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"You can choose to see a trial as a roadblock or an expressway. If you see it as a roadblock, it will obstruct your way. However, if you see it as an expressway, you can use it to learn and grow." "The gospel of Jesus Christ is the plan whereby we can become what God wants us to become. In fact, we become great people because of our trials, not in spite of them." This booklet reviews the nature, origins, and purposes of adversity and includes suggestions on how to patiently trust in the Lord and His eternal plan and how to use these trials to learn and grow stronger. You can benefit from your trials if you: Develop a relationship with God Let others help you Let God carry your burdens Trust that the Lord is in control and allow His will to be done Remember that everyone has challenges Let adversity make you a better person Live with integrity Be patient Make the best of your situation Serve others Keep a positive attitude Keep an eternal perspective
Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data.