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Haitao Chu

Publications and source records attributed to Haitao Chu.

At least 19 recordsLinked to original sources
PubMed2006

An intervention to decrease catheter-related bloodstream infections in the ICU.

BACKGROUND: Catheter-related bloodstream infections occurring in the intensive care unit (ICU) are common, costly, and potentially lethal. METHODS: We conducted a collaborative cohort study predominantly in ICUs in Michigan. An evidence-based intervention was used to reduce the incidence of catheter-related bloodstream infections. Multilevel Poisson regression modeling was used to compare infection rates before, during, and up to 18 months after implementation of the study intervention. Rates of infection per 1000 catheter-days were measured at 3-month intervals, according to the guidelines of the National Nosocomial Infections Surveillance System. RESULTS: A total of 108 ICUs agreed to participate in the study, and 103 reported data. The analysis included 1981 ICU-months of data and 375,757 catheter-days. The median rate of catheter-related bloodstream infection per 1000 catheter-days decreased from 2.7 infections at baseline to 0 at 3 months after implementation of the study intervention (P< or =0.002), and the mean rate per 1000 catheter-days decreased from 7.7 at baseline to 1.4 at 16 to 18 months of follow-up (P<0.002). The regression model showed a significant decrease in infection rates from baseline, with incidence-rate ratios continuously decreasing from 0.62 (95% confidence interval [CI], 0.47 to 0.81) at 0 to 3 months after implementation of the intervention to 0.34 (95% CI, 0.23 to 0.50) at 16 to 18 months. CONCLUSIONS: An evidence-based intervention resulted in a large and sustained reduction (up to 66%) in rates of catheter-related bloodstream infection that was maintained throughout the 18-month study period.

Adult↗
PubMed2006

Longitudinal anthropometric changes in HIV-infected and HIV-uninfected men.

BACKGROUND: Although morphologic abnormalities are common among HIV-infected persons receiving highly active antiretroviral therapy (HAART), longitudinal comparative body shape changes among HAART-treated HIV-infected men versus HIV-seronegative men of similar age remain unclear. METHODS: Since September 1999, men enrolled in the Multicenter AIDS Cohort Study underwent body mass index (BMI) and circumference measurements of the waist, hip, thigh, and arm at each semiannual visit. Changes in these measurements that occurred between 1999 and 2003 among HIV-infected men were compared with measurements of HIV-seronegative men using linear mixed effects regression models. The HIV-infected men were further stratified by treatment group (no antiretroviral therapy [ART], monotherapy or combination [mono/combo] ART, or HAART). Analyses were adjusted for age, nadir CD4 cell count, and BMI (for circumference measurements). RESULTS: Over the 4-year observation period, mean BMI increased significantly among the 392 HIV-seronegative men (0.12 kg/m/y; P < 0.001) but did not change in the 3 HIV-infected groups (combined n = 661). Mean waist and hip circumferences increased significantly in all groups. Hip circumferences increased more slowly in the HIV-positive HAART-treated group (n = 488) than in the HIV-seronegative group (0.18 vs. 0.49 cm/y; P < 0.001), however, yielding a more rapid increase in the waist/hip ratio in the HIV-positive, HAART-treated group over time (0.005 per year; P < 0.001). CONCLUSIONS: The increased rate of change in waist/hip ratio in HIV-infected men receiving HAART compared with HIV-seronegative men is attributable to slower increases in hip circumference rather than an increased rate of change in waist circumference. These findings underscore the importance of body fat composition changes in the peripheral compartment relative to the central compartment among HIV-infected men receiving HAART.

Anthropometry↗
PubMed2006

Confidence intervals for biomarker-based human immunodeficiency virus incidence estimates and differences using prevalent data.

