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Baseline Covariates (baseline + covariate)
Selected AbstractsFactors predictive of nephropathy in DCCT Type 1 diabetic patients with good or poor metabolic controlDIABETIC MEDICINE, Issue 7 2003L. Zhang Abstract Aims The study aim was to assess the time-related risk of developing diabetic nephropathy [albumin excretion rate (AER) , 40 mg/24 h] from baseline covariates in Type 1 diabetic patients with either good or poor metabolic control (MC). Methods Based on material from the Diabetes Control and Complications Trial study (n = 1441), patients were considered as under good or poor MC if their HbA1c mean level up to last visit fell in the lowest (, 6.9%) or highest (, 9.5%) quintile of the overall HbA1c distribution, respectively. Prevalence cases of nephropathy were excluded from the study. Survival analysis and Cox regression were applied to the data. Results Among patients with good MC (n = 277), 15% had developed nephropathy at the end of the study. Conversely, among patients with poor MC (n = 268), the proportion without the complication was 52%. When adjusting for MC, time to diabetic nephropathy was related to age (P < 0.0001), AER (P < 0.001), duration of diabetes (P < 0.005), body mass index (BMI) (P < 0.005), all at baseline, and to gender (P < 0.01). Patients with upper normal range AER levels, longer duration of diabetes and lower BMI were at higher risk, regardless of MC. The adverse effect of younger age on diabetic nephropathy was more marked in good than in poor MC. Although women tended to develop the complication more often under good MC, they appeared to be better protected under poor MC. Conclusions This study confirms occurrence of diabetic nephropathy under good MC and non-occurrence of the complication despite poor MC. It also demonstrates that some baseline covariates can affect, in a differential manner, time to diabetic nephropathy depending on MC. Diabet. Med. 20, 580,585 (2003) [source] Marijuana use and depression among adults: testing for causal associationsADDICTION, Issue 10 2006Valerie S. Harder ABSTRACT Aim To determine whether marijuana use predicts later development of depression after accounting for differences between users and non-users of marijuana. Design An ongoing longitudinal survey of 12 686 men and women beginning in 1979. Setting The National Longitudinal Survey of Youth of 1979, a nationally representative sample from the United States. Participants A total of 8759 adults (age range 29,37 years) interviewed in 1994 had complete data on past-year marijuana use and current depression. Measurements Self-reported past-year marijuana use was tested as an independent predictor of later adult depression using the Center for Epidemiologic Studies,Depression questionnaire. Individual's propensity to use marijuana was calculated using over 50 baseline covariates. Findings Before adjusting for group differences, the odds of current depression among past-year marijuana users is 1.4 times higher (95% CI: 1.1, 1.9) than the odds of depression among the non-using comparison group. After adjustment, the odds of current depression among past-year marijuana users is only 1.1 times higher than the comparison group (95% CI: 0.8, 1.7). Similarly, adjustment eliminates significant associations between marijuana use and depression in four additional analyses: heavy marijuana use as the risk factor, stratifying by either gender or age, and using a 4-year lag-time between marijuana use and depression. Conclusions After adjusting for differences in baseline risk factors of marijuana use and depression, past-year marijuana use does not significantly predict later development of depression. These findings are discussed in terms of their relevance for understanding possible causal effects of marijuana use on depression. [source] Methods for incorporating covariate adjustment, subgroup analysis and between-centre differences into cost-effectiveness evaluationsHEALTH ECONOMICS, Issue 12 2005Richard M. Nixon Abstract Background: Overall assessments of cost,effectiveness are now commonplace in informing medical policy decision making. It is often important, however, also to investigate how cost,effectiveness varies between patient subgroups. Yet such analyses are rarely undertaken, because appropriate methods have not been sufficiently developed. Methods: We propose a coherent set of Bayesian methods to extend cost,effectiveness analyses to adjust for baseline covariates, to investigate differences between subgroups, and to allow for differences between centres in a multicentre study using a hierarchical model. These methods consider costs and effects jointly, and allow for the typically skewed distribution of cost data. The results are presented as inferences on the cost,effectiveness plane, and as cost,effectiveness acceptability curves. Results: In applying these methods to a randomised trial of case management of psychotic patients, we show that overall cost,effectiveness can be affected by ignoring the skewness of cost data, but