Advances in meta-analysis: A unifying modelling framework with measurement error correction.

In psychological studies, multivariate outcomes measured on the same individuals are often encountered. Effects originating from these outcomes are consequently dependent. Multivariate meta-analysis examines the relationships of multivariate outcomes by estimating the mean effects and their variance-covariance matrices from series of primary studies. In this paper we discuss a unified modelling framework for multivariate meta-analysis that also incorporates measurement error corrections. We focus on two types of effect sizes, standardized mean differences (d) and correlations (r), that are common in psychological studies. Using generalized least squares estimation, we outline estimated mean vectors and variance-covariance matrices for d and r that are corrected for measurement error. Given the burgeoning research involving multivariate outcomes, and the largely overlooked ramifications of measurement error, we advocate addressing measurement error while conducting multivariate meta-analysis to enhance the replicability of psychological research.

Opportunistic CT screening of osteoporosis on thoracic and lumbar spine: a meta-analysis.

BACKGROUND: Osteoporotic fractures are a major contributor to late life morbidity and mortality, and impose a substantial societal cost, yet osteoporosis remains substantially underdiagnosed and undertreated. The purpose of this meta-analysis was to assess the pooled diagnostic sensitivity and specificity of computed tomography (CT) images for diagnosing osteoporosis in patients who meet WHO dual X-ray absorptiometry (DXA) osteoporosis criteria using specific Hounsfield unit (HU) values as a threshold. METHODS: Systematic literature searches in PubMed, Embase, Web of Science and Google Scholar were performed from the earliest available date through 1 July 2018, restricted to publications in English. Participants in all studies underwent CT scans that included the lumbar and/or thoracic spine for different indications and HU measurements were used to identify osteoporosis. DXA scans served as the reference standard. RESULTS: Ten eligible studies were identified. The mean area under the hierarchical summary receiver operating characteristic (ROC) curve for diagnosis osteoporosis was 0.84 (95% CI: 0.81, 0.87). The pooled diagnostic sensitivity and specificity of CT images to identify osteoporosis were 0.83 (95% CI: 0.73, 0.90) and 0.74 (95% CI: 0.69, 0.79). The positive likelihood ratio (LR+), negative likelihood ratio (LR-), and diagnostic odds ratio were 3.4 (95% CI: 2.7, 4.5), 0.21 (95% CI: 0.12, 0.36), and 16.4 (95% CI: 7.8, 34.3), respectively. The bias-adjusted sensitivity and specificity of CT were 0.73 and 0.71. Meta-regression demonstrated that country of study, DXA criteria and scanner manufacturer were significant factors associated with the sensitivity of CT in detecting osteoporosis while scanner manufacturer was the only factor associated with specificity of CT. CONCLUSIONS: This meta-analysis showed reasonable pooled sensitivity and specificity for using threshold values measured on CT scans to identify osteoporosis opportunistically.

Fix Your Leaky Pipeline: Support Women in Pursuit of Advanced Degrees.

Completing a doctoral program is a daunting journey for any individual. Working full-time and juggling personal demands make the journey more complex. This monograph explores the unique motivations women who travel this path possess, identifies barriers in completion of a doctoral program for women, and tells the story of one group of women who developed a working model for a supportive writing group. As numbers of female health professions students and education leaders increase, institutions must identify mechanisms for support of women interested in pursuing an academic doctoral degree, preparing academic institutions for future diversity changes in health care.

Change in quality of life of stage IA lung cancer patients after sublobar resection and lobectomy.

BACKGROUND: Few studies have examined the differential impact of sublobar resection (SL) and lobectomy (L) on quality of life (QoL) during the first postoperative year. METHODS: We used a prospective cohort of Stage IA lung cancer patients undergoing video-assisted thoracoscopic surgery (VATS) from the Initiative for Early Lung Cancer Research on Treatment. QoL was measured before surgery, and within 4, 6, and 12 months post-surgery using three validated instruments: SF-12 [physical (PCS) and mental health (MCS)], FACT-LCS (lung-cancer-symptoms), and the PHQ-4 (anxiety and depression subscales). Locally weighted smoothing curve (LOWESS) was fitted to identify the best interval knot for the change in the QoL trend post-surgery. After adjusting for demographic and clinical variables, an adjusted piecewise linear mixed effects model was developed to estimate differences in baseline and 12-month scores, and rates of change for each QoL measure. RESULTS: SL resection was performed in 127 (63.2%) and L in 74 (36.8%) patients. LOWESS plots suggested that the shift of QoL (interval knot) was at 2 months post-surgery. Decreases in PCS scores were less severe for SL than L patients 2 months post-surgery (-0.18 vs. -2.30, P=0.02); while subsequent improvements were observed for both groups (SL: +0.29 vs. L: +0.74, P=0.06). SL patients reported significantly better scores a year post-surgery compared to baseline (P=0.003), while L patients did not. Anxiety decreased at similar rates for both SL and L patients within 2 months post-surgery (P=0.18), then stabilized for the remaining months. MCS and depression scores remained stable in both groups throughout. QoL scores were lower for women than for men, but only significantly worse for the lung-cancer-symptoms (P=0.003) and anxiety (P=0.04). CONCLUSIONS: SL patients fared better in physical health and lung cancer symptoms than L patients. The first two postoperative months showed the most significant change which suggests targeting postoperative intervention during that time.

