Imputation of missing values of tumour stage in population-based cancer registration

<p>Abstract</p> <p>Background</p> <p>Missing data on tumour stage information is a common problem in population-based cancer registries. Statistical analyses on the level of tumour stage may be biased, if no adequate method for handling of missing data is applied. In order to determine a useful way to treat missing data on tumour stage, we examined different imputation models for multiple imputation with chained equations for analysing the stage-specific numbers of cases of malignant melanoma and female breast cancer.</p> <p>Methods</p> <p>This analysis was based on the malignant melanoma data set and the female breast cancer data set of the cancer registry Schleswig-Holstein, Germany. The cases with complete tumour stage information were extracted and their stage information partly removed according to a MAR missingness-pattern, resulting in five simulated data sets for each cancer entity. The missing tumour stage values were then treated with multiple imputation with chained equations, using polytomous regression, predictive mean matching, random forests and proportional sampling as imputation models. The estimated tumour stages, stage-specific numbers of cases and survival curves after multiple imputation were compared to the observed ones.</p> <p>Results</p> <p>The amount of missing values for malignant melanoma was too high to estimate a reasonable number of cases for each UICC stage. However, multiple imputation of missing stage values led to stage-specific numbers of cases of T-stage for malignant melanoma as well as T- and UICC-stage for breast cancer close to the observed numbers of cases. The observed tumour stages on the individual level, the stage-specific numbers of cases and the observed survival curves were best met with polytomous regression or predictive mean matching but not with random forest or proportional sampling as imputation models.</p> <p>Conclusions</p> <p>This limited simulation study indicates that multiple imputation with chained equations is an appropriate technique for dealing with missing information on tumour stage in population-based cancer registries, if the amount of unstaged cases is on a reasonable level.</p

Comparison of methods for handling missing data on immunohistochemical markers in survival analysis of breast cancer

Background:Tissue micro-arrays (TMAs) are increasingly used to generate data of the molecular phenotype of tumours in clinical epidemiology studies, such as studies of disease prognosis. However, TMA data are particularly prone to missingness. A variety of methods to deal with missing data are available. However, the validity of the various approaches is dependent on the structure of the missing data and there are few empirical studies dealing with missing data from molecular pathology. The purpose of this study was to investigate the results of four commonly used approaches to handling missing data from a large, multi-centre study of the molecular pathological determinants of prognosis in breast cancer.Patients and Methods:We pooled data from over 11 000 cases of invasive breast cancer from five studies that collected information on seven prognostic indicators together with survival time data. We compared the results of a multi-variate Cox regression using four approaches to handling missing data-complete case analysis (CCA), mean substitution (MS) and multiple imputation without inclusion of the outcome (MI) and multiple imputation with inclusion of the outcome (MI). We also performed an analysis in which missing data were simulated under different assumptions and the results of the four methods were compared.Results:Over half the cases had missing data on at least one of the seven variables and 11 percent had missing data on 4 or more. The multi-variate hazard ratio estimates based on multiple imputation models were very similar to those derived after using MS, with similar standard errors. Hazard ratio estimates based on the CCA were only slightly different, but the estimates were less precise as the standard errors were large. However, in data simulated to be missing completely at random (MCAR) or missing at random (MAR), estimates for MI were least biased and most accurate, whereas estimates for CCA were most biased and least accurate.Conclusion:In this study, empirical results from analyses using CCA, MS, MI and MI were similar, although results from CCA were less precise. The results from simulations suggest that in general MI is likely to be the best. Given the ease of implementing MI in standard statistical software, the results of MI and CCA should be compared in any multi-variate analysis where missing data are a problem. © 2011 Cancer Research UK. All rights reserved

A Guide to Handling Missing Data in Cost-Effectiveness Analysis Conducted Within Randomised Controlled Trials

