Authors:Tad T Brunyé, Catherine E Konold, Jason Wang, Kathleen F Kerr, Trafton Drew, Hannah Shucard, Kim Soroka, Donald L Weaver, Joann G Elmore Abstract: Research Methods in Medicine & Health Sciences, Ahead of Print. BackgroundIn pathology and other specialties of diagnostic medicine, longitudinal studies and competency assessments often involve physicians interpreting the same images multiple times. In these designs, a washout period is used to reduce the chances that later interpretations are influenced by prior exposure.Objective/sThe present study examines whether a washout period between 9 and 39 months is sufficient to prevent three effects of prior exposure when pathologists review digital breast tissue biopsies and render diagnostic decisions: faster case review durations, higher confidence, and lower perceived difficulty.MethodsIn a longitudinal breast pathology study, 48 resident pathologists reviewed a mix of five novel and five repeated digital whole slide images during Phase 2, occurring 9–39 months after an initial Phase 1 review. Importantly, cases that were repeated for some participants in Phase 2 were novel for other participants in Phase 2. We statistically tested for differences in participants’ case review duration, self-reported confidence, and self-reported difficulty in Phase 2 based on whether the case was novel or repeated.ResultsNo statistically significant difference in review time, confidence, or difficulty as a function of whether the case was repeated or novel in a Phase 2 review occurring 9-39 months after initial viewing; this same result was found in a subset of participants with a shorter (9-14-months) washout.ConclusionThese results provide evidence to support the efficacy of at least a 9-months washout period in the design of longitudinal medical imaging and informatics studies to ensure no detectable effect of initial exposure on participant’s subsequent case review. Citation: Research Methods in Medicine & Health Sciences PubDate: 2023-08-29T06:41:47Z DOI: 10.1177/26320843231199453
Authors:Wimarsha T Jayanetti, N Rao Chaganty, Sinjini Sikdar Abstract: Research Methods in Medicine & Health Sciences, Ahead of Print. BackgroundMeta-analysis is a popular approach for combining results from multiple studies investigating the same questions. Meta-analysis has gained wide popularity in genomic analysis due to the availability of large volumes of genomic study results from public databases. In genomic meta-analysis, researchers, often, tend to combine p-values related to significance testing of a gene from multiple studies where thousands of genes are tested simultaneously. The traditional p-value combination approaches aim to find genes which are differentially expressed in at least one of studies. An alternative form of meta-analysis has, recently, gained popularity where the aim is to find genes that are consistently differentially expressed in a large number, possibly a majority, of studies. An approach based on weighted ordered p-values (WOP) has been developed, in the recent past, to perform the latter type of meta-analysis.MethodsIn this article, we discuss the limitations of the WOP meta-analysis method due to its adherence to the standard null distributional assumptions of classical meta-analysis that can lead to incorrect significance testing results. Moreover, we propose a robust meta-analysis method for simultaneous significance testing of multitude of genes that improves the WOP approach using an empirical modification.ResultsThrough simulation studies, we demonstrate the superiority of our proposed method over the existing WOP meta-analysis by substantially reducing false discoveries of significant genes and controlling type-I error rates especially in the presence of unobserved confounding variables. We illustrate the utility of our proposed method through a variety of meta-analysis of genomic studies in different diseases. Citation: Research Methods in Medicine & Health Sciences PubDate: 2023-07-26T10:59:35Z DOI: 10.1177/26320843231191645
Authors:My Luong Vuong, Pham Hien Trang Tu, Khanh Linh Duong, Tat-Thang Vo Abstract: Research Methods in Medicine & Health Sciences, Ahead of Print. BackgroundCore patient characteristic sets (CPCSs) are increasingly developed to identify variables that should be reported to describe the target population of epidemiological studies in the same medical area, while keeping the additional burden on the data collection acceptable.MethodsWe conduct a systematic review of primary studies and protocols that aim to develop a CPCS, using the PubMed database. We extract information on the study design and the characteristics of the proposed CPCS. The quality of Delphi studies is assessed by a tool proposed in the literature. All results are reported descriptively.ResultsAmong 23 eligible studies, Delphi survey is the most frequently used technique to obtain consensus in CPCS development (69.6%, n = 16). Most studies do not include patients as stakeholders. The final CPCS rarely includes socioeconomic factors (26.1%, n = 6). Besides, 60.9% (n = 14) and 26.1% (n = 6) of the studies provide definitions and measurement methods for items in the CPCS, respectively.ConclusionThis review identifies considerable variation and suboptimality in many methodological aspects of CPCS studies. To improve these shortcomings, guidance on the conduct and reporting of CPCS studies should be established in the future. Citation: Research Methods in Medicine & Health Sciences PubDate: 2023-07-26T07:02:05Z DOI: 10.1177/26320843231191777
