The search for stable prognostic models in multiple imputed data sets

Background  

In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition.     

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.     

Imputation strategies for missing binary outcomes in cluster randomized trials

Background  

Attrition, which leads to missing data, is a common problem in cluster randomized trials (CRTs), where groups of patients rather than individuals are randomized. Standard multiple imputation (MI) strategies may not be appropriate to impute missing data from CRTs since they assume independent data. In this paper, under the assumption of missing completely at random and covariate dependent missing, we compared six MI strategies which account for the intra-cluster correlation for missing binary outcomes in CRTs with the standard imputation strategies and complete case analysis approach using a simulation study.     

A comparison of multiple imputation methods for missing data in longitudinal studies

Background

Multiple imputation (MI) is now widely used to handle missing data in longitudinal studies. Several MI techniques have been proposed to impute incomplete longitudinal covariates, including standard fully conditional specification (FCS-Standard) and joint multivariate normal imputation (JM-MVN), which treat repeated measurements as distinct variables, and various extensions based on generalized linear mixed models. Although these MI approaches have been implemented in various software packages, there has not been a comprehensive evaluation of the relative performance of these methods in the context of longitudinal data.

Method

Using both empirical data and a simulation study based on data from the six waves of the Longitudinal Study of Australian Children (N?=?4661), we investigated the performance of a wide range of MI methods available in standard software packages for investigating the association between child body mass index (BMI) and quality of life using both a linear regression and a linear mixed-effects model.

Results

In this paper, we have identified and compared 12 different MI methods for imputing missing data in longitudinal studies. Analysis of simulated data under missing at random (MAR) mechanisms showed that the generally available MI methods provided less biased estimates with better coverage for the linear regression model and around half of these methods performed well for the estimation of regression parameters for a linear mixed model with random intercept. With the observed data, we observed an inverse association between child BMI and quality of life, with available data as well as multiple imputation.

Conclusion

Both FCS-Standard and JM-MVN performed well for the estimation of regression parameters in both analysis models. More complex methods that explicitly reflect the longitudinal structure for these analysis models may only be needed in specific circumstances such as irregularly spaced data.

     

Missing data approaches in eHealth research: simulation study and a tutorial for nonmathematically inclined researchers

Background

Missing data is a common nuisance in eHealth research: it is hard to prevent and may invalidate research findings.

Objective

In this paper several statistical approaches to data “missingness” are discussed and tested in a simulation study. Basic approaches (complete case analysis, mean imputation, and last observation carried forward) and advanced methods (expectation maximization, regression imputation, and multiple imputation) are included in this analysis, and strengths and weaknesses are discussed.

Methods

The dataset used for the simulation was obtained from a prospective cohort study following participants in an online self-help program for problem drinkers. It contained 124 nonnormally distributed endpoints, that is, daily alcohol consumption counts of the study respondents. Missingness at random (MAR) was induced in a selected variable for 50% of the cases. Validity, reliability, and coverage of the estimates obtained using the different imputation methods were calculated by performing a bootstrapping simulation study.

Results

In the performed simulation study, the use of multiple imputation techniques led to accurate results. Differences were found between the 4 tested multiple imputation programs: NORM, MICE, Amelia II, and SPSS MI. Among the tested approaches, Amelia II outperformed the others, led to the smallest deviation from the reference value (Cohen’s d = 0.06), and had the largest coverage percentage of the reference confidence interval (96%).

Conclusions

The use of multiple imputation improves the validity of the results when analyzing datasets with missing observations. Some of the often-used approaches (LOCF, complete cases analysis) did not perform well, and, hence, we recommend not using these. Accumulating support for the analysis of multiple imputed datasets is seen in more recent versions of some of the widely used statistical software programs making the use of multiple imputation more readily available to less mathematically inclined researchers.     

Dealing with missing data in a multi-question depression scale: a comparison of imputation methods

Background  

Missing data present a challenge to many research projects. The problem is often pronounced in studies utilizing self-report scales, and literature addressing different strategies for dealing with missing data in such circumstances is scarce. The objective of this study was to compare six different imputation techniques for dealing with missing data in the Zung Self-reported Depression scale (SDS).     

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.     

Single nucleotide polymorphisms in inflammatory genes and the risk of early onset of lone atrial fibrillation

Background  

Systemic low-grade inflammation is a prognostic risk factor of atrial fibrillation (AF).     

Association of the eNOS E298D polymorphism and the risk of myocardial infarction in the Greek population

Background  

Nitric oxide (NO), produced by endothelial nitric oxide synthase (eNOS), plays a key role in the regulation of vascular tone. Endothelium-derived NO exerts vasoprotective effects by suppressing platelet aggregation, leukocyte adhesion and smooth muscle cell proliferation. The E298D polymorphic variant of eNOS has been associated with myocardial infarction (MI), but data relating to this variant are divergent in Greece. Accordingly, we examined a possible association between the E298D polymorphism of the eNOS gene and MI in a subgroup of the Greek population.     

