A clinical trial may have no subject (0%) or every subject (100%) developing the outcome of concern in either of the two comparison groups. This will cause a zero-cell in the four-cell (2x2) table of a trial using a binary outcome and make it impossible to estimate the odds ratio, a commonly used effect measure. A usual way to deal with this problem is to add 0.5 to each of the four cells in the 2x2 table. This is known as Haldane's approximation. In meta-analysis, Haldane's approximation can also be applied. Two approaches are possible: add 0.5 to only the trials with a zero cell or to all the trials in the meta-analysis. Little is known which approach is better when used in combination with different definitions of the odds ratio: the ordinary odds ratio, Peto's odds ratio and Mantel-Haenszel odds ratio. / A new formula is derived for converting Peto's odds ratio to the risk difference. The derived risk difference through the new method was then compared with the true risk difference and the risk difference derived by taking the Peto's odds ratio as the ordinary odds ratio. All simulations and analyses were conducted on the Statistical Analysis Software (SAS). / Conclusions. The estimated confidence interval of a meta-analysis would mostly exclude the truth if an inappropriate correction method is used to deal with zero cells. Counter-intuitively, the combined result of a meta-analysis will be worse as the number of studies included becomes larger. Mantel-Haenszel odds ratio without applying Haldane's approximation is recommended in general for dealing with sparse data in meta-analysis. The ordinary odds ratio with adding 0.5 to only the trials with a zero cell can be used when the trials are heterogeneous and the odds ratio is close to 1. Applying Haldane's approximation to all trials in a meta-analysis should always be avoided. Peto's odds ratio without Haldane's approximation can always be considered but the new formula should be used for converting Peto's odds ratio to the risk difference. / In addition, the odds ratio needs to be converted to a risk difference to aid decision making. Peto's odds ratio is preferable in some situations and the risk difference is derived by taking Peto's odds ratio as an ordinary odds ratio. It is unclear whether this is appropriate. / Methods. For studying the validity of Haldane's approximation, we defined 361 types of meta-analysis. Each type of meta-analysis is determined by a unique combination of the risk in the two compared groups and thus provides a unique true odds ratio. The number of trials in a meta-analysis is set at 5, 10 and 50 and the sample size of each trial in a meta-analysis varies at random but is made sufficiently small so that at least one trial in a meta-analysis will have a zero-cell. The number of outcome events in a comparison group of a trial is generated at random according to the pre-determined risk for that group. One thousand homogeneous meta-analyses and one thousand heterogeneous meta-analyses are simulated for each type of meta-analysis. Two Haldane's approximation approaches in addition to no approximation are evaluated for three definitions of the odds ratio. Thus, nine combined odds ratios are estimated for each type of meta-analysis and are all compared with the true odds ratio. The percentage of meta-analyses with the 95% confidence interval including the true odds ratio is estimated as the main index for validity of the correction methods. / Objectives. (1) We conducted a simulation study to examine the validity of Haldane's approximation as applied to meta-analysis, and (2) we derived and evaluated a new method to covert Peto's odds ratio to the risk difference, and compared it with the conventional conversion method. / Results. By using the true ordinary odds ratio, the percentage of meta-analyses with the confidence interval containing the truth was lowest (from 23.2% to 53.6%) when Haldane's approximation was applied to all the trials regardless the definition of the odds ratios used. The percentage was highest with Mantel-Haenszel odds ratio (95.0%) with no approximation applied. The validity of the corrections methods increases as the true odds ratio gets close to one, as the number of trials in a meta-analysis decreases, as the heterogeneity decreases and the trial size increases. / The proposed new formula performed better than the conventional method. The mean relative difference between the true risk difference and the risk difference obtained from the new formula is -0.006% while the mean relative difference between the true risk difference and the risk difference obtained from the conventional method is -10.9%. / The validity is relatively close (varying from 86.8% to 95.8%) when the true odds ratio is between 1/3 and 3 for all combinations of the correction methods and definitions of the odds ratio. However, Peto's odds ratio performed consistently best if the true Peto's odds ratio is used as the truth for comparison among the three definitions of the odds ratio regardless the correction method (varying from 88% to 98.7%). / Tam Wai-san Wilson. / "Jan 2006." / Adviser: J. L. Tang. / Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6488. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 151-157). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Conclusion. The missing data is a common problem in clinical trial. The methodology development is urgently needed to detect the difference of two treatments drug in patient quality of life. The modified pattern mixture model incorporating generalized estimating equation method or multiple imputation method provides a solution to tackle the non-ignorable missing data problem. Different clinical trials with various treatment schedules, missing data patterns will be formed. Further studies are needed to study the optimal choice of patterns under the methods. / Introduction. Health-related Quality of Life (HRQoL) has now been included as a major endpoint in many cancer clinical trials in addition to the traditional endpoints such as tumor response and survival. It refers to how illness or its treatment affects patients' ability to function and whether it induces symptoms. Toxicity, progression and death are common outcome affecting patient's QOL in cancer trial. Since this type of missing data are not occurred at random and are called non-ignorable missing data, conventional methods of analyses are not appropriate. It is important to develop general methods to deal with this problem so that treatment effectiveness for improving patient's QOL or those with serious side effect that is detrimental to patient's QOL can be identified. / Methods. The generalized estimating equation based on modified pattern mixture model is constructed to deal with non-ignorable missing data problem. We conducted a simulation study to examine performance of the model for different types of data. Two scenarios were examined. The first case assumes that two groups have quadratic trend but with different rates of change. The second case assumes that one group has linear trend with time while the other group has quadratic trend with time. Moreover, the second methodology is the multiple imputation based on modified pattern mixture model. The main idea is to resample the data within each pattern to create the full data set and use the standard method to analyze the data. Comparison between two methods was carried out in this study. / Recently, joint models for the QOL outcomes and the indicators of drop-outs are used in longitudinal studies to correct for non-ignorable missing. Two broad classes of joint models, selection model and pattern mixture model, were used. Most of the methodology has been developed in the selection model while the pattern mixture model has attracted less attention due to the identifiability problem. Although pattern mixture model has its own limitation, a modified version of this model incorporating Generalized Estimating Equation can be used in practice. / Result. The power of generalized estimating equation alone is higher than pattern mixture model when the missing data is missing at random. Moreover, the bias of generalized estimating equation is less than that of pattern mixture model when the missing data is missing at random. However, the pattern mixture model performs well when the missing data is missing not at random. On the other hand, the modified pattern mixture model has higher power than the standard pattern mixture model if one group has quadratic trend and other group has linear trend. However, the power of modified pattern mixture model is similar or worst than the standard when the data is both quadratic trends with different rates of change. On the other hand, the results of multiple imputation based on modified pattern mixture model were similar but the power was less than the generalized estimating equation model. / Mo Kwok Fai. / "August 2006." / Adviser: Benny Zee. / Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6051. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 91-93). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

