Analysis of local sensitivity to nonignorability with missing outcomes and predictors.

The ISNI (index of sensitivity to local nonignorability) method quantifies local sensitivity of parametric inferences to nonignorable missingness in an outcome variable. Here we extend ISNI to the situations where both outcomes and predictors can be missing and where the missingness mechanism can be either parametric or semi-parametric. We define the quantity MinNI (minimum nonignorability) to be an approximation to the norm of the smallest value of the transformed nonignorability that gives a nonnegligible displacement of the estimate of the parameter of interest. We illustrate our method in a complete data set from which we synthetically delete observations according to various patterns. We then apply the method to real-data examples involving the normal linear model and conditional logistic regression.

Survival of women diagnosed with breast cancer and who have survived a previous cancer.

PURPOSE: Many women diagnosed with breast cancer have survived previous cancer; yet little is known about the impact of previous cancer on overall and cancer-specific survival. METHODS: This population-based cohort study using SEER-Medicare data included women (age ≥ 66 years) diagnosed with breast cancer between 2005 and 2015. Separately by breast cancer stage, we estimated effect of previous cancer on overall survival using Cox regression and on cause-specific survival using competing risk regression; all survival analyses adjusted for covariates. RESULTS: Of 138,576 women diagnosed with breast cancer, 8% had a previous cancer of another organ site, most commonly colorectal or uterine cancer or melanoma. Many of these women (46.3%) were diagnosed within 5 years of breast cancer. For all breast cancer stages except IV wherein there was no difference, women with vs. without previous cancer had worse overall survival. This survival disadvantage was driven by deaths due to the previous cancer and other causes. In contrast, women with previous cancer generally had favorable breast-cancer-specific survival, although this varied by stage. Overall survival varied by previous cancer type, timing, and stage; previous lung cancer, cancer diagnosed within 1 year of incident breast cancer, and previous cancer at a distant stage were associated with the worst survival. In contrast, women with a previous melanoma had equivalent overall survival to women without previous cancer. CONCLUSION: We observed variable impact of previous cancer on overall and breast-cancer-specific survival depending on breast cancer stage at diagnosis and the type, timing, and stage of previous cancer.

Estimating the optimal timing of surgery from observational data.

Infants with hypoplastic left heart syndrome require an initial Norwood operation, followed some months later by a stage 2 palliation (S2P). The timing of S2P is critical for the operation's success and the infant's survival, but the optimal timing, if one exists, is unknown. We attempt to identify the optimal timing of S2P by analyzing data from the Single Ventricle Reconstruction Trial (SVRT), which randomized patients between two different types of Norwood procedure. In the SVRT, the timing of the S2P was chosen by the medical team; thus with respect to this exposure, the trial constitutes an observational study, and the analysis must adjust for potential confounding. To accomplish this, we propose an extended propensity score analysis that describes the time to surgery as a function of confounders in a discrete competing-risk model. We then apply inverse probability weighting to estimate a spline hazard model for predicting survival from the time of S2P. Our analysis suggests that S2P conducted at 6 months after the Norwood gives the patient the best post-S2P survival. Thus, we place the optimal time slightly later than a previous analysis in the medical literature that did not account for competing risks of death and heart transplantation.

Estimating the optimal timing of surgery by imputing potential outcomes.

Hypoplastic left heart syndrome is a congenital anomaly that is uniformly fatal in infancy without immediate treatment. The standard treatment consists of an initial Norwood procedure (stage 1) followed some months later by stage 2 palliation (S2P). The ideal timing of the S2P is uncertain. The Single Ventricle Reconstruction Trial (SVRT) randomized the procedure used in the initial Norwood operation, leaving the timing of the S2P to the discretion of the surgical team. To estimate the causal effect of the timing of S2P, we propose to impute the potential post-S2P survival outcomes using statistical models under the Rubin Causal Model framework. With this approach, it is straightforward to estimate the causal effect of S2P timing on post-S2P survival by directly comparing the imputed potential outcomes. Specifically, we consider a lognormal model and a restricted cubic spline model, evaluating their performance in Monte Carlo studies. When applied to the SVRT data, the models give somewhat different imputed values, but both support the conclusion that the optimal time for the S2P is at 6 months after the Norwood procedure.

