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author:("Li, xiaoshan")
1.  Sensitivity analysis for causal inference using inverse probability weighting 
Evaluation of impact of potential uncontrolled confounding is an important component for causal inference based on observational studies. In this article, we introduce a general framework of sensitivity analysis that is based on inverse probability weighting. We propose a general methodology that allows both non-parametric and parametric analyses, which are driven by two parameters that govern the magnitude of the variation of the multiplicative errors of the propensity score and their correlations with the potential outcomes. We also introduce a specific parametric model that offers a mechanistic view on how the uncontrolled confounding may bias the inference through these parameters. Our method can be readily applied to both binary and continuous outcomes and depends on the covariates only through the propensity score that can be estimated by any parametric or non-parametric method. We illustrate our method with two medical data sets.
PMCID: PMC3777387  PMID: 21770046
Causal inference; Inverse probability weighting; Propensity score; Sensitivity analysis; Uncontrolled confounding
2.  Plasma Osteopontin Velocity Differentiates Lung Cancers from Controls in a CT Screening Population 
As CT screening is integrated into non-small cell lung cancer (NSCLC) care, additional parameters are needed to help distinguish cancers from benign nodules. Osteopontin (OPN), a secreted phosphoprotein, has elevated plasma levels in NSCLC. We hypothesize that changes in plasma OPN over time (i.e., OPN velocity [OPNV]) can differentiate NSCLC patients from those without cancer in a CT screening population.
A nested case-control study was conducted within a NSCLC CT screening trial. Incident cancers with serial plasma were matched to controls. OPN was measured by ELISA. Demographic, OPN, and OPNV were compared between cancers and controls using Wilcoxon Signed Rank test.
Ten incident cancers were identified. The pack years distributions were similar, but cancers were older (68.0 vs. 63.3, p=0.002) and their surveillance intervals were shorter (25.5 vs. 27.0 months, p=0. 03) than matched controls. Baseline OPN was similar (median difference: −5.15 ng/ml, p=0.50), but OPNV in the cancers was significantly greater than that of matched controls, (median difference: 1.06 ng/ml/month, p = 0.01). Accuracy rate for prediction of disease status by OPNV (adjusted for age and surveillance) was 83%.
These are early evidence for utility of monitoring plasma OPN during CT screening to assist in identification of NSCLCs.
PMCID: PMC3746829  PMID: 23568008
non-small cell lung cancer; osteopontin; biomarker; early detection; CT screening
3.  A Note on Monotonicity Assumptions for Exact Unconditional Tests in Binary Matched-pairs Designs 
Biometrics  2011;67(4):1666-1668.
Exact unconditional tests have been widely applied to test the difference between two probabilities for 2×2 matched-pairs binary data with small sample size. In this context, Lloyd (2008, Biometrics 64, 716–723) proposed an E + M p-value, that showed better performance than the existing M p-value and C p-value. However, the analytical calculation of the E + M p-value requires that the Barnard convexity condition be satisfied; this can be challenging to prove theoretically. In this paper, by a simple reformulation, we show that a weaker condition, conditional monotonicity, is sufficient to calculate all three p-values (M, C and E + M) and their corresponding exact sizes. Moreover, this conditional monotonicity condition is applicable to non-inferiority tests.
PMCID: PMC3132212  PMID: 21466507
Barnard convexity; conditional monotonicity; exact unconditional test; matched-pairs; non-inferiority test
4.  Characterization of a Genomic Signature of Pregnancy in the Breast 
The objective of the current study was to comprehensively compare the genomic profiles in the breast of parous and nulliparous postmenopausal women to identify genes that permanently change their expression following pregnancy.
