A Bayesian hierarchical model for individual participant data meta-analysis of demand curves.

Individual participant data meta-analysis is a frequently used method to combine and contrast data from multiple independent studies. Bayesian hierarchical models are increasingly used to appropriately take into account potential heterogeneity between studies. In this paper, we propose a Bayesian hierarchical model for individual participant data generated from the Cigarette Purchase Task (CPT). Data from the CPT details how demand for cigarettes varies as a function of price, which is usually described as an exponential demand curve. As opposed to the conventional random-effects meta-analysis methods, Bayesian hierarchical models are able to estimate both the study-specific and population-level parameters simultaneously without relying on the normality assumptions. We applied the proposed model to a meta-analysis with baseline CPT data from six studies and compared the results from the proposed model and a two-step conventional random-effects meta-analysis approach. We conducted extensive simulation studies to investigate the performance of the proposed approach and discussed the benefits of using the Bayesian hierarchical model for individual participant data meta-analysis of demand curves.

A mixed effects model for analyzing area under the curve of longitudinally measured biomarkers with missing data.

A simple approach for analyzing longitudinally measured biomarkers is to calculate summary measures such as the area under the curve (AUC) for each individual and then compare the mean AUC between treatment groups using methods such as t test. This two-step approach is difficult to implement when there are missing data since the AUC cannot be directly calculated for individuals with missing measurements. Simple methods for dealing with missing data include the complete case analysis and imputation. A recent study showed that the estimated mean AUC difference between treatment groups based on the linear mixed model (LMM), rather than on individually calculated AUCs by simple imputation, has negligible bias under random missing assumptions and only small bias when missing is not at random. However, this model assumes the outcome to be normally distributed, which is often violated in biomarker data. In this paper, we propose to use a LMM on log-transformed biomarkers, based on which statistical inference for the ratio, rather than difference, of AUC between treatment groups is provided. The proposed method can not only handle the potential baseline imbalance in a randomized trail but also circumvent the estimation of the nuisance variance parameters in the log-normal model. The proposed model is applied to a recently completed large randomized trial studying the effect of nicotine reduction on biomarker exposure of smokers.

Randomized Trial of Reduced-Nicotine Standards for Cigarettes.

BACKGROUND: The Food and Drug Administration can set standards that reduce the nicotine content of cigarettes. METHODS: We conducted a double-blind, parallel, randomized clinical trial between June 2013 and July 2014 at 10 sites. Eligibility criteria included an age of 18 years or older, smoking of five or more cigarettes per day, and no current interest in quitting smoking. Participants were randomly assigned to smoke for 6 weeks either their usual brand of cigarettes or one of six types of investigational cigarettes, provided free. The investigational cigarettes had nicotine content ranging from 15.8 mg per gram of tobacco (typical of commercial brands) to 0.4 mg per gram. The primary outcome was the number of cigarettes smoked per day during week 6. RESULTS: A total of 840 participants underwent randomization, and 780 completed the 6-week study. During week 6, the average number of cigarettes smoked per day was lower for participants randomly assigned to cigarettes containing 2.4, 1.3, or 0.4 mg of nicotine per gram of tobacco (16.5, 16.3, and 14.9 cigarettes, respectively) than for participants randomly assigned to their usual brand or to cigarettes containing 15.8 mg per gram (22.2 and 21.3 cigarettes, respectively; P<0.001). Participants assigned to cigarettes with 5.2 mg per gram smoked an average of 20.8 cigarettes per day, which did not differ significantly from the average number among those who smoked control cigarettes. Cigarettes with lower nicotine content, as compared with control cigarettes, reduced exposure to and dependence on nicotine, as well as craving during abstinence from smoking, without significantly increasing the expired carbon monoxide level or total puff volume, suggesting minimal compensation. Adverse events were generally mild and similar among groups. CONCLUSIONS: In this 6-week study, reduced-nicotine cigarettes versus standard-nicotine cigarettes reduced nicotine exposure and dependence and the number of cigarettes smoked. (Funded by the National Institute on Drug Abuse and the Food and Drug Administration Center for Tobacco Products; ClinicalTrials.gov number, NCT01681875.).