Authors:Narayanaswamy Balakrishnan, Jan Rychtář, Dewey Taylor, Stephen D. Walter Abstract: Research Methods in Medicine & Health Sciences, Ahead of Print. BackgroundIn meta-analysis, researchers often pool the results from a set of similar studies. A number of studies, however, often tend to report only the minimum and maximum values, median, and/or the first and third quartiles. Recently, many methods have been discussed for estimating the mean and standard deviation from those sample summaries. However, these methods may provide a substantially biased estimate of the inverse variance that is needed for the meta-analysis.Research DesignWe use Basu’s theorem to derive unbiased estimators for σ−2 from the most commonly used sample summaries from the normal distribution. While there are no closed formulas for these estimators, we use simulations to obtain simple approximations for the estimators.ResultsThe proposed approximate estimators still show a little to no bias for normally distributed data and generally show smaller bias than the usual methods even for some non-normal distributions. The proposed estimators have lower mean squared error.ConclusionsThe proposed estimators are recommended for the purpose of obtaining inverse-variance weights, particularly in the context of meta-analyses. Citation: Research Methods in Medicine & Health Sciences PubDate: 2023-07-18T02:18:48Z DOI: 10.1177/26320843231190024
Authors:Patrick Merkel, Sina Ramtin, Teun Teunis, David Ring Abstract: Research Methods in Medicine & Health Sciences, Ahead of Print. ObjectiveWe tested whether intermixing mental health items with items addressing comfort and capability could limit the floor effects noted when mental health is measured in musculoskeletal specialty care.MethodsOne hundred and 31 people seeking care for upper and lower extremity musculoskeletal conditions were randomized to complete randomly ordered, unlabeled mental health items intermixed with comfort and capability items, or intact and labelled questionnaires. For the two approaches, we compared: (1) flooring and ceiling effects; (2) mean and median questionnaire scores; (3) internal consistency (Cronbach alpha); and (4) exploratory factor analysis. We sought correlations between mental health and levels of pain intensity and capability.ResultsWe found slightly more flooring in the intermixed group for symptoms of depression (66% [41 of 62] vs 46% [32 of 69], p-value = .034), no differences in the mean and median scores for each questionnaire, lower internal consistency measured by Cronbach alpha, and lower factor loading coefficients in exploratory factor analysis for symptoms of depression and anxiety in the intermixed group. The mean level of symptoms of anxiety was significantly different between two groups (intermixed: 0.87 [95% CI 0.82 to 0.92], fixed: 0.96 [95% CI 0.93 to 0.98]). There were no differences in the association of the mental health measures gathered via the two different strategies with measures of pain intensity and magnitude of capability.ConclusionThe finding that intermixing mental health questions with questions about comfort and capability did not diminish floor effects suggests no advantage to intermixing mental health items in questionnaires used in musculoskeletal care and research. Citation: Research Methods in Medicine & Health Sciences PubDate: 2023-07-17T04:02:16Z DOI: 10.1177/26320843231190318
Authors:Chrysostomos Kalyvas, Katerina Papadimitropoulou, William Malbecq, Loukia M. Spineli Abstract: Research Methods in Medicine & Health Sciences, Ahead of Print. BackgroundThe Health Technology Assessment agencies typically require an economic evaluation considering a lifetime horizon for interventions affecting survival. However, survival data are often censored and are typically analyzed assuming the censoring mechanism independent of the event process. This assumption may lead to biased results when the censoring mechanism is informative.MethodsWe propose a flexible approach to jointly model the participants experiencing an event and censored participants by incorporating the pattern-mixture (PM) model in the fractional polynomial (FP) model within the network meta-analysis (NMA) framework. We introduce the informative censoring hazard ratio parameter that quantifies the departure from the censored at random assumption. The FP-PM model is exemplified in an NMA of the overall survival from non-small cell lung carcinoma studies using Bayesian methods.ResultsThe results on hazard ratio and survival from the FP-PM model are similar to those from the FP model. However, the posterior standard deviation of the hazard ratio is slightly greater when censored data are modeled because the uncertainty induced by censoring is naturally accounted for in the FP-PM model. The between-study standard deviation is almost identical in both models due to the low censoring rate across the studies. At the end of the corresponding studies, the informative censoring hazard ratio demonstrated a possible departure from the censored at random assumption for gefitinib and best supportive care.ConclusionsThe proposed method offers a comprehensive sensitivity analysis framework to examine the robustness of the NMA results to clinically plausible censoring scenarios. Citation: Research Methods in Medicine & Health Sciences PubDate: 2023-07-14T10:15:36Z DOI: 10.1177/26320843231190026
