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

Inhibition of protein N-myristoylation blocks Plasmodium falciparum intraerythrocytic development, egress and invasion

We have combined chemical biology and genetic modification approaches to investigate the importance of protein myristoylation in the human malaria parasite, Plasmodium falciparum. Parasite treatment during schizogony in the last 10 to 15 hours of the erythrocytic cycle with IMP-1002, an inhibitor of N-myristoyl transferase (NMT), led to a significant blockade in parasite egress from the infected erythrocyte. Two rhoptry proteins were mislocalized in the cell, suggesting that rhoptry function is disrupted. We identified 16 NMT substrates for which myristoylation was significantly reduced by NMT inhibitor (NMTi) treatment, and, of these, 6 proteins were substantially reduced in abundance. In a viability screen, we showed that for 4 of these proteins replacement of the N-terminal glycine with alanine to prevent myristoylation had a substantial effect on parasite fitness. In detailed studies of one NMT substrate, glideosome-associated protein 45 (GAP45), loss of myristoylation had no impact on protein location or glideosome assembly, in contrast to the disruption caused by GAP45 gene deletion, but GAP45 myristoylation was essential for erythrocyte invasion. Therefore, there are at least 3 mechanisms by which inhibition of NMT can disrupt parasite development and growth: early in parasite development, leading to the inhibition of schizogony and formation of "pseudoschizonts," which has been described previously; at the end of schizogony, with disruption of rhoptry formation, merozoite development and egress from the infected erythrocyte; and at invasion, when impairment of motor complex function prevents invasion of new erythrocytes. These results underline the importance of P. falciparum NMT as a drug target because of the pleiotropic effect of its inhibition

CPAP pressure and flow data at 2 positive pressure levels and multiple controlled breathing rates from a trial of 30 adults

Objectives: A unique dataset of airway flow/pressure from healthy subjects on Continuous Positive Airway Pressure (CPAP) ventilation was collected. This data can be used to develop or validate models of pulmonary mechanics, and/or to develop methods to identify patient-specific parameters which cannot be measured non-invasively, during CPAP therapy. These models and values, particularly if available breath-to-breath in real-time, could assist clinicians in the prescription or optimisation of CPAP therapy, including optimising PEEP settings. Data description: Data was obtained from 30 subjects for model-based identification of patient-specific lung mechanics using a specially designed venturi sensor system comprising an array of differential and gauge pressure sensors. Relevant medical information was collected using a questionnaire, including: sex; age; weight; height; smoking history; and history of asthma. Subjects were tasked with breathing at five different rates (including passive), matched to an online pacing sound and video, at two different levels of PEEP (4 and 7 cmH2O) for between 50 and 180 s. Each data set comprises ~ 17 breaths of data, including rest periods between breathing rates and CPAP levels

Isotope effect on the transition temperature TcT_c in Fe-based superconductors: the current status

The results of the Fe isotope effect (Fe-IE) on the transition temperature $T_c$ obtained up to date in various Fe-based high temperature superconductors are summarized and reanalyzed by following the approach developed in [Phys. Rev. B 82, 212505 (2010)]. It is demonstrated that the very controversial results for Fe-IE on $T_c$ are caused by small structural changes occurring simultaneously with the Fe isotope exchange. The Fe-IE exponent on $T_c$ [$\alpha_{\rm Fe}=-(\Delta T_c/T_c)/(\Delta M/M)$, $M$ is the isotope mass] needs to be decomposed into two components with the one related to the structural changes ($\alpha_{\rm Fe}^{\rm str}$) and the genuine (intrinsic) one ($\alpha_{\rm Fe}^{\rm int}$). The validity of such decomposition is further confirmed by the fact that $\alpha_{\rm Fe}^{\rm int}$ coincides with the Fe-IE exponent on the characteristic phonon frequencies $\alpha_{\rm Fe}^{\rm ph}$ as is reported in recent EXAFS and Raman experiments.Comment: 7 pages, 4 figures. The paper is partially based on the results published in [New J. Phys. 12, 073024 (2010) = arXiv:1002.2510] and [Phys. Rev. B 82, 212505 (2010) = arXiv:1008.4540

Oxygen Isotope Effect Resulting from Polaron-induced Superconductivity in Cuprates

The planar oxygen isotope effect coefficient measured as a function of hole doping in the Pr- and La-doped YBa2Cu3O7 (YBCO) and the Ni-doped La1.85Sr0.15CuO4 (LSCO) superconductors quantitatively and qualitatively follows the form originally proposed by Kresin and Wolf, which was derived for polarons perpendicular to the superconducting planes. Interestingly, the inverse oxygen isotope effect coefficient at the pseudogap temperature also follows the same formula. These findings allow the conclusion that the superconductivity in YBCO and LSCO results from polarons or rather bipolarons in the CuO2 plane. The original formula, proposed for the perpendicular direction only, is obviously more generally valid and accounts for the superconductivity in the CuO2 planes.Comment: Dedicated to Alex M\"uller on the occasion of his 90th birthda