Analysis of self-report and biochemically verified tobacco abstinence outcomes with missing data: a sensitivity analysis using two-stage imputation.

BACKGROUND: Missing data are common in tobacco studies. It is well known that from the observed data alone, it is impossible to distinguish between missing mechanisms such as missing at random (MAR) and missing not at random (MNAR). In this paper, we propose a sensitivity analysis method to accommodate different missing mechanisms in cessation outcomes determined by self-report and urine validation results. METHODS: We propose a two-stage imputation procedure, allowing survey and urine data to be missing under different mechanisms. The motivating data were from a tobacco cessation trial examining the effects of the extended vs. standard Quit and Win contests and counseling vs. no counseling under a 2-by-2 factorial design. The primary outcome was 6-month biochemically verified tobacco abstinence. RESULTS: Our proposed method covers a wide spectrum of missing scenarios, including the widely adopted "missing = smoking" imputation by assuming a perfect smoking-missing correlation (an extreme case of MNAR), the MAR case by assuming a zero smoking-missing correlation, and many more in between. The analysis of the data example shows that the estimated effects of the studied interventions are sensitive to the different missing assumptions on the survey and urine data. CONCLUSIONS: Sensitivity analysis has played a crucial role in assessing the robustness of the findings in clinical trials with missing data. The proposed method provides an effective tool for analyzing missing data introduced at two different stages of outcome assessment, the self-report and validation time. Our methods are applicable to trials studying biochemically verified abstinence from alcohol and other substances.

Effect of Immediate vs Gradual Reduction in Nicotine Content of Cigarettes on Biomarkers of Smoke Exposure: A Randomized Clinical Trial.

Importance: The optimal temporal approach for reducing nicotine to minimally or nonaddictive levels in all cigarettes sold in the United States has not been determined. Objectives: To determine the effects of immediate vs gradual reduction in nicotine content to very low levels and as compared with usual nicotine level cigarettes on biomarkers of toxicant exposure. Design, Setting, and Participants: A double-blind, randomized, parallel-design study with 2 weeks of baseline smoking and 20 weeks of intervention was conducted at 10 US sites. A volunteer sample of daily smokers with no intention to quit within 30 days was recruited between July 2014 and September 2016, with the last follow-up completed in March 2017. Interventions: (1) Immediate reduction to 0.4 mg of nicotine per gram of tobacco cigarettes; (2) gradual reduction from 15.5 mg to 0.4 mg of nicotine per gram of tobacco cigarettes with 5 monthly dose changes; or (3) maintenance on 15.5 mg of nicotine per gram of tobacco cigarettes. Main Outcomes and Measures: Between-group differences in 3 co-primary biomarkers of smoke toxicant exposure: breath carbon monoxide (CO), urine 3-hydroxypropylmercapturic acid (3-HPMA, metabolite of acrolein), and urine phenanthrene tetraol (PheT, indicator of polycyclic aromatic hydrocarbons) calculated as area under the concentration-time curve over the 20 weeks of intervention. Results: Among 1250 randomized participants (mean age, 45 years; 549 women [44%]; 958 [77%] completed the trial), significantly lower levels of exposure were observed in the immediate vs gradual reduction group for CO (mean difference, -4.06 parts per million [ppm] [95% CI, -4.89 to -3.23]; P < .0055), 3-HPMA (ratio of geometric means, 0.83 [95% CI, 0.77 to 0.88]; P < .0055), and PheT (ratio of geometric means, 0.88 [95% CI, 0.83 to 0.93]; P < .0055). Significantly lower levels of exposure were observed in the immediate reduction vs control group for CO (mean difference, -3.38 [95% CI, -4.40 to -2.36]; P < .0055), 3-HPMA (ratio of geometric means, 0.81 [95% CI, 0.75 to 0.88]; P < .0055), and PheT (ratio of geometric means, 0.86 [95% CI, 0.81 to 0.92]; P < .0055). No significant differences were observed between the gradual reduction vs control groups for CO (mean difference, 0.68 [95% CI, -0.31 to 1.67]; P = .18), 3-HPMA (ratio of geometric means, 0.98 [95% CI, 0.91 to 1.06]; P = .64), and PheT (ratio of geometric means, 0.98 [95% CI, 0.92 to 1.04]; P = .52). Conclusions and Relevance: Among smokers, immediate reduction of nicotine in cigarettes led to significantly greater decreases in biomarkers of smoke exposure across time compared with gradual reduction or a control group, with no significant differences between gradual reduction and control. Trial Registration: clinicaltrials.gov Identifier: NCT02139930.