The study was designed as a two-phase approach. In the discovery phase, we compared breast genomic profiles of 37 parous with 18 nulliparous postmenopausal women. In the validation phase, confirmation of the genomic patterns observed in the discovery phase was sought in an independent set of 30 parous and 22 nulliparous postmenopausal women. RNA was hybridized to Affymetrix HG_U133 Plus 2.0 oligonucleotide arrays containing probes to 54,675 transcripts; scanned and the images analyzed using Affymetrix GCOS software. Surrogate variable analysis, logistic regression and significance analysis for microarrays were used to identify statistically significant differences in expression of genes. The False Discovery Rate (FDR) approach was used to control for multiple comparisons. We found that 208 genes (305 probe sets) were differentially expressed between parous and nulliparous women in both discovery and validation phases of the study at a FDR of 10% and with at least a 1.25-fold change. These genes are involved in regulation of transcription, centrosome organization, RNA splicing, cell cycle control, adhesion and differentiation. The results provide persuasive evidence that full-term pregnancy induces long-term genomic changes in the breast. The genomic signature of pregnancy could be used as an intermediate marker to assess potential chemopreventive interventions with hormones mimicking the effects of pregnancy for prevention of breast cancer.
PMCID: PMC3320726  PMID: 21622728
Pregnancy; breast; genes; expression; postmenopausal
5.  Literature Based Drug Interaction Prediction with Clinical Assessment Using Electronic Medical Records: Novel Myopathy Associated Drug Interactions 
PLoS Computational Biology  2012;8(8):e1002614.
Drug-drug interactions (DDIs) are a common cause of adverse drug events. In this paper, we combined a literature discovery approach with analysis of a large electronic medical record database method to predict and evaluate novel DDIs. We predicted an initial set of 13197 potential DDIs based on substrates and inhibitors of cytochrome P450 (CYP) metabolism enzymes identified from published in vitro pharmacology experiments. Using a clinical repository of over 800,000 patients, we narrowed this theoretical set of DDIs to 3670 drug pairs actually taken by patients. Finally, we sought to identify novel combinations that synergistically increased the risk of myopathy. Five pairs were identified with their p-values less than 1E-06: loratadine and simvastatin (relative risk or RR = 1.69); loratadine and alprazolam (RR = 1.86); loratadine and duloxetine (RR = 1.94); loratadine and ropinirole (RR = 3.21); and promethazine and tegaserod (RR = 3.00). When taken together, each drug pair showed a significantly increased risk of myopathy when compared to the expected additive myopathy risk from taking either of the drugs alone. Based on additional literature data on in vitro drug metabolism and inhibition potency, loratadine and simvastatin and tegaserod and promethazine were predicted to have a strong DDI through the CYP3A4 and CYP2D6 enzymes, respectively. This new translational biomedical informatics approach supports not only detection of new clinically significant DDI signals, but also evaluation of their potential molecular mechanisms.
Author Summary
Drug-drug interactions are a common cause of adverse drug events. In this paper, we developed an automated search algorithm which can predict new drug interactions based on published literature. Using a large electronic medical record database, we then analyzed the correlation between concurrent use of these potentially interacting drugs and the incidence of myopathy as an adverse drug event. Myopathy comprises a range of musculoskeletal conditions including muscle pain, weakness, and tissue breakdown (rhabdomyolysis). Our statistical analysis identified 5 drug interaction pairs: (loratadine, simvastatin), (loratadine, alprazolam), (loratadine, duloxetine), (loratadine, ropinirole), and (promethazine, tegaserod). When taken together, each drug pair showed a significantly increased risk of myopathy when compared to the expected additive myopathy risk from taking either of the drugs alone. Further investigation suggests that two major drug metabolism proteins, CYP2D6 and CYP3A4, are involved with these five drug pairs' interactions. Overall, our method is robust in that it can incorporate all published literature, all FDA approved drugs, and very large clinical datasets to generate predictions of clinically significant interactions. The interactions can then be further validated in future cell-based experiments and/or clinical studies.
PMCID: PMC3415435  PMID: 22912565
6.  Propensity Score-based Sensitivity Analysis Method for Uncontrolled Confounding 
American Journal of Epidemiology  2011;174(3):345-353.
