Optimal crossover designs for logistic regression models in pharmacodynamics

Pharmacodynamics (PD) is the study of the biochemical and physiological effects of drugs. The construction of optimal designs for dose-ranging trials with multiple periods is considered in this paper, where the outcome of the trial (the effect of the drug) is considered to be a binary response: the success or failure of a drug to bring about a particular change in the subject after a given amount of time. The carryover effect of each dose from one period to the next is assumed to be proportional to the direct effect. It is shown for a logistic regression model that the efficiency of optimal parallel (single-period) or crossover (two-period) design is substantially greater than a balanced design. The optimal designs are also shown to be robust to misspecification of the value of the parameters. Finally, the parallel and crossover designs are combined to provide the experimenter with greater flexibility.

Likelihood-based estimation of the regression model with scrambled responses

A significant problem in the collection of responses to potentially sensitive questions, such as relating to illegal, immoral or embarrassing activities, is non-sampling error due to refusal to respond or false responses. Eichhorn & Hayre (1983) suggested the use of scrambled responses to reduce this form of bias. This paper considers a linear regression model in which the dependent variable is unobserved but for which the sum or product with a scrambling random variable of known distribution, is known. The performance of two likelihood-based estimators is investigated, namely of a Bayesian estimator achieved through a Markov chain Monte Carlo (MCMC) sampling scheme, and a classical maximum-likelihood estimator. These two estimators and an estimator suggested by Singh, Joarder & King (1996) are compared. Monte Carlo results show that the Bayesian estimator outperforms the classical estimators in almost all cases, and the relative performance of the Bayesian estimator improves as the responses become more scrambled.

Finite mixture regression model with random effects: application to neonatal hospital length of stay

A two-component mixture regression model that allows simultaneously for heterogeneity and dependency among observations is proposed. By specifying random effects explicitly in the linear predictor of the mixture probability and the mixture components, parameter estimation is achieved by maximising the corresponding best linear unbiased prediction type log-likelihood. Approximate residual maximum likelihood estimates are obtained via an EM algorithm in the manner of generalised linear mixed model (GLMM). The method can be extended to a g-component mixture regression model with the component density from the exponential family, leading to the development of the class of finite mixture GLMM. For illustration, the method is applied to analyse neonatal length of stay (LOS). It is shown that identification of pertinent factors that influence hospital LOS can provide important information for health care planning and resource allocation. (C) 2002 Elsevier Science B.V. All rights reserved.

A review of two journals found that articles using multivariable logistic regression frequently did not report commonly recommended assumptions

Background and Objective: To examine if commonly recommended assumptions for multivariable logistic regression are addressed in two major epidemiological journals. Methods: Ninety-nine articles from the Journal of Clinical Epidemiology and the American Journal of Epidemiology were surveyed for 10 criteria: six dealing with computation and four with reporting multivariable logistic regression results. Results: Three of the 10 criteria were addressed in 50% or more of the articles. Statistical significance testing or confidence intervals were reported in all articles. Methods for selecting independent variables were described in 82%, and specific procedures used to generate the models were discussed in 65%. Fewer than 50% of the articles indicated if interactions were tested or met the recommended events per independent variable ratio of 10: 1. Fewer than 20% of the articles described conformity to a linear gradient, examined collinearity, reported information on validation procedures, goodness-of-fit, discrimination statistics, or provided complete information on variable coding. There was no significant difference (P >.05) in the proportion of articles meeting the criteria across the two journals. Conclusion: Articles reviewed frequently did not report commonly recommended assumptions for using multivariable logistic regression. (C) 2004 Elsevier Inc. All rights reserved.

Who fails to complete tuberculosis treatment? Temporal trends and risk factors for treatment interruption in a community-based directly observed therapy programme in a rural district of South Africa

SETTING: Hlabisa Tuberculosis Programme, Hlabisa, South Africa. OBJECTIVE: To determine trends in and risk factors for interruption of tuberculosis treatment. METHODS: Data were extracted from the control programme database starting in 1991. Temporal trends in treatment interruption are described; independent risk factors for treatment interruption were determined with a multiple logistic regression model, and Kaplan-Meier survival curves for treatment interruption were constructed for patients treated in 1994-1995. RESULTS: Overall 629 of 3610 surviving patients (17%) failed to complete treatment; this proportion increased from 11% (n = 79) in 1991/1992 to 22% (n = 201) in 1996. Independent risk factors for treatment interruption were diagnosis between 1994-1996 compared with 1991-1393 (odds ratio [OR] 1.9, 95% confidence interval [CT] 1.6-2.4); human immunodeficiency virus (HIV) positivity compared with HIV negativity (OR 1.8, 95% CI 1.4-2.4); supervised by village clinic compared with community health worker (OR 1.9, 95% CI 1.4-2.6); and male versus female sex (OR 1.3, 95% CI 1.1-1.6). Few patients interrupted treatment during the first 2 weeks, and the treatment interruption rate thereafter was constant at 1% per 14 days. CONCLUSIONS: Frequency of treatment interruption from this programme has increased recently. The strongest risk factor was year of diagnosis, perhaps reflecting the impact of an increased caseload on programme performance. Ensuring adherence to therapy in communities with a high level of migration remains a challenge even within community-based directly observed therapy programmes.

Geographical variation in the penetrance of CDKN2A mutations for melanoma

Background: Germline mutations in the CDKN2A gene, which encodes two proteins (p16INK4A and p14ARF), are the most common cause of inherited susceptibility to melanoma. We examined the penetrance of such mutations using data from eight groups from Europe, Australia and the United States that are part of The Melanoma Genetics Consortium Methods: We analyzed 80 families with documented CDKN2A mutations and multiple cases of cutaneous melanoma. We modeled penetrance for melanoma using a logistic regression model incorporating survival analysis. Hypothesis testing was based on likelihood ratio tests. Covariates included gender, alterations in p14APF protein, and population melanoma incidence rates. All statistical tests were two-sided. Results: The 80 analyzed families contained 402 melanoma patients, 320 of whom were tested for mutations and 291 were mutation carriers. We also tested 713 unaffected family members for mutations and 194 were carriers. Overall, CDKN2A mutation penetrance was estimated to be 0.30 (95% confidence interval (CI) = 0.12 to 0.62) by age 50 years and 0.67 (95% CI = 0.31 to 0.96) by age 80 years. Penetrance was not statistically significantly modified by gender or by whether the CDKN2A mutation altered p14ARF protein. However, there was a statistically significant effect of residing in a location with a high population incidence rate of melanoma (P = .003). By age 50 years CDKN2A mutation penetrance reached 0.13 in Europe, 0.50 in the United States, and 0.32 in Australia; by age 80 years it was 0.58 in Europe, 0.76 in the United States, and 0.91 in Australia. Conclusions: This study, which gives the most informed estimates of CDKN2A mutation penetrance available, indicates that the penetrance varies with melanoma population incidence rates. Thus, the same factors that affect population incidence of melanoma may also mediate CDKN2A penetrance.

Use of multiple biomarkers for a molecular diagnosis of prostate cancer

The identification of biomarkers capable of providing a reliable molecular diagnostic test for prostate cancer (PCa) is highly desirabie clinically. We describe here 4 biomarkers, UDP-N-Acetyl-alpha-D-galactosamine transferase (GalNAc-T3; not previously associated with PCa), PSMA, Hepsin and DD3/PCA3, which, in combination, distinguish prostate cancer from benign prostate hyperplasia (BPH). GalNAc-T3 was identified as overexpressed in PCa tissues by microarray analysis, confirmed by quantitative real-time PCR and shown immunohistochemically to be localised to prostate epithelial cells with higher expression in malignant cells. Real-time quantitative PCR analysis across 21 PCa and 34 BPH tissues showed 4.6-fold overexpression of GalNAc-T3 (p = 0.005). The noncoding mRNA (DD3/PCA3) was overexpressed 140-fold (p = 0.007) in the cancer samples compared to BPH tissues. Hepsin was overexpressed 21-fold (p = 0.049, whereas the overexpression for PSMA was 66-fold (p = 0.047). When the gene expression data for these 4 biomarkers was combined in a logistic regression model, a predictive index was obtained that distinguished 100% of the PCa samples from all of the BPH samples. Therefore, combining these genes in a real-time PCR assay represents a powerful new approach to diagnosing PCa by molecular profiling. (c) 2005 Wiley-Liss, Inc.

Factors predictive of intravenous fluid administration errors in Australian surgical care wards

Background: Intravenous (IV) fluid administration is an integral component of clinical care. Errors in administration can cause detrimental patient outcomes and increase healthcare costs, although little is known about medication administration errors associated with continuous IV infusions. Objectives: ( 1) To ascertain the prevalence of medication administration errors for continuous IV infusions and identify the variables that caused them. ( 2) To quantify the probability of errors by fitting a logistic regression model to the data. Methods: A prospective study was conducted on three surgical wards at a teaching hospital in Australia. All study participants received continuous infusions of IV fluids. Parenteral nutrition and non-electrolyte containing intermittent drug infusions ( such as antibiotics) were excluded. Medication administration errors and contributing variables were documented using a direct observational approach. Results: Six hundred and eighty seven observations were made, with 124 (18.0%) having at least one medication administration error. The most common error observed was wrong administration rate. The median deviation from the prescribed rate was 247 ml/h (interquartile range 275 to + 33.8 ml/ h). Errors were more likely to occur if an IV infusion control device was not used and as the duration of the infusion increased. Conclusions: Administration errors involving continuous IV infusions occur frequently. They could be reduced by more common use of IV infusion control devices and regular checking of administration rates.

Pre-term delivery and periodontal disease: a case-control study from Croatia

Aim: The aim of this report was to assess the strength and influence of periodontitis as a possible risk factor for pre-term birth (PTB) in a cohort of 81 primiparous Croatian mothers aged 18-39 years. Methods: PTB cases (n=17; mean age 25 +/- 2.9 years; age range 20-33 years) were defined as spontaneous delivery after less than 37 completed weeks of gestation that were followed by spontaneous labour or spontaneous rupture of membranes. Controls (full-time births) were normal births at or after 37 weeks of gestation (n=64; mean age 25 +/- 2.9 years; age range 19-39 years). Information on known risk factors and obstetric factors included the current pregnancy history, maternal age at delivery, pre-natal care, nutritional status, tobacco use, alcohol use, genitourinary infections, vaginosis, gestational age, and birth weight. Full-mouth periodontal examination was performed on all mothers within 2 days of delivery. Results: PTB cases had significantly worse periodontal status than controls (p=0.008). Multivariate logistic regression model, after controlling for other risk factors, demonstrated that periodontal disease is a significant independent risk factor for PTB, with an adjusted odds ratio of 8.13 for the PTB group (95% confidence interval 2.73-45.9). Conclusion: Periodontal disease represents a strong, independent, and clinically significant risk factor for PTB in the studied cohort. There are strong indicators that periodontal therapy should form a part of preventive prenatal care in Croatia.

The role of social capital in the promotion of conservation farming: The case of 'landcare' in the southern Philippines

'Social capital' refers to the relationships of trust, communication, and cooperation that facilitate collective action in a community. It is particularly relevant to soil conservation in developing countries, which requires collective efforts to raise awareness of soil degradation, provide effective training in soil conservation practices, and implement soil conservation measures on individual farms. The Landcare Program in the Southern Philippines promotes simple conservation practices in upland environments through establishing and supporting community landcare groups and municipal landcare associations, thus augmenting the social capital of farmers in these locations. An evaluation of the Landcare Program in Barangay Ned, South Cotabato, based on a survey of 313 farm households and case studies of nine landcare groups, shows that, despite extreme isolation and difficult working conditions, farmers responded by rapidly forming landcare groups and a landcare association, and adopting contour barriers on their maize farms. They utilized the bonding social capital inhering in their local communities to build stocks of bridging social capital, linking them to information, training and resources from outside their immediate locality. A logistic regression model of the factors affecting adoption of contour barriers shows that farmers who had undergone the practical, farmer-based training provided by the Landcare Program, and who were members of a landcare group, were significantly more likely to adopt conservation measures. These results confirm the value of investing in social capital to promote soil conservation. Copyright (c) 2005 John Wiley & Sons, Ltd.

The role of uncertainty in the relationship between fairness evaluations and willingness to pay

Participants in contingent valuation studies may be uncertain about a number of aspects of the policy and survey context. The uncertainty management model of fairness judgments states that individuals will evaluate a policy in terms of its fairness when they do not know whether they can trust the relevant managing authority or experience uncertainty due to insufficient knowledge of the general issues surrounding the environmental policy. Similarly, some researchers have suggested that, not knowing how to answer WTP questions, participants convey their general attitudes toward the public good rather than report well-defined economic preferences. These contentions were investigated in a sample of 840 residents in four urban catchments across Australia who were interviewed about their WTP for stormwater pollution abatement. Four sources of uncertainty were measured: amount of prior issue-related thought, trustworthiness of the water authority, insufficient scenario information, and WTP response uncertainty. A logistic regression model was estimated in each subsample to test the main effects of the uncertainty sources on WTP as well as their interaction with fairness and proenvironmental attitudes. Results indicated support for the uncertainty management model in only one of the four samples. Similarly, proenvironmental attitudes interacted rarely with uncertainty to a significant level, and in ways that were more complex than hypothesised. It was concluded that uncertain individuals were generally not more likely than other participants to draw on either fairness evaluations or proenvironmental attitudes when making decisions about paying for stormwater pollution abatement.

Inflammation, complement activation and endothelial function in stable and unstable coronary artery disease

Background: Endothelial dysfunction plays an important role in the pathogenesis of coronary artery disease (CAD). Apart from traditional risk factors complement activation and inflammation may trigger and sustain endothelial dysfunction. We sought to assess the association between endothelial function, high sensitivity C-reactive protein (hs-CRP) and markers of complement activation in patients with either stable or unstable coronary artery disease. Methods: We prospectively recruited 78 patients, 35 patients with stable angina pectoris (SAP) and 43 patients with unstable angina pectoris (UAP). Endothelial function was assessed as brachial artery reactivity (BAR). Hs-CRP, C3a, C5a, and C1-Inhibitor (C1 inh.) were measured enzymatically. Results: Patients with IJAP showed higher median levels of hs-CRP and C3a compared to patients with SAP, while BAR was not significantly different between patient groups. In UAP patients, hs-CRP was significantly correlated with cholesterol (r = 0.27, p < 0.02), C3a (r = 0.32, p < 0.001) and C1 INH.(r = 0.41, p < 0.003), but not with flow mediated dilatation (r = 0.09, P = 0.41). Hs-CRP and C1 INH.were found to be independant predictors of IJAP in a backward stepwise logistic regression model. Conclusions: We conclude that both hs-CRP, a marker of inflammation and C3a, a marker of complement activation are elevated in patients with UAP, but not in patients with SAP. (c) 2005 Elsevier B.V. All rights reserved.

Maternal health during pregnancy and perinatal mortality in Bangladesh: evidence form a large-scale community-based clinical trial

Perinatal mortality is very high in Bangladesh. In this setting, few community-level studies have assessed the influence of underlying maternal health factors on perinatal outcomes. We used the data from a community-based clinical controlled trial conducted between 1994 and 1997 in the catchment areas of a large MCH/FP hospital located in Mirpur, a suburban area of Dhaka in Bangladesh, to investigate the levels of perinatal mortality and its associated maternal health factors during pregnancy. A total of 2007 women were followed after recruitment up to delivery, maternal death, or until they dropped out of the study. Of these, 1584 who gave birth formed our study subjects. The stillbirth rate was 39.1 per 1000 births [95% confidence interval (CI) 39.0, 39.3] and the perinatal mortality rate (up to 3 days) was 54.3 per 1000 births [95% CI 54.0, 54.6] among the study population. In the fully adjusted logistic regression model, the risk of perinatal mortality was as high as 2.7 times [95% CI 1.5, 4.9] more likely for women with hypertensive disorders, 5.0 times [95% CI 2.3, 10.8] as high for women who had antepartum haemorrhage and 2.6 times [95% CI 1.2, 5.8] as high for women who had higher haemoglobin levels in pregnancy when compared with their counterparts. The inclusion of potential confounding variables such as poor obstetric history, sociodemographic characteristics and preterm delivery influenced only marginally the net effect of important maternal health factors associated with perinatal mortality. Perinatal mortality in the study setting was significantly associated with poor maternal health conditions during pregnancy. The results of this study point towards the urgent need for monitoring complications in high-risk pregnancies, calling for the specific components of the safe motherhood programme interventions that are designed to manage these complications of pregnancy.

A landscape-scale test of the predictive ability of a spatially explicit model for population viability analysis

1. Although population viability analysis (PVA) is widely employed, forecasts from PVA models are rarely tested. This study in a fragmented forest in southern Australia contrasted field data on patch occupancy and abundance for the arboreal marsupial greater glider Petauroides volans with predictions from a generic spatially explicit PVA model. This work represents one of the first landscape-scale tests of its type. 2. Initially we contrasted field data from a set of eucalypt forest patches totalling 437 ha with a naive null model in which forecasts of patch occupancy were made, assuming no fragmentation effects and based simply on remnant area and measured densities derived from nearby unfragmented forest. The naive null model predicted an average total of approximately 170 greater gliders, considerably greater than the true count (n = 81). 3. Congruence was examined between field data and predictions from PVA under several metapopulation modelling scenarios. The metapopulation models performed better than the naive null model. Logistic regression showed highly significant positive relationships between predicted and actual patch occupancy for the four scenarios (P = 0.001-0.006). When the model-derived probability of patch occupancy was high (0.50-0.75, 0.75-1.00), there was greater congruence between actual patch occupancy and the predicted probability of occupancy. 4. For many patches, probability distribution functions indicated that model predictions for animal abundance in a given patch were not outside those expected by chance. However, for some patches the model either substantially over-predicted or under-predicted actual abundance. Some important processes, such as inter-patch dispersal, that influence the distribution and abundance of the greater glider may not have been adequately modelled. 5. Additional landscape-scale tests of PVA models, on a wider range of species, are required to assess further predictions made using these tools. This will help determine those taxa for which predictions are and are not accurate and give insights for improving models for applied conservation management.

A zero-augmented gamma mixed model for longitudinal data with many zeros

In many occupational safety interventions, the objective is to reduce the injury incidence as well as the mean claims cost once injury has occurred. The claims cost data within a period typically contain a large proportion of zero observations (no claim). The distribution thus comprises a point mass at 0 mixed with a non-degenerate parametric component. Essentially, the likelihood function can be factorized into two orthogonal components. These two components relate respectively to the effect of covariates on the incidence of claims and the magnitude of claims, given that claims are made. Furthermore, the longitudinal nature of the intervention inherently imposes some correlation among the observations. This paper introduces a zero-augmented gamma random effects model for analysing longitudinal data with many zeros. Adopting the generalized linear mixed model (GLMM) approach reduces the original problem to the fitting of two independent GLMMs. The method is applied to evaluate the effectiveness of a workplace risk assessment teams program, trialled within the cleaning services of a Western Australian public hospital.