The inclusion of racial/ethnic minorities in treatment outcomes trials for children and adolescents with depression and anxiety is essential, particularly given the assumption, required by the NIH, that racial diversity is important to the generalizability of clinical trial outcomes. A search for randomized clinical trials on the treatment of child and adolescent depression and anxiety was conducted using the Medline and Psychinfo databases. These were then reviewed to determine whether race or ethnicity were 1) factored into recruitment strategies; 2) represented in the trial sample; and 3) included in moderator analyses to determine the extent to which they may influence trial outcomes. 37 original and 13 follow-up trials were identified (total N = 3330). None identified strategies for targeted recruitment of racial/ethnic minorities. Six did not report race. All minority groups except for Native Americans are underrepresented as compared to 2000 US Census figures; however, only one study reported Native Americans as participants. Overall, 67% of the sample was Caucasian, 26% minority, and 6% unreported. There was no trend in minority representation by year. Most studies reviewed do report the ethnic breakdown of their sample population, although methods vary. Six studies, three original and three follow-up, explored the ethnicity as a moderator. Without an increased presence of minorities in clinical trials, it is unclear that the results of these studies can reliably generalize to a diverse population. The importance of studies in minority samples becomes apparent, as does the need for a greater emphasis on recruitment.

Thesis (Ph. D.)--University of California, San Diego, 2001. / Vita. Includes bibliographical references (leaves 202-206).

Source: Dissertation Abstracts International, Volume: 70-03, Section: B, page: 1576. Advisers: Dejian Lai; Asha S. Kapadia. Includes bibliographical references.

