Outcomes after PD-103 versus I-125 for low dose rate prostate brachytherapy monotherapy: An international, multi-institutional study

BACKGROUND AND PURPOSE

Pd-103 and I-125 are commonly used in low dose rate (LDR) brachytherapy for prostate cancer. Comparisons of outcomes by isotope type are limited, but Pd-103 has distinct radiobiologic advantages over I-125 despite its lesser availability outside the United States. We evaluated oncologic outcomes after Pd-103 vs I-125 LDR monotherapy for prostate cancer.

MATERIALS AND METHODS

We retrospectively analyzed databases at 8 institutions for men who received definitive LDR monotherapy with Pd-103 (n = 1,597) or I-125 (n = 7,504) for prostate cancer. Freedom from clinical failure (FFCF) and freedom from biochemical failure (FFBF) stratified by isotope were analyzed by Kaplan-Meier univariate and Cox multivariate analyses. Biochemical cure rates (prostate-specific antigen level ≤ 0.2 ng/mL between 3.5 and 4.5 years of follow-up) by isotype were calculated for men with at least 3.5 years of follow-up and compared by univariate and multivariate logistic regression.

RESULTS

Compared with I-125, Pd-103 led to higher 7-year rates of FFBF (96.2% vs 87.6%, P < 0.001) and FFCF (96.5% vs 94.3%, P < 0.001). This difference held after multivariate adjustment for baseline factors (FFBF hazard ratio [HR] = 0.31, FFCF HR = 0.49, both P < 0.001). Pd-103 was also associated with higher cure rates on univariate (odds ratio [OR] = 5.9, P < 0.001) and multivariate (OR = 6.0, P < 0.001) analyses. Results retained significance in sensitivity analyses of data from the 4 institutions that used both isotopes (n = 2,971).

CONCLUSIONS

Pd-103 monotherapy was associated with higher FFBF, FFCF, and biochemical cure rates, and suggests that Pd-103 LDR may lead to improved oncologic outcomes compared with I-125.

Analgesic effect of oral ibuprofen 400, 600, and 800 mg; paracetamol 500 and 1000 mg; and paracetamol 1000 mg plus 60 mg codeine in acute postoperative pain: a single-dose, randomized, placebo-controlled, and double-blind study

PURPOSE

Effect size estimates of analgesic drugs can be misleading. Ibuprofen (400 mg, 600 mg, 800 mg), paracetamol (1000 mg, 500 mg), paracetamol 1000 mg/codeine 60 mg, and placebo were investigated to establish the multidimensional pharmacodynamic profiles of each drug on acute pain with calculated effect size estimates.

METHODS

A randomized, double-blind, single-dose, placebo-controlled, parallel-group, single-centre, outpatient, and single-dose study used 350 patients (mean age 25 year, range 18 to 30 years) of homogenous ethnicity after third molar surgery. Primary outcome was sum pain intensity over 6 h. Secondary outcomes were time to analgesic onset, duration of analgesia, time to rescue drug intake, number of patients taking rescue drug, sum pain intensity difference, maximum pain intensity difference, time to maximum pain intensity difference, number needed to treat values, adverse effects, overall drug assessment as patient-reported outcome measure (PROM), and the effect size estimates NNT and NNTp.

RESULTS

Ibuprofen doses above 400 mg do not significantly increase analgesic effect. Paracetamol has a very flat analgesic dose-response profile. Paracetamol 1000/codeine 60 mg gives similar analgesia as ibuprofen from 400 mg, but has a shorter time to analgesic onset. Active drugs show no significant difference in maximal analgesic effect. Other secondary outcomes support these findings. The frequencies of adverse effects were low, mild to moderate in all active groups. NNT and NTTp values did not coincide well with PROMs.

CONCLUSION

Ibuprofen doses above 400 mg for acute pain offer limited analgesic gain. Paracetamol 1000 mg/codeine 60 mg is comparable to ibuprofen doses from 400 mg. Calculated effect size estimates and PROM in our study seem not to relate well as clinical analgesic efficacy estimators.

TRIAL REGISTRATION

NCT00699114.

The association between intrapartum opioid fentanyl and early breastfeeding: A prospective observational study

INTRODUCTION

Intrapartum opioids in labor may interfere with the early breastfeeding phase and cause breastfeeding difficulties. This study examines the effects of intrapartum fentanyl given intravenously (IV) or through epidural analgesia (EDA) on early breastfeeding.

