Econometrics, Statistics, Computer science, Psychology, Data science, Mathematics, Physics, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3190657895 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2108.01041 |
| ids.doi | https://doi.org/10.48550/arxiv.2108.01041 |
| ids.mag | 3190657895 |
| ids.openalex | https://openalex.org/W3190657895 |
| fwci | |
| type | preprint |
| title | Bayesian Sample Size Calculations for SMART Studies |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11304 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9927999973297119 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2741 |
| topics[0].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[0].display_name | Advanced Neuroimaging Techniques and Applications |
| topics[1].id | https://openalex.org/T13702 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9857000112533569 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Machine Learning in Healthcare |
| topics[2].id | https://openalex.org/T10136 |
| topics[2].field.id | https://openalex.org/fields/26 |
| topics[2].field.display_name | Mathematics |
| topics[2].score | 0.9828000068664551 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2613 |
| topics[2].subfield.display_name | Statistics and Probability |
| topics[2].display_name | Statistical Methods and Inference |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C107673813 |
| concepts[0].level | 2 |
| concepts[0].score | 0.616521954536438 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q812534 |
| concepts[0].display_name | Bayesian probability |
| concepts[1].id | https://openalex.org/C129848803 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6088682413101196 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2564360 |
| concepts[1].display_name | Sample size determination |
| concepts[2].id | https://openalex.org/C198531522 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5093141794204712 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q485146 |
| concepts[2].display_name | Sample (material) |
| concepts[3].id | https://openalex.org/C149782125 |
| concepts[3].level | 1 |
| concepts[3].score | 0.4788125157356262 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[3].display_name | Econometrics |
| concepts[4].id | https://openalex.org/C105795698 |
| concepts[4].level | 1 |
| concepts[4].score | 0.3975735902786255 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[4].display_name | Statistics |
| concepts[5].id | https://openalex.org/C41008148 |
| concepts[5].level | 0 |
| concepts[5].score | 0.3720327615737915 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[5].display_name | Computer science |
| concepts[6].id | https://openalex.org/C15744967 |
| concepts[6].level | 0 |
| concepts[6].score | 0.36276131868362427 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[6].display_name | Psychology |
| concepts[7].id | https://openalex.org/C2522767166 |
| concepts[7].level | 1 |
| concepts[7].score | 0.32351452112197876 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2374463 |
| concepts[7].display_name | Data science |
| concepts[8].id | https://openalex.org/C33923547 |
| concepts[8].level | 0 |
| concepts[8].score | 0.25440946221351624 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[8].display_name | Mathematics |
| concepts[9].id | https://openalex.org/C121332964 |
| concepts[9].level | 0 |
| concepts[9].score | 0.1242654025554657 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[9].display_name | Physics |
| concepts[10].id | https://openalex.org/C97355855 |
| concepts[10].level | 1 |
| concepts[10].score | 0.07962405681610107 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11473 |
| concepts[10].display_name | Thermodynamics |
| keywords[0].id | https://openalex.org/keywords/bayesian-probability |
| keywords[0].score | 0.616521954536438 |
| keywords[0].display_name | Bayesian probability |
| keywords[1].id | https://openalex.org/keywords/sample-size-determination |
| keywords[1].score | 0.6088682413101196 |
| keywords[1].display_name | Sample size determination |
| keywords[2].id | https://openalex.org/keywords/sample |
| keywords[2].score | 0.5093141794204712 |
| keywords[2].display_name | Sample (material) |
| keywords[3].id | https://openalex.org/keywords/econometrics |
| keywords[3].score | 0.4788125157356262 |
| keywords[3].display_name | Econometrics |
| keywords[4].id | https://openalex.org/keywords/statistics |
| keywords[4].score | 0.3975735902786255 |
| keywords[4].display_name | Statistics |
| keywords[5].id | https://openalex.org/keywords/computer-science |
| keywords[5].score | 0.3720327615737915 |
| keywords[5].display_name | Computer science |
| keywords[6].id | https://openalex.org/keywords/psychology |
| keywords[6].score | 0.36276131868362427 |
| keywords[6].display_name | Psychology |
| keywords[7].id | https://openalex.org/keywords/data-science |
| keywords[7].score | 0.32351452112197876 |
| keywords[7].display_name | Data science |
| keywords[8].id | https://openalex.org/keywords/mathematics |
| keywords[8].score | 0.25440946221351624 |
| keywords[8].display_name | Mathematics |
| keywords[9].id | https://openalex.org/keywords/physics |
| keywords[9].score | 0.1242654025554657 |