that it may be difficult to gain substantial precision by adjusting for baseline covariates. While analyses of overall cost,effectiveness can mask important subgroup differences, crude differences between centres may provide an unrealistic indication of the true differences between them. Conclusions: The methods developed allow a flexible choice for the distributions used for cost data, and have a wide range of applicability , to both randomised trials and observational studies. Experience needs to be gained in applying these methods in practice, and using their results in decision making. Copyright © 2005 John Wiley & Sons, Ltd. [source] Receiver operating characteristic surfaces in the presence of verification biasJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 1 2008Yueh-Yun Chi Summary., In diagnostic medicine, the receiver operating characteristic (ROC) surface is one of the established tools for assessing the accuracy of a diagnostic test in discriminating three disease states, and the volume under the ROC surface has served as a summary index for diagnostic accuracy. In practice, the selection for definitive disease examination may be based on initial test measurements and induces verification bias in the assessment. We propose a non-parametric likelihood-based approach to construct the empirical ROC surface in the presence of differential verification, and to estimate the volume under the ROC surface. Estimators of the standard deviation are derived by both the Fisher information and the jackknife method, and their relative accuracy is evaluated in an extensive simulation study. The methodology is further extended to incorporate discrete baseline covariates in the selection process, and to compare the accuracy of a pair of diagnostic tests. We apply the proposed method to compare the diagnostic accuracy between mini-mental state examination and clinical evaluation of dementia, in discriminating between three disease states of Alzheimer's disease. [source] Factors Associated with Iowa Rural Hospitals' Decision to Convert to Critical Access Hospital StatusTHE JOURNAL OF RURAL HEALTH, Issue 1 2009Pengxiang Li PhD ABSTRACT:,Context: The Balanced Budget Act (BBA) of 1997 allowed some rural hospitals meeting certain requirements to convert to Critical Access Hospitals (CAHs) and changed their Medicare reimbursement from prospective to cost-based. Some subsequent CAH-related laws reduced restrictions and increased payments, and the number of CAHs grew rapidly. Purpose: To examine factors related to hospitals' decisions to convert and time to CAH conversion. Methods: Eighty-nine rural hospitals in Iowa were characterized and observed from 1998 to 2005. Cox proportional hazards models were used to identify the determinants of time to CAH conversion. Findings: T-test and one-covariate Cox regression indicated that, in 1998, Iowa rural hospitals with more staffed beds, discharges, and acute inpatient days, higher operating margin, lower skilled swing bed days relative to acute days, and located in relatively high density counties were more likely to convert later or not convert before 2006. Multiple Cox regression with baseline covariates indicated that lower number of discharges and average length of stay (ALOS) were significant after controlling all other covariates. Conclusion: Iowa rural hospitals' decisions regarding CAH conversion were influenced by hospital size, financial condition, skilled swing bed days relative to acute days, length of stay, proportion of Medicare acute days, and geographic factors. Although financial concerns are often cited in surveys as the main reason for conversion, lower number of discharges and ALOS are the most prominent factors affecting rural hospitals' decision on when to convert. [source] Joint Models for Multivariate Longitudinal and Multivariate Survival DataBIOMETRICS, Issue 2 2006Yueh-Yun Chi Summary Joint modeling of longitudinal and survival data is becoming increasingly essential in most cancer and AIDS clinical trials. We propose a likelihood approach to extend both longitudinal and survival components to be multidimensional. A multivariate mixed effects model is presented to explicitly capture two different sources of dependence among longitudinal measures over time as well as dependence between different variables. For the survival component of the joint model, we introduce a shared frailty, which is assumed to have a positive stable distribution, to induce correlation between failure times. The proposed marginal univariate survival model, which accommodates both zero and nonzero cure fractions for the time to event, is then applied to each marginal survival function. The proposed multivariate survival model has a proportional hazards structure for the population hazard, conditionally as well as marginally, when the baseline covariates are specified through a specific mechanism. In addition, the model is capable of dealing with survival functions with different cure rate structures. The methodology is specifically applied to the International Breast