The Prevalence of Dyslexia: A New Approach to Its Estimation.

How prevalent is dyslexia? A definitive answer to this question has been elusive because of the continuous distribution of reading performance and predictors of dyslexia and because of the heterogeneous nature of samples of poor readers. Samples of poor readers are a mixture of individuals whose reading is consistent with or expected based on their performance in other academic areas and in language, and individuals with dyslexia whose reading is not consistent with or expected based on their other performances. In the present article, we replicate and extend a new approach for determining the prevalence of dyslexia. Using model-based meta-analysis and simulation, three main results were found. First, the prevalence of dyslexia is better represented as a distribution that varies as a function of severity as opposed to any single-point estimate. Second, samples of poor readers will contain more expected poor readers than unexpected or dyslexic readers. Third, individuals with dyslexia can be found across the reading spectrum as opposed to only at the lower tail of reading performance. These results have implications for screening and identification, and for recruiting participants for scientific studies of dyslexia.

A group-specific prior distribution for effect-size heterogeneity in meta-analysis.

While both methodological and applied work on Bayesian meta-analysis have flourished, Bayesian modeling of differences between groups of studies remains scarce in meta-analyses in psychology, education, and the social sciences. On rare occasions when Bayesian approaches have been used, non-informative prior distributions have been chosen. However, more informative prior distributions have recently garnered popularity. We propose a group-specific weakly informative prior distribution for the between-studies standard-deviation parameter in meta-analysis. The proposed prior distribution incorporates a frequentist estimate of the between-studies standard deviation as the noncentrality parameter in a folded noncentral t distribution. This prior distribution is then separately modeled for each subgroup of studies, as determined by a categorical factor. Use of the new prior distribution is shown in two extensive examples based on a published meta-analysis on psychological interventions aimed at increasing optimism. We compare the folded noncentral t prior distribution to several non-informative prior distributions. We conclude with discussion, limitations, and avenues for further development of Bayesian meta-analysis in psychology and the social sciences.

Synthesising quantitative evidence in systematic reviews of complex health interventions.

Public health and health service interventions are typically complex: they are multifaceted, with impacts at multiple levels and on multiple stakeholders. Systematic reviews evaluating the effects of complex health interventions can be challenging to conduct. This paper is part of a special series of papers considering these challenges particularly in the context of WHO guideline development. We outline established and innovative methods for synthesising quantitative evidence within a systematic review of a complex intervention, including considerations of the complexity of the system into which the intervention is introduced. We describe methods in three broad areas: non-quantitative approaches, including tabulation, narrative and graphical approaches; standard meta-analysis methods, including meta-regression to investigate study-level moderators of effect; and advanced synthesis methods, in which models allow exploration of intervention components, investigation of both moderators and mediators, examination of mechanisms, and exploration of complexities of the system. We offer guidance on the choice of approach that might be taken by people collating evidence in support of guideline development, and emphasise that the appropriate methods will depend on the purpose of the synthesis, the similarity of the studies included in the review, the level of detail available from the studies, the nature of the results reported in the studies, the expertise of the synthesis team and the resources available.

A Review of Quality of Life Measures used in Surgical Outcomes for Stage I Lung Cancers.

This review summarizes the literature on QoL in early stage lung cancer patients who underwent surgery. PubMed and PsycINFO were searched. Twelve articles from 10 distinct studies were identified for a total of 992 patients. Five QoL measures were used. One study reported only on pre-surgical QoL, six only on post-surgical QoL and three studies reported on both pre- and post-surgical QoL. Timing for the administration of post-surgical QoL surveys varied. The literature on QoL in Stage I non-small-cell lung cancer patients is very sparse. Additional research is needed to explore the impact of different surgical approaches on QoL.

Initiative for Early Lung Cancer Research on Treatment: Development of Study Design and Pilot Implementation.