Statistical modeling and prediction of clinical trial recruitment.

Real-time prediction of clinical trial accrual can support logistical planning, ensuring that studies meet but do not exceed sample size targets. We describe a novel, simulation-based prediction method that is founded on a realistic model for the underlying processes of recruitment. The model reflects key features of enrollment such as the staggered initiation of new centers, heterogeneity in enrollment capacity, and declining accrual within centers. The model's first stage assumes that centers join the trial (ie, initiate accrual) according to an inhomogeneous Poisson process in discrete time. The second part assumes that each center's enrollment pattern reflects an early plateau followed by a slow decline, with a burst at the end of the trial following the announcement of an imminent closing date. By summing up achieved and projected enrollment, one can predict accrual as a function of time and, thereby, the time when the trial will achieve a planned enrollment target. We applied our method retrospectively to two real-world trials: NSABP B-38 and REMATCH (Randomized Evaluation of Mechanical Assistance for the Treatment of Congestive Heart Failure). In both studies, the proposed method produced prediction intervals for time to completion that were more accurate than those from conventional predictions that assume a constant rate of enrollment, estimated either from the entire trial to date or over a recent time window. The advantage is substantial in the early stages of NSABP B-38. We conclude that a method based on a realistic accrual model offers improved accuracy in the prediction of enrollment landmarks, especially at the early stages of large trials that involve many centers.

Statistical justification of expansion cohorts in phase 1 cancer trials.

BACKGROUND: Phase I cancer trials increasingly incorporate dose-expansion cohorts (DECs), reflecting a growing demand to acquire more information about investigational drugs. Protocols commonly fail to provide a sample-size justification or analysis plan for the DEC. In this study, we develop a statistical framework for the design of DECs. METHODS: We assume the maximum tolerated dose (MTD) for the investigational drug has been identified in the dose-escalation stage of the trial. We use the 80% lower confidence bound and the 90% upper confidence bound for the response and toxicity rates, respectively, as decision thresholds for the dose-expansion stage. We calculate the operating characteristics with reference to prespecified minimum effective response rates and maximum safe DLT rates. RESULTS: We apply our framework to specify a system of DEC plans. The design comprises three components: 1) the number of subjects enrolled at the MTD, 2) the minimum number of responses necessary to indicate provisional drug efficacy, and 3) the maximum number of dose-limiting toxicities (DLTs) permitted to indicate drug safety. We demonstrate our method in an application to a cancer immunotherapy trial. CONCLUSIONS: Our simple and practical tool enables creation of DEC designs that appropriately address the safety and efficacy objectives of the trial.

Estimating lead-time bias in lung cancer diagnosis of patients with previous cancers.

Surprisingly, survival from a diagnosis of lung cancer has been found to be longer for those who experienced a previous cancer than for those with no previous cancer. A possible explanation is lead-time bias, which, by advancing the time of diagnosis, apparently extends survival among those with a previous cancer even when they enjoy no real clinical advantage. We propose a discrete parametric model to jointly describe survival in a no-previous-cancer group (where, by definition, lead-time bias cannot exist) and in a previous-cancer group (where lead-time bias is possible). We model the lead time with a negative binomial distribution and the post-lead-time survival with a linear spline on the logit hazard scale, which allows for survival to differ between groups even in the absence of bias; we denote our model Logit-Spline/Negative Binomial. We fit Logit-Spline/Negative Binomial to a propensity-score matched subset of the Surveillance, Epidemiology, and End Results-Medicare linked data set, conducting sensitivity analyses to assess the effects of key assumptions. With lung cancer-specific death as the end point, the estimated mean lead time is roughly 11 months for stage I&II patients; with overall survival, it is roughly 3.4 months in stage I&II. For patients with higher-stage lung cancers, the mean lead time is 1 month or less for both outcomes. Accounting for lead-time bias reduces the survival advantage of the previous-cancer group when one exists, but it does not nullify it in all cases.

