Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ.

Meta-analysis using individual participant data (IPD) obtains and synthesises the raw, participant-level data from a set of relevant studies. The IPD approach is becoming an increasingly popular tool as an alternative to traditional aggregate data meta-analysis, especially as it avoids reliance on published results and provides an opportunity to investigate individual-level interactions, such as treatment-effect modifiers. There are two statistical approaches for conducting an IPD meta-analysis: one-stage and two-stage. The one-stage approach analyses the IPD from all studies simultaneously, for example, in a hierarchical regression model with random effects. The two-stage approach derives aggregate data (such as effect estimates) in each study separately and then combines these in a traditional meta-analysis model. There have been numerous comparisons of the one-stage and two-stage approaches via theoretical consideration, simulation and empirical examples, yet there remains confusion regarding when each approach should be adopted, and indeed why they may differ. In this tutorial paper, we outline the key statistical methods for one-stage and two-stage IPD meta-analyses, and provide 10 key reasons why they may produce different summary results. We explain that most differences arise because of different modelling assumptions, rather than the choice of one-stage or two-stage itself. We illustrate the concepts with recently published IPD meta-analyses, summarise key statistical software and provide recommendations for future IPD meta-analyses. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd

Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models.

Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher's information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis)

Simulation-based power calculations for planning a two-stage individual participant data meta-analysis

BACKGROUND Researchers and funders should consider the statistical power of planned Individual Participant Data (IPD) meta-analysis projects, as they are often time-consuming and costly. We propose simulation-based power calculations utilising a two-stage framework, and illustrate the approach for a planned IPD meta-analysis of randomised trials with continuous outcomes where the aim is to identify treatment-covariate interactions. METHODS The simulation approach has four steps: (i) specify an underlying (data generating) statistical model for trials in the IPD meta-analysis; (ii) use readily available information (e.g. from publications) and prior knowledge (e.g. number of studies promising IPD) to specify model parameter values (e.g. control group mean, intervention effect, treatment-covariate interaction); (iii) simulate an IPD meta-analysis dataset of a particular size from the model, and apply a two-stage IPD meta-analysis to obtain the summary estimate of interest (e.g. interaction effect) and its associated p-value; (iv) repeat the previous step (e.g. thousands of times), then estimate the power to detect a genuine effect by the proportion of summary estimates with a significant p-value. RESULTS In a planned IPD meta-analysis of lifestyle interventions to reduce weight gain in pregnancy, 14 trials (1183 patients) promised their IPD to examine a treatment-BMI interaction (i.e. whether baseline BMI modifies intervention effect on weight gain). Using our simulation-based approach, a two-stage IPD meta-analysis has < 60% power to detect a reduction of 1 kg weight gain for a 10-unit increase in BMI. Additional IPD from ten other published trials (containing 1761 patients) would improve power to over 80%, but only if a fixed-effect meta-analysis was appropriate. Pre-specified adjustment for prognostic factors would increase power further. Incorrect dichotomisation of BMI would reduce power by over 20%, similar to immediately throwing away IPD from ten trials. CONCLUSIONS Simulation-based power calculations could inform the planning and funding of IPD projects, and should be used routinely

Association of maternal serum PAPP-A levels, nuchal translucency and crown rump length in first trimester with adverse pregnancy outcomes: Retrospective cohort study.

OBJECTIVE: Are first trimester serum pregnancy-associated plasma protein-A (PAPP-A), nuchal translucency (NT) and crown rump length (CRL) prognostic factors for adverse pregnancy outcomes? METHOD: Retrospective cohort women, singleton pregnancies (UK 2011-2015). Unadjusted and multivariable logistic regression, outcomes: small for gestational age (SGA), pre-eclampsia (PE), pre-term birth (PTB), miscarriage, stillbirth, perinatal mortality and neonatal death (NND). RESULTS: 12,592 pregnancies: 852 (6.8%) PTB, 352 (2.8%) PE, 1824 (14.5%) SGA, 73 (0.6%) miscarriages, 37(0.3%) stillbirths, 73 perinatal deaths (0.6%) and 38 (0.30%) NND. Multivariable analysis: lower odds of SGA [adjusted odds ratio (aOR) 0.88 (95% CI 0.85,0.91)], PTB [0.92 (95%CI 0.88,0.97)], PE [0.91 (95% CI 0.85,0.97)] and stillbirth [ 0.71 (95% CI 0.52,0.98)] as PAPP-A increases. Lower odds of SGA [aOR 0.79 (95% CI 0.70,0.89)] but higher odds of miscarriage [aOR 1.75 95% CI (1.12,2.72)] as NT increases, and lower odds of stillbirth as CRL increases [aOR 0.94 95% CI (0.89,0.99)]. Multivariable analysis of three factors together demonstrated strong associations: a) PAPP-A, NT, CRL and SGA, b) PAPP-A and PTB, c) PAPP-A, CRL and PE, d) NT and miscarriage. CONCLUSIONS: PAPP-A, NT and CRL independent prognostic factor for adverse pregnancy outcomes, especially PAPP-A and SGA with lower PAPP-A associated with increased risk