It is well known that incorporating auxiliary covariates in the analysis of randomized clinical trials (RCTs) can increase efficiency. Questions still remain regarding how to flexibly incorporate baseline covariates while maintaining valid inference. Recent methodological advances that use semiparametric theory to develop covariate-adjusted inference for RCTs have focused on independent outcomes. In biomedical research, however, cluster randomized trials and longitudinal studies, characterized by correlated responses, are commonly used. We develop methods that flexibly incorporate baseline covariates for efficiency improvement in randomized studies with correlated outcomes. In Chapter 1, we show how augmented estimators may be used for cluster randomized trials, in which treatments are assigned to groups of individuals. We demonstrate the potential for imbalance correction and efficiency improvement through consideration of both cluster- and individual-level covariates. To improve small-sample estimation, we consider several variance adjustments. We evaluate this approach for continuous and binary outcomes through simulation and apply it to the Young Citizens study, a cluster randomized trial of a community behavioral intervention for HIV prevention in Tanzania. Chapter 2 builds upon the previous chapter by deriving semiparametric locally efficient estimators of marginal mean treatment effects when outcomes are correlated. Estimating equations are determined by the efficient score under a mean model for marginal effects when data contain baseline covariates and exhibit correlation. Locally efficient estimators are implemented for longitudinal data with continuous outcomes and clustered data with binary outcomes. Methods are illustrated through application to AIDS Clinical Trial Group Study 398, a longitudinal randomized study that compared various protease inhibitors in HIV-positive subjects. In Chapter 3, we empirically evaluate several covariate-adjusted tests of intervention effects when baseline covariates are selected adaptively and the number of randomized units is small. We demonstrate that randomization inference preserves type I error under model selection while tests based on asymptotic theory break down. Additionally, we show that covariate adjustment typically increases power, except at extremely small sample sizes using liberal selection procedures. Properties of covariate-adjusted tests are explored for independent and multivariate outcomes. We revisit Young Citizens to provide further insight into the performance of various methods in small-sample settings.

The primary purpose of this paper is to indicate how Canadian legislative reforms could provide valuable insights regarding the reform of sexual assault law in South Africa. The first section of this paper contains an examination of three particular evidentiary rules in the South African context. In the second section a feminist critique of rape law is used to explore the significance of these rules in rape trials, using the framework of significant themes of the feminist enquiry. In the third section I look at the development of these evidentiary rules in Canada and evaluate the present legal position in this regard, with particular reference to decision of the Supreme Court of Canada in R v Seaboyer, R v Gayme (1991) 83 D.L.R. (4th) 193. In the final instance, an attempt is made to identify some significant lessons for those seeking to formulate the much needed reforms to these rules in South Africa.

The intention of this dissertation is to expose the gendered experiences of rape victims, based on the notion that while it should be the purpose of rape laws to protect victims of rape, in many circumstances the legal process results in disempowering experiences for victims, particularly women. Therefore, I suggest that the courtroom, a supposedly just space, is one which is laced with patriarchal undercurrents that work specifically against women. Rape is a complex and multi-faceted subject that is fast becoming an epidemic. In relation to HIV/AIDS and sexuality, the issue of rape certainly becomes compounded. Deconstructing the historical and cultural experiences of women is not only necessary in attempting to understand rape, but also the reasons why the justice system, which is dominantly a male domain, may still cling to patriarchal principles. One reason for the marginalization of rape victims may be the continued regard of women as second class citizens. The rape trial, in which Jacob Zuma was the alleged rapist, is a starting point, and by referring to this case, I intend to reveal and discuss weaknesses with regard to rape law within the South African context. / Thesis (M.A.)-University of KwaZulu-Natal, Durban, 2009.

This research presents a comprehensive narrative of the development of slavery in early Indiana history. It chronicles the evolution from a French system of slavery to one influenced by Virginian legal code. In exploring the nature of the practiced slavery and the obstacles to slavery’s implementation, the evidence demonstrates that while Indiana did practice slavery, the state was never at risk of developing a plantation-style slave society. The 1820 Indiana Supreme Court case Polly v. Lasselle, which officially ended any legal form of slavery in the state, exemplifies the evolution of slavery and the constantly changing power relationship between owner and slave. By means of previously unused primary sources, this thesis creates a new account of the court case and places it within the context of Indiana’s slavery history. / Department of History

Thesis (J.S.M.)--Stanford University, 2009. / Submitted to the Stanford Program in International Legal Studies at the Stanford Law School, Stanford University. "April 2009." Includes bibliographical references (leaves 78-82). Abstract available online.