Adaptive parametric prediction of event times in clinical trials.

BACKGROUND: In event-based clinical trials, it is common to conduct interim analyses at planned landmark event counts. Accurate prediction of the timing of these events can support logistical planning and the efficient allocation of resources. As the trial progresses, one may wish to use the accumulating data to refine predictions. PURPOSE: Available methods to predict event times include parametric cure and non-cure models and a nonparametric approach involving Bayesian bootstrap simulation. The parametric methods work well when their underlying assumptions are met, and the nonparametric method gives calibrated but inefficient predictions across a range of true models. In the early stages of a trial, when predictions have high marginal value, it is difficult to infer the form of the underlying model. We seek to develop a method that will adaptively identify the best-fitting model and use it to create robust predictions. METHODS: At each prediction time, we repeat the following steps: (1) resample the data; (2) identify, from among a set of candidate models, the one with the highest posterior probability; and (3) sample from the predictive posterior of the data under the selected model. RESULTS: A Monte Carlo study demonstrates that the adaptive method produces prediction intervals whose coverage is robust within the family of selected models. The intervals are generally wider than those produced assuming the correct model, but narrower than nonparametric prediction intervals. We demonstrate our method with applications to two completed trials: The International Chronic Granulomatous Disease study and Radiation Therapy Oncology Group trial 0129. LIMITATIONS: Intervals produced under any method can be badly calibrated when the sample size is small and unhelpfully wide when predicting the remote future. Early predictions can be inaccurate if there are changes in enrollment practices or trends in survival. CONCLUSIONS: An adaptive event-time prediction method that selects the model given the available data can give improved robustness compared to methods based on less flexible parametric models.

Proximity and gravity: modeling heaped self-reports.

Self-reported daily cigarette counts typically exhibit a preponderance of round numbers, a phenomenon known as heaping or digit preference. Heaping can be a substantial nuisance, as scientific interest lies in the distribution of the underlying true values rather than that of the heaped data. In principle, we can estimate parameters of the underlying distribution from heaped data if we know the conditional distribution of the heaped count given the true count, denoted the heaping mechanism (analogous to the missingness mechanism for missing data). In general, it is not possible to estimate the heaping mechanism robustly from heaped data only. A doubly-coded smoking cessation trial data set that includes daily cigarette count as both a conventional heaped retrospective recall measurement and a precise instantaneous measurement offers the rare opportunity to directly estimate the heaping mechanism. We propose a novel model that describes the conditional probability of the self-reported count as a function of its proximity to the truth and its intrinsic attractiveness, denoted its gravity. We apply variations of the model to the cigarette count data, illuminating the cognitive processes that influence self-reporting behaviors. The principal application of the model will be to enabling the correct analysis of heaped-only data sets. Copyright © 2017 John Wiley & Sons, Ltd.

The Optical Biopsy: A Novel Technique for Rapid Intraoperative Diagnosis of Primary Pulmonary Adenocarcinomas.

BACKGROUND: With increasing use of chest computed tomography scans, indeterminate pulmonary nodules are frequently detected as an incidental finding and present a diagnostic challenge. Tissue biopsy followed by histological review and immunohistochemistry is the gold standard to obtain a diagnosis and the most common malignant finding is a primary lung adenocarcinoma. Our objective was to determine whether an intraoperative optical biopsy (molecular imaging) may provide an alternative approach for determining if a pulmonary nodule is a primary lung adenocarcinoma. METHODS: Before surgery, 30 patients with an indeterminate pulmonary nodule were intravenously administered a folate receptor-targeted fluorescent contrast agent specific for primary lung adenocarcinomas. During surgery, the nodule was removed and the presence of fluorescence (optical biopsy) was assessed in the operating room to determine if the nodule was a primary pulmonary adenocarcinoma. Standard-of-care frozen section and immunohistochemical staining on permanent sections were then performed as the gold standard to validate the results of the optical biopsy. RESULTS: Optical biopsies identified 19 of 19 (100%) primary pulmonary adenocarcinomas. There were no false positive or false negative diagnoses. An optical biopsy required 2.4 minutes compared to 26.5 minutes for frozen section (P < 0.001) and it proved more accurate than frozen section in diagnosing lung adenocarcinomas. CONCLUSIONS: An optical biopsy has excellent positive predictive value for intraoperative diagnosis of primary lung adenocarcinomas. With refinement, this technology may prove to be an important supplement to standard pathology for examining close surgical margins, identifying lymph node involvement, and determining whether suspicious nodules are malignant.