Authors:Hanna A Frank, Mohammad Ehsanul Karim Abstract: Research Methods in Medicine & Health Sciences, Ahead of Print. In a real-world observational data analysis setting, guessing the true model specification can be difficult for an analyst. Unfortunately, correct model specification is a core assumption for treatment effect estimation methods such as propensity score methods, G-computation, and regression techniques. Targeted maximum likelihood estimation (TMLE) is an alternative method that allows the use of data-adaptive and machine learning algorithms for model fitting. TMLE therefore does not require strict assumptions about the model specification but preserves the validity of the inference. Multiple studies have shown that TMLE outperforms other methods in certain real-world settings, making it a useful and potentially superior algorithm for causal inference. However, there is a lack of accessible resources for practitioners to understand the implementation. Hence the TMLE framework is the least-used method by practitioners in epidemiology literature. Recently a few accessible articles have been published, but they focus only on binary outcomes and demonstrations are done mainly with simulated data. This paper aims to fill the gap in the literature by providing a step-by-step TMLE implementation guide for a continuous outcome, using an openly accessible clinical dataset. Citation: Research Methods in Medicine & Health Sciences PubDate: 2023-05-26T12:58:51Z DOI: 10.1177/26320843231176662
Authors:Mihály Sulyok, Mark D. Walker Abstract: Research Methods in Medicine & Health Sciences, Ahead of Print. Relatively few epidemiological studies have utilized Random Forests (RF), possibly because the time series data often encountered in this discipline are perceived as unsuitable for supervised learning methods. We show RF can be used for such data, and demonstrate an example examining which social activities influence pertussis. Results are compared with regression with ARIMA errors modelling. Pertussis continues to be perceived as a childhood condition, despite recent incidence increases in older ages. COVID-19 provided a unique situation; social restrictions were implemented and the number of pertussis cases declined. This meant the influence of different activities on transmission could be gauged. Data detailing restrictions was used from the Oxford 'COVID-19 Government Response Tracker' (OxCGRT). The number of cases of pertussis and OxGCRT variables were lagged then embedded into a matrix, before being fitted into a RF regression model. Based on VIMP, this identified ‘international travel’ ‘public events’ and ‘workplace’ as the most important variables, suggesting adult based activities may be of most importance. An ARIMA(1,0,1), using OxCGRT categories as external regressors, similarly indicated that adult social activities better accounted for the number of cases of pertussis. Citation: Research Methods in Medicine & Health Sciences PubDate: 2023-05-19T06:52:31Z DOI: 10.1177/26320843231176660
Authors:Luke E Peters, Jie Zhao, Scott Gelzinnis, Stephen R Smith, Jennifer Martin, Peter Pockney Abstract: Research Methods in Medicine & Health Sciences, Ahead of Print. Background: High response rates for patient surveys are required in medical literature to ensure non-response bias is minimised. It is often difficult to achieve a satisfactory response rate as patient engagement in surveys is decreasing. A major barrier to phone surveys is getting patients to answer calls from unknown numbers.Purpose: To design a methodology which boosts response rates for telephone-based patient surveys.Research Design: We prospectively analysed the effectiveness of our methodology for increasing patient participation using caller ID and text messanging.Study Sample: Two waves totalling 1313 patients were contacted for participation in a patient survey for a descriptive quantitative and qualitative cohort study using our developed methadology.Data Analysis: We analysed the timepoints at which successful contact was made when using caller ID and text messanging.Results: We achieved a call answer rate of 85.4%, which was a 70.8% increase when compared to a similar patient cohort contacted via blocked caller ID (i.e. with privacy settings).Conclusion: We have developed a simple, inexpensive methodology which, when tested outside the Australian setting and for other projects, shows promise for increasing patient survey response rate. Citation: Research Methods in Medicine & Health Sciences PubDate: 2023-04-04T02:31:24Z DOI: 10.1177/26320843231167496
