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If the overall thrust of your study is to test a hypothesis, the wording of the main objectives should also indicate the direction of the relationship being tested. The study population So far we have focused on only one aspect of a study, the research problem. But every study in social sciences has a second aspect, the study population, from whom the required information to find answers to your research questions is obtained. As you narrow the research problem, similarly you need to decide very specifically and clearly who constitutes your study population, in order to select the appropriate respondents. Suppose you have designed a study to ascertain the needs of young people living in a community. In terms of the study population, one of the first questions you need to answer is: ‘Who do I consider to be a young person? Having decided the age group that constitutes your ‘young person’, the next question you need to consider is whether you want to select young people of either gender or confine the study to one only. In addition, there is another dimension to consider: that is, what constitutes the community? Which geographical area(s) or ethnic background should I select my respondents from? As a part of identifying your study population, you need to decide who would you consider an immigrant. Will you select your respondents irrespective of the country of origin or select only those who have come from a specific country(ies)? In a way you need to narrow your definition of the study population as you have done with your research problem. These issues are discussed in greater depth under ‘Establishing operational definitions’ following this section. In quantitative research, you need to narrow both the research problem and the study population and make them as specific as possible so that you and your readers are clear about them. In qualitative research, reflecting the ‘exploratory’ philosophical base of the approach, both the study population and the research problem should remain loose and flexible to ensure the freedom necessary to obtain varied and rich data if a situation emerges. Establishing operational definitions In defining the problem you may use certain words or items that are difficult to measure and/or the understanding of which may vary from respondent to respondent. In a research study it is important to develop, define or establish a set of rules, indicators or yardsticks in order to establish clearly the meaning of such words/items. It is sometimes also important to define clearly the study population from which you need to obtain the required information. When you define concepts that you plan to use either in your research problem and/or in identifying the study population in a measurable form, they are called working definitions or operational definitions. You must understand that these working definitions that you develop are only for the purpose of your study and could be quite different to legal definitions, or those used by others. As the understanding of concepts can vary markedly from person to person, your working definitions will inform your readers what exactly you mean by the concepts that you have used in your study. To measure the effectiveness of a retraining programme designed to help young people. Although these objectives clearly state the main thrust of the studies, they are not specific in terms of the main variables to be studied and the study populations. You cannot count the number of children living below the poverty line until you decide what constitutes the poverty line and how to determine it; you cannot find out the impact of immigration on family roles unless you identify which roles constitute family roles; and you cannot measure effectiveness until you define what effectiveness is. On the other hand, it is equally important to decide exactly what you mean by ‘children’, ‘immigrants’ or ‘young’. In addition, are you going to consider immigrants from every country or only a few? In many cases you need to develop operational definitions for the variables and concepts you are studying and for the population that becomes the source of the information for your study. This is achieved through the process of developing operational/working definitions. You need to develop operational definitions for the major concepts you are using in your study and develop a framework for the study population enabling you to select appropriate respondents. Operational definitions may differ from day-to-day meanings as well as dictionary or legal definitions.
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We determined that paclitaxel caused cells to transit mitosis on multipolar spindles, resulting in increased chromosome gains and losses. Methods: We identified 36 subjects with metastatic breast cancer and measurable disease who received taxane therapy and had available archived tissue. No statistically significant correlations were found between tumor type or prior chemotherapy and response to taxanes. Surprisingly, there was an inverse correlation between Ki67 and taxane response, although this did not reach statistical significance. The First Affiliated Hospital of Nanjing Medical University, 2 Nanjing, Jiangsu, China and Nanjing Medical University, Nanjing, Jiangsu, China. All of their clinical data were recorded, including age, performance status, hormone receptor status, metastatic site, primary or acquired trastuzumab resistance, previous treatment and so on. Lapatinib tended to reduce the risk of disease progression in patients resistant to trastuzumab primarily. Tohoku University Graduate School of Medicine, Sendai, Japan and Juntendo University Hospital, Tokyo, Japan. However, the molecular events orchestrating the innate and acquired therapeutic resistance are not completely understood. National Institute for Cellular Biotechnology, Dublin City