INTRODUCTION: To maximize the benefits of computed tomographic screening for lung cancer, optimal treatment for small, early lung cancers is needed. Limiting the extent of surgery spares lung tissue, preserves pulmonary function, and decreases operative time, complications, and morbidities. It also increases the likelihood of resecting future new primary lung cancers. The goal is to assess alternative treatments in a timely manner. METHODS: The focus sessions with patients and physicians separately highlighted the need to consider their perceptions. Literature reviews and analyses of treatment results using large databases were performed to formulate critical questions about long-term treatment outcomes, recurrence, and quality of life of alternative treatments. Based on these analyses, the investigators developed a prospective multi-institutional cohort study, the Initiative for Early Lung Cancer Research for Treatment, to compare treatments for stage I NSCLC. HIPAA compliant institutional review board approval was obtained and we performed a feasibility study of the first 206 surgical patients. RESULTS: Lobectomy was performed in 89 (43.2%) patients, and sublobar resection was performed in 117 (56.7%) patients. Mediastinal lymph node resection was performed in 173 (84.0%) patients, 8 had N1 and 3 N2 lymph node metastases. Patients stated that both the surgeon's opinion (93%) and the patient's own opinion (93%) were extremely important, followed by the patients' view that the chosen procedure would provide the best quality of life (90%). CONCLUSIONS: It was feasible to obtain pre- and postsurgical information from patients and surgeons. We anticipate statistically meaningful results about treatment alternatives in 3 to 5 years.

Controversies on lung cancers manifesting as part-solid nodules.

PURPOSE: Summarise survival of patients with resected lung cancers manifesting as part-solid nodules (PSNs). METHODS: PubMed/MEDLINE and EMBASE databases were searched for all studies/clinical trials on CT-detected lung cancer in English before 21 December 2015 to identify surgically resected lung cancers manifesting as PSNs. Outcome measures were lung cancer-specific survival (LCS), overall survival (OS), or disease-free survival (DFS). All PSNs were classified by the percentage of solid component to the entire nodule diameter into category PSNs <80% or category PSNs ≥80%. RESULTS: Twenty studies reported on PSNs <80%: 7 reported DFS and 2 OS of 100%, 6 DFS 96.3-98.7%, and 11 OS 94.7-98.9% (median DFS 100% and OS 97.5%). Twenty-seven studies reported on PSNs ≥80%: 1 DFS and 2 OS of 100%, 19 DFS 48.0%-98.0% (median 82.6%), and 16 reported OS 43.0%-98.0% (median DFS 82.6%, OS 85.5%). Both DFS and OS were always higher for PSNs <80%. CONCLUSION: A clear definition of the upper limit of solid component of a PSN is needed to avoid misclassification because cell-types and outcomes are different for PSN and solid nodules. The workup should be based on the size of the solid component. KEY POINTS: • Lung cancers manifesting as PSNs are slow growing with high cure rates. • Upper limits of the solid component are important for correct interpretation. • Consensus definition is important for the management of PSNs. • Median disease-free-survival (DFS) increased with decreasing size of the nodule.

Hepatic steatosis in participants in a program of low-dose CT screening for lung cancer.

OBJECTIVE: Determine the frequency of moderate-to-severe hepatic steatosis (HS) in asymptomatic participants in a low-dose CT (LDCT) screening program for lung cancer, to identify risk factors, and develop recommendations. METHODS: Baseline LDCT scans of the chest of 170 participants in an IRB-approved study between August 2011 and April 2016 were reviewed. Demographic variables, comorbidities, and liver function tests were documented. Hepatic and splenic attenuation values hounsfield unit (HU) were measured. Regression analyses were performed. RESULTS: Average liver attenuation was 57.6HU (standard deviation (SD) 9.3) and average liver/spleen (L/S) ratio was 1.3 (SD 0.3). Liver attenuation was <40HU for 9 (5.3%), liver/spleen (L/S) ratio <0.8 for 6 (3.5%), and either <40HU or L/S ratio <0.8 for 9 (5.3%). Male sex (p=0.004), diabetes (p=0.0005), emphysema (p=0.03), and high BMI (p=0.0006) were significant predictors of HS. Aspartate aminotransferase (p=0.0018) and alanine aminotransferase (p=0.012) were negatively correlated with liver attenuation. Reduced serum levels of alpha-1-antitrypsin may be a common factor of emphysema and HS. CONCLUSION: LDCT can detect HS in asymptomatic participants with frequencies similar to previous reports. If liver attenuation is below 40HU and/or L/S ratio below 0.8, further evaluation of HS to the primary care physician or liver specialist is recommended.