The authors would like to thank Professor Adrian Grant and the team at the University of Aberdeen (Professor Craig Ramsay, Janice Cruden, Charles Boachie, Professor Marion Campbell and Seonaidh Cotton) who kindly allowed the REFLUX dataset to be used for this work, and Eldon Spackman for kindly sharing the Stata (R) code for calculating the probability that an intervention is cost effective following MI. The authors are grateful to the reviewers for their comments, which greatly improved this paper. M. G. is recipient of a Medical Research Council Early Career Fellowship in Economics of Health (grant number: MR/K02177X/1). I. R. W. was supported by the Medical Research Council [Unit Programme U105260558]. No specific funding was obtained to produce this paper. The authors declare no conflicts of interest.Missing data are a frequent problem in cost-effectiveness analysis (CEA) within a randomised controlled trial. Inappropriate methods to handle missing data can lead to misleading results and ultimately can affect the decision of whether an intervention is good value for money. This article provides practical guidance on how to handle missing data in within-trial CEAs following a principled approach: (i) the analysis should be based on a plausible assumption for the missing data mechanism, i.e. whether the probability that data are missing is independent of or dependent on the observed and/or unobserved values; (ii) the method chosen for the base-case should fit with the assumed mechanism; and (iii) sensitivity analysis should be conducted to explore to what extent the results change with the assumption made. This approach is implemented in three stages, which are described in detail: (1) descriptive analysis to inform the assumption on the missing data mechanism; (2) how to choose between alternative methods given their underlying assumptions; and (3) methods for sensitivity analysis. The case study illustrates how to apply this approach in practice, including software code. The article concludes with recommendations for practice and suggestions for future research.Medical Research Council Early Career Fellowship in Economics of Health MR/K02177X/1Medical Research Council UK (MRC) U105260558Medical Research Council UK (MRC) MC_U105260558 MR/K02177X/

The impact of hyperactivity and leptin on recovery from anorexia nervosa

In anorexia nervosa (AN), hyperactivity is observed in about 80% of patients and has been associated with low leptin levels in the acute stage of AN and in anorexia animal models. To further understand the importance of this correlation in AN, we investigated the relationship between hypoleptinaemia and hyperactivity in AN patients longitudinally and assessed their predictive value for recovery

The relation between plasma tyrosine concentration and fatigue in primary biliary cirrhosis and primary sclerosing cholangitis

BACKGROUND: In primary biliary cirrhosis (PBC) and primary sclerosing cholangitis (PSC) fatigue is a major clinical problem. Abnormal amino acid (AA) patterns have been implicated in the development of fatigue in several non-hepatological conditions but for PBC and PSC no data are available. This study aimed to identify abnormalities in AA patterns and to define their relation with fatigue. METHODS: Plasma concentrations of tyrosine, tryptophan, phenylalanine, valine, leucine and isoleucine were determined in plasma of patients with PBC (n = 45), PSC (n = 27), chronic hepatitis C (n = 22) and healthy controls (n = 73). Fatigue and quality of life were quantified using the Fisk fatigue severity scale, a visual analogue scale and the SF-36. RESULTS: Valine, isoleucine, leucine were significantly decreased in PBC and PSC. Tyrosine and phenylalanine were increased (p < 0.0002) and tryptophan decreased (p < 0.0001) in PBC. In PBC, but not in PSC, a significant inverse relation between tyrosine concentrations and fatigue and quality of life was found. Patients without fatigue and with good quality of life had increased tyrosine concentrations compared to fatigued patients. Multivariate analysis indicated that this relation was independent from disease activity or severity or presence of cirrhosis. CONCLUSION: In patients with PBC and PSC, marked abnormalities in plasma AA patterns occur. Normal tyrosine concentrations, compared to increased concentrations, may be associated with fatigue and diminished quality of life

Is TEA an inhibitor for human Aquaporin-1?

Excessive water uptake through aquaporins can be life threatening, and disregulation of water permeability causes many diseases. Therefore, reversible aquaporin inhibitors are highly desired. In this paper, we identified the binding site for tetraethylammonium (TEA) of the membrane water channel aquaporin-1 by a combined molecular docking and molecular dynamics simulation approach. The binding site identified from docking studies was independently confirmed with an unbiased molecular dynamics simulation of an aquaporin tetramer embedded in a lipid membrane, surrounded by a 100-mM tetraethylammonium solution in water. A third independent assessment of the binding site was obtained by umbrella sampling simulations. These simulations, in addition, revealed a binding affinity of more than 17 kJ/mol, corresponding to an IC50 value of << 3 mM. Finally, we observed in our simulations a 50% reduction of the water flux in the presence of TEA, in agreement with water permeability measurements on aquaporin expressed in oocytes. These results confirm TEA as a putative lead for an aquaporin-1 inhibitor

The search for stable prognostic models in multiple imputed data sets

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping.</p> <p>Results</p> <p>Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model.</p> <p>Conclusion</p> <p>In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability.</p

Obesity, antenatal depression, diet and gestational weight gain in a population cohort study

Purpose: The aims of this paper are to examine: (1) the relationship between high pre-pregnancy BMI and antenatal depression; (2) whether BMI and antenatal depression interact to predict diet and gestational weight gain (GWG). Methods: Data came from the Avon Longitudinal Study of Parents and Children (ALSPAC). Underweight women were excluded. Pre-pregnancy BMI was self-reported and antenatal depression was assessed using the Edinburgh Postnatal Depression Scale at 18 and 32 weeks’ gestation to identify persistently elevated depressive symptoms (EPDS>12). Dietary patterns were calculated from food frequency questionnaires at 32 weeks’ gestation. GWG was categorised using the USA Institute of Medicine guidelines. Results: This study included 13,314 pregnant women. Obese women had significantly higher odds of antenatal depression than normal weight controls after adjusting for sociodemographics and health behaviours (aOR 1.39, 95%CI 1.05–1.84). Every unit increase in pre-pregnancy BMI was associated with approximately 3% higher odds of antenatal depression (aOR 1.03, 95%CI 1.01-1.05). Antenatal depression was not meaningfully associated with dietary patterns after adjusting for confounders and was not associated with inadequate or excessive GWG. There was no evidence for an interaction of depression and BMI on either diet or GWG. Conclusions Healthcare professionals should be aware of the dose-response relationship between high pre-pregnancy BMI and antenatal depression

Optimizing the diagnostic work-up of acute uncomplicated urinary tract infections

<p>Abstract</p> <p>Background</p> <p>Most diagnostic tests for acute uncomplicated urinary tract infections (UTIs) have been previously studied in so-called single-test evaluations. In practice, however, clinicians use more than one test in the diagnostic work-up. Since test results carry overlapping information, results from single-test studies may be confounded. The primary objective of the Amsterdam Cystitis/Urinary Tract Infection Study (ACUTIS) is to determine the (additional) diagnostic value of relevant tests from patient history and laboratory investigations, taking into account their mutual dependencies. Consequently, after suitable validation, an easy to use, multivariable diagnostic rule (clinical index) will be derived.</p> <p>Methods</p> <p>Women who contact their GP with painful and/or frequent micturition undergo a series of possibly relevant tests, consisting of patient history questions and laboratory investigations. Using urine culture as the reference standard, two multivariable models (diagnostic indices) will be generated: a model which assumes that patients attend the GP surgery and a model based on telephone contact only. Models will be made more robust using the bootstrap. Discrimination will be visualized in high resolution histograms of the posterior UTI probabilities and summarized as 5^th, 10^th, 25^th50^th, 75^th, 90^th, and 95^thcentiles of these, Brier score and the area under the receiver operating characteristics curve (ROC) with 95% confidence intervals. Using the regression coefficients of the independent diagnostic indicators, a diagnostic rule will be derived, consisting of an efficient set of tests and their diagnostic values.</p> <p>The course of the presenting complaints is studied using 7-day patient diaries. To learn more about the natural history of UTIs, patients will be offered the opportunity to postpone the use of antibiotics.</p> <p>Discussion</p> <p>We expect that our diagnostic rule will allow efficient diagnosis of UTIs, necessitating the collection of diagnostic indicators with proven added value. GPs may use the rule (preferably after suitable validation) to estimate UTI probabilities for women with different combinations of test results. Finally, in a subcohort, an attempt is made to identify which indicators (including antibiotic treatment) are useful to prognosticate recovery from painful and/or frequent micturition.</p