Cancer Study Group (IBCSG) trial to investigate the relationship between quality of life, disease-free survival, and overall survival. [source] Maximum Likelihood Methods for Nonignorable Missing Responses and Covariates in Random Effects ModelsBIOMETRICS, Issue 4 2003Amy L. Stubbendick Summary. This article analyzes quality of life (QOL) data from an Eastern Cooperative Oncology Group (ECOG) melanoma trial that compared treatment with ganglioside vaccination to treatment with high-dose interferon. The analysis of this data set is challenging due to several difficulties, namely, nonignorable missing longitudinal responses and baseline covariates. Hence, we propose a selection model for estimating parameters in the normal random effects model with nonignorable missing responses and covariates. Parameters are estimated via maximum likelihood using the Gibbs sampler and a Monte Carlo expectation maximization (EM) algorithm. Standard errors are calculated using the bootstrap. The method allows for nonmonotone patterns of missing data in both the response variable and the covariates. We model the missing data mechanism and the missing covariate distribution via a sequence of one-dimensional conditional distributions, allowing the missing covariates to be either categorical or continuous, as well as time-varying. We apply the proposed approach to the ECOG quality-of-life data and conduct a small simulation study evaluating the performance of the maximum likelihood estimates. Our results indicate that a patient treated with the vaccine has a higher QOL score on average at a given time point than a patient treated with high-dose interferon. [source] Pharmacokinetics and pharmacokinetic/pharmacodynamic associations of ofatumumab, a human monoclonal CD20 antibody, in patients with relapsed or refractory chronic lymphocytic leukaemia: a phase 1,2 studyBRITISH JOURNAL OF HAEMATOLOGY, Issue 1 2010Bertrand Coiffier Summary The purpose of this phase 1,2 study was to investigate the association between the pharmacokinetic properties of ofatumumab, a human monoclonal CD20 antibody, and outcomes in 33 patients with relapsed/refractory chronic lymphocytic leukaemia receiving 4 weekly infusions of ofatumumab. The ofatumumab concentration profiles were fitted well by a two-compartment model with different elimination rate constant at first infusion compared to the remaining infusions in line with the observed rapid and sustained B-cell depletion. Exposure to ofatumumab was linked to clinical outcomes: high exposure was associated with higher probability of overall clinical response and longer progression-free survival. This association still remained statistically significant even when adjusting for relevant baseline covariates including tumour burden. The trial was registered at http://www.clinicaltrials.gov (NCT00093314). [source] The prognostic value of hemoglobin change after initiating androgen-deprivation therapy for newly diagnosed metastatic prostate cancerCANCER, Issue 3 2006A Multivariate Analysis of Southwest Oncology Group Study 889 Abstract BACKGROUND. The objective of this study was to characterize changes in hemoglobin (HGB) levels after the initiation of androgen-deprivation therapy (ADT) in patients with previously untreated, metastatic prostate cancer who were enrolled in a large clinical trial. METHODS. The multivariate associations between 3-month change in HGB and baseline characteristics were evaluated with a linear regression model. The associations between 3-month change in HGB level and time-to-event outcomes, including overall survival and progression-free survival, were evaluated by using proportional hazards regression models. RESULTS. Quartiles of baseline HGB levels were ,12.0 g/dL, from 12.1 to 13.7 g/dL, from 13.8 to 14.7 g/dL, and >14.7 g/dL. Overall, 3 months after initiating ADT, the mean HGB level declined 0.54 g/dL (standard deviation [SD], 1.68 g/dL); however, the mean HGB level increased by 0.99 g/dL (SD, 1.83 g/dL) in patients who had baseline HGB levels <12 g/dL and decreased 1.04 g/dL (SD, 1.28 g/dL) in patients who had baseline HGB levels ,12 g/dL. After adjusting for potential confounders, including baseline HGB level, a decline in HGB after 3 months of ADT was associated independently with shorter survival (hazards ratio [HR], 1.10 per 1 g/dL decline; P = .0035) and shorter progression-free survival (HR, 1.08 per 1 g/dL decline; P = .013). An unexpected finding was that the effect of baseline HGB on overall and progression-free survival varied significantly by race. CONCLUSIONS. In a sample of men with newly diagnosed, metastatic prostate cancer, a decline in HGB level after 3 months of ADT was associated with shorter survival and progression-free survival after adjusting for disease status and other baseline covariates. Although race alone was not a strong predictor of death or disease progression, the effect of the baseline HGB level on overall and progression-free survival varied significantly by race. Cancer 2006. © 2006 American Cancer Society. [source] | |||||||||||||