Blockade of lymphocyte chemotaxis in visceral graft-versus-host disease.

BACKGROUND: Graft-versus-host disease (GVHD) is a major barrier to successful allogeneic hematopoietic stem-cell transplantation (HSCT). The chemokine receptor CCR5 appears to play a role in alloreactivity. We tested whether CCR5 blockade would be safe and limit GVHD in humans. METHODS: We tested the in vitro effect of the CCR5 antagonist maraviroc on lymphocyte function and chemotaxis. We then enrolled 38 high-risk patients in a single-group phase 1 and 2 study of reduced-intensity allogeneic HSCT that combined maraviroc with standard GVHD prophylaxis. RESULTS: Maraviroc inhibited CCR5 internalization and lymphocyte chemotaxis in vitro without impairing T-cell function or formation of hematopoietic-cell colonies. In 35 patients who could be evaluated, the cumulative incidence rate (±SE) of grade II to IV acute GVHD was low at 14.7±6.2% on day 100 and 23.6±7.4% on day 180. Acute liver and gut GVHD were not observed before day 100 and remained uncommon before day 180, resulting in a low cumulative incidence of grade III or IV GVHD on day 180 (5.9±4.1%). The 1-year rate of death that was not preceded by disease relapse was 11.7±5.6% without excessive rates of relapse or infection. Serum from patients receiving maraviroc prevented CCR5 internalization by CCL5 and blocked T-cell chemotaxis in vitro, providing evidence of antichemotactic activity. CONCLUSIONS: In this study, inhibition of lymphocyte trafficking was a specific and potentially effective new strategy to prevent visceral acute GVHD. (Funded by Pfizer and others; ClinicalTrials.gov number, NCT00948753.).

Lonidamine induces intracellular tumor acidification and ATP depletion in breast, prostate and ovarian cancer xenografts and potentiates response to doxorubicin.

We demonstrate that the effects of lonidamine (LND, 100 mg/kg, i.p.) are similar for a number of xenograft models of human cancer including DB-1 melanoma and HCC1806 breast, BT-474 breast, LNCaP prostate and A2870 ovarian carcinomas. Following treatment with LND, each of these tumors exhibits a rapid decrease in intracellular pH, a small decrease in extracellular pH, a concomitant monotonic decrease in nucleoside triphosphate and an increase in inorganic phosphate over a 2-3 h period. We have previously demonstrated that selective intracellular tumor acidification potentiates response of this melanoma model to melphalan (7.5 mg/kg, i.v.), producing an estimated 89% cell kill based on tumor growth delay analysis. We now show that, in both DB-1 melanoma and HCC1806 breast carcinoma, LND potentiates response to doxorubicin, producing 95% cell kill in DB-1 melanoma at 7.5 mg/kg, i.v. doxorubicin and 98% cell kill at 10.0 mg/kg doxorubicin, and producing a 95% cell kill in HCC1806 breast carcinoma at 12.0 mg/kg doxorubicin. Potentiation of doxorubicin may result from cation trapping of the weakly basic anthracycline. Recent experience with the clinical treatment of melanoma and other forms of human cancer suggests that these diseases will probably not be cured by a single therapeutic procedure other than surgery. A multimodality therapeutic approach will be required. As a potent modulator of tumor response to N-mustards and anthracyclines as well as tumor thermo- and radiosensitivity, LND promises to play an important clinical role in the management and possible complete local control of a number of prevalent forms of human cancer.

