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Statistical techniques are needed to analyze data structures with complex dependencies such that clinically useful information can be extracted. Individual-specific networks, which capture dependencies in complex biological systems, are often summarized by graph-theoretical features. These features, which lend themselves to outcome modeling, can be subject to high variability due to arbitrary decisions in network inference and noise. Correlation-based adjacency matrices often need to be sparsified before meaningful graph-theoretical features can be extracted, requiring the data analysts to determine an optimal threshold. To address this issue, we propose to incorporate a flexible weighting function over the full range of possible thresholds to capture the variability of graph-theoretical features over the threshold domain. The potential of this approach, which extends concepts from functional data analysis to a graph-theoretical setting, is explored in a plasmode simulation study using real functional magnetic resonance imaging (fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) Preprocessed initiative. The simulations show that our modeling approach yields accurate estimates of the functional form of the weight function, improves inference efficiency, and achieves a comparable or reduced root mean square prediction error compared to competitor modeling approaches. This assertion holds true in settings where both complex functional forms underlie the outcome-generating process and a universal threshold value is employed. We demonstrate the practical utility of our approach by using resting-state fMRI data to predict biological age in children. Our study establishes the flexible modeling approach as a statistically principled, serious competitor to ad-hoc methods with superior performance., (© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Statistical models are often fitted to obtain a concise description of the association of an outcome variable with some covariates. Even if background knowledge is available to guide preselection of covariates, stepwise variable selection is commonly applied to remove irrelevant ones. This practice may introduce additional variability and selection is rarely certain. However, these issues are often ignored and model stability is not questioned. Several resampling-based measures were proposed to describe model stability, including variable inclusion frequencies (VIFs), model selection frequencies, relative conditional bias (RCB), and root mean squared difference ratio (RMSDR). The latter two were recently proposed to assess bias and variance inflation induced by variable selection. Here, we study the consistency and accuracy of resampling estimates of these measures and the optimal choice of the resampling technique. In particular, we compare subsampling and bootstrapping for assessing stability of linear, logistic, and Cox models obtained by backward elimination in a simulation study. Moreover, we exemplify the estimation and interpretation of all suggested measures in a study on cardiovascular risk. The VIF and the model selection frequency are only consistently estimated in the subsampling approach. By contrast, the bootstrap is advantageous in terms of bias and precision for estimating the RCB as well as the RMSDR. Though, unbiased estimation of the latter quantity requires independence of covariates, which is rarely encountered in practice. Our study stresses the importance of addressing model stability after variable selection and shows how to cope with it., (© 2020 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Purpose: Overactive bladder (OAB) syndrome has severe effects on quality of life. Certain drugs are known risk factors for OAB but have not been investigated in a population-wide cohort. The objective of this study was to investigate the role of prescription drugs in the etiology of the OAB., Methods: Retrospective cohort study using a population-wide database of 4 185 098 OAB-naïve women followed Strengthening the Reporting of Observational Studies in Epidemiology guidelines. We investigated the subscription use of anticholinergic medication and 188 chemical substances, which are suspected triggers for OAB (trigger medications [TMs]). We hypothesized a relationship between the prescription for one or more TM and the prescription for anticholinergic medication against OAB (marker medication [MM])., Results: The use of MM in Austria increased from 2009 to 2012 on average by 0.025 percentage points per year (95% confidence interval [CI]: 0.015-0.036). In December 2012, 1 in 123 women filled a prescription for any MM, equaling an average utilization of 0.84%. The relative risk of filling a prescription for a MM 6 months after filling a prescription for a TM was 2.70 (95% CI: 2.64-2.77). All investigated medication classes showed a higher risk for the prescription for MM. Medication from classes "genitourinary system and sex hormones" and "systemic anti-infectives" caused the highest increase in risk (109% and 89%, respectively). Prescriptions for class "cardiovascular system" caused the lowest increase in the risk (15%)., Conclusion: Certain prescription medications are a significant risk factor for the need to take anticholinergic medication as a consequence., (© 2019 The Authors. Pharmacoepidemiology & Drug Safety Published by John Wiley & Sons Ltd.)

Firth's logistic regression has become a standard approach for the analysis of binary outcomes with small samples. Whereas it reduces the bias in maximum likelihood estimates of coefficients, bias towards one-half is introduced in the predicted probabilities. The stronger the imbalance of the outcome, the more severe is the bias in the predicted probabilities. We propose two simple modifications of Firth's logistic regression resulting in unbiased predicted probabilities. The first corrects the predicted probabilities by a post hoc adjustment of the intercept. The other is based on an alternative formulation of Firth's penalization as an iterative data augmentation procedure. Our suggested modification consists in introducing an indicator variable that distinguishes between original and pseudo-observations in the augmented data. In a comprehensive simulation study, these approaches are compared with other attempts to improve predictions based on Firth's penalization and to other published penalization strategies intended for routine use. For instance, we consider a recently suggested compromise between maximum likelihood and Firth's logistic regression. Simulation results are scrutinized with regard to prediction and effect estimation. We find that both our suggested methods do not only give unbiased predicted probabilities but also improve the accuracy conditional on explanatory variables compared with Firth's penalization. While one method results in effect estimates identical to those of Firth's penalization, the other introduces some bias, but this is compensated by a decrease in the mean squared error. Finally, all methods considered are illustrated and compared for a study on arterial closure devices in minimally invasive cardiac surgery. Copyright © 2017 John Wiley & Sons, Ltd., (Copyright © 2017 John Wiley & Sons, Ltd.)

Second generation antipsychotics (SGA) are associated with adverse cardiometabolic side effects contributing to premature mortality in patients. While mechanisms mediating these cardiometabolic side effects remain poorly understood, three independent studies recently demonstrated that melatonin was protective against cardiometabolic risk in SGA-treated patients. As one of the main target areas of circulating melatonin in the brain is the suprachiasmatic nucleus (SCN), we hypothesized that the SCN is involved in SGA-induced early cardiovascular effects in Wistar rats. We evaluated the acute effects of olanzapine and melatonin in the biological clock, paraventricular nucleus and autonomic nervous system using immunohistochemistry, invasive cardiovascular measurements, and Western blot. Olanzapine induced c-Fos immunoreactivity in the SCN followed by the paraventricular nucleus and dorsal motor nucleus of the vagus indicating a potent induction of parasympathetic tone. The involvement of a SCN-parasympathetic neuronal pathway after olanzapine administration was further documented using cholera toxin-B retrograde tracing and vasoactive intestinal peptide immunohistochemistry. Olanzapine-induced decrease in blood pressure and heart rate confirmed this. Melatonin abolished olanzapine-induced SCN c-Fos immunoreactivity, including the parasympathetic pathway and cardiovascular effects while brain areas associated with olanzapine beneficial effects including the striatum, ventral tegmental area, and nucleus accumbens remained activated. In the SCN, olanzapine phosphorylated the GSK-3β, a regulator of clock activity, which melatonin prevented. Bilateral lesions of the SCN prevented the effects of olanzapine on parasympathetic activity. Collectively, results demonstrate the SCN as a key region mediating the early effects of olanzapine on cardiovascular function and show melatonin has opposing and potentially protective effects warranting additional investigation., (© 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Purpose: Double medication is defined as the unintended overlapping prescription of two identical substances with the same route of administration by two different prescribers to the same patient. Consequences of double medication are reduced patient safety and excess healthcare costs. Based on nationwide prescription data from 2011 covering 97% of Austria's population, we estimated double medication prevalences for treatment of hypertension, hyperlipidemia, and diabetes mellitus., Methods: We investigated prescriptions of 88 antihypertensive, 16 lipid-lowering and 29 hypoglycemic substances in 7,971,323 persons in 2011. Prevalence of double medication was calculated patientwise (prevalence by patients) and timewise (prevalence by patient-years). Risk factors for double medication were identified by logistic regression., Results: For antihypertensive, lipid-lowering, and hypoglycemic subtances, overall 15.0% (men: 15.1%, women: 15.0%), 13.1% (13.7%, 12.5%), and 13.0% (13.0%, 13.4%) of patients were doubly medicated, respectively. Corresponding prevalences by patient-years were 1.6%, 2.0%, and 1.2%. Logistic regression confirmed lower age and copayment waiver as independent risk factors of double medication. Furthermore, double medication occurred more often with prescriptions from hospitals or internal medicine specialists compared with general practitioners, as well as in August compared with earlier or later in the calendar year., Conclusion: While appropriate care or comanagement of patients by internal medicine specialists and general practitioners may explain some of the double prescriptions, our data indicate that unintended double medication is frequent. In Austria, lack of financial incentives of patients to avoid filling duplicate prescriptions explains a considerable fraction of double medication occurrences., (Copyright © 2015 John Wiley & Sons, Ltd.)

In the logistic regression analysis of a small-sized, case-control study on Alzheimer's disease, some of the risk factors exhibited missing values, motivating the use of multiple imputation. Usually, Rubin's rules (RR) for combining point estimates and variances would then be used to estimate (symmetric) confidence intervals (CIs), on the assumption that the regression coefficients were distributed normally. Yet, rarely is this assumption tested, with or without transformation. In analyses of small, sparse, or nearly separated data sets, such symmetric CI may not be reliable. Thus, RR alternatives have been considered, for example, Bayesian sampling methods, but not yet those that combine profile likelihoods, particularly penalized profile likelihoods, which can remove first order biases and guarantee convergence of parameter estimation. To fill the gap, we consider the combination of penalized likelihood profiles (CLIP) by expressing them as posterior cumulative distribution functions (CDFs) obtained via a chi-squared approximation to the penalized likelihood ratio statistic. CDFs from multiple imputations can then easily be averaged into a combined CDF c , allowing confidence limits for a parameter β  at level 1 - α to be identified as those β* and β** that satisfy CDF c (β*) = α ∕ 2 and CDF c (β**) = 1 - α ∕ 2. We demonstrate that the CLIP method outperforms RR in analyzing both simulated data and data from our motivating example. CLIP can also be useful as a confirmatory tool, should it show that the simpler RR are adequate for extended analysis. We also compare the performance of CLIP to Bayesian sampling methods using Markov chain Monte Carlo. CLIP is available in the R package logistf., (Copyright © 2013 John Wiley & Sons, Ltd.)

The analysis of correlations within pairs of survival times is of interest to many research topics in medicine, such as the correlation of survival-type endpoints of twins, the correlation of times till failure in paired organs, or the correlation of survival time with a surrogate endpoint. The dependence of such times is assumed monotonic and thus quantification by rank correlation coefficients appropriate. The typical censoring of such times requires more involved methods of estimation and inference as have been developed in recent years. The paper focuses on semiparametric approaches, and in particular on the normal copula-based estimation of Spearman correlation coefficients. The copula approach, often presented for a mathematically inclined readership, is reviewed from the viewpoint of an applied statistician. As an alternative to the maximum likelihood methodology for the normal copula approach (NCE) we introduce an iterative multiple imputation (IMI) method which requires only about 0.05% of the computing time of NCE, without sacrificing statistical performance. For IMI, survival probabilities at death or censoring times are first transformed to normal deviates. Then, those deviates that relate to censored times are iteratively augmented, by using conditional multiple imputation, until convergence is obtained for the normal scores rank correlation, which is similar to Spearman's rank correlation. Statistical properties of NCE and IMI are compared by means of a Monte Carlo study and by means of three real data sets, which also give an impression of the typical range of applications, and of their problems., (Copyright © 2013 John Wiley & Sons, Ltd.)

Aims: To compare the predictive value of estimated renal function calculated by the Chronic Kidney Disease Epidemiology Collaboration (eGFR(CKD-EPI)), four-variable Modification of Diet in Renal Disease (eGFR(MDRD-4)), and Cockcroft-Gault [estimated creatinine clearance (eCcr)] equation in terms of all-cause mortality in heart failure. Renal function is an important prognostic factor in heart failure. Established methods of estimating renal function are known to under-/overestimate true function in certain settings., Methods and Results: A total of 800 systolic heart failure outpatients (mean age 57 ± 11.5 years, 82% male) were studied over a median follow-up of 121 (Q1-Q3: 110-130) months. The highest systematic difference was seen between eCcr and eGFR(MDRD-4) [+12.33 points (mean), 95% limits of agreement -22.35 to 47.01; generalized kappa = 0.36]. eGFR(MDRD-4) and eGFR(CKD-EPI) were the most similar [-4.16 points (mean), 95% limits of agreement -11.56 to 3.25; generalized kappa = 0.74]. Up to 35.4% of patients were reclassified into different estimated glomerular filtration rate (eGFR) categories when comparing eGFR(CKD-EPI) with eCcr and eGFR(MDRD-4). eGFR(CKD-EPI) performed marginally better in terms of predicting all-cause mortality than eGFR(MDRD-4), as univariate areas under the time-dependent receiver operating characteristic curves (AUC), marginal and partial proportions of explained variation (PEV), net reclassification improvement (NRI), and the integrated discrimination improvement (IDI) for 5 years of follow-up were significantly higher for eGFR(CKD-EPI) than for eGFR(MDRD-4)., Conclusion: In this cohort of heart failure patients, eGFR(CKD-EPI) was marginally better in predicting all-cause mortality than eGFR(MDRD-4). Estimated function differed widely between equations and is likely to have an effect on therapy choice.

Background: TRAIL (tumor necrosis factor related apoptosis-inducing ligand) is involved in tumor immune surveillance and, thus, may be a potential cancer therapy. TRAIL expression in the tumor microenvironment has been shown to impact cancer survival in multiple tumor types, including ovarian cancer. We studied TRAIL expression and outcomes in patients with prostate cancer., Methods: A tissue microarray (TMA) of 200 prostate cancer patients and benign prostate tissue controls was used to assess the epithelial and stromal protein expression of TRAIL, death receptors (DR4 and DR5), decoy receptors (DcR1 and DcR2), and the FLICE inhibitory protein (FLIP(L) ). We correlated these expression patterns with clinicopathological parameters and determined its impact on recurrence-free survival., Results: Nearly all (99.5%) prostate cancer tissues examined displayed either decreased expression of pro-apoptotic TRAIL receptors, increased FLIP(L) expression, or both. We observed elevated death receptor, decoy receptor, FLIP(L) , and epithelial TRAIL expression in prostate cancer epithelium. TRAIL expression in the stromal tumor microenvironment surrounding the prostate cancer was markedly lower. Elevated TRAIL expression in the tumor microenvironment was also significantly associated with increased recurrence-free survival (P = .014), after controlling for other prognostic markers. In contrast, epithelial expression of TRAIL did not have an effect on overall survival., Conclusions: Expression of the components of the pro-apoptotic TRAIL pathway is altered in prostate cancer. Moreover, TRAIL expression in the tumor microenvironment may affect recurrence-free survival rate of prostate cancer patients. Consequently, these results may be useful in devising future therapeutic strategies targeting the TRAIL pathway in prostate cancer., (Copyright © 2010 American Cancer Society.)

Often, interesting candidate tumor markers are not only genes that show homogeneously higher expression (HHE) in tumor samples compared to control samples, but also genes with only predominantly higher expression (PHE), i.e. genes which exhibit higher expression in at least 80 per cent of tumor samples. Standard parametric test statistics used in the analysis of microarray experiments may fail with PHE as a consequence of the mixture of distributions present in the tumor group. As alternative we consider trimmed t-statistics which compare group mean values after removing outliers in each group. The trimming proportion can be chosen adaptively, either based on a boxplot outlier detection rule or by optimization over a series of tests with varying trimming proportions. The trimmed t-statistics can be plugged into the 'significance analysis of microarrays' (SAM) procedure, yielding the modified boxplot rule test (modBox) and the modified optimization test (modOpt), respectively. By means of simulation of microarray experiments, we show that modOpt is superior to contenders in detecting PHE, while there is only little loss in efficiency under HHE compared to SAM. Analysis of a real microarray experiment revealed that, out of nearly 29 000 genes, about 417 genes exhibiting PHE are detected by modOpt but missed by SAM., (Copyright © 2010 John Wiley & Sons, Ltd.)

Many exposures investigated in epidemiological case-control studies may vary over time. The effects of these exposures are usually estimated using logistic regression, which does not directly account for changes in covariate values over time within individuals. By contrast, the Cox model with time-dependent covariates directly accounts for these changes over time. However, the over-sampling of cases in case-control studies, relative to controls, requires manipulating the risk sets in the Cox partial likelihood. A previous study showed that simple inclusion or exclusion of future cases in each risk set induces an under- or over-estimation bias in the regression parameters, respectively. We investigate the performance of a weighted Cox model that weights subjects according to age-conditional probabilities of developing the disease of interest in the source population. In a simulation study, the lifetime experience of a source population is first generated and a case-control study is then simulated within each population. Different characteristics of exposure are generated, including time-varying intensity. The results show that the estimates from the weighted Cox model are much less biased than the Cox models that simply include or exclude future cases, and are superior to logistic regression estimates in terms of bias and mean-squared error. An application to frequency-matched population-based case-control data on lung cancer illustrates similar differences in the estimated effects of different smoking variables. The investigated weighted Cox model is a potential alternative method to analyse matched or unmatched population-based case-control studies with time-dependent exposures.

Conditional logistic regression is used for the analysis of binary outcomes when subjects are stratified into several subsets, e.g. matched pairs or blocks. Log odds ratio estimates are usually found by maximizing the conditional likelihood. This approach eliminates all strata-specific parameters by conditioning on the number of events within each stratum. However, in the analyses of both an animal experiment and a lung cancer case-control study, conditional maximum likelihood (CML) resulted in infinite odds ratio estimates and monotone likelihood. Estimation can be improved by using Cytel Inc.'s well-known LogXact software, which provides a median unbiased estimate and exact or mid-p confidence intervals. Here, we suggest and outline point and interval estimation based on maximization of a penalized conditional likelihood in the spirit of Firth's (Biometrika 1993; 80:27-38) bias correction method (CFL). We present comparative analyses of both studies, demonstrating some advantages of CFL over competitors. We report on a small-sample simulation study where CFL log odds ratio estimates were almost unbiased, whereas LogXact estimates showed some bias and CML estimates exhibited serious bias. Confidence intervals and tests based on the penalized conditional likelihood had close-to-nominal coverage rates and yielded highest power among all methods compared, respectively. Therefore, we propose CFL as an attractive solution to the stratified analysis of binary data, irrespective of the occurrence of monotone likelihood. A SAS program implementing CFL is available at: http://www.muw.ac.at/msi/biometrie/programs.

Often the effect of at least one of the prognostic factors in a Cox regression model changes over time, which violates the proportional hazards assumption of this model. As a consequence, the average hazard ratio for such a prognostic factor is under- or overestimated. While there are several methods to appropriately cope with non-proportional hazards, in particular by including parameters for time-dependent effects, weighted estimation in Cox regression is a parsimonious alternative without additional parameters. The methodology, which extends the weighted k-sample logrank tests of the Tarone-Ware scheme to models with multiple, binary and continuous covariates, has been introduced in the nineties of the last century and is further developed and re-evaluated in this contribution. The notion of an average hazard ratio is defined and its connection to the effect size measure P(X

Serum concentrations of the amino-terminal pro-B-type natriuretic peptide (NT-proBNP) may be used to monitor cardiac function during pregnancy but normal values are not established for this purpose. Therefore, we investigated NT-proBNP in normotensive healthy pregnancies compared to a non-pregnant control group. Serum NT-proBNP was measured in 94 normotensive, healthy pregnant women (32+/-6 years) every five weeks beginning from 12th gestational week (GW) in a longitudinal study and compared to a non-pregnant control group of 521 women (32+/-7 years). Pooled median serum NT-proBNP levels (25th; 75th percentile) were significantly higher in pregnant women compared to non-pregnant women (56 (33; 95) pg/ml vs. 38 (22; 62) pg/ml (p<0.001)). NT-proBNP increased during pregnancy to 73 (51; 124) pg/ml in the 11+6 to 15+6 GW (p<0.001). However, NT-proBNP levels from 23+0 GW towards term were comparable to non-pregnant controls. NT-proBNP is significantly elevated in healthy pregnancies until mid-pregnancy. As preeclampsia and gestational hypertension are associated with increased NT-proBNP, our results have to be considered in future diagnostic approaches using NT-proBNP for these pathologic conditions.

We address the phenomenon of monotone likelihood in Cox regression with time-dependent effects. Monotone likelihood occurs in the fitting process of a Cox model if at least one parameter estimate diverges to +/- infinity. We show that the probability of monotone likelihood is increased by the inclusion of time-dependent effects, particularly in small samples with several unbalanced and highly predictive covariates, and with a high percentage of censoring. Firth's bias reduction procedure was shown to provide an ideal solution to monotone likelihood. Here we extend his idea to Cox regression with time-dependent effects. By penalized maximum likelihood estimation, finite hazard ratio estimates of constant and time-dependent effects can be obtained. Penalized likelihood ratio tests and profile penalized likelihood confidence intervals are proposed as tools for inference. A Monte Carlo study of Cox regression with time-dependent effects confirms advantages of Firth-corrected (FC) over standard Cox analysis in terms of average bias and median absolute deviation. We also compare the FC and standard Cox approaches by means of analyses of two studies with time-dependent effects. An SAS macro and an R package for FC Cox regression with time-varying covariates and time-dependent effects are available at: http://www.muw.ac.at/msi/biometrie/programs., (Copyright 2008 John Wiley & Sons, Ltd.)

Metal-on-metal articulations were reintroduced to reduce polyethylene particle-induced osteolysis. Elevated serum metal levels have been detected at short- and intermediate-term follow-up. There is little knowledge about long-term effects of increased ionic metal generation. Our study was undertaken to provide information about long-term survival, clinical outcome, radiographic appearance, and serum metal concentrations after a minimum 10 years follow-up with a metal-on-metal hip prosthesis: 105 cementless primary total hip prostheses with metal-on-metal articulating surfaces were implanted in 98 patients with a mean age of 56 years. Clinical data, radiographs, and blood samples were obtained at a follow-up visit 10 years after implantation. Serum cobalt and chromium levels were determined with use of atomic absorption spectrometry. General laboratory analyses included a full blood count and kidney function parameters. The Harris score was 92 points and the UCLA score 6 points after 10 years. Small osteolytic lesions and radiolucent lines were found in Gruen's zones 1, 7, 8, and 14. The probability of survival was 98.6%. The median serum cobalt concentration of the 22 patients with their hip replacement as the only source of cobalt was 0.75 microg/L (range, 0.3-50.1 microg/L). No patient was diagnosed with renal insufficiency during the study period. Five patients were diagnosed with a malignancy between surgery and the follow-up. We do not have evidence of an increased rate of primary malignancies nor could we detect renal failure in our study group. Serum metal levels did not differ from short- and intermediate term follow-up values., (Copyright (c) 2007 Orthopaedic Research Society.)

In logistic regression analysis of small or sparse data sets, results obtained by classical maximum likelihood methods cannot be generally trusted. In such analyses it may even happen that the likelihood meets the convergence criteria while at least one parameter estimate diverges to +/-infinity. This situation has been termed 'separation', and it typically occurs whenever no events are observed in one of the two groups defined by a dichotomous covariate. More generally, separation is caused by a linear combination of continuous or dichotomous covariates that perfectly separates events from non-events. Separation implies infinite or zero maximum likelihood estimates of odds ratios, which are usually considered unrealistic. I provide some examples of separation and near-separation in clinical data sets and discuss some options to analyse such data, including exact logistic regression analysis and a penalized likelihood approach. Both methods supply finite point estimates in case of separation. Profile penalized likelihood confidence intervals for parameters show excellent behaviour in terms of coverage probability and provide higher power than exact confidence intervals. General advantages of the penalized likelihood approach are discussed., (Copyright 2006 John Wiley & Sons, Ltd.)

Background: Genes encoding enzymes involved in estrogen metabolism are held to be candidate genes for associations with breast disease. In these candidate genes, no critical combination of single-nucleotide polymorphisms (SNPs) for assessing breast carcinoma risk has been reported to date., Methods: In a large case-control study, the authors investigated 10 estrogen-metabolizing SNPs in 396 patients with breast carcinoma, 154 patients with fibroadenoma, and 1936 healthy control patients without breast carcinoma in their personal history. The following 10 SNPs were analyzed using sequencing-on-chip technology via a solid-phase polymerase chain reaction assay performed on oligonucleotide microarrays: catechol-O-methyltransferase Val158Met G-->A, 17-beta-hydroxysteroid dehydrogenase type 1 vIV A-->C, cytochrome P-450 (CYP) family 17 A2 allele T-->C, CYP1A1-1 MspI restriction fragment length polymorphism (RFLP) T-->C, CYP1A1-2 Ile462Val A-->G, CYP19-1 Trp39Arg T-->C, CYP19-2 Arg264Cys C-->T, CYP19-3 Cys1558Thr C-->T, steroid-5-alpha reductase type 2 Val89Leu G-->C, and vitamin D receptor BsmI RFLP. A total of 21,350 genotypes were evaluated. Associations and two-way interaction models were calculated using stepwise logistic regression., Results: In a multiple model, CYP1A1-1 (P = 0.004) and CYP1A1-2 (P = 0.03) were found to be associated with significantly decreased and increased risks of breast carcinoma, respectively. When two-way interactions involving investigated SNPs were ascertained, no significant interactions among polymorphisms were noted. Comparison of patients with fibroadenoma with control patients revealed significantly increased and decreased risks of fibroadenoma when the mutant alleles of CYP17 (P = 0.02) and CYP1A1-1 (P = 0.04), respectively, were present., Conclusions: The authors obtained the first SNP data indicating that CYP17 and CYP1A1-1 play a role in the pathogenesis of fibroadenoma. Although the authors were not able to develop interaction models involving SNPs, they did provide evidence that CYP1A1 is a low-penetrance susceptibility gene with respect to breast carcinoma in a large series of Caucasian women., (Copyright 2004 American Cancer Society.)

The phenomenon of separation or monotone likelihood is observed in the fitting process of a logistic model if the likelihood converges while at least one parameter estimate diverges to +/- infinity. Separation primarily occurs in small samples with several unbalanced and highly predictive risk factors. A procedure by Firth originally developed to reduce the bias of maximum likelihood estimates is shown to provide an ideal solution to separation. It produces finite parameter estimates by means of penalized maximum likelihood estimation. Corresponding Wald tests and confidence intervals are available but it is shown that penalized likelihood ratio tests and profile penalized likelihood confidence intervals are often preferable. The clear advantage of the procedure over previous options of analysis is impressively demonstrated by the statistical analysis of two cancer studies., (Copyright 2002 John Wiley & Sons, Ltd.)

Background: Adhesion molecules have been demonstrated to be involved in placental growth and development in normal pregnancy. Experimental evidence indicates that adhesion molecules are key factors of endothelial activation in preeclampsia. The aim of our study was to evaluate serum levels of the adhesion molecule Leukocyte Functional Antigen (LFA)-3 in healthy, non pregnant, female controls, healthy pregnant women, and preeclamptic women., Methods: In our study we included 45 healthy, non pregnant, female controls, 45 healthy pregnant women, and 45 preeclamptic women. An enzyme-linked immunosorbent assay was used to determine serum levels of LFA-3. Results were correlated to clinical data., Results: The median LFA-3 serum level in healthy, non pregnant, female controls was 0.2 (range 0 to 8.6) ng/mL. LFA-3 serum levels in healthy pregnant women were 4.8 (range 0 to 18) ng/mL and were significantly elevated compared to healthy, non pregnant, female controls (Mann-Whitney U-test, p=0.004). A cut-off level of 4.8 ng/mL was selected according to the 75th quantile of serum levels measured in the panel of healthy, non pregnant, female controls. In preeclamptic women, whose pregnancies had to be terminated due to exacerbation of preeclamptic symptoms, LFA-3 serum levels above the cut-off level were seen in 14/27 (52%) cases. In contrast, preeclamptic women, who went into spontaneous labor showed elevated LFA-3 serum levels in 17/18 (95%) cases (chi-square test, p=0.002). LFA-3 serum levels revealed a statistically significant influence on the odds of termination of pregnancy due to exacerbation of preeclamptic symptoms (unconditional logistic regression, p=0.02) with an odds ratio of 0.1 (95% CI, 0.006 to 0.7) by every doubling of LFA-3 values., Conclusions: Our results suggest that LFA-3 expression is upregulated in healthy pregnant women compared to healthy, non pregnant, female controls. Failure of LFA-3 upregulation in preeclampsia is associated with an increased risk for termination of pregnancy due to exacerbation of preeclamptic symptoms.

The analysis of prognostic factor studies by Cox or logistic regression models is often impeded by missing covariate values. In 1990 Schemper and Smith recommended a conditional probability imputation technique (PIT) for the analysis of treatment studies which can be easily applied using standard software and which has been demonstrated to outperform the complete case and omission of covariates strategies. Recent research, however, showed that PIT cannot universally be recommended and it was concluded that model-based methods should be preferred. We agree with these conclusions but also think that there is enough empirical evidence to judge the performance of PIT to be satisfactory in typical prognostic factor studies. Furthermore, comparisons of PIT with multiple imputation in the same context did not indicate an advantage of the latter more involved technique. By means of an analysis of a prostate cancer data set various aspects of application of PIT are discussed, in particular that PIT permits direct comparability of marginal and partial effects analyses. We conclude that PIT continues to be an appropriate and attractive choice for analyses of prognostic factor studies.