The authors developed a sensitivity analysis method to address the issue of uncontrolled confounding in observational studies. In this method, the authors use a 1-dimensional function of the propensity score, which they refer to as the sensitivity function (SF), to quantify the hidden bias due to unmeasured confounders. The propensity score is defined as the conditional probability of being treated given the measured covariates. Then the authors construct SF-corrected inverse-probability-weighted estimators to draw inference on the causal treatment effect. This approach allows analysts to conduct a comprehensive sensitivity analysis in a straightforward manner by varying sensitivity assumptions on both the functional form and the coefficients in the 1-dimensional SF. Furthermore, 1-dimensional continuous functions can be well approximated by low-order polynomial structures (e.g., linear, quadratic). Therefore, even if the imposed SF is practically certain to be incorrect, one can still hope to obtain valuable information on treatment effects by conducting a comprehensive sensitivity analysis using polynomial SFs with varying orders and coefficients. The authors demonstrate the new method by implementing it in an asthma study which evaluates the effect of clinician prescription patterns regarding inhaled corticosteroids for children with persistent asthma on selected clinical outcomes.
PMCID: PMC3202161  PMID: 21659349
confounding factors (epidemiology); inverse probability weighting; propensity score; sensitivity analysis; sensitivity function; uncontrolled confounding
7.  Evaluation of computer-generated reminders to improve CD4 laboratory monitoring in sub-Saharan Africa: a prospective comparative study 
Little evidence exists on effective interventions to integrate HIV-care guidelines into practices within developing countries. This study tested the hypothesis that clinical summaries with computer-generated reminders could improve clinicians' compliance with CD4 testing guidelines in the resource-limited setting of sub-Saharan Africa.
A prospective comparative study of two randomly selected outpatient adult HIV clinics in western Kenya. Printed summaries with reminders for overdue CD4 tests were made available to clinicians in the intervention clinic but not in the control clinic.
Changes in order rates for overdue CD4 tests were compared between and within the two clinics.
The computerized reminder system identified 717 encounters (21%) with overdue CD4 tests. Analysis by study assignment (regardless of summaries being printed or not) revealed that with computer-generated reminders, CD4 order rates were significantly higher in the intervention clinic compared to the control clinic (53% vs 38%, OR=1.80, CI 1.34 to 2.42, p<0.0001). When comparison was restricted to encounters where summaries with reminders were printed, order rates in intervention clinic were even higher (63%). The intervention clinic increased CD4 ordering from 42% before reminders to 63% with reminders (50% increase, OR=2.32, CI 1.67 to 3.22, p<0.0001), compared to control clinic with only 8% increase from prestudy baseline (CI 0.83 to 1.46, p=0.51).
Evaluation was conducted at two clinics in a single institution.
Clinical summaries with computer-generated reminders significantly improved clinician compliance with CD4 testing guidelines in the resource-limited setting of sub-Saharan Africa. This technology can have broad applicability to improve quality of HIV care in these settings.
PMCID: PMC3116261  PMID: 21252053
8.  A note on overadjustment in inverse probability weighted estimation 
Biometrika  2010;97(4):997-1001.
Standardized means, commonly used in observational studies in epidemiology to adjust for potential confounders, are equal to inverse probability weighted means with inverse weights equal to the empirical propensity scores. More refined standardization corresponds with empirical propensity scores computed under more flexible models. Unnecessary standardization induces efficiency loss. However, according to the theory of inverse probability weighted estimation, propensity scores estimated under more flexible models induce improvement in the precision of inverse probability weighted means. This apparent contradiction is clarified by explicitly stating the assumptions under which the improvement in precision is attained.
PMCID: PMC3371719  PMID: 22822256
Causal inference; Propensity score; Standardized mean
9.  Data Visualization Speeds Review of Potential Adverse Drug Events in Patients on Multiple Medications 
Journal of biomedical informatics  2009;43(2):326-331.
Patients on multiple medications are at increased risk for adverse drug events. While physicians can reduce this risk by regularly reviewing the side-effect profiles of their patients’ medications, this process can be time-consuming. We created a decision support system designed to expedite reviewing potential adverse reactions through information visualization. The system includes a database containing 16,340 unique drug and side-effect pairs, representing 250 common medications. A numeric score is assigned to each pair reflecting the strength of association between drug and effect. Based on these scores, the system generates graphical adverse reaction maps for any user-selected combination of drugs. A study comparing speed and accuracy of retrieving side-effect data using this tool versus UpToDate® demonstrated a 60% reduction in time to complete a query (61 seconds vs. 155 seconds, p<0.0001) with no decrease in accuracy. These findings suggest that information visualization can significantly expedite review of potential adverse drug events.