Effects of hyperglycemia on lonidamine-induced acidification and de-energization of human melanoma xenografts and sensitization to melphalan.

We seek to exploit the natural tendency of melanomas and other tumors to convert glucose to lactate as a method for the selective intracellular acidification of cancer cells and for the potentiation of the activity of nitrogen-mustard antineoplastic agents. We performed this study to evaluate whether the induction of hyperglycemia (26 mM) could enhance the effects of lonidamine (LND, 100 mg/kg; intraperitoneally) on the induction of intracellular acidification, bioenergetic decline and potentiation of the activity of melphalan (LPAM) against DB-1 melanoma xenografts in mice. Intracellular pH (pHi ), extracellular pH (pHe ) and bioenergetics (ß-nucleoside triphosphate to inorganic phosphate ratio, ß-NTP/Pi) were reduced by 0.7 units (p < 0.001), 0.3 units (p > 0.05) and 51.4% (p < 0.05), respectively. The therapeutic response to LPAM (7.5 mg/kg; intravenously) + LND (100 mg/kg; intraperitoneally) was reduced by about a factor of three under hyperglycemic conditions relative to normoglycemia, producing a growth delay of 7.76 days (tumor doubling time, 5.31 days; cell kill, 64%) compared with LND alone of 1.70 days and LPAM alone of 0.29 days. Under normoglycemic conditions, LND plus LPAM produced a growth delay of 17.75 days, corresponding to a cell kill of 90% at the same dose for each of these agents. The decrease in tumor cell kill under hyperglycemic conditions correlates with an increase in tumor ATP levels resulting from increased glycolytic activity. However, hyperglycemia substantially increases lactic acid production in tumors by a factor of approximately six (p < 0.05), but hyperglycemia did not increase the effects of LND on acidification of the tumor, most probably because of the strong buffering action of carbon dioxide (the pKa of carbonic acid is 6.4). Therefore, this study demonstrates that the addition of glucose during treatment with LND diminishes the activity of this agent.

Test-Retest Reliability and Stability of the Nicotine Metabolite Ratio Among Treatment-Seeking Smokers.

INTRODUCTION: The nicotine metabolite ratio (NMR), the ratio of 3-hydroxycotinine to cotinine, is a biomarker used in smoking cessation research, with several retrospective studies suggesting that NMR predicts treatment outcome. To be maximally useful in tailoring treatment, estimates of NMR should be stable over time. The present study is the first to examine the short-term test-retest reliability of NMR among treatment-seeking smokers. METHODS: Blood NMR was assessed at two time points, approximately 2-3 weeks apart and prior to intervention, among 72 healthy adult smokers (49% female; 35% non-White) enrolled in a cessation trial (http://ClinicalTrials.gov ID: NCT01314001). RESULTS: Mean NMR was stable from Time-1 to Time-2, with no significant change between assessments; test-retest reliability for NMR values was excellent (ICC[2,1] = 0.87). Test-retest reliability remained acceptable to high when NMR was categorized, as in recent clinical trials. Classification of participants as slow (quartile 1, NMR ≤ 0.24) or normal/fast NMR (quartiles 2-4, NMR ≥ 0.25) was consistent from Time-1 to Time-2 for 96% of participants (κ = 0.89). Though classification of participants into NMR quartiles was less consistent from Time-1 to Time-2 (67% agreement; weighted κ = 0.73), all reclassifications occurred between adjacent quartiles. CONCLUSIONS: Overall, these data support the use of a single NMR assessment for association studies with smoking phenotypes and in smokers seeking to quit, and they encourage large-scale efforts to determine optimal NMR cutpoints for tailoring treatment selection.

Suspension of accrual in phase II cancer clinical trials.

