Statistical Methods for Assessing Patterns of Change in Cancer-Control Behavior

评估癌症控制行为变化模式的统计方法

• 批准号: 8009761
• 负责人: Shira Ilana Dunsiger
• 金额: $ 8.36万
• 依托单位: MIRIAM HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-22 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8009761
• 关键词: Adopted; Adoption; Adult; Affect; American; Back; Behavior; Behavior Control; Behavior Therapy; Behavioral; Cancer Control; Categories; Characteristics; Cluster Analysis; Computer software; Computers; Data; Data Analyses; Data Set; Dose; Drops; Effectiveness; Effectiveness of Interventions; Exercise; Future; Goals; Guidelines; Intervention; Knowledge; Life Style; Maintenance; Malignant Neoplasms; Maps; Methodology; Methods; Metric; Modeling; Multivariate Analysis; Outcome; Participant; Pattern; Phase; Physical activity; Population; Population Group; Relapse; Research; Research Personnel; Risk; Running; Series; Statistical Methods; Time; United States National Center for Health Statistics; arm; base; behavior change; cancer risk; clinically significant; design; follow-up; high risk; innovation; meetings; psychosocial; response; sedentary; trend

DESCRIPTION (provided by applicant): Regular physical activity has been shown to be associated with reduced cancer risk (Lee et al, 2006 & McTiernan, 2008). Despite being aware of this, the majority of American adults do not currently meet physical activity guidelines (National Center for Health Statistics, 2007). Our research team has conducted numerous behavioral interventions aimed at encouraging physical activity adoption amongst previously sedentary adults (Marcus et al, 2007; Marcus, Lewis, Williams, Dunsiger et al, 2007; Pekmezi, Neighbors, Lee, Gans, Bock, Morrow, Marquez, Dunsiger & Marcus, 2009). Standard statistical methodology for analyzing data from these longitudinal trials may not suffice to describe patterns of physical activity adoption, maintenance and relapse. We propose a more sophisticated methodology aimed specifically at finding patterns of behavior change in six physical activity interventions. The ability to compare resulting sub-types of behavior change across studies (which themselves overlap in that they contain at least one treatment arm in common) will allow us to determine whether there are consistent patterns across studies and whether the distribution of these sub-types differs by treatment arm. In addition, we will identify potential predictors and moderators of pattern of behavior change by modifying our proposed computing routine to allow for other baseline variables to contribute to the distribution of sub-type. Finally, we will use these sub-types to identify key times in the treatment phase after which relapse to sedentary behavior is more likely. Each of these aims has specific implications for both design and analysis of future physical activity interventions. Using current methods we would be unable to carry out these same analyses and thus might risk missing key information about how participants change their cancer risk.

描述（由申请人提供）：定期的体力活动已被证明与降低癌症风险相关（Lee et al，2006 & McTiernan，2008）。尽管意识到这一点，但大多数美国成年人目前并不符合身体活动指南（国家卫生统计中心，2007年）。我们的研究团队进行了许多行为干预，旨在鼓励以前久坐的成年人进行体育活动（Marcus et al，2007; Marcus，刘易斯，威廉姆斯，Dunsiger et al，2007; Pekmezi，Neighbors，Lee，Gans，Bock，Morrow，Marquez，Dunsiger & Marcus，2009）。分析这些纵向试验数据的标准统计方法可能不足以描述体力活动的采用、维持和复发模式。我们提出了一个更复杂的方法，旨在专门寻找六个身体活动干预的行为变化模式。能够比较不同研究中产生的行为变化子类型（它们本身重叠，因为它们至少包含一个共同的治疗组）将使我们能够确定研究之间是否存在一致的模式，以及这些亚型的分布是否因治疗组而异。此外，我们将通过修改我们提出的计算程序来确定行为变化模式的潜在预测因素和调节因素，以允许其他基线变量对行为变化模式的影响。子类型的分布。最后，我们将使用这些子类型来确定治疗阶段的关键时间，在此之后更有可能复发到久坐行为。这些目标中的每一个都对未来身体活动干预措施的设计和分析具有特定的影响。使用目前的方法，我们将无法进行这些相同的分析，因此可能会错过有关参与者如何改变其癌症风险的关键信息。