Quasi-experimental study designs series-paper 9: collecting data from quasi-experimental studies.

OBJECTIVE: To identify variables that must be coded when synthesizing primary studies that use quasi-experimental designs. STUDY DESIGN AND SETTING: All quasi-experimental (QE) designs. RESULTS: When designing a systematic review of QE studies, potential sources of heterogeneity-both theory-based and methodological-must be identified. We outline key components of inclusion criteria for syntheses of quasi-experimental studies. We provide recommendations for coding content-relevant and methodological variables and outlined the distinction between bivariate effect sizes and partial (i.e., adjusted) effect sizes. Designs used and controls used are viewed as of greatest importance. Potential sources of bias and confounding are also addressed. CONCLUSION: Careful consideration must be given to inclusion criteria and the coding of theoretical and methodological variables during the design phase of a synthesis of quasi-experimental studies. The success of the meta-regression analysis relies on the data available to the meta-analyst. Omission of critical moderator variables (i.e., effect modifiers) will undermine the conclusions of a meta-analysis.

Quasi-experimental study designs series-paper 10: synthesizing evidence for effects collected from quasi-experimental studies presents surmountable challenges.

OBJECTIVE: To outline issues of importance to analytic approaches to the synthesis of quasi-experiments (QEs) and to provide a statistical model for use in analysis. STUDY DESIGN AND SETTING: We drew on studies of statistics, epidemiology, and social-science methodology to outline methods for synthesis of QE studies. The design and conduct of QEs, effect sizes from QEs, and moderator variables for the analysis of those effect sizes were discussed. RESULTS: Biases, confounding, design complexities, and comparisons across designs offer serious challenges to syntheses of QEs. Key components of meta-analyses of QEs were identified, including the aspects of QE study design to be coded and analyzed. Of utmost importance are the design and statistical controls implemented in the QEs. Such controls and any potential sources of bias and confounding must be modeled in analyses, along with aspects of the interventions and populations studied. Because of such controls, effect sizes from QEs are more complex than those from randomized experiments. A statistical meta-regression model that incorporates important features of the QEs under review was presented. CONCLUSION: Meta-analyses of QEs provide particular challenges, but thorough coding of intervention characteristics and study methods, along with careful analysis, should allow for sound inferences.

Quasi-experimental study designs series-paper 6: risk of bias assessment.

OBJECTIVES: Rigorous and transparent bias assessment is a core component of high-quality systematic reviews. We assess modifications to existing risk of bias approaches to incorporate rigorous quasi-experimental approaches with selection on unobservables. These are nonrandomized studies using design-based approaches to control for unobservable sources of confounding such as difference studies, instrumental variables, interrupted time series, natural experiments, and regression-discontinuity designs. STUDY DESIGN AND SETTING: We review existing risk of bias tools. Drawing on these tools, we present domains of bias and suggest directions for evaluation questions. RESULTS: The review suggests that existing risk of bias tools provide, to different degrees, incomplete transparent criteria to assess the validity of these designs. The paper then presents an approach to evaluating the internal validity of quasi-experiments with selection on unobservables. CONCLUSION: We conclude that tools for nonrandomized studies of interventions need to be further developed to incorporate evaluation questions for quasi-experiments with selection on unobservables.

Acceptability of mHealth Technology for Self-Monitoring Eating and Activity among Rural Men.

OBJECTIVE: To examine rural men's use and perceptions of mobile and wireless devices to self-monitor eating and physical activity (mHealth). DESIGN AND SAMPLE: Men in this 3-week pilot study used FitBit One® to log daily food intake and monitor activity. A companion application (app) allowed activity monitoring of fellow participants. Health-related text messages were received 1-3 times daily. A purposive sample of 12 rural men (ages 40-67) was recruited by community leaders. MEASURES: (1) baseline heart rate, blood pressure, and BMI, (2) FitBit One® usage, (3) investigator-generated surveys on acceptability of mHealth, and (4) focus group on experience with mHealth. RESULTS: Men were overweight (n = 3) or obese (n = 9) and 9 of 12 were hypertensive. Nine of twelve wore FitBit One® all 21 days. Eleven of 12 men logged food, with 9 of 12 doing this at least 15 of 21 days. Self-monitoring and daily text messaging increased awareness of energy intake and output. Companion app's food log needed targeting for rural foods. Rotating seasons (occupational, religious, recreational) and weak cellular signals created contextual barriers to self-monitoring eating and activity. CONCLUSIONS: FitBit One® and text messaging were perceived as useful among the rural men, while the companion apps require adaptation to reflect dietary norms.

