Search

Incidence rates for diseases are widely used in the field of medical research because they lead to clear and simple physical and clinical interpretations. In this study, we propose an efficient estimation method that incorporates auxiliary subgroup information related to the incidence rate into the estimation of the Cox proportional hazard model. The results show that utilizing the incidence rate information improves the efficiency of the estimation of regression parameters based on the double empirical likelihood method compared to that for conventional models that do not incorporation such information. We show that estimators of regression parameters asymptotically follow a multivariate normal distribution with a variance-covariance matrix that can be consistently estimated. Simulation results indicate that the proposed estimators significantly increase efficiency. Finally, an example of the effects of type 2 diabetes on stroke is applied to demonstrate the proposed method. [ABSTRACT FROM AUTHOR]

Objective: We assessed the quality of life (QoL) and pregnancy outcomes of in vitro fertilization (IVF) treatment among infertile women with endometriosis, as compared to infertile women without endometriosis.Study Design: Eighty-one (81) endometriosis women (with 142 embryo transfer [ET] cycles) and 605 non-endometriosis women (with 1063 ET cycles) were included. QoL was measured by FertiQoL at the date before ET. Pregnancy outcomes included biochemical pregnancy, ongoing pregnancy and live birth. Generalized estimating equation analyses were performed to assess the association between QoL and IVF pregnancy.Results: Endometriosis-affected women had significantly lower QoL, as indicated by mind/body, treatment environment and total treatment scores, and total scores of FertiQoL (p < .05), compared to those without endometriosis. Among non-endometriosis women, QoL scores were significantly associated with successful IVF pregnancy; with one unit increase in QoL scores as measured by emotional domain of FertiQoL, the probabilities of ongoing pregnancy and live birth significantly increased by 2.5% and 2.8%, respectively (p < .05). This association was also observed among endometriosis women but it did not reach statistical significance.Conclusions: Lower QoL among women with endometriosis versus non-endometriosis during IVF treatment highlights the importance of developing strategies to improve their QoL, which may enhance following pregnancy rates in this population. [ABSTRACT FROM AUTHOR]

This study investigated the incidence, rationales, and associated factors of inappropriate urinary catheter use among hospitalized older patients by gender. A longitudinal study of 321 patients with urinary catheter was conducted. Demographic factors, present health factors, urinary catheter factors, and indications of catheter use were collected. A total of 53.7% of urinary catheter-days were inappropriate. For both men and women, there was no significant difference in the incidence and common rationales of inappropriate use. Women, however, have another associated factor with inappropriate use. More tailored alternatives are needed for women to increase comfort to avoid inappropriate catheter use. [ABSTRACT FROM AUTHOR]

Sample size determination is essential during the planning phases of clinical trials. To calculate the required sample size for paired right-censored data, the structure of the within-paired correlations needs to be pre-specified. In this article, we consider using popular parametric copula models, including the Clayton, Gumbel, or Frank families, to model the distribution of joint survival times. Under each copula model, we derive a sample size formula based on the testing framework for rank-based tests and non-rank-based tests (i.e., logrank test and Kaplan- Meier statistic, respectively). We also investigate how the power or the sample size was affected by the choice of testing methods and copula model under different alternative hypotheses. In addition to this, we examine the impacts of paired-correlations, accrual times, follow-up times, and the loss to follow-up rates on sample size estimation. Finally, two real-world studies are used to illustrate our method and R code is available to the user. [ABSTRACT FROM AUTHOR]

Sample size determination is essential during the planning phases of a study. When the study endpoint is the time to an event, Cox proportional hazard model is the traditional technique to analyze the effects of covariates on survival time. In contrast to the proportional hazard model, the additive hazard (AH) model specifies that the effect of covariates additively increases or decreases the hazard function. The popularity of this model is that it gives a more intuitive interpretation without the proportional hazard assumption. Because there is no literature estimating the required sample size based on the AH model, we provide a flexible formula for calculating the required sample size. The proposed formula incorporates time-independent and time-dependent effects of covariates without complicated mathematical calculations. The performance of the method was evaluated by extensive simulations. Finally, some pilot studies are shown to illustrate the applications. [ABSTRACT FROM PUBLISHER]

Determination of an adequate sample size is critical to the design of research ventures. For clustered right-censored data, Manatunga and Chen [Sample size estimation for survival outcomes in cluster-randomized studies with small cluster sizes. Biometrics. 2000;56(2):616–621] proposed a sample size calculation based on considering the bivariate marginal distribution as Clayton copula model. In addition to the Clayton copula, other important family of copulas, such as Gumbel and Frank copulas are also well established in multivariate survival analysis. However, sample size calculation based on these assumptions has not been fully investigated yet. To broaden the scope of Manatunga and Chen [Sample size estimation for survival outcomes in cluster-randomized studies with small cluster sizes. Biometrics. 2000;56(2):616–621]'s research and achieve a more flexible sample size calculation for clustered right-censored data, we extended the work by assuming the marginal distribution as bivariate Gumbel and Frank copulas. We evaluate the performance of the proposed method and investigate the impacts of the accrual times, follow-up times and the within-clustered correlation effect of the study. The proposed method is applied to two real-world studies, and the R code is made available to users. [ABSTRACT FROM PUBLISHER]

The Shewhart-type control chart is traditionally developed under the normality assumption. In practice, however, this assumption may not hold. Because the skew normal distribution represents a broad distribution class and is more flexible than is the normal distribution, we propose two new control charts to monitor process mean and spread for skew normal distributed data. Moreover, to facilitate practical implementation, tables of charting constants are provided. We conducted simulation studies to compare the false alarm rates, and the results show that new proposed charts perform better than others as skewness increases. Finally, an illustrative example is provided. [ABSTRACT FROM PUBLISHER]