The Minnesota Green Tea Trial (MGTT), a randomized controlled trial of the efficacy of green tea extract on biomarkers of breast cancer risk: study rationale, design, methods, and participant characteristics.

PURPOSE: The Minnesota Green Tea Trial (MGTT) was a randomized, placebo-controlled, double-blinded trial investigating the effect of daily green tea extract consumption for 12 months on biomarkers of breast cancer risk. METHODS: Participants were healthy postmenopausal women at high risk of breast cancer due to dense breast tissue with differing catechol-O-methyltransferase (COMT) genotypes. The intervention was a green tea catechin extract containing 843.0 ± 44.0 mg/day epigallocatechin gallate or placebo capsules for 1 year. Annual digital screening mammograms were obtained at baseline and month 12, and fasting blood and 24-h urine samples were provided at baseline and at months 6 and 12. Primary endpoints included changes in percent mammographic density, circulating endogenous sex hormones, and insulin-like growth factor axis proteins; secondary endpoints were changes in urinary estrogens and estrogen metabolites and circulating F2-isoprostanes, a biomarker of oxidative stress. RESULTS: The MGTT screened more than 100,000 mammograms and randomized 1,075 participants based on treatment (green tea extract vs. placebo), stratified by COMT genotype activity (high COMT vs. low/intermediate COMT genotype activity). A total of 937 women successfully completed the study and 138 dropped out (overall dropout rate = 12.8 %). CONCLUSIONS: In this paper we report the rationale, design, recruitment, participant characteristics, and methods for biomarker and statistical analyses.

Carcinogenicity and DNA adduct formation of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone and enantiomers of its metabolite 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol in F-344 rats.

4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is metabolized to enantiomers of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), found in the urine of virtually all people exposed to tobacco products. We assessed the carcinogenicity in male F-344 rats of (R)-NNAL (5 ppm in drinking water), (S)-NNAL (5 ppm), NNK (5 ppm) and racemic NNAL (10 ppm) and analyzed DNA adduct formation in lung and pancreas of these rats after 10, 30, 50 and 70 weeks of treatment. All test compounds induced a high incidence of lung tumors, both adenomas and carcinomas. NNK and racemic NNAL were most potent; (R)-NNAL and (S)-NNAL had equivalent activity. Metastasis was observed from primary pulmonary carcinomas to the pancreas, particularly in the racemic NNAL group. DNA adducts analyzed were O (2)-[4-(3-pyridyl)-4-oxobut-1-yl]thymidine (O (2)-POB-dThd), 7-[4-(3-pyridyl)-4-oxobut-1-yl]guanine(7-POB-Gua),O (6)-[4-(3-pyridyl)-4-oxobut-1-yl]deoxyguanosine(O (6)-POB-dGuo),the 4-(3-pyridyl)-4-hydroxybut-1-yl(PHB)adductsO (2)-PHB-dThd and 7-PHB-Gua, O (6)-methylguanine (O (6)-Me-Gua) and 4-hydroxy-1-(3-pyridyl)-1-butanone (HPB)-releasing adducts. Adduct levels significantly decreased with time in the lungs of rats treated with NNK. Pulmonary POB-DNA adducts and O (6)-Me-Gua were similar in rats treated with NNK and (S)-NNAL; both were significantly greater than in the (R)-NNAL rats. In contrast, pulmonary PHB-DNA adduct levels were greatest in the rats treated with (R)-NNAL. Total pulmonary DNA adduct levels were similar in (S)-NNAL and (R)-NNAL rats. Similar trends were observed for DNA adducts in the pancreas, but adduct levels were significantly lower than in the lung. The results of this study clearly demonstrate the potent pulmonary carcinogenicity of both enantiomers of NNAL in rats and provide important new information regarding DNA damage by these compounds in lung and pancreas.