University, Dublin, 2 3 4 Ireland; Royal College of Surgeons in Ireland, Dublin, Ireland; University of California Los Angeles, Los Angeles and St. However, we have also shown that long-term exposure to tyrosine kinase inhibitors leads to the development of acquired resistance. To examine the prevention of the development of afatinib resistance, cells were treated twice weekly with afatinib, dasatinib, or the combination and stained with crystal violet when confluent. Furthermore, the resistant cells were cross-resistant to lapatinib, neratinib and trastuzumab. These cells were then inoculated into immunodeficient mice and treated with trastuzumab twice weekly. While many resistance mechanisms have been explored, clinical trials testing associated therapeutics have had mixed outcomes. Understanding mechanisms of resistance and finding therapeutic strategies to target them, therefore, remain important challenges. The results of this study provide important biological and clinically relevant insights. Recent evidence suggests that nodal metastases have different clones and subclones compared to the primary tumour. Whole-transcriptome AmpliSeq targeted-sequencing has been analysed for 4 patients. However, the expression levels of some remained high in the on-treatment node samples in all 4 patients analysed compared with the matched primary tumour on treatment. Cell growth and migration/invasion were measured by a bright-field automated cell counter and Transwell insert system. The expressions of 50 genes were measured in baseline samples and surgical specimens using the nCounter platform. Association between two variables was evaluated using χ2 test or Pearson correlation. Results: Gene expression profile was feasible in 58 pre/post sample pairs with a median of 7. Up to 30 patients per arm were permitted, to allow 12 subjects per arm with evaluable paired biopsies obtained at baseline, and after 4. Changes in marker expression (both absolute and %) between biopsies were calculated, and compared between the two groups. Cells were incubated in phenol red minus medium containing 5% charcoal-dextran treated serum for 5 days to remove exogenous hormones. However, over 30% of patients relapse with endocrine resistance emphasising the need for improved therapeutic strategies. Efficacy in combination with everolimus, palbociclib and abemaciclib was also evaluated.
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As longas these posterior error rates are well controlled at given levels of α∗ and β∗, we are able to accept any trial outcome (either positive or negative) at an acceptable conﬁdence level. Then, the traditional frequentist sample size formula can be used to determine the needed sample size. Note that such a procedure is not only very simple but also free of model and testingprocedures. Hence, it can be directly applied to essentially any type of clinical trial and any type of the testingprocedures. For illustration purposes, we consider its usage for comparingmeans and survival in the next two subsections. For illustration purpose, we consider only the most commonly used two-arm parallel design with equal sample size allocation. The situation with multiple arms and unequal sample size allocation can be similarly obtained. Followingthe notations of Chapter 3, denote xij the response observed from the jth subject in the ith treatment group, j =1. Bayesian Sample Size Calculation When σ2 is unknown, the null hypothesis H is rejected at the α level of 0 signiﬁcance if x¯1· − x¯2· >t. Furthermore, one needs to specify the value of (P1,P2), which controls the posterior error rate. It is assumed that the clinical meaningdifference is given by = 5% and the standard deviation is σ = 10%. It, however, should be noted that the resultant sample sizes 63 and 49 are not directly comparable, because they are controllingdifferent error rates. On the other hand, the 49 subjects per treatment group is selected to control the two posterior error rates α∗ and β∗ at the levels of 0. Therefore, which sample size should be used depends on what statistical test will be used for testingthe hypotheses. To provide a better understanding, various sample sizes needed at different posterior error conﬁguration are summarized in Table 13. The method of posterior credible interval approach explicitly takes into consideration the prior information. On the other hand, the method of posterior error rate does not suffer from this limitation. First, if there is no reliable prior information available, is there still a need for Bayesian sample size calculation method? Second, if there is a need for Bayesian’s method for sample size calculation, then how does one incorporate it into the framework that is accepted from the frequentist point of view? With an attempt to address these two questions, we will introduce an alternative method. Sample size calculation is done under certain assumptions regarding the parameters. In practice, since the standard deviation of the target patient population is usually unknown, a typical approach is to use estimates from some pilot studies as the surrogate for the true parameters. We then treat these estimates as the true parameters to justify the sample size needed for the intended clinical trial. It should be noted that the parameter estimate obtained from the pilot study inevitably suffers from the samplingerror, which causes the uncertainty (variation) about the true parameter. Consequently, it is likely to yield an unstable estimate of sample size for the intended clinical trial. In order to ﬁx the idea, we conduct a simple simulation study to examine the effect of the pilot study uncertainty. Furthermore, we assume that the population standard deviation σ = 1 and the true mean response is =25%. The Bootstrap-Median Approach 351 are known, are given by (z + z)2σ2 α/2 β n = 2 ≈ 169.
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