PMCID: PMC2838979  PMID: 19995616
adverse drug events; side-effects; information visualization; polypharmacy
10.  Suicide Ideation in Adult Survivors of Childhood Cancer: A Report From the Childhood Cancer Survivor Study 
Journal of Clinical Oncology  2009;28(4):655-661.
To evaluate risk of suicide ideation (SI) after childhood cancer, prevalence of SI in a cohort of adult survivors of pediatric cancers was compared with prevalence in a sibling comparison group. The relationship of SI to cancer treatment and current health was examined, and the hypothesis that poor physical health is significantly associated with suicidality, after adjusting for depression, was specifically tested.
Nine thousand one hundred twenty-six adult survivors of childhood cancer and 2,968 siblings enrolled onto the Childhood Cancer Survivor Study completed a survey describing their demographics and medical and psychological functioning, including SI in the prior week.
Of survivors, 7.8% reported SI compared with 4.6% of controls (odds ratio = 1.79; 95% CI, 1.4 to 2.4). Suicidality was unrelated to age, age at diagnosis, sex, cancer therapy, recurrence, time since diagnosis, or second malignancy. SI was associated with primary CNS cancer diagnosis, depression, and poor health outcomes including chronic conditions, pain, and poor global health rating. A logistic regression analysis showed that poor current physical health was significantly associated with SI even after adjusting for cancer diagnosis and depression.
Adult survivors of childhood cancers are at increased risk for SI. Risk of SI is related to cancer diagnosis and post-treatment mental and physical health, even many years after completion of therapy. The association of suicidal symptoms with physical health problems is important because these may be treatable conditions for which survivors seek follow-up care and underscores the need for a multidisciplinary approach to survivor care.
PMCID: PMC2816000  PMID: 19841325
11.  Adequacy of Hospital Discharge Summaries in Documenting Tests with Pending Results and Outpatient Follow-up Providers 
Journal of General Internal Medicine  2009;24(9):1002-1006.
Poor communication of tests whose results are pending at hospital discharge can lead to medical errors.
To determine the adequacy with which hospital discharge summaries document tests with pending results and the appropriate follow-up providers.
Retrospective study of a randomly selected sample
Six hundred ninety-six patients discharged from two large academic medical centers, who had test results identified as pending at discharge through queries of electronic medical records.
Each patient’s discharge summary was reviewed to identify whether information about pending tests and follow-up providers was mentioned. Factors associated with documentation were explored using clustered multivariable regression models.
Discharge summaries were available for 99.2% of 668 patients whose data were analyzed. These summaries mentioned only 16% of tests with pending results (482 of 2,927). Even though all study patients had tests with pending results, only 25% of discharge summaries mentioned any pending tests, with 13% documenting all pending tests. The documentation rate for pending tests was not associated with level of experience of the provider preparing the summary, patient’s age or race, length of hospitalization, or duration it took for results to return. Follow-up providers’ information was documented in 67% of summaries.
Discharge summaries are grossly inadequate at documenting both tests with pending results and the appropriate follow-up providers.
PMCID: PMC2726888  PMID: 19575268
tests with pending results; continuity of care; patient safety; discharge summary; medical errors
12.  Natural language processing to extract follow-up provider information from hospital discharge summaries 
We evaluate the performance of a Natural Language Processing (NLP) application designed to extract follow-up provider information from free-text discharge summaries at two hospitals.
We compare performance by the NLP application, called the Regenstrief EXtracion tool (REX), to performance by three physician reviewers at extracting follow-up provider names, phone/fax numbers and location information. Precision, recall, and F-measures are reported, with 95% CI for pairwise comparisons.
Of 556 summaries with follow-up information, REX performed as follows in precision, recall, F-measure respectively: Provider Name 0.96, 0.92, 0.94; Phone/Fax 0.99, 0.92, 0.96; Location 0.83, 0.82, 0.82. REX was as good as all physician-reviewers in identifying follow-up provider names and phone/fax numbers, and slightly inferior to two physicians at identifying location information. REX took about four seconds (vs. 3–5 minutes for physician-reviewers) to extract follow-up information.
A NLP program had physician-like performance at extracting provider follow-up information from discharge summaries.
PMCID: PMC3041312  PMID: 21347103

Results 1-12 (12)