Analysis of 4-hydroxy-1-(3-pyridyl)-1-butanone (HPB)-releasing DNA adducts in human exfoliated oral mucosa cells by liquid chromatography-electrospray ionization-tandem mass spectrometry.

Quantitation of DNA adducts could provide critical information on the relationship between exposure to tobacco smoke and cancer risk in smokers. In this study, we developed a robust and sensitive liquid chromatography-tandem mass spectrometry method for the analysis of 4-hydroxy-1-(3-pyridyl)-1-butanone (HPB)-releasing DNA adducts in human oral cells, a noninvasive source of DNA for biomarker studies. Isolated DNA undergoes acid hydrolysis, after which samples are purified by solid-phase extraction and analyzed by LC-ESI-MS/MS. The developed method was applied to the analysis of samples obtained via collection with a commercial mouthwash from 30 smokers and 15 nonsmokers. In smokers, the levels of HPB-releasing DNA adducts averaged 12.0 pmol HPB/mg DNA (detected in 20 out of 28 samples with quantifiable DNA yield), and in nonsmokers, the levels of adducts averaged 0.23 pmol/mg DNA (detected in 3 out of 15 samples). For the 30 smoking subjects, matching buccal brushings were also analyzed, and HPB-releasing DNA adducts were detected in 24 out of 27 samples with quantifiable DNA yield, averaging 44.7 pmol HPB/mg DNA. The levels of adducts in buccal brushings correlated with those in mouthwash samples of smokers (R = 0.73, p < 0.0001). Potentially, the method can be applied in studies of individual susceptibility to tobacco-induced cancers in humans.

Donor selection for natural killer cell receptor genes leads to superior survival after unrelated transplantation for acute myelogenous leukemia.

Killer-cell immunoglobulin-like receptor (KIR) genes form a diverse, immunogenetic system. Group A and B KIR haplotypes have distinctive centromeric (Cen) and telomeric (Tel) gene-content motifs. Aiming to develop a donor selection strategy to improve transplant outcome, we compared the contribution of these motifs to the clinical benefit conferred by B haplotype donors. We KIR genotyped donors from 1409 unrelated transplants for acute myelogenous leukemia (AML; n = 1086) and acute lymphoblastic leukemia (ALL; n = 323). Donor KIR genotype influenced transplantation outcome for AML but not ALL. Compared with A haplotype motifs, centromeric and telomeric B motifs both contributed to relapse protection and improved survival, but Cen-B homozygosity had the strongest independent effect. With Cen-B/B homozygous donors the cumulative incidence of relapse was 15.4% compared with 36.5% for Cen-A/A donors (relative risk of relapse 0.34; 95% confidence interval 0.2-0.57; P < .001). Overall, significantly reduced relapse was achieved with donors having 2 or more B gene-content motifs (relative risk 0.64; 95% confidence interval 0.48-0.86; P = .003) for both HLA-matched and mismatched transplants. KIR genotyping of several best HLA-matched potential unrelated donors should substantially increase the frequency of transplants by using grafts with favorable KIR gene content. Adopting this practice could result in superior disease-free survival for patients with AML.

Two-sample survival probability curves: A graphical approach for the analysis of time to event data in clinical trials.

With the aim to improve the communication of trial results, we introduce a novel graphical approach that complements the analysis of time to event outcomes in two-arm randomized trials. We define the so-called two-sample survival probability curve and propose a nonparametric estimator of the curve based on a random walk using Kaplan-Meier survival estimates for the two arms. We then use the estimated curve to visualize treatment effect as well as potential effect modification of factors of interest. We also propose to estimate two-sample survival probability curves within the framework of the Cox model to graphically assess model fit. The proposed two-sample survival probability plot puts trials in a standardized [0,1] × [0,1] space, allowing for a simple visualization of the main effect, effect modification, and the adequacy of a model fit.