Prevalent biologic specimens can be used to estimate human immunodeficiency virus (HIV) incidence using a two-stage immunologic testing algorithm that hinges on the average time, T, between testing HIV-positive on highly sensitive enzyme immunoassays and testing HIV-positive on less sensitive enzyme immunoassays. Common approaches to confidence interval (CI) estimation for this incidence measure have included 1) ignoring the random error in T or 2) employing a Bonferroni adjustment of the box method. The authors present alternative Monte Carlo-based CIs for this incidence measure, as well as CIs for the biomarker-based incidence difference; standard approaches to CIs are typically appropriate for the incidence ratio. Using American Red Cross blood donor data as an example, the authors found that ignoring the random error in T provides a 95% CI for incidence as much as 0.26 times the width of the Monte Carlo CI, while the Bonferroni-box method provides a 95% CI as much as 1.57 times the width of the Monte Carlo CI. Further research is needed to understand under what circumstances the proposed Monte Carlo methods fail to provide valid CIs. The Monte Carlo-based CI may be preferable to competing methods because of the ease of extension to the incidence difference or to exploration of departures from assumptions.

Algorithms↗
PubMed2006

Combined analysis of retrospective and prospective occurrences in cohort studies: HIV-1 serostatus and incident pneumonia.

BACKGROUND: The authors show how information collected on retrospective occurrence times may be combined with prospective occurrence times in the analysis of recurrent events from cohort studies. METHODS: We demonstrate how the observed data can be expanded from one to two records per participant and account for the within-individual dependence when estimating variances. We illustrate our methods using data from the Women's Interagency HIV Study, which recorded 384 retrospective and 352 prospective occurrences of pneumonia in 9478 retrospective and 7857 prospective person-years among 2610 adult women. RESULTS: The hazard of non-Pneumocystis carinii pneumonia among the 2056 HIV-1 infected women was 2.24 times (95% confidence limits: 1.74, 2.89) that of the 554 uninfected women, independent of age. This hazard ratio was homogeneous across retrospective and prospective occurrences (P for interaction = 0.96) and combining occurrence types increased the precision by reducing the standard error by about a fourth. CONCLUSIONS: As expected, HIV-1 infection increases the hazard of pneumonia, with more precise inference obtained by combining information available on bidirectional occurrences. The proposed method for the analysis of bidirectional occurrence times will improve precision when the estimated associations are homogeneous across occurrence types, or may provide added insight into either the data collection or disease process when the estimated associations are heterogeneous.

AIDS-Related Opportunistic Infections↗
PubMed2006

Sample size and statistical power assessing the effect of interventions in the context of mixture distributions with detection limits.

Often in randomized clinical trials and observational cohort studies, a non-negative continuously distributed response variable is measured in treatment and control groups. In the presence of true zeros for the response variable, a two-part zero-inflated log-normal model (which assumes that the data has a probability mass at zero and a continuous response for values greater than zero) is usually recommended. However, in some environmental health and human immunodeficiency virus (HIV) studies, quantitative assays for metabolites of toxicants, or quantitative HIV RNA measurements are subject to left-censoring due to values falling below the limit of detection (LD). Here, a zero-inflated log-normal mixture model is often suggested since true zeros are indistinguishable from left-censored values due to the LD. When the probabilities of true zeros in the two groups are not restricted to be equal, the information contributed by values falling below LD is used only to estimate the probability of true zeros in the context of mixture distributions. We derived the required sample size to assess the effect of a treatment in the context of mixture models with equal and unequal variances based on the left-truncated log-normal distribution. Methods for calculation of statistical power are also presented. We calculate the required sample size and power for a recent study estimating the effect of oltipraz on reducing urinary levels of the hydroxylated metabolite aflatoxin M(1) (AFM(1)) in a randomized, placebo-controlled, double-blind phase IIa chemoprevention trial in Qidong, China. A Monte Carlo simulation study is conducted to investigate the performance of the proposed methods.

Aflatoxin M1↗
PubMed2006

Sensitivity analysis of misclassification: a graphical and a Bayesian approach.

PURPOSE: Misclassification can produce bias in measures of association. Sensitivity analyses have been suggested to explore the impact of such bias, but do not supply formally justified interval estimates. METHODS: To account for exposure misclassification, recently developed Bayesian approaches were extended to incorporate prior uncertainty and correlation of sensitivity and specificity. Under nondifferential misclassification, a contour plot is used to depict relations among the corrected odds ratio, sensitivity, and specificity. RESULTS: Methods are illustrated by application to a case-control study of cigarette smoking and invasive pneumococcal disease while varying the distributional assumptions about sensitivity and specificity. Results are compared with those of conventional methods, which do not account for misclassification, and a sensitivity analysis, which assumes fixed sensitivity and specificity. CONCLUSION: By using Bayesian methods, investigators can incorporate uncertainty about misclassification into probabilistic inferences.

Bayes Theorem↗
PubMed2006

Multiple-imputation for measurement-error correction.

BACKGROUND: There are many methods for measurement-error correction. These methods remain rarely used despite the ubiquity of measurement error. METHODS: Treating measurement error as a missing-data problem, the authors show how multiple-imputation for measurement-error (MIME) correction can be done using SAS software and evaluate the approach with a simulation experiment. RESULTS: Based on hypothetical data from a planned cohort study of 600 children with chronic kidney disease, the estimated hazard ratio for end-stage renal disease from the complete data was 2.0 [95% confidence limits (95% CL) 1.4, 2.8] and was reduced to 1.5 (95% CL 1.1, 2.1) using a misclassified exposure of low glomerular filtration rate at study entry (sensitivity of 0.9 and specificity of 0.7). The MIME correction hazard ratio was 2.0 (95% CL 1.2, 3.3), the regression calibration (RC) hazard ratio was 2.0 (95% CL 1.1, 3.7), and restriction to a 25% validation substudy yielded a hazard ratio of 2.0 (95% CL 1.0, 3.7). Based on Monte Carlo simulations across eight scenarios, MIME was approximately unbiased, had approximately correct coverage, and was sometimes more powerful than misclassified or RC analyses. Using root mean squared error as a criterion, the MIME bias correction is sometimes outweighed by added imprecision. CONCLUSION: The choice between MIME and RC depends on performance, ease, and objectives. The usefulness of MIME correction in specific applications will depend upon the sample size or the proportion validated. MIME correction may be valuable in interpreting imperfectly measured epidemiological data.

Bias↗
PubMed2006

On estimation of vaccine efficacy using validation samples with selection bias.

Using validation sets for outcomes can greatly improve the estimation of vaccine efficacy (VE) in the field (Halloran and Longini, 2001; Halloran and others, 2003). Most statistical methods for using validation sets rely on the assumption that outcomes on those with no cultures are missing at random (MAR). However, often the validation sets will not be chosen at random. For example, confirmational cultures are often done on people with influenza-like illness as part of routine influenza surveillance. VE estimates based on such non-MAR validation sets could be biased. Here we propose frequentist and Bayesian approaches for estimating VE in the presence of validation bias. Our work builds on the ideas of Rotnitzky and others (1998, 2001), Scharfstein and others (1999, 2003), and Robins and others (2000). Our methods require expert opinion about the nature of the validation selection bias. In a re-analysis of an influenza vaccine study, we found, using the beliefs of a flu expert, that within any plausible range of selection bias the VE estimate based on the validation sets is much higher than the point estimate using just the non-specific case definition. Our approach is generally applicable to studies with missing binary outcomes with categorical covariates.

Adolescent↗
PubMed2006

Estimating biomarker-based HIV incidence using prevalence data in high risk groups with missing outcomes.

