The Bias and Efficiency of Incomplete-Data Estimators in Small Univariate Normal Samples

Widely used methods for analyzing missing data can be biased in small samples. To understand these biases, we evaluate in detail the situation where a small univariate normal sample, with values missing at random, is analyzed using either observed-data maximum likelihood (ML) or multiple imputation (MI). We evaluate two types of MI: the usual Bayesian approach, which we call posterior draw (PD) imputation, and a little-used alternative, which we call ML imputation, in which values are imputed conditionally on an ML estimate. We find that observed-data ML is more efficient and has lower mean squared error than either type of MI. Between the two types of MI, ML imputation is more efficient than PD imputation, and ML imputation also has less potential for bias in small samples. The bias and efficiency of PD imputation can be improved by a change of prior.Comment: 32 pages, 3 figures, 3 tables, 2 Appendice

Sensitivity of the Hazard Ratio to Non-Ignorable Treatment Assignment in an Observational Study

In non-randomized studies, estimation of treatment effects generally requires adjustment for imbalances in observed covariates. One such method, based on the propensity score, is useful in many applications but may be biased when the assumption of strongly ignorable treatment assignment is violated. Because it is not possible to evaluate this assumption from the data, it is advisable to assess the sensitivity of conclusions to violations of strong ignorability. Lin et al [1] have implemented this idea by investigating how an unmeasured covariate may affect the conclusions of an observational study. We extend their method to assess sensitivity of the treatment hazard ratio to hidden bias under a range of covariate distributions. We derive simple formulas for approximating the true from the apparent treatment hazard ratio estimated under a specific survival model, and assess the validity of these formulas in simulation studies. We demonstrate the method in an analysis of SEER-Medicare data on the effects of chemotherapy in elderly colon cancer patients

Contributions to Causal Inference in Observational Studies

The electronic health record (EHR) is a digital version of the patient chart. All clinically relevant patient information can be accessed from the EHR by professionals involved in the patient’s care. For researchers, the EHR is a rich, convenient source for data to address a vast range of medical research questions. In observational studies with EHR data, it is common to define the treatment/exposure status as a binary indicator reflecting whether patient was documented to receive a particular medication or procedure. The outcome can be any type of information on patient status documented in the EHR after the treatment has taken place. The EHR, although not designed primarily for research, can serve as a platform for observational studies in clinical medicine. An advantage of the EHR is that it can document treatments unequivocally, provided the treatment – medication or procedure – appears in the record. For example, in a study in which treatment is the route of medication (intravenous= treated, oral=control), the EHR makes it clear which route was used. This does not, however, relieve the investigator from the responsibility of defining and measuring confounding variables, and properly adjusting for them in comparative analyses. In Chapter 1, we demonstrate the use of longitudinal EHR data in an evaluation of the effects of treatment of 12,754 children with overweight/obesity in greater Dallas. Our objective in this study is to estimate the causal effect of clinician attention to elevated body v mass index (BMI), measured at up to 10 timepoints per child, on subsequent weight change. To account for bias from confounding, we use the propensity score stratification method, applied longitudinally at each timepoint. We specify the propensity score model to include baseline covariates, current values of time-varying covariates, and treatment status at the most recent visit. An alternative method of causal inference when treatments are applied longitudinally in an observational study relies on the marginal structural model (MSM). When estimating an MSM, one eliminates confounding bias by constructing a series of propensity score models for treatment at each time, then weighting the subjects based on these scores. The MSM has the interpretation of a causal model for the effect of the series of treatments on the outcome. Although MSMs are in wide use, there has been relatively little evaluation of the properties of model estimates in small samples. One can conduct a simulation study to assess properties such as the suitability of asymptotic approximations to moderate samples, best methods for computing the standard errors, choice of the weighting method, and robustness to incorrect assumptions about the MSM or the underlying propensity score model. Several simulation methods have been proposed, each with its pros and cons. In Chapter 2, we introduce a new, simplified simulation method that addresses the limitations of the existing methods. We demonstrate the use of our method in a Monte Carlo study to assess the properties of an estimated MSM involving treatment at two timepoints. An oft-cited concern with MSMs is the sensitivity of model estimates to large weights. This issue arises in particular when there are multiple timepoints. As the number of timepoints increases, an individual’s propensity score can become very small, while the estimation weights – defined as the inverse of the propensity score – becomes correspondingly large. Having a few subjects with large weights can result in an unstable estimate. In Chapter 3, we use the novel simulation method that we introduced in Chapter 2 to conduct a Monte Carlo assessment of the impact of large weights on the validity of MSM estimates. Finally, vi we estimate a series of MSMs for the child obesity example from Chapter 1 and interpret the results in light of our simulation findings

Prevalence of Hepatitis C Virus Antibody in Patients With Sexually Transmitted Diseases Attending a Harrisburg, PA, STD Clinic

Objective: The prevalence of hepatitis B and hepatitis C in a sexually transmitted disease (STD) clinic population was studied, along with the prevalence of various STD agents, in an attempt to identify possible STD markers for the hepatitis C virus and help delineate the role of hepatitis C as an STD. The hepatitis C antibody rates found in the STD clinic were also compared with those found among patients attending a local OB/GYN clinic and those enrolled in a blood donor program, all from the same geographical area

(31) P and (1) H MRS of DB-1 melanoma xenografts: lonidamine selectively decreases tumor intracellular pH and energy status and sensitizes tumors to melphalan.