Donors with group B KIR haplotypes improve relapse-free survival after unrelated hematopoietic cell transplantation for acute myelogenous leukemia.

Survival for patients with acute myeloid leukemia (AML) is limited by treatment-related mortality (TRM) and relapse after unrelated donor (URD) hematopoietic cell transplantation (HCT). Natural killer (NK)-cell alloreactivity, determined by donor killer-cell immunoglobulin-like receptors (KIRs) and recipient HLA, correlates with successful HCT for AML. Hypothesizing that donor KIR genotype (A/A: 2 A KIR haplotypes; B/x: at least 1 B haplotype) would affect outcomes, we genotyped donors and recipients from 209 HLA-matched and 239 mismatched T-replete URD transplantations for AML. Three-year overall survival was significantly higher after transplantation from a KIR B/x donor (31% [95% CI: 26-36] vs 20% [95% CI: 13-27]; P = .007). Multivariate analysis demonstrated a 30% improvement in the relative risk of relapse-free survival with B/x donors compared with A/A donors (RR: 0.70 [95% CI: 0.55-0.88]; P = .002). B/x donors were associated with a higher incidence of chronic graft-versus-host disease (GVHD; RR: 1.51 [95% CI: 1.01-2.18]; P = .03), but not of acute GVHD, relapse, or TRM. This analysis demonstrates that unrelated donors with KIR B haplotypes confer significant survival benefit to patients undergoing T-replete HCT for AML. KIR genotyping of prospective donors, in addition to HLA typing, should be performed to identify HLA-matched donors with B KIR haplotypes.

A prospectively measured serum biomarker for a tobacco-specific carcinogen and lung cancer in smokers.

BACKGROUND: No prior studies have related a tobacco-specific carcinogen to the risk of lung cancer in smokers. Of the over 60 known carcinogens in cigarette smoke, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is specific to tobacco and causes lung cancer in laboratory animals. Its metabolites, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol and its glucuronides (total NNAL), have been studied as biomarkers of exposure to NNK. We studied the relation of prospectively measured NNK biomarkers to lung cancer risk. METHODS: In a case-control study nested in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, we randomly selected 100 lung cancer cases and 100 controls who smoked at baseline and analyzed their baseline serum for total NNAL, cotinine, and r-1,t-2,3,c-4-tetrahydroxy-1,2,3,4-tetrahydrophenanthrene (PheT), a biomarker of polycyclic aromatic hydrocarbon exposure and metabolic activation. To examine the association of the biomarkers with all lung cancers and for histologic subtypes, we computed odds ratios for total NNAL, PheT, and cotinine using logistic regression to adjust for potential confounders. FINDINGS: Individual associations of age, smoking duration, and total NNAL with lung cancer risk were statistically significant. After adjustment, total NNAL was the only biomarker significantly associated with risk (odds ratio, 1.57 per unit SD increase; 95% confidence interval, 1.08-2.28). A similar statistically significant result was obtained for adenocarcinoma risk, but not for nonadenocarcinoma. CONCLUSIONS: This first reporting of the effect of the prospectively measured tobacco-specific biomarker total NNAL, on risk of lung cancer in smokers provides insight into the etiology of smoking-related lung cancer and reinforces targeting NNK for cancer prevention.

Longitudinal stability in cigarette smokers of urinary eicosanoid biomarkers of oxidative damage and inflammation.