PURPOSE: Phase II cancer clinical trial designs commonly incorporate an interim analysis for lack of efficacy. To strictly and ethically implement such designs, one should suspend accrual in cases where pending patient outcomes can affect early termination decisions. This article aims to evaluate various options for accrual suspension and illustrate how the suspension strategy affects operating characteristics of the trial. METHODS: We define a strict suspension strategy for determining whether one should continue, suspend, or restart accrual at any point within the trial. The strategy is compared to a naive implementation of suspension and a strategy of no suspension. We evaluate the methods' operating characteristics by simulation. RESULTS: The suspension strategy has little effect on type I error, power, and early termination probability. Methods that involve stricter suspension policies generally lead to smaller but longer trials. Differences across strategies are substantial when the ratio of enrollment rate to outcome availability rate is high. CONCLUSION: The suspension strategy is most relevant in trials that accrue rapidly and require lengthy observation of each subject. The choice of suspension strategy involves a tradeoff between the cost of implementing a potentially complex suspension algorithm in real time versus the cost of enrolling more patients and exposing them to a potentially toxic and ineffective treatment regimen.

Assessing the fit of parametric cure models.

Survival data can contain an unknown fraction of subjects who are "cured" in the sense of not being at risk of failure. We describe such data with cure-mixture models, which separately model cure status and the hazard of failure among non-cured subjects. No diagnostic currently exists for evaluating the fit of such models; the popular Schoenfeld residual (Schoenfeld, 1982. Partial residuals for the proportional hazards regression-model. Biometrika 69, 239-241) is not applicable to data with cures. In this article, we propose a pseudo-residual, modeled on Schoenfeld's, to assess the fit of the survival regression in the non-cured fraction. Unlike Schoenfeld's approach, which tests the validity of the proportional hazards (PH) assumption, our method uses the full hazard and is thus also applicable to non-PH models. We derive the asymptotic distribution of the residuals and evaluate their performance by simulation in a range of parametric models. We apply our approach to data from a smoking cessation drug trial.

Predicting Hospital Readmission in Medicaid Patients With COPD Using Administrative and Claims Data.

BACKGROUND: The goals of this study were to develop a model that predicts the risk of 30-d all-cause readmission in hospitalized Medicaid patients diagnosed with COPD and to create a predictive model in a retrospective study of a population cohort. METHODS: We analyzed 2016-2019 Medicaid claims data from 7 United States states. A COPD admission was one in which either the admission diagnosis or the first or second clinical (discharge) diagnosis bore an International Classification of Diseases, Tenth Revision code for COPD. A readmission was an admission for any condition (not necessarily COPD) that occurred within 30 d of a COPD discharge. We estimated a mixed-effects logistic model to predict 30-d readmission from patient demographic data, comorbidities, past health care utilization, and features of the index hospitalization. We evaluated model fit graphically and measured predictive accuracy by the area under the receiver operating characteristic (ROC) curve. RESULTS: Among 12,283 COPD hospitalizations contributed by 9,437 subjects, 2,534 (20.6%) were 30-d readmissions. The final model included demographics, comorbidities, claims history, admission and discharge variables, length of stay, and seasons of admission and discharge. The observed versus predicted plot showed reasonable fit, and the estimated area under the ROC curve of 0.702 was robust in sensitivity analyses. CONCLUSIONS: Our model identified with acceptable accuracy hospitalized Medicaid patients with a diagnosis of COPD who are at high risk of readmission. One can use the model to develop post-discharge management interventions for reducing readmissions, for adjusting comparisons of readmission rates between sites/providers or over time, and to guide a patient-centered approach to patient care.

(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 < 0.001), a slight decrease in extracellular pH (pH(e) ) from 7.00 ± 0.04 to 6.80 ± 0.07 (p > 0.05) and a monotonic decline in bioenergetics (nucleoside triphosphate/inorganic phosphate) of 66.8 ± 5.7% (p < 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 > 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 > 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 > 0.1) or skeletal muscle (pH(i) , pH(e) , bioenergetics; p > 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.