MATERIAL AND METHODS

This is a prospective observational study conducted in a regional maternity unit. We included 1101 healthy mothers of term singleton babies in vertex presentation born between 2016 and 2018 (468 nulliparous and 633 multiparous). The main data were collected prospectively, and additional data were retrieved from hospital records. The main outcome measures were exclusive breastfeeding at discharge, spontaneous suckling, and breastfeeding problems after birth. We assessed the outcomes in four groups categorized by intrapartum opioid exposure: none, IV fentanyl, EDA fentanyl and IV+EDA fentanyl. We also analyzed the dose-response relation of fentanyl administered by epidural or IV and early breastfeeding. Ultimately, we dichotomized the IV fentanyl group into two groups (≤200 µg and >200 µg) to further study the effect on early breastfeeding.

RESULTS

The odds of non-exclusive breastfeeding were doubled with EDA fentanyl (odds ratio [OR] 2.45, 95% CI 1.34-4.48, p = 0.004) and four times higher with IV+EDA fentanyl (OR 4.20, 95% CI 2.49-7.09, p < 0.001) compared with no opioid exposure. Spontaneous suckling was negatively associated with intrapartum fentanyl use (p < 0.001) irrespective of mode of administration. When the IV fentanyl doses exceeded 200 µg compared with less than 200 µg, we found a reduction in exclusive breastfeeding (81% vs. 89%; p = 0.014) and spontaneous suckling (68% vs. 83%; p < 0.001) and an increase in breastfeeding problems (41% vs. 27%; p = 0.004).

CONCLUSIONS

Fentanyl in labor is associated with breastfeeding difficulties. However, IV fentanyl in low doses (≤200 µg) seems to affect breastfeeding less than EDA fentanyl and is therefore a viable alternative when labor analgesia is needed. This could be most relevant for multiparous women, where a shorter labor is expected. More research is needed to determine the optimal dose and route of administration of fentanyl for labor analgesia.

A Bayesian two-way latent structure model for genomic data integration reveals few pan-genomic cluster subtypes in a breast cancer cohort

MOTIVATION

Unsupervised clustering is important in disease subtyping, among having other genomic applications. As genomic data has become more multifaceted, how to cluster across data sources for more precise subtyping is an ever more important area of research. Many of the methods proposed so far, including iCluster and Cluster of Cluster Assignments (COCAs), make an unreasonable assumption of a common clustering across all data sources, and those that do not are fewer and tend to be computationally intensive.

RESULTS

We propose a Bayesian parametric model for integrative, unsupervised clustering across data sources. In our two-way latent structure model, samples are clustered in relation to each specific data source, distinguishing it from methods like COCAs and iCluster, but cluster labels have across-dataset meaning, allowing cluster information to be shared between data sources. A common scaling across data sources is not required, and inference is obtained by a Gibbs Sampler, which we improve with a warm start strategy and modified density functions to robustify and speed convergence. Posterior interpretation allows for inference on common clusterings occurring among subsets of data sources. An interesting statistical formulation of the model results in sampling from closed-form posteriors despite incorporation of a complex latent structure. We fit the model with Gaussian and more general densities, which influences the degree of across-dataset cluster label sharing. Uniquely among integrative clustering models, our formulation makes no nestedness assumptions of samples across data sources so that a sample missing data from one genomic source can be clustered according to its existing data sources. We apply our model to a Norwegian breast cancer cohort of ductal carcinoma in situ and invasive tumors, comprised of somatic copy-number alteration, methylation and expression datasets. We find enrichment in the Her2 subtype and ductal carcinoma among those observations exhibiting greater cluster correspondence across expression and CNA data. In general, there are few pan-genomic clusterings, suggesting that models assuming a common clustering across genomic data sources might yield misleading results.

AVAILABILITY AND IMPLEMENTATION

The model is implemented in an R package called twl ('two-way latent'), available on CRAN. Data for analysis are available within the R package.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Contrasting DCIS and invasive breast cancer by subtype suggests basal-like DCIS as distinct lesions

Ductal carcinoma in situ (DCIS) is a non-invasive type of breast cancer with highly variable potential of becoming invasive and affecting mortality. Currently, many patients with DCIS are overtreated due to the lack of specific biomarkers that distinguish low risk lesions from those with a higher risk of progression. In this study, we analyzed 57 pure DCIS and 313 invasive breast cancers (IBC) from different patients. Three levels of genomic data were obtained; gene expression, DNA methylation, and DNA copy number. We performed subtype stratified analyses and identified key differences between DCIS and IBC that suggest subtype specific progression. Prominent differences were found in tumors of the basal-like subtype: Basal-like DCIS were less proliferative and showed a higher degree of differentiation than basal-like IBC. Also, core basal tumors (characterized by high correlation to the basal-like centroid) were not identified amongst DCIS as opposed to IBC. At the copy number level, basal-like DCIS exhibited fewer copy number aberrations compared with basal-like IBC. An intriguing finding through analysis of the methylome was hypermethylation of multiple protocadherin genes in basal-like IBC compared with basal-like DCIS and normal tissue, possibly caused by long range epigenetic silencing. This points to silencing of cell adhesion-related genes specifically in IBC of the basal-like subtype. Our work confirms that subtype stratification is essential when studying progression from DCIS to IBC, and we provide evidence that basal-like DCIS show less aggressive characteristics and question the assumption that basal-like DCIS is a direct precursor of basal-like invasive breast cancer.