The urinary metabolites (Z)-7-[1R,2R,3R,5S)-3,5-dihydroxy-2-[(E,3S)-3-hydroxyoct-1-enyl]cyclopentyl]hept-5-enoic acid (8-iso-PGF2α), an F2-isoprostane and biomarker of oxidative damage, and "prostaglandin E2 metabolite" (PGE-M), a biomarker of inflammation, are elevated in cigarette smokers. However, there is little information in the literature on the longitudinal stability of these widely used biomarkers. In a large clinical trial involving 10 institutional sites, smokers were given, free of charge over a period of 20 weeks, Spectrum NRC600/601 research cigarettes containing 15.5 mg nicotine/g tobacco. All participants were instructed to smoke these cigarettes for the duration of the study. At weeks 4, 8, 12, 16, and 20, first morning urine voids were collected and analyzed for 8-iso-PGF2α and PGE-M using validated liquid chromatography-electrospray ionization-tandem mass spectrometry methods. The mean level of 8-iso-PGF2α at Week 4 was 1.34 ± 1.08 (S.D.) pmol/mg creatinine (N = 226) while that of PGE-M was 73.7 ± 113 (S.D.) pmol/mg creatinine (N = 232). The corresponding levels at Week 20 were 1.35 ± 0.93 (S.D.) pmol/mg creatinine (N = 209) for 8-iso-PGF2α and 74.2 ± 142 (S.D.) pmol/mg creatinine (N = 210) for PGE-M. There was variation in these values in the intervening weeks. The intra-class correlation coefficients (ICC) were 0.51 (95% CI, 0.45, 0.57) and 0.36 (0.30, 0.43), for 8-iso-PGF2α and PGE-M, respectively, indicating fair longitudinal stability for 8-iso-PGF2α and poorer longitudinal stability for PGE-M in cigarette smokers. Males had higher ICC values than females for both 8-iso-PGF2α and PGE-M. These results indicate that, in addition to cigarette smoking, endogenous processes of oxidative damage and inflammation influence the levels of these biomarkers over time among current smokers.

Longitudinal stability in cigarette smokers of urinary biomarkers of exposure to the toxicants acrylonitrile and acrolein.

The urinary metabolites cyanoethyl mercapturic acid (CEMA) and 3-hydroxypropyl mercapturic acid (3-HPMA) have been widely used as biomarkers of exposure to acrylonitrile and acrolein, respectively, but there are no published data on their consistency over time in the urine of cigarette smokers. We provided, free of charge over a 20 week period, Spectrum NRC600/601 research cigarettes to cigarette smokers in the control arm of a randomized clinical trial of the reduced nicotine cigarette. Urine samples were collected at weeks 4, 8, 12, 16, and 20 and analyzed for CEMA and 3-HPMA, and total nicotine equivalents (TNE) using validated methods. Creatinine-corrected intra-class correlation coefficients for CEMA, 3-HPMA, and TNE were 0.67, 0.46, and 0.68, respectively, indicating good longitudinal consistency for CEMA, while that of 3-HPMA was fair. A strong correlation between CEMA and TNE values was observed. These data support the use of CEMA as a reliable biomarker of tobacco smoke exposure. This is the first report of the longitudinal stability of the biomarkers of acrylonitrile and acrolein exposure in smokers. The data indicate that CEMA, the biomarker of acrylonitrile exposure, is consistent over time in cigarette smokers, supporting its use. While 3-HPMA levels were less stable over time, this biomarker is nevertheless a useful monitor of human acrolein exposure because of its specificity to this toxicant.

Does the hematopoietic cell transplantation specific comorbidity index predict transplant outcomes? A validation study in a large cohort of umbilical cord blood and matched related donor transplants.

The hematopoietic cell transplantation specific comorbidity index (HCT-CI) has been recently proposed to predict the probability of nonrelapse mortality (NRM) and overall survival (OS) in allogeneic HCT recipients while taking into account any pretransplant comorbidity. We tested the validity of the HCT-CI in a cohort of 373 adult HCT recipients (184 matched-related donor and 189 unrelated umbilical cord blood) who received a myeloablative (N = 150) or nonmyeloablative (N = 223) conditioning regimen. HCT-CI scores of 0, 1, 2, and > or =3 were present in 58 (16%), 56 (15%), 64 (17%), and 195 (52%) patients, respectively. Pulmonary conditions were the most common comorbidity. Cumulative incidence of NRM at 2 years was 10%, 20%, 24%, and 28% for HCT-CI scores of 0, 1, 2, and > or =3, respectively (P = .01). The corresponding probability of OS at 2 years was 72%, 67%, 51%, and 48%, respectively (P < .01). On multivariate analyses adjusted for recipient age, disease risk, donor source, and conditioning regimen intensity, the relative risks for NRM for HCT-CI scores of 1, 2, and > or =3 (compared to a score of 0) were 2.0 (95% confidence intervals, 0.8-5.3), 2.6 (1.0-6.7), and 3.2 (1.4-7.4), respectively. The risks for overall mortality were 1.2 (0.6-2.1), 2.0 (1.1-3.4), and 2.1 (1.3-3.3), respectively. In subgroup analyses, the HCT-CI score did not consistently predict NRM and OS among different donor sources and conditioning regimens. The HCT-CI, although a useful tool for capturing pretransplant comorbidity and risk-assessment, needs to be further validated prior to adopting it for routine clinical use.