Authors:Loukia M Spineli Abstract: Research Methods in Medicine & Health Sciences, Ahead of Print. BackgroundUsing evidence synthesis to design a clinical trial has long been advocated as the key against research waste. However, the relevant methodology does not deal with possible missing participants (MP) that may occur in a future trial. We illustrated the synergism of the baseline effects model and network meta-analysis (NMA) to predict the percentage of MP for a future trial.MethodsWe considered the network of a published systematic review as a case study. We applied the baseline effects model, followed by the relative effects model using Bayesian methods to predict the percentage of MP in each intervention when conducting NMA and a series of pairwise meta-analyses. We illustrated the posterior distribution of the predicted percentage MP under both synthesis methods alongside the MP reported in the corresponding trials for each intervention.ResultsSelecting different interventions for the baseline effects model yielded different predicted baseline effects and led to different predicted percentages of MP for the remaining interventions, highlighting the need to carefully pre-specifying the intervention for the baseline effects model. Both synthesis methods provided almost identical posterior distributions of predicted percentage MP for estimating similar summary odds ratios. There was great variability in the percentage of MP across the trials for each intervention, manifesting as considerable variability in the percentage difference in MP compared to NMA.ConclusionsIncorporating predictions and absolute effects in the context of MP in NMA aids in determining the anticipated percentage of MP in the compared interventions to plan a future trial efficiently. Citation: Research Methods in Medicine & Health Sciences PubDate: 2023-03-29T06:55:09Z DOI: 10.1177/26320843231167502
Authors:Nishadi Gamage, Priyanga Ranasinghe, Ranil Jayawardena Abstract: Research Methods in Medicine & Health Sciences, Ahead of Print. BackgroundWhen conducting reviews, obtaining unreported information by contacting corresponding authors via traditional methods of correspondence, such as email/postage has become increasingly challenging.Objective/sThe current study aimed to identify the different non-traditional sources and approaches to obtain unreported data from respective authors of primary studies eligible for systematic reviews and meta-analyses.MethodsUnreported data were obtained initially through traditional methods (email/telephone, searching forward citations of the articles, review of other publications of the same research team and perusal of authors’ institutional profiles). The second stage included communication through digital/social media, which comprised Facebook, ResearchGate, WhatsApp, Viber, LinkedIn, and the online Global Health Data Exchange (GHDx).ResultsDuring data extraction, 41 individual data items were missing/unreported, and we were able to identify 36 (87.8%) during data tracing, using both traditional (n = 10, 27.8%) and digital and social media-based (n = 26, 72.2%) methods. These 26 data items were identified through the following methods, (a) Facebook (n = 6), (b) ResearchGate (n = 3), (c) WhatsApp (n = 3), (d) Viber (n = 1), (e) LinkedIn (n = 1) and GHDx database (n = 12).ConclusionDigital/social media platforms were found to be more successful to obtain unreported data. We believe that a combination of both methods is likely to yield the best results in tracing missing data for systematic reviews. Journals should consider including social media links and non-institutional research profiles in addition to traditional methods for correspondence. Citation: Research Methods in Medicine & Health Sciences PubDate: 2023-03-06T10:36:24Z DOI: 10.1177/26320843231162587
Authors:Victoria Yorke-Edwards, Carlos Diaz-Montana, Macey L Murray, Matthew R Sydes, Sharon B Love Abstract: Research Methods in Medicine & Health Sciences, Ahead of Print. Background: Over the last decade, there has been an increasing interest in risk-based monitoring (RBM) in clinical trials, resulting in a number of guidelines from regulators and its inclusion in ICH GCP. However, there is a lack of detail on how to approach RBM from a practical perspective, and insufficient understanding of best practice.Purpose: We present a method for clinical trials units to track their metrics within clinical trials using descriptive statistics and visualisations.Research Design: We suggest descriptive statistics and visualisations within a SWAT methodology.Study Sample: We illustrate this method using the metrics from TEMPER, a monitoring study carried out in three trials at the MRC Clinical Trials Unit at UCL.Data Collection: The data collection for TEMPER is described in DOI : 10.1177/1740774518793379.Results: We show the results and discuss a protocol for a Study-Within-A-Trial (SWAT 167) for those wishing to use the method.Conclusions: The potential benefits metric tracking brings to clinical trials include enhanced assessment of sites for potential corrective action, improved evaluation and contextualisation of the influence of metrics and their thresholds, and the establishment of best practice in RBM. The standardisation of the collection of such monitoring data would benefit both individual trials and the clinical trials community. Citation: Research Methods in Medicine & Health Sciences PubDate: 2022-12-22T06:12:34Z
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