Dataset Information

Analysis of Randomised Trials Including Multiple Births When Birth Size Is Informative.

ABSTRACT: Informative birth size occurs when the average outcome depends on the number of infants per birth. Although analysis methods have been proposed for handling informative birth size, their performance is not well understood. Our aim was to evaluate the performance of these methods and to provide recommendations for their application in randomised trials including infants from single and multiple births.Three generalised estimating equation (GEE) approaches were considered for estimating the effect of treatment on a continuous or binary outcome: cluster weighted GEEs, which produce treatment effects with a mother-level interpretation when birth size is informative; standard GEEs with an independence working correlation structure, which produce treatment effects with an infant-level interpretation when birth size is informative; and standard GEEs with an exchangeable working correlation structure, which do not account for informative birth size. The methods were compared through simulation and analysis of an example dataset.Treatment effect estimates were affected by informative birth size in the simulation study when the effect of treatment in singletons differed from that in multiples (i.e. in the presence of a treatment group by multiple birth interaction). The strength of evidence supporting the effectiveness of treatment varied between methods in the example dataset.Informative birth size is always a possibility in randomised trials including infants from both single and multiple births, and analysis methods should be pre-specified with this in mind. We recommend estimating treatment effects using standard GEEs with an independence working correlation structure to give an infant-level interpretation.

SUBMITTER: Yelland LN

PROVIDER: S-EPMC4847643 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Analysis of Randomised Trials Including Multiple Births When Birth Size Is Informative.

Yelland Lisa N LN Sullivan Thomas R TR Pavlou Menelaos M Seaman Shaun R SR

Paediatric and perinatal epidemiology 20150901 6

<h4>Background</h4>Informative birth size occurs when the average outcome depends on the number of infants per birth. Although analysis methods have been proposed for handling informative birth size, their performance is not well understood. Our aim was to evaluate the performance of these methods and to provide recommendations for their application in randomised trials including infants from single and multiple births.<h4>Methods</h4>Three generalised estimating equation (GEE) approaches were c ...[more]

PMID: 26332368

Similar Datasets

Project description:BackgroundRecent work has shown that cluster-randomised trials can estimate two distinct estimands: the participant-average and cluster-average treatment effects. These can differ when participant outcomes or the treatment effect depends on the cluster size (termed informative cluster size). In this case, estimators that target one estimand (such as the analysis of unweighted cluster-level summaries, which targets the cluster-average effect) may be biased for the other. Furthermore, commonly used estimators such as mixed-effects models or generalised estimating equations with an exchangeable correlation structure can be biased for both estimands. However, there has been little empirical research into whether informative cluster size is likely to occur in practice.MethodWe re-analysed a cluster-randomised trial comparing two different thresholds for red blood cell transfusion in patients with acute upper gastrointestinal bleeding to explore whether estimates for the participant- and cluster-average effects differed, to provide empirical evidence for whether informative cluster size may be present. For each outcome, we first estimated a participant-average effect using independence estimating equations, which are unbiased under informative cluster size. We then compared this to two further methods: (1) a cluster-average effect estimated using either weighted independence estimating equations or unweighted cluster-level summaries, and (2) estimates from a mixed-effects model or generalised estimating equations with an exchangeable correlation structure. We then performed a small simulation study to evaluate whether observed differences between cluster- and participant-average estimates were likely to occur even if no informative cluster size was present.ResultsFor most outcomes, treatment effect estimates from different methods were similar. However, differences of >10% occurred between participant- and cluster-average estimates for 5 of 17 outcomes (29%). We also observed several notable differences between estimates from mixed-effects models or generalised estimating equations with an exchangeable correlation structure and those based on independence estimating equations. For example, for the EQ-5D VAS score, the independence estimating equation estimate of the participant-average difference was 4.15 (95% confidence interval: -3.37 to 11.66), compared with 2.84 (95% confidence interval: -7.37 to 13.04) for the cluster-average independence estimating equation estimate, and 3.23 (95% confidence interval: -6.70 to 13.16) from a mixed-effects model. Similarly, for thromboembolic/ischaemic events, the independence estimating equation estimate for the participant-average odds ratio was 0.43 (95% confidence interval: 0.07 to 2.48), compared with 0.33 (95% confidence interval: 0.06 to 1.77) from the cluster-average estimator.ConclusionIn this re-analysis, we found that estimates from the various approaches could differ, which may be due to the presence of informative cluster size. Careful consideration of the estimand and the plausibility of assumptions underpinning each estimator can help ensure an appropriate analysis methods are used. Independence estimating equations and the analysis of cluster-level summaries (with appropriate weighting for each to correspond to either the participant-average or cluster-average treatment effect) are a desirable choice when informative cluster size is deemed possible, due to their unbiasedness in this setting.