A Bayesian hierarchical model for demand curve analysis.

Drug self-administration experiments are a frequently used approach to assessing the abuse liability and reinforcing property of a compound. It has been used to assess the abuse liabilities of various substances such as psychomotor stimulants and hallucinogens, food, nicotine, and alcohol. The demand curve generated from a self-administration study describes how demand of a drug or non-drug reinforcer varies as a function of price. With the approval of the 2009 Family Smoking Prevention and Tobacco Control Act, demand curve analysis provides crucial evidence to inform the US Food and Drug Administration's policy on tobacco regulation, because it produces several important quantitative measurements to assess the reinforcing strength of nicotine. The conventional approach popularly used to analyze the demand curve data is individual-specific non-linear least square regression. The non-linear least square approach sets out to minimize the residual sum of squares for each subject in the dataset; however, this one-subject-at-a-time approach does not allow for the estimation of between- and within-subject variability in a unified model framework. In this paper, we review the existing approaches to analyze the demand curve data, non-linear least square regression, and the mixed effects regression and propose a new Bayesian hierarchical model. We conduct simulation analyses to compare the performance of these three approaches and illustrate the proposed approaches in a case study of nicotine self-administration in rats. We present simulation results and discuss the benefits of using the proposed approaches.

A Bayesian hierarchical model for demand curve analysis.

Drug self-administration experiments are a frequently used approach to assess the abuse liability and reinforcing property of a compound. It has been used to assess the abuse liabilities of various substances such as psychomotor stimulants and hallucinogens, food, nicotine, and alcohol. The demand curve generated from a self-administration study describes how demand of a drug or non-drug reinforcer varies as a function of price. With the approval of the 2009 Family Smoking Prevention and Tobacco Control Act, demand curve analysis provides crucial evidence to inform the US Food and Drug Administration's policy on tobacco regulation because it produces several important quantitative measurements to assess the reinforcing strength of nicotine. The conventional approach popularly used to analyze the demand curve data is individual-specific non-linear least square regression. The non-linear least square approach sets out to minimize the residual sum of squares for each subject in the dataset; however, this one-subject-at-a-time approach does not allow for the estimation of between- and within-subject variability in a unified model framework. In this paper, we review the existing approaches to analyze the demand curve data, non-linear least square regression, and the mixed effects regression and propose a new Bayesian hierarchical model. We conduct simulation analyses to compare the performance of these three approaches and illustrate the proposed approaches in a case study of nicotine self-administration in rats. We present simulation results and discuss the benefits of using the proposed approaches.

Short versus continuous gemcitabine treatment of non-small cell lung cancer in an in vitro cell culture bioreactor system.

Five-year survival for non-small cell lung cancer is 15%. Gemcitabine is a nucleoside analogue that inhibits ribonucleotide reductase and interferes with DNA replication. In this study, we sought to compare short versus continuous infusion gemcitabine in an in vitro bioreactor system using pharmacokinetic-guided dosing. Gemcitabine was infused over either 0.5 or 2.5h to produce concentration-time profiles that mimic those measured in biological samples (i.e., patient plasma). The effects of gemcitabine on the growth and survival of H2009 cells were examined using trypan blue staining, cell cycle analysis, TUNEL assay, and clonogenic assay. Data were analyzed with two ways analysis of variance. Maximum gemcitabine (Cmax) concentrations during the short infusion were 51.2+/-10.4 microM and for the continuous, 14.8+/-2.93 microM. Steady-state concentrations during the continuous infusions were 14.9+/-2.90 microM. Gemcitabine treatment resulted in a decrease for G1 fraction relative to controls. G2/M, subG1 and TUNEL were higher following gemcitabine relative to controls. Survival was approximately 20-fold higher following the short infusion compared with the continuous infusion (p = 0.0085). In conclusion, gemcitabine infused by this novel method induced apoptosis after both the short and continuous infusions, and long-term survival was significantly diminished following continuous compared with the short infusion.

