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

ABSTRACT: 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

SUBMITTER: Ensor J

PROVIDER: S-EPMC5960205 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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

Ensor Joie J Burke Danielle L DL Snell Kym I E KIE Hemming Karla K Riley Richard D RD

BMC medical research methodology 20180518 1

<h4>Background</h4>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.<h4>Methods</h4>The simulation approach has four steps: ( ...[more]

PMID: 29776399

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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.

Project description:BackgroundIndividual participant data meta-analysis (IPD-MA) is considered the gold standard for investigating subgroup effects. Frequently used regression-based approaches to detect subgroups in IPD-MA are: meta-regression, per-subgroup meta-analysis (PS-MA), meta-analysis of interaction terms (MA-IT), naive one-stage IPD-MA (ignoring potential study-level confounding), and centred one-stage IPD-MA (accounting for potential study-level confounding). Clear guidance on the analyses is lacking and clinical researchers may use approaches with suboptimal efficiency to investigate subgroup effects in an IPD setting. Therefore, our aim is to overview and compare the aforementioned methods, and provide recommendations over which should be preferred.MethodsWe conducted a simulation study where we generated IPD of randomised trials and varied the magnitude of subgroup effect (0, 25, 50% relative reduction), between-study treatment effect heterogeneity (none, medium, large), ecological bias (none, quantitative, qualitative), sample size (50,100,200), and number of trials (5,10) for binary, continuous and time-to-event outcomes. For each scenario, we assessed the power, false positive rate (FPR) and bias of aforementioned five approaches.ResultsNaive and centred IPD-MA yielded the highest power, whilst preserving acceptable FPR around the nominal 5% in all scenarios. Centred IPD-MA showed slightly less biased estimates than naïve IPD-MA. Similar results were obtained for MA-IT, except when analysing binary outcomes (where it yielded less power and FPR < 5%). PS-MA showed similar power as MA-IT in non-heterogeneous scenarios, but power collapsed as heterogeneity increased, and decreased even more in the presence of ecological bias. PS-MA suffered from too high FPRs in non-heterogeneous settings and showed biased estimates in all scenarios. Meta-regression showed poor power (< 20%) in all scenarios and completely biased results in settings with qualitative ecological bias.ConclusionsOur results indicate that subgroup detection in IPD-MA requires careful modelling. Naive and centred IPD-MA performed equally well, but due to less bias of the estimates in the presence of ecological bias, we recommend the latter.

Project description:BackgroundPrevious narrative reviews have concluded that dietary nitrate (NO3-) improves maximal neuromuscular power in humans. This conclusion, however, was based on a limited number of studies, and no attempt has been made to quantify the exact magnitude of this beneficial effect. Such information would help ensure adequate statistical power in future studies and could help place the effects of dietary NO3- on various aspects of exercise performance (i.e., endurance vs. strength vs. power) in better context. We therefore undertook a systematic review and individual participant data meta-analysis to quantify the effects of NO3- supplementation on human muscle power.MethodsThe literature was searched using a strategy developed by a health sciences librarian. Data sources included Medline Ovid, Embase, SPORTDiscus, Scopus, Clinicaltrials.gov , and Google Scholar. Studies were included if they used a randomized, double-blind, placebo-controlled, crossover experimental design to measure the effects of dietary NO3- on maximal power during exercise in the non-fatigued state and the within-subject correlation could be determined from data in the published manuscript or obtained from the authors.ResultsNineteen studies of a total of 268 participants (218 men, 50 women) met the criteria for inclusion. The overall effect size (ES; Hedge's g) calculated using a fixed effects model was 0.42 (95% confidence interval (CI) 0.29, 0.56; p = 6.310 × 10- 11). There was limited heterogeneity between studies (i.e., I2 = 22.79%, H2 = 1.30, p = 0.3460). The ES estimated using a random effects model was therefore similar (i.e., 0.45, 95% CI 0.30, 0.61; p = 1.064 × 10- 9). Sub-group analyses revealed no significant differences due to subject age, sex, or test modality (i.e., small vs. large muscle mass exercise). However, the ES in studies using an acute dose (i.e., 0.54, 95% CI 0.37, 0.71; p = 6.774 × 10- 12) was greater (p = 0.0211) than in studies using a multiple dose regimen (i.e., 0.22, 95% CI 0.01, 0.43; p = 0.003630).ConclusionsAcute or chronic dietary NO3- intake significantly increases maximal muscle power in humans. The magnitude of this effect-on average, ~ 5%-is likely to be of considerable practical and clinical importance.

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

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

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