Asymptomatic Clostridium difficile colonization in two Australian tertiary hospitals, 2012-2014: prospective, repeated cross-sectional study.

OBJECTIVES: To investigate the prevalence and risk factors for asymptomatic toxigenic (TCD) and nontoxigenic Clostridium difficile (NTCD) colonization in a broad cross section of the general hospital population over a 3-year period. METHODS: Patients without diarrhoea admitted to two Australian tertiary hospitals were randomly selected through six repeated cross-sectional surveys conducted between 2012 and 2014. Stool specimens were cultured under anaerobic conditions, and C. difficile isolates were tested for the presence of toxin genes and ribotyped. Patients were then grouped into noncolonized, TCD colonized or NTCD colonized for identifying risk factors using multinomial logistic regression models. RESULTS: A total of 1380 asymptomatic patients were enrolled; 76 patients (5.5%) were TCD colonized and 28 (2.0%) were NTCD colonized. There was a decreasing annual trend in TCD colonization, and asymptomatic colonization was more prevalent during the summer than winter months. TCD colonization was associated with gastro-oesophageal reflux disease (relative risk ratio (RRR) = 2.20; 95% confidence interval (CI) 1.17-4.14), higher number of admissions in the previous year (RRR = 1.24; 95% CI 1.10-1.39) and antimicrobial exposure during the current admission (RRR = 2.78; 95% CI 1.23-6.28). NTCD colonization was associated with chronic obstructive pulmonary disease (RRR = 3.88; 95% CI 1.66-9.07) and chronic kidney failure (RRR = 5.78; 95% CI 2.29-14.59). Forty-eight different ribotypes were identified, with 014/020 (n = 23), 018 (n = 10) and 056 (n = 6) being the most commonly isolated. CONCLUSIONS: Risk factors differ between patients with asymptomatic colonization by toxigenic and nontoxigenic strains. Given that morbidity is largely driven by toxigenic strains, this novel finding has important implications for disease control and prevention

Region-Specific Responses of Adductor Longus Muscle to Gravitational Load-Dependent Activity in Wistar Hannover Rats

Response of adductor longus (AL) muscle to gravitational unloading and reloading was studied. Male Wistar Hannover rats (5-wk old) were hindlimb-unloaded for 16 days with or without 16-day ambulation recovery. The electromyogram (EMG) activity in AL decreased after acute unloading, but that in the rostral region was even elevated during continuous unloading. The EMG levels in the caudal region gradually increased up to 6th day, but decreased again. Approximately 97% of fibers in the caudal region were pure type I at the beginning of experiment. Mean percentage of type I fibers in the rostral region was 61% and that of type I+II and II fiber was 14 and 25%, respectively. The percent type I fibers decreased and de novo appearance of type I+II was noted after unloading. But the fiber phenotype in caudal, not rostral and middle, region was normalized after 16-day ambulation. Pronounced atrophy after unloading and re-growth following ambulation was noted in type I fibers of the caudal region. Sarcomere length in the caudal region was passively shortened during unloading, but that in the rostral region was unchanged or even stretched slightly. Growth-associated increase of myonuclear number seen in the caudal region of control rats was inhibited by unloading. Number of mitotic active satellite cells decreased after unloading only in the caudal region. It was indicated that the responses of fiber properties in AL to unloading and reloading were closely related to the region-specific neural and mechanical activities, being the caudal region more responsive

