Cure models, survival probabilities, and solid organ transplantation for patients with colorectal cancer.

A previous cancer diagnosis can preclude patients from consideration for solid organ transplantation. Statistical models may improve candidate selection. We fitted statistical cure models and estimated five-year cancer-specific survival (5yCSS) for colorectal cancer patients in the United States using registry data. The median cure probability at cancer diagnosis for patients in the general population was 0.67. Among 956 colorectal cancer patients who underwent solid organ transplantation, the median time since diagnosis was 6.3 years and the median 5yCSS at transplantation was 0.96. Patients with a 5yCSS below 0.90 had increased posttransplant cancer-specific mortality (hazard ratio 3.31, 95% confidence interval 1.52-7.21). Compared with recently published guidelines, our models suggested shorter wait times for some groups of colorectal cancer patients (e.g., stage IIA cancers) and longer wait times for others (stages IIB, IIIB, IIIC, IV). In conclusion, colorectal cancer patients undergoing solid organ transplantation had excellent prognoses, reflecting selection incorporating existing guidelines and clinical judgement. Nonetheless, 5yCSS probabilities estimated from cure models offer additional prognostic information for patients considered for transplantation and identify situations where current guidelines might be revised. We developed a web-based tool for clinicians to calculate 5yCSS probabilities for use in transplant evaluation for individual colorectal cancer patients (https://dceg.cancer.gov/tools/risk-assessment/calculator-of-colorectal-cancer-survival-probability).

To evaluate hypertrichosis with low dose oral minoxidil and spironolactone combination therapy for alopecia.

Low dose oral minoxidil (LDOM) is an efficacious and safe treatment for alopecia, however, a notable side effect is hypertrichosis. Spironolactone, known for treating hirsutism, is also used off-label for the treatment of certain forms of alopecia and may reduce LDOM-induced hypertrichosis. We performed a retrospective review of 54 patients seen at NYU Langone Health and compared hypertrichosis rates in female alopecia patients on LDOM monotherapy versus those on combination therapy with spironolactone. Among 54 patients, 37 received LDOM alone and 17 received the combination. Hypertrichosis developed in 33.3% of patients, with lower rates in the combination group (17.6% vs. 40.5% for monotherapy). Although not statistically significant, the trend suggests spironolactone may mitigate hypertrichosis. The study highlights the potential of combination therapy to address hypertrichosis and calls for larger studies to confirm these findings.

Ambient ultraviolet A, ultraviolet B and risk of melanoma in a nationwide United States cohort, 1984-2014.

BACKGROUND: Ultraviolet radiation (UVR) exposure is the primary risk factor for melanoma although the relationship is complex. Compared to radiation from UVB wavelengths, UVA makes up a majority of the surface solar UVR, penetrates the skin more deeply, is the principal range emitted by tanning beds, and is less filtered by sunscreens and window glass. Few studies have examined the relationship between ambient UVA and UVB and melanoma risk. METHODS: Incidence rate ratios (IRRs) and 95% confidence intervals (CIs) were estimated for the association between satellite-based ambient (based on residential history) UVA, UVB and melanoma in non-Hispanic White participants using data from the United States Radiologic Technologists study, a large, nationwide prospective cohort. Associations of UVA and UVB quartile (Q) were examined in mutually adjusted and stratified models, additionally adjusted for demographic and sun sensitivity characteristics. RESULTS: There were 837 incident melanoma cases among 62,785 participants. Incidence of melanoma was statistically significantly increased for the highest quartile of childhood UVA exposure after adjustment for UVB (IRR = 2.82; 95%CI:1.46,5.44), but not for higher childhood UVB after adjustment for UVA. Childhood UVA was associated with increased melanoma risk within strata of UVB. Childhood UVB was not associated with melanoma after adjustment for UVA, but there was some evidence of lower risk with increased lifetime ambient UVB after UVA adjustment. CONCLUSIONS: Melanoma risk was elevated among participants living in locations with high annual childhood and lifetime UVA after controlling for UVB. With confirmation, these findings support increased protection from solar UVA for melanoma prevention.