Project description:BackgroundMultiple primary outcomes may be specified in randomised controlled trials (RCTs). When analysing multiple outcomes it's important to control the family wise error rate (FWER). A popular approach to do this is to adjust the p-values corresponding to each statistical test used to investigate the intervention effects by using the Bonferroni correction. It's also important to consider the power of the trial to detect true intervention effects. In the context of multiple outcomes, depending on the clinical objective, the power can be defined as: 'disjunctive power', the probability of detecting at least one true intervention effect across all the outcomes or 'marginal power' the probability of finding a true intervention effect on a nominated outcome. We provide practical recommendations on which method may be used to adjust for multiple comparisons in the sample size calculation and the analysis of RCTs with multiple primary outcomes. We also discuss the implications on the sample size for obtaining 90% disjunctive power and 90% marginal power.MethodsWe use simulation studies to investigate the disjunctive power, marginal power and FWER obtained after applying Bonferroni, Holm, Hochberg, Dubey/Armitage-Parmar and Stepdown-minP adjustment methods. Different simulation scenarios were constructed by varying the number of outcomes, degree of correlation between the outcomes, intervention effect sizes and proportion of missing data.ResultsThe Bonferroni and Holm methods provide the same disjunctive power. The Hochberg and Hommel methods provide power gains for the analysis, albeit small, in comparison to the Bonferroni method. The Stepdown-minP procedure performs well for complete data. However, it removes participants with missing values prior to the analysis resulting in a loss of power when there are missing data. The sample size requirement to achieve the desired disjunctive power may be smaller than that required to achieve the desired marginal power. The choice between whether to specify a disjunctive or marginal power should depend on the clincial objective.

Project description:OBJECTIVES:The aim of our work was to analyse the effect of maternal smoking on body size and body proportions of newborns when the mother had smoked only during the first trimester, in comparison with continued smoking after the first trimester. Furthermore, we have evaluated how growth restriction associated with maternal smoking contributes to changes in body proportions. DESIGN:Register-based cohort study SETTING: Maternal Exposure (MATEX) cohort identified from the Finnish Medical Birth Register. PARTICIPANTS:Singleton births without congenital anomalies and missing data (1.38?million) from 1 January 1991 to 31 December 2016. METHODS:Logistic regression was used to quantify the effect of maternal smoking, stratified by the maternal smoking status. OUTCOME MEASURES:Body proportions indicated by low brain-to-body ratio (defined as <10th percentile); high ponderal index and high head-to-length ratio (defined as >90th percentile); small body size for gestational age at birth (defined as weight, length or head circumference <10th percentile) and preterm birth (<37?weeks) and low birth weight (2500?g). RESULTS:Continued smoking after the first trimester was associated with high ponderal index (OR 1.26, 95%?CI 1.23 to 1.28), low brain-to-body ratio (1.11, 1.07-1.15) and high head-to-length ratio (1.22, 1.19-1.26), corresponding with absolute risks of 22%, 10% and 19%, respectively). The effects were slightly lower when smoking had been quit during the first trimester. Similar effects were seen for the body size variables and low birth weight. Preterm birth was not associated with smoking only during first trimester. CONCLUSIONS:Maternal smoking, independent of smoking duration during pregnancy, was associated with abnormal body proportions resulting from larger reduction of length and head circumference in comparison to weight. The effects of having quit smoking during the first trimester and having continued smoking after the first trimester were similar, suggesting the importance of early pregnancy as a sensitive exposure window.

Dataset Information

Analysis of Randomised Trials Including Multiple Births When Birth Size Is Informative.

Publications

Analysis of Randomised Trials Including Multiple Births When Birth Size Is Informative.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets