Combining fractional polynomial model building with multiple imputation.

Multivariable fractional polynomial (MFP) models are commonly used in medical research. The datasets in which MFP models are applied often contain covariates with missing values. To handle the missing values, we describe methods for combining multiple imputation with MFP modelling, considering in turn three issues: first, how to impute so that the imputation model does not favour certain fractional polynomial (FP) models over others; second, how to estimate the FP exponents in multiply imputed data; and third, how to choose between models of differing complexity. Two imputation methods are outlined for different settings. For model selection, methods based on Wald-type statistics and weighted likelihood-ratio tests are proposed and evaluated in simulation studies. The Wald-based method is very slightly better at estimating FP exponents. Type I error rates are very similar for both methods, although slightly less well controlled than analysis of complete records; however, there is potential for substantial gains in power over the analysis of complete records. We illustrate the two methods in a dataset from five trauma registries for which a prognostic model has previously been published, contrasting the selected models with that obtained by analysing the complete records only

Utility of repeat cytological assessment of thyroid nodules initially classified as benign: clinical insights from multidisciplinary care in an Irish tertiary referral centre.

BACKGROUND: Fine needle aspiration biopsy (FNAB) is the tool of choice for evaluating thyroid nodules with the majority classified as benign following initial assessment. However, concern remains about false negative results and some guidelines have recommended routine repeat aspirates. We aimed to assess the utility of routine repeat FNAB for nodules classified as benign on initial biopsy and to examine the impact of establishing a multidisciplinary team for the care of these patients. METHODS: We performed a retrospective review of 400 consecutive patients (413 nodules) who underwent FNAB of a thyroid nodule at our hospital between July 2008 and July 2011. Data recorded included demographic, clinical, histological and radiological variables. RESULTS: Three hundred and fifty seven patients (89 %) were female. Median follow-up was 5.5 years. Two hundred and fifty eight (63 %) nodules were diagnosed as benign. The rate of routine repeat biopsy increased significantly over the time course of the study (p for trend = 0.012). Nine Thy 2 nodules were classified differently on the basis of routine repeat biopsy; one patient was classified as malignant on repeat biopsy and was diagnosed with papillary thyroid carcinoma. Eight were classified as a follicular lesions on repeat biopsy-six diagnosed as benign following lobectomy; two declined lobectomy and were followed radiologically with no nodule size increase. CONCLUSIONS: The false negative rate of an initial benign cytology result, from a thyroid nodule aspirate, is low. In the setting of an experienced multidisciplinary thyroid team, routine repeat aspiration is not justified

Recurring pulmonary hamartomas: cause for concern?

We report the case of a well-controlled female asthmatic who developed \u27multiple pulmonary hamartomas\u27 on three separate occasions over a period of 25 years that necessitated surgical resection. To our knowledge, this is the first report of recurrent hamartomas in a single individual necessitating multiple thoracotomies

Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines

Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to obtain the estimates of interest. The estimates from each imputed dataset are then combined into one overall estimate and variance, incorporating both the within and between imputation variability. Rubin's rules for combining these multiply imputed estimates are based on asymptotic theory. The resulting combined estimates may be more accurate if the posterior distribution of the population parameter of interest is better approximated by the normal distribution. However, the normality assumption may not be appropriate for all the parameters of interest when analysing prognostic modelling studies, such as predicted survival probabilities and model performance measures. Methods: Guidelines for combining the estimates of interest when analysing prognostic modelling studies are provided. A literature review is performed to identify current practice for combining such estimates in prognostic modelling studies. Results: Methods for combining all reported estimates after MI were not well reported in the current literature. Rubin's rules without applying any transformations were the standard approach used, when any method was stated. Conclusion: The proposed simple guidelines for combining estimates after MI may lead to a wider and more appropriate use of MI in future prognostic modelling studies

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Type I error rates of multi-arm multi-stage clinical trials: strong control and impact of intermediate outcomes

BACKGROUND: The multi-arm multi-stage (MAMS) design described by Royston et al. [Stat Med. 2003;22(14):2239-56 and Trials. 2011;12:81] can accelerate treatment evaluation by comparing multiple treatments with a control in a single trial and stopping recruitment to arms not showing sufficient promise during the course of the study. To increase efficiency further, interim assessments can be based on an intermediate outcome (I) that is observed earlier than the definitive outcome (D) of the study. Two measures of type I error rate are often of interest in a MAMS trial. Pairwise type I error rate (PWER) is the probability of recommending an ineffective treatment at the end of the study regardless of other experimental arms in the trial. Familywise type I error rate (FWER) is the probability of recommending at least one ineffective treatment and is often of greater interest in a study with more than one experimental arm. METHODS: We demonstrate how to calculate the PWER and FWER when the I and D outcomes in a MAMS design differ. We explore how each measure varies with respect to the underlying treatment effect on I and show how to control the type I error rate under any scenario. We conclude by applying the methods to estimate the maximum type I error rate of an ongoing MAMS study and show how the design might have looked had it controlled the FWER under any scenario. RESULTS: The PWER and FWER converge to their maximum values as the effectiveness of the experimental arms on I increases. We show that both measures can be controlled under any scenario by setting the pairwise significance level in the final stage of the study to the target level. In an example, controlling the FWER is shown to increase considerably the size of the trial although it remains substantially more efficient than evaluating each new treatment in separate trials. CONCLUSIONS: The proposed methods allow the PWER and FWER to be controlled in various MAMS designs, potentially increasing the uptake of the MAMS design in practice. The methods are also applicable in cases where the I and D outcomes are identical

P3MC: A double blind parallel group randomised placebo controlled trial of Propranolol and Pizotifen in preventing migraine in children

<p>Abstract</p> <p>Background</p> <p>A recent Cochrane Review demonstrated the remarkable lack of reliable clinical trials of migraine treatments for children, especially for the two most prescribed preventative treatments in the UK, <it>Propranolol </it>and <it>Pizotifen</it>.</p> <p>Migraine trials in both children and adults have high placebo responder rates, e.g. of 23%, but for a trial's results to be generalisable "placebo responders" should not be excluded and for a drug to be worthwhile it should be clearly superior, both clinically and statistically, to placebo.</p> <p>Methods/Design</p> <p>Two multicentre, two arm double blind parallel group randomised controlled trials, with allocation ratio of 2:1 for each comparison, Propranolol versus placebo and Pizotifen versus placebo. The trial is designed to test whether Propranolol is superior to placebo and whether Pizotifen is superior to placebo for the prevention of migraine attacks in children aged 5 - 16 years referred to secondary care out-patient settings with frequent migraine (2-6/4 weeks). The primary outcome measure is the number of migraine attacks during trial weeks 11 to 14.</p> <p>Discussion</p> <p>A strength of this trial is the participation of clinically well defined migraine patients who will also be approached to help with future longer-term follow-up studies.</p> <p>Trial Registration</p> <p>ISRCTN97360154</p

ICE COLD ERIC – International collaborative effort on chronic obstructive lung disease: exacerbation risk index cohorts – Study protocol for an international COPD cohort study

<p>Abstract</p> <p>Background</p> <p>Chronic Obstructive Pulmonary Disease (COPD) is a systemic disease; morbidity and mortality due to COPD are on the increase, and it has great impact on patients' lives. Most COPD patients are managed by general practitioners (GP). Too often, GPs base their initial assessment of patient's disease severity mainly on lung function. However, lung function correlates poorly with COPD-specific health-related quality of life and exacerbation frequency. A validated COPD disease risk index that better represents the clinical manifestations of COPD and is feasible in primary care seems to be useful. The objective of this study is to develop and validate a practical COPD disease risk index that predicts the clinical course of COPD in primary care patients with GOLD stages 2–4.</p> <p>Methods/Design</p> <p>We will conduct 2 linked prospective cohort studies with COPD patients from GPs in Switzerland and the Netherlands. We will perform a baseline assessment including detailed patient history, questionnaires, lung function, history of exacerbations, measurement of exercise capacity and blood sampling. During the follow-up of at least 2 years, we will update the patients' profile by registering exacerbations, health-related quality of life and any changes in the use of medication. The primary outcome will be health-related quality of life. Secondary outcomes will be exacerbation frequency and mortality. Using multivariable regression analysis, we will identify the best combination of variables predicting these outcomes over one and two years and, depending on funding, even more years.</p> <p>Discussion</p> <p>Despite the diversity of clinical manifestations and available treatments, assessment and management today do not reflect the multifaceted character of the disease. This is in contrast to preventive cardiology where, nowadays, the treatment in primary care is based on patient-specific and fairly refined cardiovascular risk profile corresponding to differences in prognosis. After completion of this study, we will have a practical COPD-disease risk index that predicts the clinical course of COPD in primary care patients with GOLD stages 2–4. In a second step we will incorporate evidence-based treatment effects into this model, such that the instrument may guide physicians in selecting treatment based on the individual patients' prognosis.</p> <p>Trial registration</p> <p>ClinicalTrials.gov Archive NCT00706602</p

Effect of the Pre-erythrocytic Candidate Malaria Vaccine RTS,S/AS01E on Blood Stage Immunity in Young Children

(See the article by Greenhouse et al, on pages 19-26.