Impact of atopic dermatitis on the quality of life of Nigerian children: a hospital-based cross-sectional study

Background. Atopic dermatitis (AD) is the most common inflammatory skin disease in childhood. A skin disorder with a relapsing course,AD exerts a significant disease burden on affected children. However, there is a dearth of knowledge about the impact of AD on the quality of life (QOL) of affected children in Nigeria.Objectives. To examine the impact of AD on QOL in children of various age groups, and to identify the relationship between patient variables (age, gender, socioeconomic status), disease severity and QOL in AD.Method. This was a cross-sectional descriptive study of children with AD attending the dermatology clinic of Lagos University Teaching Hospital, Idi-Araba, Lagos, Nigeria. AD cases were recruited from new paediatric patients ≤16 years who attended the clinic over a 6-month period. English and Yoruba versions of the Infants' Dermatitis Quality of Life Index (IDQOL) and the Children's Dermatology Life Quality Index (CDLQI) were used to determine the QOL of the subjects. AD severity was evaluated using the Objective SCORing ofAtopic Dermatitis (obj-SCORAD) index.Results. Forty-seven subjects with AD were identified. Their ages ranged from newborn to 16 years. The median(interquartile range (IQR))IDQOL score was 6.0 (3.0 - 15.5; n=25), and the median (IQR) CDLQI score was 9.5 (7.75 - 17.75; n=22). The mean (standard deviation)obj-SCORAD score was 34.4 (17.2). The question on itching was the highest-scoring question in both QOL questionnaires. There was no significant difference in QOL across age, gender and socioeconomic status groups. However, greater QOL scores were significantly correlated with higher AD severity scores.Conclusion. The study confirms that AD impairs the QOL of affected children in all age groups. QOL assessments are relevant tools which provide a patient's perspective, thus improving the understanding of the impact of AD on afflicted individuals

A two stage conditional power adaptive design adjusting for treatment by covariate interaction.

During the design and planning phase of clinical trials, researchers often assume that no covariate by treatment interaction exists. This assumption has led to many trials being underpowered to detect such interactions and perhaps inaccurate interpretation of treatment effects. We propose a two-stage adaptive design that incorporates the likely existence of a treatment by covariate interaction into the design and implementation of the clinical trial. The information in stage 1 is used to test for the presence of the covariate by treatment interaction. A statistically significant interaction influences how the second stage of the trial will be implemented, thereby aiding in the full understanding and consequently, an accurate interpretation of the treatment effect. We examine the statistical properties of the proposed design using a binary outcome under different types of covariate by treatment interactions and treatment allocation schemes. A conditional power approach is used to prevent inflation of the overall trial type I error rate while maintaining adequate statistical power conditional on the statistically significant interaction.

Stochastically curtailed phase II clinical trials.

Phase II trials often test the null hypothesis H(0): p or=p(1), where p is the true unknown proportion responding to the new treatment, p(0) is the greatest response proportion which is deemed clinically ineffective, and p(1) is the smallest response proportion which is deemed clinically effective. In order to expose the fewest number of patients to an ineffective therapy, phase II clinical trials should terminate early when the trial fails to produce sufficient evidence of therapeutic activity (i.e. if p or=p(1)), the trial should declare the drug effective in the fewest patients possible to allow for advancement to a phase III comparative trial. Several statistical designs, including Simon's minimax and optimal designs, have been developed that meet these requirements. In this paper, we propose three alternative designs that rely upon stochastic curtailment based on conditional power. We compare and contrast the properties of the three approaches: (1) stochastically curtailed (SC) binomial tests, (2) stochastically curtailed (SC) Simon's optimal design, and (3) SC Simon's minimax design to those of Simon's minimax and Simon's optimal designs. For each of these designs we compare and contrast the number of opportunities for study termination, the expected sample size of the trial under the null hypothesis (p