Browsing by Author "Wang, Meng"

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    A comparison of meta-analytic methods for synthesizing evidence from explanatory and pragmatic trials
    (2018-01-25) Sajobi, Tolulope T; Li, Guowei; Awosoga, Oluwagbohunmi; Wang, Meng; Menon, Bijoy K; Hill, Michael D; Thabane, Lehana
    Abstract Background The pragmatic–explanatory continuum indicator summary version 2 (PRECIS-2) tool has recently been developed to classify randomized clinical trials (RCTs) as pragmatic or explanatory based on their design characteristics. Given that treatment effects in explanatory trials may be greater than those obtained in pragmatic trials, conventional meta-analytic approaches may not accurately account for the heterogeneity among the studies and may result in biased treatment effect estimates. This study investigates if the incorporation of PRECIS-2 classification of published trials can improve the estimation of overall intervention effects in meta-analysis. Methods Using data from 31 published trials of intervention aimed at reducing obesity in children, we evaluated the utility of incorporating PRECIS-2 ratings of published trials into meta-analysis of intervention effects in clinical trials. Specifically, we compared random-effects meta-analysis, stratified meta-analysis, random-effects meta-regression, and mixture random-effects meta-regression methods for estimating overall pooled intervention effects. Results Our analyses revealed that mixture meta-regression models that incorporate PRECIS-2 classification as covariate resulted in a larger pooled effect size (ES) estimate (ES = − 1.01, 95%CI = [− 1.52, − 0.43]) than conventional random-effects meta-analysis (ES = − 0.15, 95%CI = [− 0.23, − 0.08]). Conclusions In addition to the original intent of PRECIS-2 tool of aiding researchers in their choice of trial design, PRECIS-2 tool is useful for explaining between study variations in systematic review and meta-analysis of published trials. We recommend that researchers adopt mixture meta-regression methods when synthesizing evidence from explanatory and pragmatic trials.
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    Dementia risk prediction in individuals with mild cognitive impairment: a comparison of Cox regression and machine learning models
    (2022-11-02) Wang, Meng; Greenberg, Matthew; Forkert, Nils D.; Chekouo, Thierry; Afriyie, Gabriel; Ismail, Zahinoor; Smith, Eric E.; Sajobi, Tolulope T.
    Abstract Background Cox proportional hazards regression models and machine learning models are widely used for predicting the risk of dementia. Existing comparisons of these models have mostly been based on empirical datasets and have yielded mixed results. This study examines the accuracy of various machine learning and of the Cox regression models for predicting time-to-event outcomes using Monte Carlo simulation in people with mild cognitive impairment (MCI). Methods The predictive accuracy of nine time-to-event regression and machine learning models were investigated. These models include Cox regression, penalized Cox regression (with Ridge, LASSO, and elastic net penalties), survival trees, random survival forests, survival support vector machines, artificial neural networks, and extreme gradient boosting. Simulation data were generated using study design and data characteristics of a clinical registry and a large community-based registry of patients with MCI. The predictive performance of these models was evaluated based on three-fold cross-validation via Harrell’s concordance index (c-index), integrated calibration index (ICI), and integrated brier score (IBS). Results Cox regression and machine learning model had comparable predictive accuracy across three different performance metrics and data-analytic conditions. The estimated c-index values for Cox regression, random survival forests, and extreme gradient boosting were 0.70, 0.69 and 0.70, respectively, when the data were generated from a Cox regression model in a large sample-size conditions. In contrast, the estimated c-index values for these models were 0.64, 0.64, and 0.65 when the data were generated from a random survival forest in a large sample size conditions. Both Cox regression and random survival forest had the lowest ICI values (0.12 for a large sample size and 0.18 for a small sample size) among all the investigated models regardless of sample size and data generating model. Conclusion Cox regression models have comparable, and sometimes better predictive performance, than more complex machine learning models. We recommend that the choice among these models should be guided by important considerations for research hypotheses, model interpretability, and type of data.
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    Global assessment of migraine severity measure: preliminary evidence of construct validity
    (2019-04-04) Sajobi, Tolulope T; Amoozegar, Farnaz; Wang, Meng; Wiebe, Natalie; Fiest, Kirsten M; Patten, Scott B; Jette, Nathalie
    Abstract Background In persons with migraine, severity of migraine is an important determinant of several health outcomes (e.g., patient quality of life and health care resource utilization). This study investigated how migraine patients rate the severity of their disease and how these ratings correlate with their socio-demographic, clinical, and psycho-social characteristics. Methods This is a cohort of 263 adult migraine patients consecutively enrolled in the Neurological Disease and Depression Study (NEEDs). We obtained a broad range of clinical and patient-reported measures (e.g., patients’ ratings of migraine severity using the Global Assessment of Migraine Severity (GAMS), and migraine-related disability, as measured by the Migraine Disability Scale (MIDAS)). Depression was measured using the 9-item Patient Health Questionnaire (PHQ-9) and the 14-item Hospital Anxiety and Depression Scale (HADS). Median regression analysis was used to examine the predictors of patient ratings of migraine severity. Results The mean age for the patients was 42.5 years (SD = 13.2). While 209 (79.4%) patients were females, 177 (67.4%) participants reported “moderately severe” to “extremely severe” migraine on the GAMS, and 100 (31.6%) patients had chronic migraine. Patients’ report of severity on the GAMS was strongly correlated with patients’ ratings of MIDAS global severity question, overall MIDAS score, migraine type, PHQ-9 score, and frequency of migraine attacks. Mediation analyses revealed that MIDAS mediated the effect of depression on patient ratings of migraine severity, accounting for about 32% of the total effect of depression. Overall, migraine subtype, frequency of migraine, employment status, depression, and migraine-related disability were statistically significant predictors of patient-ratings of migraine severity. Conclusions This study highlights the impact of clinical and psychosocial determinants of patient-ratings of migraine severity. GAMS is a brief and valid tool that can be used to assess migraine severity in busy clinical settings.