Meta-analysis of non-linear exposure-outcome relationships using individual participant data: A comparison of two methods.
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SUBMITTER: White IR
PROVIDER: S-EPMC6492097 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A fundamental aspect of epidemiological studies concerns the estimation of factor-outcome associations to identify risk factors, prognostic factors and potential causal factors. Because reliable estimates for these associations are important, there is a growing interest in methods for combining the results from multiple studies in individual participant data meta-analyses (IPD-MA). When there is substantial heterogeneity across studies, various random-effects meta-analysis models are possible that employ a one-stage or two-stage method. These are generally thought to produce similar results, but empirical comparisons are few.We describe and compare several one- and two-stage random-effects IPD-MA methods for estimating factor-outcome associations from multiple risk-factor or predictor finding studies with a binary outcome. One-stage methods use the IPD of each study and meta-analyse using the exact binomial distribution, whereas two-stage methods reduce evidence to the aggregated level (e.g. odds ratios) and then meta-analyse assuming approximate normality. We compare the methods in an empirical dataset for unadjusted and adjusted risk-factor estimates.Though often similar, on occasion the one-stage and two-stage methods provide different parameter estimates and different conclusions. For example, the effect of erythema and its statistical significance was different for a one-stage (OR?=?1.35, [Formula: see text]) and univariate two-stage (OR?=?1.55, [Formula: see text]). Estimation issues can also arise: two-stage models suffer unstable estimates when zero cell counts occur and one-stage models do not always converge.When planning an IPD-MA, the choice and implementation (e.g. univariate or multivariate) of a one-stage or two-stage method should be prespecified in the protocol as occasionally they lead to different conclusions about which factors are associated with outcome. Though both approaches can suffer from estimation challenges, we recommend employing the one-stage method, as it uses a more exact statistical approach and accounts for parameter correlation.
| S-EPMC3621872 | biostudies-other
Project description:Multiple imputation is a popular method for addressing missing data, but its implementation is difficult when data have a multilevel structure and one or more variables are systematically missing. This systematic missing data pattern may commonly occur in meta-analysis of individual participant data, where some variables are never observed in some studies, but are present in other hierarchical data settings. In these cases, valid imputation must account for both relationships between variables and correlation within studies. Proposed methods for multilevel imputation include specifying a full joint model and multiple imputation with chained equations (MICE). While MICE is attractive for its ease of implementation, there is little existing work describing conditions under which this is a valid alternative to specifying the full joint model. We present results showing that for multilevel normal models, MICE is rarely exactly equivalent to joint model imputation. Through a simulation study and an example using data from a traumatic brain injury study, we found that in spite of theoretical differences, MICE imputations often produce results similar to those obtained using the joint model. We also assess the influence of prior distributions in MICE imputation methods and find that when missingness is high, prior choices in MICE models tend to affect estimation of across-study variability more than compatibility of conditional likelihoods. Copyright © 2017 John Wiley & Sons, Ltd.
| S-EPMC5582994 | biostudies-literature
Project description:BackgroundIndividual participant data (IPD) meta-analyses that obtain "raw" data from studies rather than summary data typically adopt a "two-stage" approach to analysis whereby IPD within trials generate summary measures, which are combined using standard meta-analytical methods. Recently, a range of "one-stage" approaches which combine all individual participant data in a single meta-analysis have been suggested as providing a more powerful and flexible approach. However, they are more complex to implement and require statistical support. This study uses a dataset to compare "two-stage" and "one-stage" models of varying complexity, to ascertain whether results obtained from the approaches differ in a clinically meaningful way.Methods and findingsWe included data from 24 randomised controlled trials, evaluating antiplatelet agents, for the prevention of pre-eclampsia in pregnancy. We performed two-stage and one-stage IPD meta-analyses to estimate overall treatment effect and to explore potential treatment interactions whereby particular types of women and their babies might benefit differentially from receiving antiplatelets. Two-stage and one-stage approaches gave similar results, showing a benefit of using anti-platelets (Relative risk 0.90, 95% CI 0.84 to 0.97). Neither approach suggested that any particular type of women benefited more or less from antiplatelets. There were no material differences in results between different types of one-stage model.ConclusionsFor these data, two-stage and one-stage approaches to analysis produce similar results. Although one-stage models offer a flexible environment for exploring model structure and are useful where across study patterns relating to types of participant, intervention and outcome mask similar relationships within trials, the additional insights provided by their usage may not outweigh the costs of statistical support for routine application in syntheses of randomised controlled trials. Researchers considering undertaking an IPD meta-analysis should not necessarily be deterred by a perceived need for sophisticated statistical methods when combining information from large randomised trials.