Impact of smoking reduced nicotine content cigarettes on sensitivity to cigarette price: further results from a multi-site clinical trial.

AIMS: To assess the impact of a reduction in the nicotine content of cigarettes on estimated consumption of reduced nicotine cigarettes and usual brand cigarettes at a variety of hypothetical prices. DESIGN: Double-blind study with participants assigned randomly to receive cigarettes for 6 weeks that were either usual brand or an investigational cigarette with one of five nicotine contents. SETTING: Ten sites across the United States. PARTICIPANTS: A total of 839 eligible adult smokers randomized from 2013 to 2014. INTERVENTION AND COMPARATOR: Participants received their usual brand or an investigational cigarette with one of five nicotine contents: 15.8 (primary control), 5.2, 2.4, 1.3, or 0.4 mg/g. MEASUREMENTS: The Cigarette Purchase Task was completed at baseline and at the week 6 post-randomization visit. FINDINGS: Compared with normal nicotine content controls, the lowest nicotine content (0.4 mg/g) reduced the number of study cigarettes participants estimated they would smoke at a range of prices [mean reduction relative to 15.8 mg/g at a price of $4.00/pack: 9.50, 95% confidence interval (CI) = 6.81,12.19]. The lowest nicotine content also reduced the maximum amount of money allocated to study cigarettes and the price at which participants reported they would stop buying study cigarettes [median reduction relative to 15.8 mg/g, 95% CI = $8.21 (4.27,12.15) per day and $0.44 (0.17,0.71) per cigarette, respectively]. A reduction in nicotine content to the lowest level also reduced the maximum amount of money allocated to usual brand cigarettes (median reduction relative to 15.8 mg/g: $4.39 per day, 95% CI = 1.88,6.90). CONCLUSIONS: In current smokers, a reduction in nicotine content may reduce cigarette consumption, reduce the reinforcement value of cigarettes and increase cessation if reduced nicotine content cigarettes were the only cigarette available for purchase.

A Randomized Controlled Trial of Green Tea Extract Supplementation and Mammographic Density in Postmenopausal Women at Increased Risk of Breast Cancer.

Epidemiologic and animal studies suggest a protective role of green tea against breast cancer. However, the underlying mechanism is not understood. We conducted a randomized, double-blinded, placebo-controlled phase II clinical trial to investigate whether supplementation with green tea extract (GTE) modifies mammographic density (MD), as a potential mechanism, involving 1,075 healthy postmenopausal women. Women assigned to the treatment arm consumed daily 4 decaffeinated GTE capsules containing 1,315 mg total catechins, including 843 mg epigallocatechin-3-gallate (EGCG) for 12 months. A computer-assisted method (Madena) was used to assess MD in digital mammograms at baseline and month 12 time points in 932 completers (462 in GTE and 470 in placebo). GTE supplementation for 12 months did not significantly change percent MD (PMD) or absolute MD in all women. In younger women (50-55 years), GTE supplementation significantly reduced PMD by 4.40% as compared with the placebo with a 1.02% PMD increase from pre- to postintervention (P = 0.05), but had no effect in older women (Pinteraction = 0.07). GTE supplementation did not induce MD change in other subgroups of women stratified by catechol-O-methyltransferase genotype or level of body mass index. In conclusion, 1-year supplementation with a high dose of EGCG did not have a significant effect on MD measures in all women, but reduced PMD in younger women, an age-dependent effect similar to those of tamoxifen. Further investigation of the potential chemopreventive effect of green tea intake on breast cancer risk in younger women is warranted. Cancer Prev Res; 10(12); 710-8. ©2017 AACR.