Carbon and nitrogen isotopic ratios of urine and faeces as novel nutritional biomarkers of meat and fish intake

Purpose Meat and fish consumption are associated with changes in the risk of chronic diseases. Intake is mainly assessed using self-reporting, as no true quantitative nutritional biomarker is available. The measurement of plasma fatty acids, often used as an alternative, is expensive and time-consuming. As meat and fish differ in their stable isotope ratios, δ13C and δ15N have been proposed as biomarkers. However, they have never been investigated in controlled human dietary intervention studies. Objective In a short-term feeding study, we investigated the suitability of δ13C and δ15N in blood, urine and faeces as biomarkers of meat and fish intake. Methods The dietary intervention study (n = 14) followed a randomised cross-over design with three eight-day dietary periods (meat, fish and half-meat–half-fish). In addition, 4 participants completed a vegetarian control period. At the end of each period, 24-h urine, fasting venous blood and faeces were collected and their δ13C and δ15N analysed. Results There was a significant difference between diets in isotope ratios in faeces and urine samples, but not in blood samples (Kruskal–Wallis test, p < 0.0001). In pairwise comparisons, δ13C and δ15N were significantly higher in urine and faecal samples following a fish diet when compared with all other diets, and significantly lower following a vegetarian diet. There was no significant difference in isotope ratio between meat and half-meat–half-fish diets for blood, urine or faecal samples. Conclusions The results of this study show that urinary and faecal δ13C and δ15N are suitable candidate biomarkers for short-term meat and fish intake

Adapting a Program to Inform African American and Hispanic American Women About Cancer Clinical Trials

The dearth of evidence-based clinical trial education programs may contribute to the underrepresentation of African American and Hispanic American women in cancer research studies. This study used focus group-derived data from 80 women distributed among eight Spanish- and English-language focus groups. These data guided the researchers’ adaptation and refinement of the National Cancer Institute’s various clinical trials education programs into a program that was specifically focused on meeting the information needs of minority women and addressing the barriers to study participation that they perceived. A “sisterhood” theme was adopted and woven throughout the presentation

Evaluating the Quality of Research into a Single Prognostic Biomarker: A Systematic Review and Meta-analysis of 83 Studies of C-Reactive Protein in Stable Coronary Artery Disease

Background Systematic evaluations of the quality of research on a single prognostic biomarker are rare. We sought to evaluate the quality of prognostic research evidence for the association of C-reactive protein (CRP) with fatal and nonfatal events among patients with stable coronary disease. Methods and Findings We searched MEDLINE (1966 to 2009) and EMBASE (1980 to 2009) and selected prospective studies of patients with stable coronary disease, reporting a relative risk for the association of CRP with death and nonfatal cardiovascular events. We included 83 studies, reporting 61,684 patients and 6,485 outcome events. No study reported a prespecified statistical analysis protocol; only two studies reported the time elapsed (in months or years) between initial presentation of symptomatic coronary disease and inclusion in the study. Studies reported a median of seven items (of 17) from the REMARK reporting guidelines, with no evidence of change over time. The pooled relative risk for the top versus bottom third of CRP distribution was 1.97 (95% confidence interval [CI] 1.78–2.17), with substantial heterogeneity (I2 = 79.5). Only 13 studies adjusted for conventional risk factors (age, sex, smoking, obesity, diabetes, and low-density lipoprotein [LDL] cholesterol) and these had a relative risk of 1.65 (95% CI 1.39–1.96), I2 = 33.7. Studies reported ten different ways of comparing CRP values, with weaker relative risks for those based on continuous measures. Adjusting for publication bias (for which there was strong evidence, Egger's p<0.001) using a validated method reduced the relative risk to 1.19 (95% CI 1.13–1.25). Only two studies reported a measure of discrimination (c-statistic). In 20 studies the detection rate for subsequent events could be calculated and was 31% for a 10% false positive rate, and the calculated pooled c-statistic was 0.61 (0.57–0.66). Conclusion Multiple types of reporting bias, and publication bias, make the magnitude of any independent association between CRP and prognosis among patients with stable coronary disease sufficiently uncertain that no clinical practice recommendations can be made. Publication of prespecified statistical analytic protocols and prospective registration of studies, among other measures, might help improve the quality of prognostic biomarker research