Are longitudinal randomised controlled oral health trials properly analysed? A meta-epidemiological study

Samer Mheissen; Haris Khan; Jadbinder Seehra; Nikolaos Pandis

doi:10.1016/j.jdent.2022.104182

Are longitudinal randomised controlled oral health trials properly analysed? A meta-epidemiological study

Samer Mheissen^*, Haris Khan, Jadbinder Seehra, Nikolaos Pandis

^*Corresponding author for this work

Centre for Dental Education

Research output: Contribution to journal › Review article › peer-review

1 Citation (Scopus)

Abstract

Introduction: Longitudinal designs with multiple outcome measurements are commonly encountered in oral health randomised controlled trials (RCTs). The aim of this meta epidemiological study was to assess whether optimal statistical analysis approaches have been used in longitudinal oral health RCTs. Data sources: PubMed search was undertaken in September 2021 for longitudinal oral health RCTs with at least 3 repeated outcome measurements which have been published between 2016 and 2020 in the highest impact general and specialty dental journals. Study selection: Study selection and data extraction were accomplished independently and in duplicate. The statistical methods undertaken in the selected articles were tabulated, and the association between study characteristics and use of optimal analyses were assessed using X² or Fisher's exact test and logistic regression. Results: Five hundred and five oral health RCTs were deemed eligible for inclusion. Of these, only 28.3% RCTs used optimal statistical analyses for a longitudinal trial design. For the trials with an optimal statistical approach, the most frequent test used was repeated measures analysis of variance (RM-ANOVA) followed by mixed effect models (MEM). The use of optimal statistical tests was predicated by the involvement of a statistician (OR: 2, 95% CI:1.27–3.18, p < 0.01), the journal impact factor (OR:1.19, 95% CI;1.1–1.29), continent of first author (likelihood ratio test p = 0.01), number of the authors (OR:1.22, 95% CI;1.12–1.3, p < 0.001), protocol registration (OR: 1.48, 95%CI; 1 to 2.2, p = 0.05), funding(OR:2.4, 95%CI; 1.6–3.7, p < 0.001), and dental specialty (likelihood ratio test p < 0.001). Conclusions: Most longitudinal oral health RCTs did not use optimal statistical analyses. Greater awareness of optimal analyses used to assess longitudinal data reported in oral health trials is required to circumvent the reporting of suboptimal inferences, selective reporting and research waste. Clinical significance: Further progress is required to avoid suboptimal statistical analyses and fully utilise the benefits of the repeated measurements over time in oral health RCTs.

Original language	English
Article number	104182
Journal	Journal of Dentistry
Volume	124
Early online date	9 Jul 2022
DOIs	https://doi.org/10.1016/j.jdent.2022.104182
Publication status	Published - Sept 2022

Keywords

Clinical trials
Dental
Longitudinal data
Oral health
Statistical analysis

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10.1016/j.jdent.2022.104182

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title = "Are longitudinal randomised controlled oral health trials properly analysed? A meta-epidemiological study",

abstract = "Introduction: Longitudinal designs with multiple outcome measurements are commonly encountered in oral health randomised controlled trials (RCTs). The aim of this meta epidemiological study was to assess whether optimal statistical analysis approaches have been used in longitudinal oral health RCTs. Data sources: PubMed search was undertaken in September 2021 for longitudinal oral health RCTs with at least 3 repeated outcome measurements which have been published between 2016 and 2020 in the highest impact general and specialty dental journals. Study selection: Study selection and data extraction were accomplished independently and in duplicate. The statistical methods undertaken in the selected articles were tabulated, and the association between study characteristics and use of optimal analyses were assessed using X2 or Fisher's exact test and logistic regression. Results: Five hundred and five oral health RCTs were deemed eligible for inclusion. Of these, only 28.3% RCTs used optimal statistical analyses for a longitudinal trial design. For the trials with an optimal statistical approach, the most frequent test used was repeated measures analysis of variance (RM-ANOVA) followed by mixed effect models (MEM). The use of optimal statistical tests was predicated by the involvement of a statistician (OR: 2, 95% CI:1.27–3.18, p < 0.01), the journal impact factor (OR:1.19, 95% CI;1.1–1.29), continent of first author (likelihood ratio test p = 0.01), number of the authors (OR:1.22, 95% CI;1.12–1.3, p < 0.001), protocol registration (OR: 1.48, 95%CI; 1 to 2.2, p = 0.05), funding(OR:2.4, 95%CI; 1.6–3.7, p < 0.001), and dental specialty (likelihood ratio test p < 0.001). Conclusions: Most longitudinal oral health RCTs did not use optimal statistical analyses. Greater awareness of optimal analyses used to assess longitudinal data reported in oral health trials is required to circumvent the reporting of suboptimal inferences, selective reporting and research waste. Clinical significance: Further progress is required to avoid suboptimal statistical analyses and fully utilise the benefits of the repeated measurements over time in oral health RCTs.",

keywords = "Clinical trials, Dental, Longitudinal data, Oral health, Statistical analysis",

author = "Samer Mheissen and Haris Khan and Jadbinder Seehra and Nikolaos Pandis",

note = "Funding Information: None. Publisher Copyright: {\textcopyright} 2022",

year = "2022",

month = sep,

doi = "10.1016/j.jdent.2022.104182",

language = "English",

volume = "124",

journal = "Journal of Dentistry",

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T1 - Are longitudinal randomised controlled oral health trials properly analysed? A meta-epidemiological study

AU - Mheissen, Samer

AU - Khan, Haris

AU - Seehra, Jadbinder

AU - Pandis, Nikolaos

PY - 2022/9

Y1 - 2022/9

N2 - Introduction: Longitudinal designs with multiple outcome measurements are commonly encountered in oral health randomised controlled trials (RCTs). The aim of this meta epidemiological study was to assess whether optimal statistical analysis approaches have been used in longitudinal oral health RCTs. Data sources: PubMed search was undertaken in September 2021 for longitudinal oral health RCTs with at least 3 repeated outcome measurements which have been published between 2016 and 2020 in the highest impact general and specialty dental journals. Study selection: Study selection and data extraction were accomplished independently and in duplicate. The statistical methods undertaken in the selected articles were tabulated, and the association between study characteristics and use of optimal analyses were assessed using X2 or Fisher's exact test and logistic regression. Results: Five hundred and five oral health RCTs were deemed eligible for inclusion. Of these, only 28.3% RCTs used optimal statistical analyses for a longitudinal trial design. For the trials with an optimal statistical approach, the most frequent test used was repeated measures analysis of variance (RM-ANOVA) followed by mixed effect models (MEM). The use of optimal statistical tests was predicated by the involvement of a statistician (OR: 2, 95% CI:1.27–3.18, p < 0.01), the journal impact factor (OR:1.19, 95% CI;1.1–1.29), continent of first author (likelihood ratio test p = 0.01), number of the authors (OR:1.22, 95% CI;1.12–1.3, p < 0.001), protocol registration (OR: 1.48, 95%CI; 1 to 2.2, p = 0.05), funding(OR:2.4, 95%CI; 1.6–3.7, p < 0.001), and dental specialty (likelihood ratio test p < 0.001). Conclusions: Most longitudinal oral health RCTs did not use optimal statistical analyses. Greater awareness of optimal analyses used to assess longitudinal data reported in oral health trials is required to circumvent the reporting of suboptimal inferences, selective reporting and research waste. Clinical significance: Further progress is required to avoid suboptimal statistical analyses and fully utilise the benefits of the repeated measurements over time in oral health RCTs.

AB - Introduction: Longitudinal designs with multiple outcome measurements are commonly encountered in oral health randomised controlled trials (RCTs). The aim of this meta epidemiological study was to assess whether optimal statistical analysis approaches have been used in longitudinal oral health RCTs. Data sources: PubMed search was undertaken in September 2021 for longitudinal oral health RCTs with at least 3 repeated outcome measurements which have been published between 2016 and 2020 in the highest impact general and specialty dental journals. Study selection: Study selection and data extraction were accomplished independently and in duplicate. The statistical methods undertaken in the selected articles were tabulated, and the association between study characteristics and use of optimal analyses were assessed using X2 or Fisher's exact test and logistic regression. Results: Five hundred and five oral health RCTs were deemed eligible for inclusion. Of these, only 28.3% RCTs used optimal statistical analyses for a longitudinal trial design. For the trials with an optimal statistical approach, the most frequent test used was repeated measures analysis of variance (RM-ANOVA) followed by mixed effect models (MEM). The use of optimal statistical tests was predicated by the involvement of a statistician (OR: 2, 95% CI:1.27–3.18, p < 0.01), the journal impact factor (OR:1.19, 95% CI;1.1–1.29), continent of first author (likelihood ratio test p = 0.01), number of the authors (OR:1.22, 95% CI;1.12–1.3, p < 0.001), protocol registration (OR: 1.48, 95%CI; 1 to 2.2, p = 0.05), funding(OR:2.4, 95%CI; 1.6–3.7, p < 0.001), and dental specialty (likelihood ratio test p < 0.001). Conclusions: Most longitudinal oral health RCTs did not use optimal statistical analyses. Greater awareness of optimal analyses used to assess longitudinal data reported in oral health trials is required to circumvent the reporting of suboptimal inferences, selective reporting and research waste. Clinical significance: Further progress is required to avoid suboptimal statistical analyses and fully utilise the benefits of the repeated measurements over time in oral health RCTs.

KW - Clinical trials

KW - Dental

KW - Longitudinal data

KW - Oral health

KW - Statistical analysis

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U2 - 10.1016/j.jdent.2022.104182

DO - 10.1016/j.jdent.2022.104182

M3 - Review article

AN - SCOPUS:85133798727

SN - 0300-5712

VL - 124

JO - Journal of Dentistry

JF - Journal of Dentistry

M1 - 104182

ER -

Are longitudinal randomised controlled oral health trials properly analysed? A meta-epidemiological study

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