Multiple imputation for estimation of an occurrence rate in cohorts with attrition and discrete follow-up time points: a simulation study

Background  

In longitudinal cohort studies, subjects may be lost to follow-up at any time during the study. This leads to attrition and thus to a risk of inaccurate and biased estimations. The purpose of this paper is to show how multiple imputation can take advantage of all the information collected during follow-up in order to estimate the cumulative probability P(E) of an event E, when the first occurrence of this event is observed at t successive time points of a longitudinal study with attrition.     

Reporting performance of prognostic models in cancer: a review

Background  

Appropriate choice and use of prognostic models in clinical practice require the use of good methods for both model development, and for developing prognostic indices and risk groups from the models. In order to assess reliability and generalizability for use, models need to have been validated and measures of model performance reported. We reviewed published articles to assess the methods and reporting used to develop and evaluate performance of prognostic indices and risk groups from prognostic models.     

Testing the Effectiveness of Motivational Interviewing as a Weight Reduction Strategy for Obese Cardiac Patients: A Pilot Study

Background

Obesity has reached epidemic proportions in the USA and is a particular threat to those with coronary disease. Motivational interviewing (MI) is a client-centered, directive method for enhancing intrinsic motivation to change by exploring and resolving ambivalence about altering behavior.

Purpose

This study examined the efficacy of MI compared to nutritional counseling for weight loss in a small sample of obese cardiac patients.

Method

Participants were assigned to either MI or to nutrition counseling and followed up over 3 months. Trained undergraduate students delivered the MI intervention.

Results

There were significant reductions in weight in women in the MI intervention, but not in men.

Conclusion

The results suggest that MI may be effective for obese female cardiac patients, in particular, even when delivered by nonprofessional interviewers. Limitations of the study include a small sample size, nonrandomized assignment to conditions, and attrition over time.     

Nearest neighbor imputation algorithms: a critical evaluation

Background

Nearest neighbor (NN) imputation algorithms are efficient methods to fill in missing data where each missing value on some records is replaced by a value obtained from related cases in the whole set of records. Besides the capability to substitute the missing data with plausible values that are as close as possible to the true value, imputation algorithms should preserve the original data structure and avoid to distort the distribution of the imputed variable. Despite the efficiency of NN algorithms little is known about the effect of these methods on data structure.

Methods

Simulation on synthetic datasets with different patterns and degrees of missingness were conducted to evaluate the performance of NN with one single neighbor (1NN) and with k neighbors without (kNN) or with weighting (wkNN) in the context of different learning frameworks: plain set, reduced set after ReliefF filtering, bagging, random choice of attributes, bagging combined with random choice of attributes (Random-Forest-like method).

Results

Whatever the framework, kNN usually outperformed 1NN in terms of precision of imputation and reduced errors in inferential statistics, 1NN was however the only method capable of preserving the data structure and data were distorted even when small values of k neighbors were considered; distortion was more severe for resampling schemas.

Conclusions

The use of three neighbors in conjunction with ReliefF seems to provide the best trade-off between imputation error and preservation of the data structure. The very same conclusions can be drawn when imputation experiments were conducted on the single proton emission computed tomography (SPECTF) heart dataset after introduction of missing data completely at random.

     

Assessment of a motivational interviewing curriculum for year 3 medical students using a standardized patient case

Objective

We sought to evaluate a year 3 motivational interviewing (MI) curriculum using a standardized patient case.

Methods

The 2-h small group MI curriculum included a didactic presentation followed by interactive role plays. During a clinical skills assessment at the end of year 3 the MI skills of 80 students who had participated in the curriculum were compared with those of 19 students who had not participated.

Results

The standardized patient reliably rated the students on their performance of 8 items. Students who had participated in the MI curriculum were significantly more proficient than nonparticipating students in the performance of 2 strategic MI skills, importance and confidence rulers (ps < .006). The groups did not differ in their use of patient-centered counseling skills or collaborative change planning commonly used in MI.

Conclusions

Third year medical students can learn to use MI skills that specifically aim to enhance patients’ motivations for change.

Practice implications

Medical schools should consider providing students with MI training and MI skill assessments using standardized patient cases to help students prepare to counsel patients for behavior change.     

Sleep Contribution to Motor Memory Consolidation: A Motor Imagery Study

Study Objectives:

Sleep is known to enhance performance following physical practice (PP) of a new sequence of movements. Apart from a pilot study, it is still unknown whether a similar sleep-dependent consolidation effect can be observed following motor imagery (MI) and whether this mnemonic process is related to MI speed.

Design:

Counterbalanced within-subject design.

Setting:

The laboratory.

Participants:

Thirty-two participants.

Interventions:

PP, real-time MI, fast MI, and NoSleep (control) groups.