| keywords[9].display_name | Physics |
| keywords[10].id | https://openalex.org/keywords/thermodynamics |
| keywords[10].score | 0.07962405681610107 |
| keywords[10].display_name | Thermodynamics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2108.01041 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2108.01041 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2108.01041 |
| locations[1].id | doi:10.48550/arxiv.2108.01041 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2108.01041 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5021869883 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-6490-0110 |
| authorships[0].author.display_name | Armando Turchetta |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Armando Turchetta |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5048550846 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-7225-3977 |
| authorships[1].author.display_name | Erica E. M. Moodie |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Erica E. M. Moodie |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5085069223 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-9811-7140 |
| authorships[2].author.display_name | David A. Stephens |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | David A. Stephens |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5001224758 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-9202-9251 |
| authorships[3].author.display_name | Sylvie Lambert |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Sylvie D. Lambert |
| authorships[3].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2108.01041 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Bayesian Sample Size Calculations for SMART Studies |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T11304 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9927999973297119 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2741 |
| primary_topic.subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| primary_topic.display_name | Advanced Neuroimaging Techniques and Applications |
| related_works | https://openalex.org/W4388813866, https://openalex.org/W2999104021, https://openalex.org/W4293088233, https://openalex.org/W2850804095, https://openalex.org/W1481656249, https://openalex.org/W2162280767, https://openalex.org/W3012357814, https://openalex.org/W1570799877, https://openalex.org/W2800688113, https://openalex.org/W1997267813 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2108.01041 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2108.01041 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2108.01041 |
| primary_location.id | pmh:oai:arXiv.org:2108.01041 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2108.01041 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2108.01041 |
| publication_date | 2021-08-02 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2081236418, https://openalex.org/W2460537066, https://openalex.org/W2962738156, https://openalex.org/W2159155203, https://openalex.org/W2108782374, https://openalex.org/W1987132730, https://openalex.org/W2783709266, https://openalex.org/W1986546901, https://openalex.org/W2475300008, https://openalex.org/W2485345981, https://openalex.org/W1999562241, https://openalex.org/W2949118362, https://openalex.org/W3047307095, https://openalex.org/W2766446970, https://openalex.org/W2761659193, https://openalex.org/W2021710102, https://openalex.org/W1971712327, https://openalex.org/W2768092002, https://openalex.org/W2564845374, https://openalex.org/W2036136173, https://openalex.org/W2098101547, https://openalex.org/W2167371237, https://openalex.org/W2276801646, https://openalex.org/W2087649022, https://openalex.org/W2130607817, https://openalex.org/W1991676478 |
| referenced_works_count | 26 |
| abstract_inverted_index.a | 27, 111, 127, 186, 199, 211 |
| abstract_inverted_index.In | 0, 120 |
| abstract_inverted_index.an | 203 |
| abstract_inverted_index.as | 24, 41 |
| abstract_inverted_index.be | 104 |
| abstract_inverted_index.by | 8 |
| abstract_inverted_index.in | 22, 62, 83, 126, 141, 185 |
| abstract_inverted_index.is | 183, 191 |
| abstract_inverted_index.of | 3, 11, 52, 58, 117, 202 |
| abstract_inverted_index.on | 99, 160, 210, 215 |
| abstract_inverted_index.or | 78 |
| abstract_inverted_index.to | 48, 70, 114, 130, 150, 158, 175, 193 |
| abstract_inverted_index.us | 157 |
| abstract_inverted_index.we | 123 |
| abstract_inverted_index.The | 107, 180 |
| abstract_inverted_index.and | 31, 86, 134, 166, 190 |
| abstract_inverted_index.can | 103 |
| abstract_inverted_index.for | 81, 139, 198 |
| abstract_inverted_index.has | 60, 67 |
| abstract_inverted_index.may | 75 |
| abstract_inverted_index.not | 76, 88 |
| abstract_inverted_index.the | 1, 9, 42, 50, 56, 71, 144, 151, 168, 171, 176, 195 |
| abstract_inverted_index.two | 220 |
| abstract_inverted_index.`two | 145 |
| abstract_inverted_index.been | 20 |
| abstract_inverted_index.from | 170, 219 |
| abstract_inverted_index.have | 19, 38 |
| abstract_inverted_index.lack | 10 |
| abstract_inverted_index.more | 28, 132 |
| abstract_inverted_index.most | 4, 43 |
| abstract_inverted_index.path | 113 |
| abstract_inverted_index.rely | 98, 159 |
| abstract_inverted_index.size | 92, 174, 197 |
| abstract_inverted_index.some | 116 |