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

Standardisation of rates using logistic regression: a comparison with the direct method

<p>Abstract</p> <p>Background</p> <p>Standardisation of rates in health services research is generally undertaken using the direct and indirect arithmetic methods. These methods can produce unreliable estimates when the calculations are based on small numbers. Regression based methods are available but are rarely applied in practice. This study demonstrates the advantages of using logistic regression to obtain smoothed standardised estimates of the prevalence of rare disease in the presence of covariates.</p> <p>Methods</p> <p>Step by step worked examples of the logistic and direct methods are presented utilising data from BETS, an observational study designed to estimate the prevalence of subclinical thyroid disease in the elderly. Rates calculated by the direct method were standardised by sex and age categories, whereas rates by the logistic method were standardised by sex and age as a continuous variable.</p> <p>Results</p> <p>The two methods produce estimates of similar magnitude when standardising by age and sex. The standard errors produced by the logistic method were lower than the conventional direct method.</p> <p>Conclusion</p> <p>Regression based standardisation is a practical alternative to the direct method. It produces more reliable estimates than the direct or indirect method when the calculations are based on small numbers. It has greater flexibility in factor selection and allows standardisation by both continuous and categorical variables. It therefore allows standardisation to be performed in situations where the direct method would give unreliable results.</p

Experience and Challenges from Clinical Trials with Malaria Vaccines in Africa.

Malaria vaccines are considered amongst the most important modalities for potential elimination of malaria disease and transmission. Research and development in this field has been an area of intense effort by many groups over the last few decades. Despite this, there is currently no licensed malaria vaccine. Researchers, clinical trialists and vaccine developers have been working on many approached to make malaria vaccine available.African research institutions have developed and demonstrated a great capacity to undertake clinical trials in accordance to the International Conference on Harmonization-Good Clinical Practice (ICH-GCP) standards in the last decade; particularly in the field of malaria vaccines and anti-malarial drugs. This capacity is a result of networking among African scientists in collaboration with other partners; this has traversed both clinical trials and malaria control programmes as part of the Global Malaria Action Plan (GMAP). GMAP outlined and support global strategies toward the elimination and eradication of malaria in many areas, translating in reduction in public health burden, especially for African children. In the sub-Saharan region the capacity to undertake more clinical trials remains small in comparison to the actual need.However, sustainability of the already developed capacity is essential and crucial for the evaluation of different interventions and diagnostic tools/strategies for other diseases like TB, HIV, neglected tropical diseases and non-communicable diseases. There is urgent need for innovative mechanisms for the sustainability and expansion of the capacity in clinical trials in sub-Saharan Africa as the catalyst for health improvement and maintained

Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study

<p>Abstract</p> <p>Background</p> <p>The appropriate handling of missing covariate data in prognostic modelling studies is yet to be conclusively determined. A resampling study was performed to investigate the effects of different missing data methods on the performance of a prognostic model.</p> <p>Methods</p> <p>Observed data for 1000 cases were sampled with replacement from a large complete dataset of 7507 patients to obtain 500 replications. Five levels of missingness (ranging from 5% to 75%) were imposed on three covariates using a missing at random (MAR) mechanism. Five missing data methods were applied; a) complete case analysis (CC) b) single imputation using regression switching with predictive mean matching (SI), c) multiple imputation using regression switching imputation, d) multiple imputation using regression switching with predictive mean matching (MICE-PMM) and e) multiple imputation using flexible additive imputation models. A Cox proportional hazards model was fitted to each dataset and estimates for the regression coefficients and model performance measures obtained.</p> <p>Results</p> <p>CC produced biased regression coefficient estimates and inflated standard errors (SEs) with 25% or more missingness. The underestimated SE after SI resulted in poor coverage with 25% or more missingness. Of the MI approaches investigated, MI using MICE-PMM produced the least biased estimates and better model performance measures. However, this MI approach still produced biased regression coefficient estimates with 75% missingness.</p> <p>Conclusions</p> <p>Very few differences were seen between the results from all missing data approaches with 5% missingness. However, performing MI using MICE-PMM may be the preferred missing data approach for handling between 10% and 50% MAR missingness.</p

DYNAMO-HIA–A Dynamic Modeling Tool for Generic Health Impact Assessments

Currently, no standard tool is publicly available that allows researchers or policy-makers to quantify the impact of policies using epidemiological evidence within the causal framework of Health Impact Assessment (HIA). A standard tool should comply with three technical criteria (real-life population, dynamic projection, explicit risk-factor states) and three usability criteria (modest data requirements, rich model output, generally accessible) to be useful in the applied setting of HIA. With DYNAMO-HIA (Dynamic Modeling for Health Impact Assessment), we introduce such a generic software tool specifically designed to facilitate quantification in the assessment of the health impacts of policies