Combating "dread shed": The impact of overlapping topical and oral minoxidil on temporary hair shedding during oral minoxidil initiation.

Background: Low dose oral minoxidil (LDOM) is a preferred treatment for alopecia due to ease of use and efficacy. While LDOM is typically well tolerated, patients may experience a temporary increase in hair shedding starting treatment, colloquially regarded as "dread shed". One proposed method to combat this is to overlap therapies by maintaining use of topical minoxidil when initiating LDOM. Objective: To evaluate the impact of maintaining topical minoxidil when initiating LDOM on "dread shed". Methods: We performed a retrospective chart review of patients seen at New York University Langone Health Dermatology from January 1, 2008 to August 1, 2023 prescribed LDOM. Results: A total of 115 patients met inclusion criteria, of whom 37 maintained use of topical minoxidil when initiating LDOM. Six patients experienced "dread shed" when initiating LDOM, 2 of whom overlapped therapies. We did not find that overlapping therapies had a significant impact on decreasing rates of "dread shed". Limitations: Limitations include retrospective design, sample size, and subjective patient-reported assessment of hair shedding. Conclusions: A total of 5.2% of patients experienced dread shed, which is lower than previously reported in literature. Maintaining topical minoxidil during LDOM initiation does not significantly impact "dread shed". This remains a significant side effect deserving of further research.

Who enrolls and why? Examining center-specific underlying patterns behind enrollment: a New York City-based traumatic brain injury model systems study.

BACKGROUND: To elucidate the sociodemographic and study factors involved in enrollment in the Traumatic Brain Injury Model System (TBIMS) database, this study examined the effect of a variety of variables on enrollment at a local TBIMS center. METHODS: A sample of 654 individuals from the local TBIMS center was studied examining enrollment by age, gender, race, ethnicity, homelessness status at date of injury, history of homelessness, health insurance status, presence of social support, primary language, consenting in hospital or after discharge, and the need for an interpreter. Binary logistic regression was conducted to identify variables that predict center-based enrollment into TBIMS. RESULTS: Results demonstrated that older age was associated with decreasing enrollment (OR = 0.99, p = 0.01), needing an interpreter made enrollment less likely (OR = 0.33, p < 0.01), being primarily Spanish speaking predicted enrollment (OR = 3.20, p = 0.02), Hispanic ethnicity predicted enrollment (OR = 7.31, p = 0.03), and approaching individuals in the hospital predicted enrollment (OR = 6.94, p < 0.01). Here, OR denotes the odds ratio estimate from a logistic regression model and P denotes the corresponding p-value. CONCLUSIONS: These results can be useful in driving enrollment strategies at this center for other similar TBI research, and to contribute a representative TBI sample to the national database.

Estimating the overall fraction of phenotypic variance attributed to high-dimensional predictors measured with error.

In prospective genomic studies (e.g., DNA methylation, metagenomics, and transcriptomics), it is crucial to estimate the overall fraction of phenotypic variance (OFPV) attributed to the high-dimensional genomic variables, a concept similar to heritability analyses in genome-wide association studies (GWAS). Unlike genetic variants in GWAS, these genomic variables are typically measured with error due to technical limitation and temporal instability. While the existing methods developed for GWAS can be used, ignoring measurement error may severely underestimate OFPV and mislead the design of future studies. Assuming that measurement error variances are distributed similarly between causal and noncausal variables, we show that the asymptotic attenuation factor equals to the average intraclass correlation coefficients of all genomic variables, which can be estimated based on a pilot study with repeated measurements. We illustrate the method by estimating the contribution of microbiome taxa to body mass index and multiple allergy traits in the American Gut Project. Finally, we show that measurement error does not cause meaningful bias when estimating the correlation of effect sizes for two traits.

Incorporating survival data into case-control studies with incident and prevalent cases.