Estimating the Prevalence of Pseudomyxoma Peritonei in Europe Using a Novel Statistical Method

Background

The determination of the incidence and prevalence of rare diseases is important for economists and health-care providers. Pseudomyxoma peritonei (PMP) is a rare, slow-growing abdominal cancer that represents a substantial burden on both patients and health-care systems. The incidence rate was previously approximated at 1–2 people per million per year; this incidence has never been challenged, and the prevalence has not been estimated.

Methods

Epidemiological data from Norway and England were obtained and analysed to calculate a minimum incidence rate based on the number of patients having a first surgical intervention for PMP. A novel method was then used to determine a prevalence rate for PMP, incorporating incidence, death, and cure rates in a multi-year analysis that accounted for the increasing population of Europe over a 10-year period.

Results

An incidence rate of 3.2 people per million per year was calculated, with a corresponding estimated prevalence rate of 22 people per million per year. By this calculation, 11,736 people in Europe were estimated to be living with PMP in 2018.

Conclusion

Incidence and prevalence are essential tools for assessment of the financial and human cost of a disease. For rare diseases, such as PMP, the lack of accurate registries presents a particular challenge in determining such health-related statistical parameters. Based on our calculations, a significant number of people are living with PMP in Europe, underlining the need for appropriate resource allocation to ensure that adequate health-care measures are provided.

Persistent lumbar radicular and low back pain; impact of genetic variability versus emotional distress

OBJECTIVE

Earlier studies documenting the effect of candidate genes on recovery have seldom taken into consideration the impact of emotional distress. Thus, we aimed to assess the modifying effect of emotional distress on genetic variability as a predictor for pain recovery in lumbar radicular (LRP) and low back pain (LBP).

RESULTS

The study population comprised 201 patients and mean age was 41.7 years. The significant association between MMP9 rs17576 (B = 0.71, 95% CI 0.18 to 1.24, p = 0.009) and pain recovery remained statistically significant after adjusting for pain intensity at baseline, age, gender, smoking, body mass index, pain localization and emotional distress (B = 0.68, 95% CI 0.18 to 1.18, p = 0.008). In contrast, the association between OPRM1 (B = - 0.85, 95% CI - 1.66 to - 0.05, p = 0.038) and pain recovery was abolished in the multivariate analysis (B = - 0.72, 95% CI - 1.46 to 0.02, p = 0.058). Hence, MMP9 rs17576 and emotional distress independently seem to predict persistent back pain. The predictive effect of OPRM1 rs179971 with regard to the same outcome is probably dependent on other factors including emotional processing. Trial registration The Regional Committee for Medical Research and Ethics reference number 2014/1754.

Hypothesis Tests for Neyman's Bias in Case-Control Studies

Survival bias is a long-recognized problem in case-control studies, and many varieties of bias can come under this umbrella term. We focus on one of them, termed Neyman's bias or "prevalence-incidence bias." It occurs in case-control studies when exposure affects both disease and disease-induced mortality, and we give a formula for the observed, biased odds ratio under such conditions. We compare our result with previous investigations into this phenomenon and consider models under which this bias may or may not be important. Finally, we propose three hypothesis tests to identify when Neyman's bias may be present in case-control studies. We apply these tests to three data sets, one of stroke mortality, another of brain tumors, and the last of atrial fibrillation, and find some evidence of Neyman's bias in the former two cases, but not the last case.

Research participant compensation: A matter of statistical inference as well as ethics

The ethics of compensation of research subjects for participation in clinical trials has been debated for years. One ethical issue of concern is variation among subjects in the level of compensation for identical treatments. Surprisingly, the impact of variation on the statistical inferences made from trial results has not been examined. We seek to identify how variation in compensation may influence any existing dependent censoring in clinical trials, thereby also influencing inference about the survival curve, hazard ratio, or other measures of treatment efficacy. In simulation studies, we consider a model for how compensation structure may influence the censoring model. Under existing dependent censoring, we estimate survival curves under different compensation structures and observe how these structures induce variability in the estimates. We show through this model that if the compensation structure affects the censoring model and dependent censoring is present, then variation in that structure induces variation in the estimates and affects the accuracy of estimation and inference on treatment efficacy. From the perspectives of both ethics and statistical inference, standardization and transparency in the compensation of participants in clinical trials are warranted.