The novel two-step serologic sensitive/less sensitive testing algorithm for detecting recent HIV seroconversion (STARHS) provides a simple and practical method to estimate HIV-1 incidence using cross-sectional HIV seroprevalence data. STARHS has been used increasingly in epidemiologic studies. However, the uncertainty of incidence estimates using this algorithm has not been well described, especially for high risk groups or when missing data is present because a fraction of sensitive enzyme immunoassay (EIA) positive specimens are not tested by the less sensitive EIA. Ad hoc methods used in practice provide incorrect confidence limits and thus may jeopardize statistical inference. In this report, we propose maximum likelihood and Bayesian methods for correctly estimating the uncertainty in incidence estimates obtained using prevalence data with a fraction missing, and extend the methods to regression settings. Using a study of injection drug users participating in a drug detoxification program in New York city as an example, we demonstrated the impact of underestimating the uncertainty in incidence estimates using ad hoc methods. Our methods can be applied to estimate the incidence of other diseases from prevalence data using similar testing algorithms when missing data is present.

Bayes Theorem↗
PubMed2006

A general approach for sample size and statistical power calculations assessing of interventions using a mixture model in the presence of detection limits.

A zero-inflated log-normal mixture model (which assumes that the data has a probability mass at zero and a continuous response for values greater than zero) with left censoring due to assay measurements falling below detection limits has been applied to compare treatment groups in randomized clinical trials and observational cohort studies. The sample size calculation (for a given type I error rate and a desired statistical power) has not been studied for this type of data under the assumption of equal proportions of true zeros in the treatment and control groups. In this article, we derive the sample sizes based on the expected differences between the non-zero values of individuals in treatment and control groups. Methods for calculation of statistical power are also presented. When computing the sample sizes, caution is needed as some irregularities occur, namely that the location parameter is sometimes underestimated due to the mixture distribution and left censoring. In such cases, the aforementioned methods fail. We calculated the required sample size for a recent randomized chemoprevention trial estimating the effect of oltipraz on reducing aflatoxin. A Monte Carlo simulation study was also conducted to investigate the performance of the proposed methods. The simulation results illustrate that the proposed methods provide adequate sample size estimates. However, when the aforementioned irregularity occurs, our methods are restricted and further research is needed.

Anticarcinogenic Agents↗
PubMed2006

Bimodal virological response to antiretroviral therapy for HIV infection: an application using a mixture model with left censoring.

STUDY OBJECTIVE: To assess whether HIV RNA levels (log(10) scale) in highly active antiretroviral therapy (HAART) treated population have a bimodal distribution, suggesting optimal or suboptimal response to HAART. METHODS: The study population from two ongoing cohort studies comprised 564 men (4785 person visits) and 1173 women (8675 person visits) with known dates of HAART initiation and with HIV RNA measurements before and after initiation. Values below detection limit of assays were treated in the analysis as left censored. Maximum likelihood methods were used to estimate parameters and to determine possible bimodality of HIV RNA distributions. RESULTS: A two component mixture model fitted HIV RNA levels significantly better than did a single component distribution at different years from HAART initiation in both therapy experienced and therapy naive patients. In the fifth year after HAART initiation, 32% of men and 44% of women had HIV RNA in the higher component with medians of 5247 and 9253 copies/ml, respectively, suggesting suboptimal virological response to HAART, which was associated with poor adherence and lower frequency of CCR5 heterozygous genotype. CONCLUSION: The bimodal distribution of HIV RNA persisted during the years after HAART initiation. The high occurrence of suboptimal virological response at the fifth year after HAART initiation underscore the needs for careful monitoring and patient education about the importance of treatment adherence. This data analysis overcomes limitations of measurement techniques of observations having values below detection limits and serves to characterise the dynamics of the virological response to therapies.

Adult↗
PubMed2005

Individual variation in CD4 cell count trajectory among human immunodeficiency virus-infected men and women on long-term highly active antiretroviral therapy: an application using a Bayesian random change-point model.

The authors evaluated population- and individual-level CD4-positive T-lymphocyte (CD4 cell) count trajectories over a 7-year period (July 1995-March 2004) following initiation of highly active antiretroviral therapy (HAART) in the Multicenter AIDS Cohort Study and the Women's Interagency HIV Study. The study population included 404 human immunodeficiency virus (HIV)-infected men and 609 HIV-infected women who 1) had a CD4 cell count measurement available from their last pre-HAART study visit, 2) provided at least four post-HAART CD4 cell count measurements, and 3) reported HAART usage for at least 80% of the post-HAART visits. The CD4 cell count trajectory was analyzed by means of a Bayesian random change-point model. The results indicated that CD4 cell count trajectories for long-term frequent HAART users can be well modeled with change points at both the population and individual levels. At the population level, regardless of CD4 cell count before HAART initiation, the gains in CD4 cell count ended approximately 2 years after HAART initiation in both men and women. At the individual level, 35% of men in the Multicenter AIDS Cohort Study versus 25% of women in the Women's Interagency HIV Study had a statistically significant change in CD4 cell count trajectory within 7 years after HAART initiation.

Adult↗
PubMed2005

Assessing the effect of interventions in the context of mixture distributions with detection limits.

Many quantitative assay measurements of metabolites of environmental toxicants in clinical investigations are subject to left censoring due to values falling below assay detection limits. Moreover, when observations occur in both unexposed individuals and exposed individuals who reflect a mixture of two distributions due to differences in exposure, metabolism, response to intervention and other factors, the measurements of these biomarkers can be bimodally distributed with an extra spike below the limit of detection. Therefore, estimating the effect of interventions on these biomarkers becomes an important and challenging problem. In this article, we present maximum likelihood methods to estimate the effect of intervention in the context of mixture distributions when a large proportion of observations are below the limit of detection. The selection of the number of components of mixture distributions was carried out using both bootstrap-based and cross-validation-based information criterion. We illustrate our methods using data from a randomized clinical trial conducted in Qidong, People's Republic of China.

Anticarcinogenic Agents↗
PubMed2005

Effect of acyclovir on herpetic ocular recurrence using a structural nested model.

Noncompliance with assigned therapies is ubiquitous in randomized clinical trials. Treatment effects may be corrected for noncompliance using Robins' structural nested models, but few examples have been published. The Herpetic Eye Disease Study randomized 703 ocular herpes patients to 365 days of acyclovir or placebo between 1992 and 1996, and achieved over 90% compliance in both arms. The hazard of recurrence in the acyclovir arm was 0.55 times the hazard in the placebo arm using an intent-to-treat approach (95% confidence interval [CI]: 0.41, 0.75). Assuming a structural nested model with a Weibull distribution, the hazard of recurrence under constant exposure to acyclovir was 0.41 times that of the non-exposed (test-based 95% CI: 0.28, 0.72), or 34% larger than the intent-to-treat estimate. Notwithstanding excellent compliance, intent-to-treat estimates may notably undervalue the causal effect of a treatment.

Acyclovir↗
PubMed2005

A note on comparing exposure data to a regulatory limit in the presence of unexposed and a limit of detection.

In some occupational health studies, observations occur in both exposed and unexposed individuals. If the levels of all exposed individuals have been detected, a two-part zero-inflated log-normal model is usually recommended, which assumes that the data has a probability mass at zero for unexposed individuals and a continuous response for values greater than zero for exposed individuals. However, many quantitative exposure measurements are subject to left censoring due to values falling below assay detection limits. A zero-inflated log-normal mixture model is suggested in this situation since unexposed zeros are not distinguishable from those exposed with values below detection limits. In the context of this mixture distribution, the information contributed by values falling below a fixed detection limit is used only to estimate the probability of unexposed. We consider sample size and statistical power calculation when comparing the median of exposed measurements to a regulatory limit. We calculate the required sample size for the data presented in a recent paper comparing the benzene TWA exposure data to a regulatory occupational exposure limit. A simulation study is conducted to investigate the performance of the proposed sample size calculation methods.

Algorithms↗
PubMed2005

Pulmonary outcomes of off-pump vs on-pump coronary artery bypass surgery in a randomized trial.

