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

The measurement of low pay in the UK labour force survey

Consideration of the National Minimum Wage requires estimates of the distribution of hourly pay. The UK Labour Force Survey (LFS) is a key source of such estimates. The approach most frequently adopted by researchers has been to measure hourly earnings from several questions on pay and hours. The Office for National Statistics is now applying a new approach, based on an alternative more direct measurement introduced in March 1999. These two measures do not produce identical values and this paper investigates sources of discrepancies and concludes that the new variable is more accurate. The difficulty with using the new variable is that it is only available on a subset of respondents. An approach is developed in which missing values of the new variable are replaced by imputed values. The assumptions underlying this imputation approach and results of applying it to LFS data are presented. The relation to weighting approaches is also discussed

A Hot‐Deck Multiple Imputation Procedure for Gaps in Longitudinal Recurrent Event Histories

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89547/1/j.1541-0420.2011.01558.x.pd

Analysis of transplant urgency and benefit via multiple imputation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109279/1/sim6250.pd

(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

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

A Comparison of Recall and Diary Food Expenditure Data

Recall food expenditure data, which is the basis of a great deal of empirical work, is believed to suffer from considerable measurement error. Diary records are believed to be more accurate. We study an unusual data set that collects recall and diary data from the same households and so allows a direct comparison of the two methods of data collection. The diary data imply measurement errors in recall food expenditure data that are substantial, and which do not have the properties of classical measurement error. However, we also present evidence that the diary measures are themselves imperfect

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