In vivo (31) P MRS demonstrates that human melanoma xenografts in immunosuppressed mice treated with lonidamine (LND, 100 mg/kg intraperitoneally) exhibit a decrease in intracellular pH (pH(i) ) from 6.90 ± 0.05 to 6.33 ± 0.10 (p \u3c 0.001), a slight decrease in extracellular pH (pH(e) ) from 7.00 ± 0.04 to 6.80 ± 0.07 (p \u3e 0.05) and a monotonic decline in bioenergetics (nucleoside triphosphate/inorganic phosphate) of 66.8 ± 5.7% (p \u3c 0.001) relative to the baseline level. Both bioenergetics and pH(i) decreases were sustained for at least 3 h following LND treatment. Liver exhibited a transient intracellular acidification by 0.2 ± 0.1 pH units (p \u3e 0.05) at 20 min post-LND, with no significant change in pH(e) and a small transient decrease in bioenergetics (32.9 ± 10.6%, p \u3e 0.05) at 40 min post-LND. No changes in pH(i) or adenosine triphosphate/inorganic phosphate were detected in the brain (pH(i) , bioenergetics; p \u3e 0.1) or skeletal muscle (pH(i) , pH(e) , bioenergetics; p \u3e 0.1) for at least 120 min post-LND. Steady-state tumor lactate monitored by (1) H MRS with a selective multiquantum pulse sequence with Hadamard localization increased approximately three-fold (p = 0.009). Treatment with LND increased the systemic melanoma response to melphalan (LPAM; 7.5 mg/kg intravenously), producing a growth delay of 19.9 ± 2.0 days (tumor doubling time, 6.15 ± 0.31 days; log(10) cell kill, 0.975 ± 0.110; cell kill, 89.4 ± 2.2%) compared with LND alone of 1.1 ± 0.1 days and LPAM alone of 4.0 ± 0.0 days. The study demonstrates that the effects of LND on tumor pH(i) and bioenergetics may sensitize melanoma to pH-dependent therapeutics, such as chemotherapy with alkylating agents or hyperthermia

An Active Learning Approach for Rapid Characterization of Endothelial Cells in Human Tumors

Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers

A phase I/II trial of hydroxychloroquine in conjunction with radiation therapy and concurrent and adjuvant temozolomide in patients with newly diagnosed glioblastoma multiforme

Preclinical studies indicate autophagy inhibition with hydroxychloroquine (HCQ) can augment the efficacy of DNA-damaging therapy. The primary objective of this trial was to determine the maximum tolerated dose (MTD) and efficacy of HCQ in combination with radiation therapy (RT) and temozolomide (TMZ) for newly diagnosed glioblastoma (GB). A 3 + 3 phase I trial design followed by a noncomparative phase II study was conducted in GB patients after initial resection. Patients received HCQ (200 to 800 mg oral daily) with RT and concurrent and adjuvant TMZ. Quantitative electron microscopy and immunoblotting were used to assess changes in autophagic vacuoles (AVs) in peripheral blood mononuclear cells (PBMC). Population pharmacokinetic (PK) modeling enabled PK-pharmacodynamic correlations. Sixteen phase I subjects were evaluable for dose-limiting toxicities. At 800 mg HCQ/d, 3/3 subjects experienced Grade 3 and 4 neutropenia and thrombocytopenia, 1 with sepsis. HCQ 600 mg/d was found to be the MTD in this combination. The phase II cohort (n = 76) had a median survival of 15.6 mos with survival rates at 12, 18, and 24 mo of 70%, 36%, and 25%. PK analysis indicated dose-proportional exposure for HCQ. Significant therapy-associated increases in AV and LC3-II were observed in PBMC and correlated with higher HCQ exposure. These data establish that autophagy inhibition is achievable with HCQ, but dose-limiting toxicity prevented escalation to higher doses of HCQ. At HCQ 600 mg/d, autophagy inhibition was not consistently achieved in patients treated with this regimen, and no significant improvement in overall survival was observed. Therefore, a definitive test of the role of autophagy inhibition in the adjuvant setting for glioma patients awaits the development of lower-toxicity compounds that can achieve more consistent inhibition of autophagy than HCQ