Refractive prediction by various intraocular lens formulas using optical biometry and effect of ocular parameters on their accuracy

Purpose: To assess the prediction accuracy of intraocular lens (IOL) formulas and study the effect of axial length (AL), central corneal thickness (CCT), anterior chamber depth (ACD), and lens thickness (LT) on the accuracy of formulas using optic biometry. Methods: This study was performed on 164 eyes of 164 patients who underwent uneventful cataract surgery. Ocular biometry values were measured using Lenstar?900, and intraocular lens (IOL) power was calculated using the SRK/T, SRK II, Hoffer Q, Holladay 2, and Barrett Universal II formulas. We evaluated the extent of bias within each formula for different ocular biometric measurements and explored the relationship between the prediction error and the ocular parameters by using various IOL formulas. Results: The summarization of refractive prediction error and absolute prediction error for each IOL formulation was performed after adjusting the mean refractive error to zero. The deviation in the error values was minimum for SRK/T (0.265) followed by Holladay 2 (0.327) and Barret (0.382). Further, SRK/T had the lowest median (0.15) and mean (0.198) absolute error as compared to other formulations. For the above formulations, 100% of the eyes were in the diopter range of ±1.0. It was observed that the overall distribution of error was closer to zero for SRK/T, followed by Holladay 2 and then Barrett. Conclusion: In summary, we found that accuracy was better in SRK/T formula. We achieved a better understanding of how each variable in the formulas is relatively weighed and the influencing factors in the refraction prediction.

Analysis of Repeated Data of Multi-center Clinical Trial for Traditional Chinese Medicine by Hierarchical Linear Model / 中国中医药信息杂志

Objective To explore the applicability and feasibility of the hierarchical linear model in dealing with the repeated measurement data by applying it into multi-center clinical trials’ evaluation of new Chinese medicine. Methods The theoretical research described the basic concepts and principles of hierarchical linear model and compared the applying conditions of traditional statistical methods with hierarchical linear model. In the empirical study, the hierarchical linear model was used into a real multicenter clinical trial research of traditional Chinese medicine, with the TCM syndrome score as the analysis indicator. Results The hierarchical linear model added time, group and time×group as random variables into the model to get the final result. All of the three variables were significant in the result. The interaction (time×group) statistical result was t=2.65, P=0.008 1. During the whole treatment, the TCM syndrome mean score of trial group reduced 8.5 and the control group reduced 7.47. At the end of treatment, the TCM syndrome mean score of trial group was 2.46, and the control group was 3.31, which was higher than trail group. For this result, combining with the TCM syndrome score’s developing trend, we can see that the efficacy of trial group is not worse than the control group. Conclusion By comparing the data requirements, applying conditions and analysis results, the hierarchical linear model could be an effective method which can be used in multi-center trial to evaluate traditional Chinese drug.

Analysis of repeated measurement data in the clinical trials.

Statistics is an integral part of Clinical Trials. Elements of statistics span Clinical Trial design, data monitoring, analyses and reporting. A solid understanding of statistical concepts by clinicians improves the comprehension and the resulting quality of Clinical Trials. In biomedical research it has been seen that researcher frequently use t‑test and ANOVA to compare means between the groups of interest irrespective of the nature of the data. In Clinical Trials we record the data on the patients more than two times. In such a situation using the standard ANOVA procedures is not appropriate as it does not consider dependencies between observations within subjects in the analysis. To deal with such types of study data Repeated Measure ANOVA should be used. In this article the application of One‑way Repeated Measure ANOVA has been demonstrated by using the software SPSS (Statistical Package for Social Sciences) Version 15.0 on the data collected at four time points 0 day, 15th day, 30th day, and 45th day of multicentre clinical trial conducted on Pandu Roga (~Iron Deficiency Anemia) with an Ayurvedic formulation Dhatrilauha.