STUDY OBJECTIVES: Comparison of pulmonary outcomes after off-pump coronary artery bypass (OPCAB) vs on-pump coronary artery grafting with cardiopulmonary bypass (CABG/CPB). STUDY DESIGN: We examined preoperative and postoperative respiratory compliance, fluid balance, hemodynamics, arterial blood gases, chest radiographs, spirometry, pulmonary complications, and time to extubation in a prospective trial of 200 patients randomized to OPCAB vs CABG/CPB performed by one surgeon. RESULTS: One CABG/CPB patient and two OPCAB patients required mitral valve repair or replacement and were withdrawn. After three crossovers from CABG/CBP to OPCAB and one crossover from OPCAB to CABG, 97 CABG/CPB patients and 100 OPCAB patients remained. There were no significant preoperative demographic differences between groups. Postoperative compliance was reduced more after OPCAB than after CABG/CPB (- 15.4 +/- 10.7 mL/cm H(2)O vs - 11.2 +/- 10.1 mL/cm H(2)O [mean +/- SD]; p = 0.007), associated with rotation of the heart into the right chest to perform posterolateral bypasses (p < 0.001) and the concomitant increased fluid requirements necessary to maintain hemodynamic stability during rotation of the heart. In addition to higher intraoperative fluid intake (4,541 +/- 1,311 mL vs 3,585 +/- 1,033 mL, p < 0.0001), OPCAB patients had higher intraoperative fluid balance (3,903 +/- 1,315 mL vs 1,772 +/- 1,373 mL, p < 0.0001), and higher postoperative pulmonary arterial diastolic pressure (15.0 +/- 5.5 mm Hg vs 11.8 +/- 5.2 mm Hg, p < 0.0001) and central venous pressure (10.4 +/- 4.5 mm Hg vs 8.4 +/- 4.7 mm Hg, p < 0.0001). Despite lower compliance, immediate postoperative Pao(2) on fraction of inspired oxygen of 1.0 (275 +/- 97 torr vs 221 +/- 92 torr, p = 0.001) was higher after OPCAB and extubation was earlier (p = 0.001). Postoperative chest radiographs, spirometry, mortality, reintubation, or readmission for pulmonary complications were not different between groups. CONCLUSIONS: Compared to CABG/CPB, OPCAB was associated with a greater reduction in postoperative respiratory compliance associated with increased fluid administration and rotation of the heart into the right chest to perform posterolateral grafts. OPCAB yielded better gas exchange and earlier extubation but no difference in chest radiographs, spirometry, or rates of death, pneumonia, pleural effusion, or pulmonary edema.

Cardiopulmonary Bypass↗
PubMed2004

Estimating vaccine efficacy using auxiliary outcome data and a small validation sample.

In vaccine studies, a specific diagnosis of a suspected case by culture or serology of the infectious agent is expensive and difficult. Implementing validation sets in the study is less expensive and is easier to carry out. In studies using validation sets, the non-specific or auxiliary outcome is measured on each participant while the specific outcome is measured only for a small proportion of the participants. Vaccine efficacy, defined as one minus some measure of relative risk, could be severely attenuated if based only on the auxiliary outcome. Applying missing data analysis techniques could thus correct the bias while maintaining statistical efficiency. However, when the sample size in the validation sets is small and the vaccine is highly efficacious, all specific outcomes are likely to be negative in the validation set in the vaccinated group. Two commonly used missing data analysis methods, the mean score method and multiple imputation, depend on the ad hoc continuity correction when none of the specific outcomes are positive and the normality or log-normality assumption of relative risk, which may not hold when the relative risk is highly skewed, to estimate the confidence interval. In this paper, we propose a Bayesian method to estimate vaccine efficacy and its highest probability density (HPD) credible set using Monte Carlo (MC) methods when using auxiliary outcome data and a small validation sample. Comparing the performance of these approaches using data from a field study of influenza vaccine and simulations, we recommend to use the Bayesian method in this situation.

Adolescent↗