Quiet Eye and Performance in Sport: A Meta-Analysis.

Research linking the "quiet eye" (QE) period to subsequent performance has not been systematically synthesized. In this paper we review the literature on the link between the two through nonintervention (Synthesis 1) and intervention (Synthesis 2) studies. In the first synthesis, 27 studies with 38 effect sizes resulted in a large mean effect (d = 1.04) reflecting differences between experts' and novices' QE periods, and a moderate effect size (d = 0.58) comparing QE periods for successful and unsuccessful performances within individuals. Studies reporting QE duration as a percentage of the total time revealed a larger mean effect size than studies reporting an absolute duration (in milliseconds). The second synthesis of 9 articles revealed very large effect sizes for both the quiet-eye period (d = 1.53) and performance (d = 0.84). QE also showed some ability to predict performance effects across studies.

Biobehavioral determinants of glycemic control in type 2 diabetes: A systematic review and meta-analysis.

OBJECTIVES: To conduct a model-driven meta-analysis of correlational research on psychological and motivational predictors of diabetes outcomes, with adherence factors as mediators. METHODS: A comprehensive literature search of published and unpublished studies located a sample of 775 individual correlational or predictive studies reported across 739 research reports. RESULTS: Results varied according to the outcome variable included in the regression models. Depression had a larger negative effect on adherence to physical activity than on dietary adherence. Coping and self-efficacy were strongly related to dietary adherence, which was strongly related to improved glycemic control. Medication adherence was related to glycosylated hemoglobin, whereas medications and self-monitoring were related to fasting blood glucose. Adding appointment keeping to the models did not significantly alter the results. CONCLUSION: Self-efficacy was the most consistent predictor of all adherence behaviors and dietary adherence was the most significant predictor of HbA1c. Physical activity was the most predictive factor of BMI and glucose self-monitoring the most predictive of FBG. PRACTICE IMPLICATIONS: Metabolic control is a primary goal in T2DM, so the best pathway to attaining that goal appears to be an emphasis on self-efficacy and dietary adherence.

Model-driven meta-analyses for informing health care: a diabetes meta-analysis as an exemplar.

A relatively novel type of meta-analysis, a model-driven meta-analysis, involves the quantitative synthesis of descriptive, correlational data and is useful for identifying key predictors of health outcomes and informing clinical guidelines. Few such meta-analyses have been conducted and thus, large bodies of research remain unsynthesized and uninterpreted for application in health care. We describe the unique challenges of conducting a model-driven meta-analysis, focusing primarily on issues related to locating a sample of published and unpublished primary studies, extracting and verifying descriptive and correlational data, and conducting analyses. A current meta-analysis of the research on predictors of key health outcomes in diabetes is used to illustrate our main points.

The impact of multiple endpoint dependency on Q and I(2) in meta-analysis.

A common assumption in meta-analysis is that effect sizes are independent. When correlated effect sizes are analyzed using traditional univariate techniques, this assumption is violated. This research assesses the impact of dependence arising from treatment-control studies with multiple endpoints on homogeneity measures Q and I(2) in scenarios using the unbiased standardized-mean-difference effect size. Univariate and multivariate meta-analysis methods are examined. Conditions included different overall outcome effects, study sample sizes, numbers of studies, between-outcomes correlations, dependency structures, and ways of computing the correlation. The univariate approach used typical fixed-effects analyses whereas the multivariate approach used generalized least-squares (GLS) estimates of a fixed-effects model, weighted by the inverse variance-covariance matrix. Increased dependence among effect sizes led to increased Type I error rates from univariate models. When effect sizes were strongly dependent, error rates were drastically higher than nominal levels regardless of study sample size and number of studies. In contrast, using GLS estimation to account for multiple-endpoint dependency maintained error rates within nominal levels. Conversely, mean I(2) values were not greatly affected by increased amounts of dependency. Last, we point out that the between-outcomes correlation should be estimated as a pooled within-groups correlation rather than using a full-sample estimator that does not consider treatment/control group membership.

Development of an evidence-based exercise and education cancer recovery program.

Cancer-related fatigue is one of the most common and distressing side effects experienced by patients with cancer. Increased activity and exercise have been shown to significantly impact cancer-related fatigue and are beneficial during and after treatment. This article describes the development and implementation of a 12-week evidence-based exercise and education program for cancer survivors in a community medical center. Participants consistently reported significant improvements in fatigue, depression, sleep disturbances, pain, and quality of life. The improvements were independent of the type of cancer, extent of disease, or treatment status. Additional benefits described by participants were support, a sense of belonging, and being understood.