| S-EPMC3463584 | biostudies-literature
Project description:BackgroundPrevious research on the depression scale of the Patient Health Questionnaire (PHQ-9) has found that different latent factor models have maximized empirical measures of goodness-of-fit. The clinical relevance of these differences is unclear. We aimed to investigate whether depression screening accuracy may be improved by employing latent factor model-based scoring rather than sum scores.MethodsWe used an individual participant data meta-analysis (IPDMA) database compiled to assess the screening accuracy of the PHQ-9. We included studies that used the Structured Clinical Interview for DSM (SCID) as a reference standard and split those into calibration and validation datasets. In the calibration dataset, we estimated unidimensional, two-dimensional (separating cognitive/affective and somatic symptoms of depression), and bi-factor models, and the respective cut-offs to maximize combined sensitivity and specificity. In the validation dataset, we assessed the differences in (combined) sensitivity and specificity between the latent variable approaches and the optimal sum score (⩾10), using bootstrapping to estimate 95% confidence intervals for the differences.ResultsThe calibration dataset included 24 studies (4378 participants, 652 major depression cases); the validation dataset 17 studies (4252 participants, 568 cases). In the validation dataset, optimal cut-offs of the unidimensional, two-dimensional, and bi-factor models had higher sensitivity (by 0.036, 0.050, 0.049 points, respectively) but lower specificity (0.017, 0.026, 0.019, respectively) compared to the sum score cut-off of ⩾10.ConclusionsIn a comprehensive dataset of diagnostic studies, scoring using complex latent variable models do not improve screening accuracy of the PHQ-9 meaningfully as compared to the simple sum score approach.
| S-EPMC9393567 | biostudies-literature
Project description:BackgroundIn non-randomized studies (NRSs) where a continuous outcome variable (e.g., depressive symptoms) is assessed at baseline and follow-up, it is common to observe imbalance of the baseline values between the treatment/exposure group and control group. This may bias the study and consequently a meta-analysis (MA) estimate. These estimates may differ across statistical methods used to deal with this issue. Analysis of individual participant data (IPD) allows standardization of methods across studies. We aimed to identify methods used in published IPD-MAs of NRSs for continuous outcomes, and to compare different methods to account for baseline values of outcome variables in IPD-MA of NRSs using two empirical examples from the Thyroid Studies Collaboration (TSC).MethodsFor the first aim we systematically searched in MEDLINE, EMBASE, and Cochrane from inception to February 2021 to identify published IPD-MAs of NRSs that adjusted for baseline outcome measures in the analysis of continuous outcomes. For the second aim, we applied analysis of covariance (ANCOVA), change score, propensity score and the naïve approach (ignores the baseline outcome data) in IPD-MA from NRSs on the association between subclinical hyperthyroidism and depressive symptoms and renal function. We estimated the study and meta-analytic mean difference (MD) and relative standard error (SE). We used both fixed- and random-effects MA.ResultsTen of 18 (56%) of the included studies used the change score method, seven (39%) studies used ANCOVA and one the propensity score (5%). The study estimates were similar across the methods in studies in which groups were balanced at baseline with regard to outcome variables but differed in studies with baseline imbalance. In our empirical examples, ANCOVA and change score showed study results on the same direction, not the propensity score. In our applications, ANCOVA provided more precise estimates, both at study and meta-analytical level, in comparison to other methods. Heterogeneity was higher when change score was used as outcome, moderate for ANCOVA and null with the propensity score.ConclusionANCOVA provided the most precise estimates at both study and meta-analytic level and thus seems preferable in the meta-analysis of IPD from non-randomized studies. For the studies that were well-balanced between groups, change score, and ANCOVA performed similarly.