Typically, case-control studies to estimate odds-ratios associating risk factors with disease incidence only include newly diagnosed cases. Recently proposed methods allow incorporating information on prevalent cases, individuals who survived from disease diagnosis to sampling, into cross-sectionally sampled case-control studies under parametric assumptions for the survival time after diagnosis. Here we propose and study methods to additionally use prospectively observed survival times from prevalent and incident cases to adjust logistic models for the time between diagnosis and sampling, the backward time, for prevalent cases. This adjustment yields unbiased odds-ratio estimates from case-control studies that include prevalent cases. We propose a computationally simple two-step generalized method-of-moments estimation procedure. First, we estimate the survival distribution assuming a semiparametric Cox model using an expectation-maximization algorithm that yields fully efficient estimates and accommodates left truncation for prevalent cases and right censoring. Then, we use the estimated survival distribution in an extension of the logistic model to three groups (controls, incident, and prevalent cases), to adjust for the survival bias in prevalent cases. In simulations, under modest amounts of censoring, odds-ratios from the two-step procedure were equally efficient as those estimated from a joint logistic and survival data likelihood under parametric assumptions. This indicates that utilizing the cases' prospective survival data lessens model dependencies and improves precision of association estimates for case-control studies with prevalent cases. We illustrate the methods by estimating associations between single nucleotide polymorphisms and breast cancer risk using controls, and incident and prevalent cases sampled from the US Radiologic Technologists Study cohort.

A comparative study of two-sample tests for interval-censored data.

Interval-censored data are ubiquitous in clinical studies where actual time-to-event is difficult to measure. A number of nonparametric tests have been proposed to conduct a two-sample test using interval-censored data, and these tests can be used for assessing and comparing treatment effects over the control group. Alternatively, as commonly perceived, parametric tests can also be used assuming data are generated from a parametric family of distributions. To provide some guidance on choosing an appropriate method, in this paper, the performance of parametric tests and a series of nonparametric tests are compared through extensive simulation studies that cover a wide range of scenarios with varying sample sizes, varying censoring mechanisms and varying alternative hypotheses. For the purpose of illustration, we also apply these procedures to analyse three real datasets.

Analysis of linear transformation models with covariate measurement error and interval censoring.

Among several semiparametric models, the Cox proportional hazard model is widely used to assess the association between covariates and the time-to-event when the observed time-to-event is interval-censored. Often, covariates are measured with error. To handle this covariate uncertainty in the Cox proportional hazard model with the interval-censored data, flexible approaches have been proposed. To fill a gap and broaden the scope of statistical applications to analyze time-to-event data with different models, in this paper, a general approach is proposed for fitting the semiparametric linear transformation model to interval-censored data when a covariate is measured with error. The semiparametric linear transformation model is a broad class of models that includes the proportional hazard model and the proportional odds model as special cases. The proposed method relies on a set of estimating equations to estimate the regression parameters and the infinite-dimensional parameter. For handling interval censoring and covariate measurement error, a flexible imputation technique is used. Finite sample performance of the proposed method is judged via simulation studies. Finally, the suggested method is applied to analyze a real data set from an AIDS clinical trial.

On the Reproducibility of Psychological Science.

Investigators from a large consortium of scientists recently performed a multi-year study in which they replicated 100 psychology experiments. Although statistically significant results were reported in 97% of the original studies, statistical significance was achieved in only 36% of the replicated studies. This article presents a reanalysis of these data based on a formal statistical model that accounts for publication bias by treating outcomes from unpublished studies as missing data, while simultaneously estimating the distribution of effect sizes for those studies that tested nonnull effects. The resulting model suggests that more than 90% of tests performed in eligible psychology experiments tested negligible effects, and that publication biases based on p-values caused the observed rates of nonreproducibility. The results of this reanalysis provide a compelling argument for both increasing the threshold required for declaring scientific discoveries and for adopting statistical summaries of evidence that account for the high proportion of tested hypotheses that are false. Supplementary materials for this article are available online.