21 Survival Analysis

Psychologists often ask whether-and, if so, when-critical events occur, over the life-course. Such questions about event occurrence present unique design and analytic difficulties. The core problem is that no matter when data collection begins and no matter its length of follow-up, some participants may not experience the target event while observed. The event-histories of such participants are said to be censored. Although the prospect of censoring complicates research design and the presence of censoring complicates statistical analysis, the censored cases themselves reveal important insights into patterns of event occurrence. Why? Because these cases provide information on how long a subset of participants can continue without experiencing the event of interest. Consequently, they cannot be ignored but must be incorporated into data-analyses in some reasonable and informative way, if event histories are to be modeled unbiasedly. Typically, this is achieved by application of the methods of survival analysis. These methods permit the investigator to specify sensible statistical models for the risk of event occurrence over time and to compare these patterns among groups, while controlling for covariates, even in the face of censoring. Data collection can be prospective or retrospective; research designs can be experimental or observational. The passage of time can be measured continuously or discretely. In this chapter, we present a nonmathematical introduction to survival analysis. After describing the conceptual and statistical “building blocks” of the approach, we focus on important issues in research design and data analysis. In each case, we survey the problems that must be faced when adapting the methodology to psychological research and we provide guidelines for making informed decisions. In the process, we review how psychologists have used survival analysis successfully in the past and we point out new applications of the methods in the future. We conclude with recommendations for follow-up reading. Keywords: data collection; research design; survival analysis

[1]  James Trussell,et al.  A hazards-Model analysis of the covariates of infant and child mortality in Sri Lanka , 1983, Demography.

[2]  R. Mojtabai,et al.  Factors Affecting Relapse in Patients Discharged from a Public Hospital: Results from Survival Analysis , 2004, Psychiatric Quarterly.

[3]  E. Petkova,et al.  Psychological versus pharmacological treatments of bulimia nervosa: predictors and processes of change. , 1999, Journal of consulting and clinical psychology.

[4]  Michael S. Hurlburt,et al.  Providing independent housing for the homeless mentally ill: A novel approach to evaluating long‐term longitudinal housing patterns , 1998 .

[5]  E. Foster.  Does the Continuum of Care Influence Time in Treatment? , 1998 .

[6]  S. Davidson,et al.  Physician retention in community and migrant health centers: who stays and for how long? , 1998, Medical care.

[7]  J. Singer,et al.  The design and analysis of longitudinal studies of development and psychopathology in context: Statistical models and methodological recommendations , 1998, Development and Psychopathology.

[8]  M. Strober,et al.  The long-term course of severe anorexia nervosa in adolescents: survival analysis of recovery, relapse, and outcome predictors over 10-15 years in a prospective study. , 1997, The International journal of eating disorders.

[9]  P. Lavori,et al.  ANALYSIS OF COURSE OF PSYCHOPATHOLOGY: TRANSITIONS AMONG STATES OF HEALTH AND ILLNESS , 1996 .

[10]  B. Frueh,et al.  Cognitive-Behavioral Treatment of Incarcerated Offenders , 1996 .

[11]  M. Somers.  Modelling employee withdrawal behaviour over time: A study of turnover using survival analysis , 1996 .

[12]  A. Diekmann,et al.  Social status and aggression: a field study analyzed by survival analysis. , 1996, The Journal of social psychology.

[13]  Ludwig Fahrmeir,et al.  Smoothing Hazard Functions and Time-Varying Effects in Discrete Duration and Competing Risks Models , 1996 .

[14]  M. M. Ward,et al.  Abstinence effects as predictors of 28-day relapse in smokers. , 1996, Addictive behaviors.

[15]  M. Stoolmiller,et al.  Predicting the timing of first sexual intercourse for at-risk adolescent males. , 1996, Child development.

[16]  C. Ritter,et al.  The effects of role socialization on the initiation of cocaine use: an event history analysis from adolescence into middle adulthood. , 1996, Journal of health and social behavior.

[17]  T. Chung,et al.  Onset and staging of DSM-IV alcohol dependence using mean age and survival-hazard methods. , 1995, Journal of abnormal psychology.

[18]  Zheng Wu.  Premarital Cohabitation and Postmarital Cohabiting Union Formation , 1995, Journal of family issues.

[19]  Melissa Roderick.  Grade Retention and School Dropout: Investigating the Association , 1994 .

[20]  T. Miyoshi,et al.  A survival analysis of the revictimization of children: the case of Colorado. , 1994, Child abuse & neglect.

[21]  J. Singer,et al.  Investigating onset, cessation, relapse, and recovery: why you should, and how you can, use discrete-time survival analysis to examine event occurrence. , 1993, Journal of consulting and clinical psychology.

[22]  W F Velicer,et al.  Assessing outcome in smoking cessation studies. , 1992, Psychological bulletin.

[23]  John B. Willett,et al.  From Whether to When: New Methods for Studying Student Dropout and Teacher Attrition , 1991 .

[24]  B. Garfinkel,et al.  Predictors of length of stay of psychiatric adolescent inpatients. , 1991, Journal of the American Academy of Child and Adolescent Psychiatry.

[25]  J. Singer,et al.  QUANTITATIVE METHODS IN PSYCHOLOGY Modeling the Days of Our Lives: Using Survival Analysis When Designing and Analyzing Longitudinal Studies of Duration and the Timing of Events , 1991 .

[26]  D J Kupfer,et al.  Conceptualization and rationale for consensus definitions of terms in major depressive disorder. Remission, recovery, relapse, and recurrence. , 1991, Archives of general psychiatry.

[27]  S. Hall,et al.  Effects of commitment to abstinence, positive moods, stress, and coping on relapse to cocaine use. , 1991, Journal of consulting and clinical psychology.

[28]  C. Eastman,et al.  The temporal onset of individual symptoms in winter depression: differentiating underlying mechanisms. , 1991, Journal of affective disorders.

[29]  H. Koot,et al.  Longitudinal research in child and adolescent psychiatry. , 1991, Journal of the American Academy of Child and Adolescent Psychiatry.

[30]  D J Kupfer,et al.  Methodologic issues in maintenance therapy clinical trials. , 1991, Archives of general psychiatry.

[31]  William D. Dupont,et al.  Power and sample size calculations. A review and computer program. , 1990, Controlled clinical trials.

[32]  R. Wetzel,et al.  The lifetime risk of suicide in alcoholism. , 1990, Archives of general psychiatry.

[33]  P. Lelliott,et al.  Onset of panic disorder with agoraphobia. Toward an integrated model. , 1989, Archives of general psychiatry.

[34]  J. Hollis,et al.  Preventing smoking relapse, using an individually tailored skills-training technique. , 1989, Journal of consulting and clinical psychology.

[35]  N. Bolger,et al.  The onset of suicidal ideation in childhood and adolescence , 1989, Journal of youth and adolescence.

[36]  T. Micceri.  The unicorn, the normal curve, and other improbable creatures. , 1989 .

[37]  G. Marlatt,et al.  A longitudinal analysis of unaided smoking cessation. , 1988, Journal of consulting and clinical psychology.

[38]  B. Efron.  Logistic Regression, Survival Analysis, and the Kaplan-Meier Curve , 1988 .

[39]  M. A. Moussa.  Planning the size of survival time clinical trials with allowance for stratification. , 1988, Statistics in medicine.

[40]  Elja Arjas,et al.  A Graphical Method for Assessing Goodness of Fit in Cox's Proportional Hazards Model , 1988 .

[41]  E. Lichtenstein,et al.  Classification and prediction of smoking relapse episodes: an exploration of individual differences. , 1988, Journal of consulting and clinical psychology.

[42]  R. Glasgow,et al.  Variables associated with participation and outcome in a worksite smoking control program. , 1988, Journal of consulting and clinical psychology.

[43]  P. Nathan,et al.  Outcomes of treatment for alcohol problems: current methods, problems, and results. , 1987, Journal of consulting and clinical psychology.

[44]  D. Kandel,et al.  Drug Use and Other Determinants of Premarital Pregnancy and Its Outcome: A Dynamic Analysis of Competing Life Events. , 1987 .

[45]  L. Rips,et al.  Answering autobiographical questions: the impact of memory and inference on surveys. , 1987, Science.

[46]  R. Parker,et al.  Longitudinal Design and Longitudinal Analysis , 1986, Research on aging.

[47]  J M Lachin,et al.  Evaluation of sample size and power for analyses of survival with allowance for nonuniform patient entry, losses to follow-up, noncompliance, and stratification. , 1986, Biometrics.

[48]  G A Marlatt,et al.  Understanding and preventing relapse. , 1986, The American psychologist.

[49]  M. Reznikoff,et al.  Psychosocial factors in outcome in two opiate addiction treatments. , 1986, Journal of clinical psychology.

[50]  Marjorie S. Zatz.  Los Cholos: Legal Processing of Chicano Gang Members , 1985 .

[51]  D. Fergusson,et al.  A proportional hazards model of family breakdown , 1984 .

[52]  D. Kandel,et al.  Patterns of drug use from adolescence to young adulthood: III. Predictors of progression. , 1984, American journal of public health.

[53]  S. Hall,et al.  Preventing relapse to cigarette smoking by behavioral skill training. , 1984, Journal of consulting and clinical psychology.

[54]  R. J. Coelho.  Self-Efficacy and Cessation of Smoking , 1984, Psychological reports.

[55]  P. Allison.  Event History Analysis , 1984 .

[56]  D. Kandel,et al.  Risk Periods for Drug Involvement in Adolescence in France and in Israel: Application of Survival Analysis to Cross-Sectional Data , 1983 .

[57]  Diane H Felmlee,et al.  Contextual effects in the classroom: The impact of ability groups on student attention. , 1983 .

[58]  A. Diekmann,et al.  the 'sickle-hypothesis'. a time dependent poisson model with applications to deviant behavior and occupational mobility , 1983 .

[59]  Norman M. Bradburn,et al.  Chapter 8 – Response Effects1 , 1983 .

[60]  D. Schoenfeld,et al.  Nomograms for calculating the number of patients needed for a clinical trial with survival as an endpoint. , 1982, Biometrics.

[61]  R W Makuch,et al.  Sample size requirements for comparing time-to-failure among k treatment groups. , 1982, Journal of chronic diseases.

[62]  L. Freedman.  Tables of the number of patients required in clinical trials using the logrank test. , 1982, Statistics in medicine.

[63]  J. Lachin.  Introduction to sample size determination and power analysis for clinical trials. , 1981, Controlled clinical trials.

[64]  M H Gail,et al.  Planning the duration of a comparative clinical trial with loss to follow-up and a period of continued observation. , 1981, Journal of chronic diseases.

[65]  J. Eiser,et al.  Dependence, relapse and extinction: a theoretical critique and a behavioral examination. , 1979, Journal of clinical psychology.

[66]  W A Hunt,et al.  An evaluation of current methods of modifying smoking behavior. , 1974, Journal of clinical psychology.

[67]  B. Turnbull.  Nonparametric Estimation of a Survivorship Function with Doubly Censored Data , 1974 .

[68]  W A Hunt,et al.  Relapse rates in addiction programs. , 1971, Journal of clinical psychology.

[69]  William J. Horvath,et al.  A statistical model for the duration of wars and strikes , 1968 .