| S-EPMC8902696 | biostudies-literature
Project description:ObjectiveTo investigate if cerebroplacental ratio (CPR) adds to the predictive value of umbilical artery pulsatility index (UA PI) alone - standard of practice - for adverse perinatal outcome in singleton pregnancies.Design and settingMeta-analysis based on individual participant data (IPD).Population or sampleTen centres provided 17 data sets for 21 661 participants, 18 731 of which could be included. Sample sizes per data set ranged from 207 to 9215 individuals. Patient populations varied from uncomplicated to complicated pregnancies.MethodsIn a collaborative, pooled analysis, we compared the prognostic value of combining CPR with UA PI, versus UA PI only and CPR only, with a one-stage IPD approach. After multiple imputation of missing values, we used multilevel multivariable logistic regression to develop prediction models. We evaluated the classification performance of all models with receiver operating characteristics analysis. We performed subgroup analyses according to gestational age, birthweight centile and estimated fetal weight centile.Main outcome measuresComposite adverse perinatal outcome, defined as perinatal death, caesarean section for fetal distress or neonatal unit admission.ResultsAdverse outcomes occurred in 3423 (18%) participants. The model with UA PI alone resulted in an area under the curve (AUC) of 0.775 (95% CI 0.709-0.828) and with CPR alone in an AUC of 0.778 (95% CI 0.715-0.831). Addition of CPR to the UA PI model resulted in an increase in the AUC of 0.003 points (0.778, 95% CI 0.714-0.831). These results were consistent across all subgroups.ConclusionsCerebroplacental ratio added no predictive value for adverse perinatal outcome beyond UA PI, when assessing singleton pregnancies, irrespective of gestational age or fetal size.Tweetable abstractDoppler measurement of cerebroplacental ratio in clinical practice has limited added predictive value to umbilical artery alone.
| S-EPMC7818434 | biostudies-literature
Project description:BackgroundIndividual patient data (IPD) meta-analysis allows for the exploration of heterogeneity and can identify subgroups that most benefit from an intervention (or exposure), much more successfully than meta-analysis of aggregate data. One-stage or two-stage IPD meta-analysis is possible, with the former using mixed-effects regression models and the latter obtaining study estimates through simpler regression models before aggregating using standard meta-analysis methodology. However, a comprehensive comparison of the two methods, in practice, is lacking.MethodsWe generated 1000 datasets for each of many simulation scenarios covering different IPD sizes and different between-study variance (heterogeneity) assumptions at various levels (intercept and exposure). Numerous simulation settings of different assumptions were also used, while we evaluated performance both on main effects and interaction effects. Performance was assessed on mean bias, mean error, coverage, and power.ResultsFully specified one-stage models (random study intercept or fixed study-specific intercept; random exposure effect; and fixed study-specific effects for covariate) were the best performers overall, especially when investigating interactions. For main effects, performance was almost identical across models unless intercept heterogeneity was present, in which case the fully specified one-stage and the two-stage models performed better. For interaction effects, differences across models were greater with the two-stage model consistently outperformed by the two fully specified one-stage models.ConclusionsA fully specified one-stage model should be preferred (accounting for potential exposure, intercept, and, possibly, interaction heterogeneity), especially when investigating interactions. If non-convergence is encountered with a random study intercept, the fixed study-specific intercept one-stage model should be used instead.
| S-EPMC6175226 | biostudies-literature
Project description: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.
| S-EPMC5960205 | biostudies-literature
Project description:When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models.
| S-EPMC4847645 | biostudies-literature
Project description:BackgroundIn twin pregnancies, the rates of adverse perinatal outcome and subsequent long-term morbidity are substantial, and mainly result from preterm birth (PTB).ObjectivesTo assess the effectiveness of progestogen treatment in the prevention of neonatal morbidity or PTB in twin pregnancies using individual participant data meta-analysis (IPDMA).Search strategyWe searched international scientific databases, trial registration websites, and references of identified articles.Selection criteriaRandomised clinical trials (RCTs) of 17-hydroxyprogesterone caproate (17Pc) or vaginally administered natural progesterone, compared with placebo or no treatment.Data collection and analysisInvestigators of identified RCTs were asked to share their IPD. The primary outcome was a composite of perinatal mortality and severe neonatal morbidity. Prespecified subgroup analyses were performed for chorionicity, cervical length, and prior spontaneous PTB.Main resultsThirteen trials included 3768 women and their 7536 babies. Neither 17Pc nor vaginal progesterone reduced the incidence of adverse perinatal outcome (17Pc relative risk, RR 1.1; 95% confidence interval, 95% CI 0.97-1.4, vaginal progesterone RR 0.97; 95% CI 0.77-1.2). In a subgroup of women with a cervical length of ?25 mm, vaginal progesterone reduced adverse perinatal outcome when cervical length was measured at randomisation (15/56 versus 22/60; RR 0.57; 95% CI 0.47-0.70) or before 24 weeks of gestation (14/52 versus 21/56; RR 0.56; 95% CI 0.42-0.75).Author's conclusionsIn unselected women with an uncomplicated twin gestation, treatment with progestogens (intramuscular 17Pc or vaginal natural progesterone) does not improve perinatal outcome. Vaginal progesterone may be effective in the reduction of adverse perinatal outcome in women with a cervical length of ?25 mm; however, further research is warranted to confirm this finding.
| S-EPMC5909188 | biostudies-literature