Measurements and Results:

Subjects practiced an explicitly known sequence of finger movements, and were assigned to PP, real-time MI, or fast MI, in which they intentionally imagined the sequence at a faster pace. A NoSleep group subjected to real-time MI, but without any intervening sleep, was also tested. Performance was evaluated before practice, as well as prior to, and after a night of sleep or a similar time interval during the daytime. Compared with the NoSleep group, the results revealed offline gains in performance after sleep in the PP, real-time MI, and fast MI groups. There was no correlation between a measure of underestimation of the time to imagine the motor sequence and the actual speed gains after sleep, neither between the ease/difficulty to form mental images and performance gains.

Conclusions:

These results provide evidence that sleep contributes to the consolidation of motor sequence learning acquired through MI and further suggests that offline delayed gains are not related to the MI content per se. They extend our previous findings and strongly confirm that performance enhancement following MI is sleep dependent.

Citation:

Debarnot U; Creveaux T; Collet C; Doyon J; Guillot A. Sleep contribution to motor memory consolidation: a motor imagery study. SLEEP 2009;32(12):1559-1565.     

Risk groups defined by Recursive Partitioning Analysis of patients with colorectal adenocarcinoma treated with colorectal resection

Background  

To define different prognostic groups of surgical colorectal adenocarcinoma patients derived from recursive partitioning analysis (RPA).     

Prediction of two month modified Rankin Scale with an ordinal prediction model in patients with aneurysmal subarachnoid haemorrhage

Background  

Aneurysmal subarachnoid haemorrhage (aSAH) is a devastating event with a frequently disabling outcome. Our aim was to develop a prognostic model to predict an ordinal clinical outcome at two months in patients with aSAH.     

Lasso regularization for left-censored Gaussian outcome and high-dimensional predictors

Background

Biological assays for the quantification of markers may suffer from a lack of sensitivity and thus from an analytical detection limit. This is the case of human immunodeficiency virus (HIV) viral load. Below this threshold the exact value is unknown and values are consequently left-censored. Statistical methods have been proposed to deal with left-censoring but few are adapted in the context of high-dimensional data.

Methods

We propose to reverse the Buckley-James least squares algorithm to handle left-censored data enhanced with a Lasso regularization to accommodate high-dimensional predictors. We present a Lasso-regularized Buckley-James least squares method with both non-parametric imputation using Kaplan-Meier and parametric imputation based on the Gaussian distribution, which is typically assumed for HIV viral load data after logarithmic transformation. Cross-validation for parameter-tuning is based on an appropriate loss function that takes into account the different contributions of censored and uncensored observations. We specify how these techniques can be easily implemented using available R packages. The Lasso-regularized Buckley-James least square method was compared to simple imputation strategies to predict the response to antiretroviral therapy measured by HIV viral load according to the HIV genotypic mutations. We used a dataset composed of several clinical trials and cohorts from the Forum for Collaborative HIV Research (HIV Med. 2008;7:27-40). The proposed methods were also assessed on simulated data mimicking the observed data.

Results

Approaches accounting for left-censoring outperformed simple imputation methods in a high-dimensional setting. The Gaussian Buckley-James method with cross-validation based on the appropriate loss function showed the lowest prediction error on simulated data and, using real data, the most valid results according to the current literature on HIV mutations.

Conclusions

The proposed approach deals with high-dimensional predictors and left-censored outcomes and has shown its interest for predicting HIV viral load according to HIV mutations.

     

VAMP-8 gene variant is associated with increased risk of early myocardial infarction

Introduction

Single nucleotide polymorphism in the 3’ untranslated region of the vesicle-associated membrane protein gene (VAMP-8) has been associated with increased risk of early-onset myocardial infarction (MI). In this study the risk of early onset MI conferred by VAMP-8 gene polymorphism was investigated in a group of 171 male subjects.

Material and methods

Male patients with a history of MI who underwent coronary angiography were enrolled and divided into early (incident < 55 years of age) and late (incident ≥ 55 years of age) MI onset groups. Apart from the RFLP-PCR based analysis of the VAMP-8 variant, history of hypertension, lipid abnormalities, smoking and body mass index were recorded. In statistical analyses odds ratios and relative risk were used as a measure of genotype-MI association while logistic regression was implemented for evaluation of MI risk factor strength.

Results

VAMP-8 A allele frequency proved to be significantly higher in the early-onset MI group and conferred higher relative risk of early MI in the investigated cohort, when calculated for the individual A allele (p = 0.029). In logistic regression analyses no association between risk genotypes and traditional risk factors was observed.

Conclusions

In this study VAMP-8 A variant was identified as a risk allele for early MI in male subjects.     

Survivin gene levels in the peripheral blood of patients with gastric cancer independently predict survival

Background  

The detection of circulating tumor cells (CTC) is considered a promising tool for improving risk stratification in patients with solid tumors. We investigated on whether the expression of CTC related genes adds any prognostic power to the TNM staging system in patients with gastric carcinoma.