| abstract_inverted_index.that | 102, 137 |
| abstract_inverted_index.they | 25 |
| abstract_inverted_index.this | 121, 154 |
| abstract_inverted_index.SMART | 201, 213 |
| abstract_inverted_index.While | 55 |
| abstract_inverted_index.allow | 131 |
| abstract_inverted_index.based | 209 |
| abstract_inverted_index.fewer | 161 |
| abstract_inverted_index.focus | 169 |
| abstract_inverted_index.fully | 77 |
| abstract_inverted_index.hence | 87 |
| abstract_inverted_index.offer | 26 |
| abstract_inverted_index.pilot | 212 |
| abstract_inverted_index.study | 51, 189 |
| abstract_inverted_index.their | 65 |
| abstract_inverted_index.these | 53, 118 |
| abstract_inverted_index.trial | 46 |
| abstract_inverted_index.which | 74 |
| abstract_inverted_index.yield | 89 |
| abstract_inverted_index.(ATSs) | 18 |
| abstract_inverted_index.SMARTs | 59 |
| abstract_inverted_index.Stress | 206 |
| abstract_inverted_index.allows | 156 |
| abstract_inverted_index.design | 47, 66, 84 |
| abstract_inverted_index.easily | 105 |
| abstract_inverted_index.effect | 173 |
| abstract_inverted_index.gained | 39 |
| abstract_inverted_index.inputs | 142 |
| abstract_inverted_index.number | 57 |
| abstract_inverted_index.offers | 110 |
| abstract_inverted_index.paper, | 122 |
| abstract_inverted_index.recent | 63 |
| abstract_inverted_index.robust | 135 |
| abstract_inverted_index.sample | 91, 196 |
| abstract_inverted_index.switch | 167 |
| abstract_inverted_index.trials | 36 |
| abstract_inverted_index.years, | 64 |
| abstract_inverted_index.account | 80, 138 |
| abstract_inverted_index.chronic | 5 |
| abstract_inverted_index.disease | 217 |
| abstract_inverted_index.growing | 21 |
| abstract_inverted_index.limited | 69 |
| abstract_inverted_index.minimal | 177 |
| abstract_inverted_index.priors' | 146 |
| abstract_inverted_index.provide | 124 |
| abstract_inverted_index.setting | 129 |
| abstract_inverted_index.several | 100 |
| abstract_inverted_index.through | 143 |
| abstract_inverted_index.(SMARTs) | 37 |
| abstract_inverted_index.Adaptive | 205 |
| abstract_inverted_index.Bayesian | 108, 128 |
| abstract_inverted_index.Canadian | 221 |
| abstract_inverted_index.adaptive | 15 |
| abstract_inverted_index.clinical | 45 |
| abstract_inverted_index.compared | 149 |
| abstract_inverted_index.estimate | 194 |
| abstract_inverted_index.formulae | 97 |
| abstract_inverted_index.multiple | 33 |
| abstract_inverted_index.patients | 218 |
| abstract_inverted_index.proposed | 181 |
| abstract_inverted_index.remained | 68 |
| abstract_inverted_index.setting, | 73 |
| abstract_inverted_index.standard | 95, 152 |
| abstract_inverted_index.suitable | 44 |
| abstract_inverted_index.thorough | 187 |
| abstract_inverted_index.alleviate | 115 |
| abstract_inverted_index.approach, | 30 |
| abstract_inverted_index.approach. | 147 |
| abstract_inverted_index.attention | 40 |
| abstract_inverted_index.concerns. | 119 |
| abstract_inverted_index.conducted | 214 |
| abstract_inverted_index.effective | 13 |
| abstract_inverted_index.estimates | 136 |
| abstract_inverted_index.evaluated | 184 |
| abstract_inverted_index.formalize | 49 |
| abstract_inverted_index.formulae, | 153 |
| abstract_inverted_index.framework | 109 |
| abstract_inverted_index.increased | 61 |
| abstract_inverted_index.integrate | 163 |
| abstract_inverted_index.pre-trial | 164 |
| abstract_inverted_index.realistic | 133 |
| abstract_inverted_index.treatment | 16 |
| abstract_inverted_index.Management | 207 |
| abstract_inverted_index.assignment | 34 |
| abstract_inverted_index.conditions | 6 |
| abstract_inverted_index.detectable | 178 |
| abstract_inverted_index.full-scale | 200 |
| abstract_inverted_index.knowledge, | 165 |
| abstract_inverted_index.management | 2 |
| abstract_inverted_index.parameters | 85 |
| abstract_inverted_index.popularity | 23 |
| abstract_inverted_index.provinces. | 222 |
| abstract_inverted_index.randomized | 35 |
| abstract_inverted_index.sequential | 32 |
| abstract_inverted_index.simulation | 188 |
| abstract_inverted_index.strategies | 17 |
| abstract_inverted_index.appropriate | 90 |
| abstract_inverted_index.assumptions | 101 |
| abstract_inverted_index.difference. | 179 |
| abstract_inverted_index.frequentist | 72, 96 |
| abstract_inverted_index.implemented | 192 |
| abstract_inverted_index.methodology | 155, 182 |
| abstract_inverted_index.strategies. | 54 |
| abstract_inverted_index.treatments, | 14 |
| abstract_inverted_index.uncertainty | 82, 140 |
| abstract_inverted_index.universally | 12 |
| abstract_inverted_index.assumptions, | 162 |
| abstract_inverted_index.calculations | 125 |
| abstract_inverted_index.intervention | 208 |
| abstract_inverted_index.standardized | 172 |
| abstract_inverted_index.Additionally, | 148 |
| abstract_inverted_index.Specifically, | 94 |
| abstract_inverted_index.appropriately | 79 |
| abstract_inverted_index.characterized | 7 |
| abstract_inverted_index.misspecified. | 106 |
| abstract_inverted_index.Internet-Based | 204 |
| abstract_inverted_index.cardiovascular | 216 |
| abstract_inverted_index.individualized | 29 |
| abstract_inverted_index.straightforward | 112 |
| abstract_inverted_index.recommendations. | 93 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile |