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PubMed2000

Hierarchical logistic regression models for clustered binary outcomes in studies of IVF-ET.

OBJECTIVE: To describe a hierarchical logistic regression model for clustered binary data, apply it to data from a study on the effect of hydrosalpinx on embryo implantation, and compare the results with analyses that do not account for clustering. DESIGN: Observational study. SETTING: Academic research environment. PATIENT(S): Women undergoing IVF-ET for tubal disease. MAIN OUTCOME MEASURE(S): Odds of per embryo implantation. RESULT(S): Although regression estimates are largely similar between the models, the hierarchical model properly reflects the added variation due to clustering. Standard errors are higher, confidence intervals are wider, and P values indicate fewer "statistically significant" effects. CONCLUSION(S): Ignoring important sources of variation in any analysis can lead to incorrect confidence intervals and P values. In studies of IVF-ET, where clustered data are common, unexplained heterogeneity can be substantial. In this setting, hierarchical logistic regression is an appropriate alternative to standard logistic regression.

Adult↗
PubMed2000

Anthropometric and pharmacotherapeutic variables on acute emesis induced by cisplatin-containing chemotherapy.

OBJECTIVE: To characterize the effects of anthropometric and pharmacotherapeutic variables on acute emesis induced by cisplatin-containing regimens with dosages > or =50 mg x m(-2). METHODS: A prospective, cross-sectional, noncontrolled study was performed to analyze acute vomiting during the first 24 hours in patients treated in a Spanish hospital. The patients received an intravenous combination of drugs (2 doses of metoclopramide 3 mg/kg, dexamethasone 20 mg) as first-choice antiemetic therapy. Intravenous ondansetron 8 mg and dexamethasone 20 mg served as an alternative regimen in patients <30 years old with a history of extrapyramidal manifestations or emesis in previous cycles. Therapeutic failure was used as a dependent variable, defined as three or more vomiting episodes documented by the patients. Other variables were the chemotherapeutic regimen; antiemetic regimen; patient gender, age, weight, and height; and cycle number. The reference logistic model and two reduced-models derived from the latter were designed. The logistic models were subsequently validated by means of receiving operating characteristic curves. RESULTS: A total of 319 cycles involving 106 patients were studied. The metoclopramide regimen was administered in 66% of the cycles. The therapeutic failure rate was 21% for the metoclopramide regimen and 32% for the ondansetron treatment. The logistic model developed identified the type of chemotherapeutic regimen provided as the most significant prognostic variable (p < 0.0001). Patient weight (odds ratio 1.64) and height (odds ratio 1.28) were identified as prognostic factors related with therapeutic failure. CONCLUSIONS: The type of chemotherapeutic regimen administered and the anthropometric characteristics of the patients exert a clear conditioning effect on risks associated with therapeutic failure against acute emesis following high-dose cisplatin therapy. Such anthropometric parameters have not been previously identified as prognostic factors.

Acute Disease↗
PubMed1999

A transition rate model for first admissions to psychiatric institutions.

This paper describes the application of a parametric transition rate model, the generalized log-logistic model, to the duration of first admissions to psychiatric institutions. The final model included diagnosis, gender, age, living conditions and year of admission as covariates. Characteristics of the log-logistic model are described extensively. Parametric transition models offer challenging and promising possibilities to model duration of hospital stay.

Adolescent↗
PubMed2004

Predicting blood pressure change caused by rapid injection of propofol during anesthesia induction with a logistic regression model.

BACKGROUND: Propofol is a common intravenous agent for induction and maintenance of anesthesia. The advantage of propofol is rapid recovery of consciousness when the continuous infusion is stopped. Additionally, it has antiemetic effect of reducing postoperative nausea and vomiting. On the other hand, rapid infusion of propofol is painful and may cause hypotension. In this study, we aimed to develop a logistic regression model to accurately predict blood pressure change caused by rapid infusion of propofol. METHODS: Seventeen variables (including demographic data, past medical history, laboratory data, and blood pressure before induction) were assessed in 200 patients who received propofol for induction of anesthesia for routine surgery. A logistic regression model was derived using these values as independent variables to predict whether a patient would suffer a significant blood pressure change (> 30% decrease from baseline). Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of our prediction model. RESULTS: A cut-off value of 0.17 in the logistic regression model predicted decreased blood pressure with 90.0% sensitivity and, 67.3% specificity. The area under the receiver operating characteristic curve was 0.855. CONCLUSIONS: Our prediction model predicts propofol-induced hypotension with acceptable accuracy. Because of the straightforward mathematic formula used, our model can be integrated effortlessly into a hospital information system, providing a reliable and useful decision support for clinical anesthesia staff.

Adolescent↗
PubMed1999

Empirical Bayes estimation of random effects parameters in mixed effects logistic regression models.

We extend an approach for estimating random effects parameters under a random intercept and slope logistic regression model to include standard errors, thereby including confidence intervals. The procedure entails numerical integration to yield posterior empirical Bayes (EB) estimates of random effects parameters and their corresponding posterior standard errors. We incorporate an adjustment of the standard error due to Kass and Steffey (KS; 1989, Journal of the American Statistical Association 84, 717-726) to account for the variability in estimating the variance component of the random effects distribution. In assessing health care providers with respect to adult pneumonia mortality, comparisons are made with the penalized quasi-likelihood (PQL) approximation approach of Breslow and Clayton (1993, Journal of the American Statistical Association 88, 9-25) and a Bayesian approach. To make comparisons with an EB method previously reported in the literature, we apply these approaches to crossover trials data previously analyzed with the estimating equations EB approach of Waclawiw and Liang (1994, Statistics in Medicine 13, 541-551). We also perform simulations to compare the proposed KS and PQL approaches. These two approaches lead to EB estimates of random effects parameters with similar asymptotic bias. However, for many clusters with small cluster size, the proposed KS approach does better than the PQL procedures in terms of coverage of nominal 95% confidence intervals for random effects estimates. For large cluster sizes and a few clusters, the PQL approach performs better than the KS adjustment. These simulation results agree somewhat with those of the data analyses.

Adult↗
PubMed1993

Determinants of success of coronary angioplasty in patients with a chronic total occlusion: a multiple logistic regression model to improve selection of patients.

OBJECTIVE: To study the determinants of success of coronary angioplasty in patients with chronic total occlusions, and to formulate a multiple logistic regression model to improve selection of patients. DESIGN: A retrospective analysis of clinical and angiographic data on a consecutive series of patients. PATIENTS: 312 patients (mean age 55, range 31 to 79 years, 86% men) who underwent coronary angioplasty procedure for a chronic total occlusion between 1981 and 1992. RESULTS: Procedural success was achieved in 191 lesions (61.2%). A major complication occurred in six patients (1.9%). Multiple stepwise logistic regression analysis identified the presence of bridging collaterals (p < 0.001), the absence of a tapered entry configuration (p < 0.001), estimated duration of occlusion of greater than three months (p = 0.001), and a vessel diameter of less than 3 mm (p = 0.003) as independent predictors of procedural failure. The logistic regression model was used to classify patients into groups of high, intermediate, and low probability of procedural success with cut off points of 70% and 30%. The predictive value for procedural success (probability > or = 70%) was 91% (95% confidence intervals (95% CI) 83% to 96%) and predictive value for procedural failure (probability < 30%) was 81% (95% CI 64% to 92%). CONCLUSIONS: Percutaneous transluminal coronary angioplasty of chronic total occlusions is associated with a low risk of acute complication. Procedural success is influenced by easily identifiable clinical and angiographic features and the multiple regression model described may help to improve selection of patients.

Adult↗
PubMed2005

Performance of a mixed effects logistic regression model for binary outcomes with unequal cluster size.

When a clustered randomized controlled trial is considered at a design stage of a clinical trial, it is useful to consider the consequences of unequal cluster size (i.e., sample size per cluster). Furthermore, the assumption of independence of observations within cluster does not hold, of course, because the subjects share the same cluster. Moreover, when the clustered outcomes are binary, a mixed effect logistic regression model is applicable. This article compares the performance of a maximum likelihood estimation of the mixed effects logistic regression model with equal and unequal cluster sizes. This was evaluated in terms of type I error rate, power, bias, and standard error through computer simulations that varied treatment effect, number of clusters, and intracluster correlation coefficients. The results show that the performance of the mixed effects logistic regression model is very similar, regardless of inequality in cluster size. This is illustrated using data from the Prevention Of Suicide in Primary care Elderly: Collaborative Trial (PROSPECT) study.

Algorithms↗
PubMed1993

Logistic regression model to estimate the risk of unbalanced offspring in reciprocal translocations.

The aim of this study was to estimate the risk of viable unbalanced offspring for a parental carrier of reciprocal translocation. On a large computerized database of reciprocal translocations we used logistic regression to model this risk. The status of the progeny is the outcome variable. Explanatory covariates are cytogenetic characteristics of the translocation, age and sex of the parental carrier, and potential viability of the gametes. The results obtained by the logistic model demonstrate the important role of certain variables such as the sex of the parental carrier and the R band length of the translocated segments. Within the group of lower risk (risk of viable unbalanced offspring less than 5%), 97% of the individuals are correctly classified with this model. For this group, the choice prenatal diagnosis can be best discussed by considering both the risk for viable unbalanced offspring and the risk of induced abortion following prenatal diagnosis.

Adolescent↗
PubMed2006

[Estimation on gene-environment interaction in the partial case-control study].

OBJECTIVE: To introduce the approaches for estimating gene-environment interaction based on partial case-control studies. METHODS: The effects of logistic model and log-linear model for estimating the main effects and gene-environment interaction effect were estimated by means of maximum likelihood methods in traditional case-control studies, case-only studies and partial case-control studies, respectively. An example was also illustrated. RESULTS: In traditional case-control study with complete data, the results of logistic model and log-linear model were equivalent. In case-only study without any information about controls, the logistic model can also efficiently estimate gene-environment interaction. In partial case-control study, environmental information was collected from all of the cases and controls, while genetic information was only collected from cases. For this case-control study with incomplete data, a suitable parameterized log-linear model could simultaneously and efficiently estimate the main effect of environment and gene-environment interaction, whereas the logistic model could not. CONCLUSION: For a partial case-control study, log-linear model could estimate not only the main effect of environment but also gene-environment interaction. If genotype and exposure were independent, estimators from partial case-control were as precisely as those from complete-data case-control studies.

Case-Control Studies↗
PubMed2005

Determination of osteoporosis risk factors using a multiple logistic regression model in postmenopausal Turkish women.

OBJECTIVE: To determine the risk factors of osteoporosis using a multiple binary logistic regression method and to assess the risk variables for osteoporosis, which is a major and growing health problem in many countries. METHODS: We presented a case-control study, consisting of 126 postmenopausal healthy women as control group and 225 postmenopausal osteoporotic women as the case group. The study was carried out in the Department of Physical Medicine and Rehabilitation, Dicle University, Diyarbakir, Turkey between 1999-2002. The data from the 351 participants were collected using a standard questionnaire that contains 43 variables. A multiple logistic regression model was then used to evaluate the data and to find the best regression model. RESULTS: We classified 80.1% (281/351) of the participants using the regression model. Furthermore, the specificity value of the model was 67% (84/126) of the control group while the sensitivity value was 88% (197/225) of the case group. We found the distribution of residual values standardized for final model to be exponential using the Kolmogorow-Smirnow test (p=0.193). The receiver operating characteristic curve was found successful to predict patients with risk for osteoporosis. This study suggests that low levels of dietary calcium intake, physical activity, education, and longer duration of menopause are independent predictors of the risk of low bone density in our population. CONCLUSION: Adequate dietary calcium intake in combination with maintaining a daily physical activity, increasing educational level, decreasing birth rate, and duration of breast-feeding may contribute to healthy bones and play a role in practical prevention of osteoporosis in Southeast Anatolia. In addition, the findings of the present study indicate that the use of multivariate statistical method as a multiple logistic regression in osteoporosis, which maybe influenced by many variables, is better than univariate statistical evaluation.

Age Distribution↗
PubMed2005

Substantial effective sample sizes were required for external validation studies of predictive logistic regression models.

BACKGROUND AND OBJECTIVES: The performance of a prediction model is usually worse in external validation data compared to the development data. We aimed to determine at which effective sample sizes (i.e., number of events) relevant differences in model performance can be detected with adequate power. METHODS: We used a logistic regression model to predict the probability that residual masses of patients treated for metastatic testicular cancer contained only benign tissue. We performed standard power calculations and Monte Carlo simulations to estimate the numbers of events that are required to detect several types of model invalidity with 80% power at the 5% significance level. RESULTS: A validation sample with 111 events was required to detect that a model predicted too high probabilities, when predictions were on average 1.5 times too high on the odds scale. A decrease in discriminative ability of the model, indicated by a decrease in the c-statistic from 0.83 to 0.73, required 81 to 106 events, depending on the specific scenario. CONCLUSION: We suggest a minimum of 100 events and 100 nonevents for external validation samples. Specific hypotheses may, however, require substantially higher effective sample sizes to obtain adequate power.

Humans↗
PubMed2006

Predicting postoperative nausea and vomiting with the application of an artificial neural network.

BACKGROUND: Several medications have proved to be useful in preventing postoperative nausea and vomiting (PONV). However, routine antiemetic prophylaxis is not cost-effective. We evaluated the accuracy and discriminating power of an artificial neural network (ANN) to predict PONV. METHODS: We analysed data from 1086 in-patients who underwent various surgical procedures under general anaesthesia without antiemetic prophylaxis. Predictors used for ANN training were selected by computing the value of chi(2) statistic and information gain with respect to PONV. The configuration of the ANN was chosen by using a software tool. Then the training of the ANN was performed based on data from a training set (n=656). Testing validation was performed with the remaining patients (n=430) whose outcome regarding PONV was unknown to the ANN. Area under the receiver operating characteristic (ROC) curves were used to quantify predictive performance. ANN performance was compared with those of the Naïve Bayesian classifier model, logistic regression model, simplified Apfel score and Koivuranta score. RESULTS: ANN accuracy was 83.3%, sensitivity 77.9% and specificity 85.0% in predicting PONV. The areas under the ROC curve follow: ANN, 0.814 (0.774-0.850); Naïve Bayesian classifier, 0.570 (0.522-0.617); logistic regression, 0.669 (0.623-0.714); Koivuranta score, 0.626 (0.578-0.672); simplified Apfel score, 0.624 (0.576-0.670). ANN discriminatory power was superior to those of the other predicting models (P<0.05). CONCLUSIONS: The ANN provided the best predictive performance among all tested models.

Adult↗
PubMed2003

Psychometric analysis of performance on categories of client needs and nursing process with the NLN Diagnostic Readiness Test.

This article provides psychometric analysis of the performance of nursing students on categories of client needs (CN) and nursing process (NP) measured by the NLN Diagnostic Readiness Test (NLN-DRT) for RN licensure. While analyses of items and number-right score performance with NLN tests are well documented, the analysis of proficiency on categories that organize items at the conceptual level is limited to reporting basic classical statistics (e.g., proportion of correct scores and percentiles). The psychometric analysis of proficiency on categories of CN and NP in this article is based on item response theory and takes into account that the binary scores on these 2 categories (1 = mastered, 0 = nonmastered) are obtained through summative standardized scoring and not through direct responses of examinees. NLN-DRT data for a local population of 646 students enrolled in an NLN accredited associate degree program was obtained 3 weeks prior to graduation. This article illustrates the application of IRT using the Rasch Model and the 2-parameter logistic model in a method of psychometric analysis that deals with proficiency on conceptual categories and provides measurement feedback to nursing educators for curriculum (or instruction) intervention in a specific educational context. Among the components of such feedback provided in this article are: (a) difficulty, discrimination, and characteristic curves of CN and NP categories, (b) performance patterns by level of success on each category, and (c) domain scores by ability levels for the population of nursing students. The Rasch Model, which was calculated using RASCAL, did not fit the data with the categories of CN or NP at the .05 level of statistical significance. However, the 2-parameter logistic model fit the data with both CN and NP categories while using the XCALIBRE computer program. The IRT approach used in this article demonstrated some measurement perspectives on linking an instrument's conceptual base to theory in the context of nursing education, and provide valuable measurement feedback for improving the quality of curriculum and teaching in institutions that use the NLN-DRT in their assessment practice.

Education, Nursing, Associate↗
PubMed2005

Testing goodness-of-fit of the logistic regression model in case-control studies using sample reweighting.

A new goodness-of-fit test for the logistic regression model is proposed. It exploits the property of this model that when it is correct, i.e. not misspecified, the parameter estimates are (asymptotically) invariant under reweighting the observations by weights wi that are a function of the binary (0/1) outcomes yi. Misspecification of the model can thus be concluded when parameter estimates change under reweighting. A local test, considering weights of the form wi=(1 + epsilonyi) is explored. The test is especially suitable for case-control studies but may be used in other contexts as well.

Adult↗
PubMed2005

MELPREDICT: a logistic regression model to estimate CDKN2A carrier probability.

BACKGROUND: Heritable alterations in CDKN2A account for a subset of familial melanoma cases although no robust method exists to identify those at risk of being a mutation carrier. METHODS: We set out to construct a model for estimating CDKN2A mutation carrier probability using a cohort of 116 consecutive familial cutaneous melanoma patients evaluated at Massachusetts General Hospital Pigmented Lesion Center between April 2001 and September 2004. Germline CDKN2A and CDK4 status on the familial melanoma cases and clinical features associated with mutational status were then used to build a multiple logistic regression model to predict carrier probability and performance of model on external validation. RESULTS: From the 116 kindreds prone to melanoma in the Boston area, 13 CDKN2A mutation carriers were identified and 12 were subsequently used in the modeling. Proband age at diagnosis, number of proband primaries, and number of additional family primaries were most closely associated with germline mutations. The estimated probability of the proband being a mutation carrier based on the logistic regression model (MELPREDICT) is given by e(L)/(1 + e(L) where L = 1.99+[0.92x(no. of proband primaries)]+[0.74x(no. of additional family primaries)]-[2.11xln(age)]. The mean estimated probabilities for subjects in the Boston dataset were 55.4% and 5.1% for the mutation carriers and non-carriers respectively. In a receiver operator characteristic analysis, the area under the curve was 0.881 (95% confidence interval 0.739 to 1.000) for the Boston model set (n = 116) and 0.803 (0.729 to 0.877) for an external Toronto hereditary melanoma cohort (n = 143). CONCLUSIONS: These results represent the first-iteration logistic regression model to approximate CDKN2A carrier probability. Validation of this model with an external dataset revealed relatively robust performance.

Adolescent↗
PubMed2001

Maximum likelihood analysis of logistic regression models with incomplete covariate data and auxiliary information.

This article presents a new method for maximum likelihood estimation of logistic regression models with incomplete covariate data where auxiliary information is available. This auxiliary information is extraneous to the regression model of interest but predictive of the covariate with missing data. Ibrahim (1990, Journal of the American Statistical Association 85, 765-769) provides a general method for estimating generalized linear regression models with missing covariates using the EM algorithm that is easily implemented when there is no auxiliary data. Vach (1997, Statistics in Medicine 16, 57-72) describes how the method can be extended when the outcome and auxiliary data are conditionally independent given the covariates in the model. The method allows the incorporation of auxiliary data without making the conditional independence assumption. We suggest tests of conditional independence and compare the performance of several estimators in an example concerning mental health service utilization in children. Using an artificial dataset, we compare the performance of several estimators when auxiliary data are available.

Analysis of Variance↗
PubMed1998

Comparison of population-averaged and subject-specific approaches for analyzing repeated binary outcomes.

Several approaches have been proposed to model binary outcomes that arise from longitudinal studies. Most of the approaches can be grouped into two classes: the population-averaged and subject-specific approaches. The generalized estimating equations (GEE) method is commonly used to estimate population-averaged effects, while random-effects logistic models can be used to estimate subject-specific effects. However, it is not clear to many epidemiologists how these two methods relate to one another or how these methods relate to more traditional stratified analysis and standard logistic models. The authors address these issues in the context of a longitudinal smoking prevention trial, the Midwestern Prevention Project. In particular, the authors compare results from stratified analysis, standard logistic models, conditional logistic models, the GEE models, and random-effects models by analyzing a binary outcome from two and seven repeated measurements, respectively. In the comparison, the authors focus on the interpretation of both time-varying and time-invariant covariates under different models. Implications of these methods for epidemiologic research are discussed.

Epidemiologic Methods↗