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.

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.