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PubMed2006

Use of OSWALD for analyzing longitudinal data with informative dropout.

OSWALD (Object-oriented Software for the Analysis of Longitudinal Data) is flexible and powerful software written for S-PLUS for the analysis of longitudinal data with dropout for which there is little other software available in the public domain. The implementation of OSWALD is described through analysis of a psychiatric clinical trial that compares antidepressant effects in an elderly depressed sample and a simulation study. In the simulation study, three different dropout mechanisms: completely random dropout (CRD), random dropout (RD) and informative dropout (ID), are considered and the results from using OSWALD are compared across mechanisms. The parameter estimates for ID-simulated data show less bias with OSWALD under the ID missing data assumption than under the CRD or RD assumptions. Under an ID mechanism, OSWALD does not provide standard error estimates. We supplement OSWALD with a bootstrap procedure to derive the standard errors. This report illustrates the usage of OSWALD for analyzing longitudinal data with dropouts and how to draw appropriate conclusions based on the analytic results under different assumptions regarding the dropout mechanism.

Humans↗
PubMed2005

A systematic literature review of attrition between waves in longitudinal studies in the elderly shows a consistent pattern of dropout between differing studies.

OBJECTIVES: Longitudinal studies of the elderly are complicated by the loss of individuals between waves due to death or other dropout mechanisms. Factors that affect dropout may well be similar from one study to another. This article systematically reviews all large population-based studies of the elderly (published 1966-2002) that report on differences in individual characteristics between people who remain and people who dropout at follow-up. STUDY DESIGN AND SETTING: A systematic review of articles that investigate attrition after baseline interview. RESULTS: Twelve studies were found that investigated dropout other than death using unadjusted, multivariable methods or both. The unadjusted analyses showed many significant factors related to attrition. Multivariable analyses showed two main independent factors were related to increased attrition: increasing age and cognitive impairment. People who were very ill or frail had higher dropout rates, and people in worse health were less likely to be recontactable. CONCLUSIONS: Multivariable methods of analyzing attrition in longitudinal studies show consistent patterns of dropout between differing studies, with a small number of key relationships. These findings will assist researchers when planning studies of older people, and provide insight into the possible biases in longitudinal studies introduced by differential dropout.

Age Factors↗
PubMed1997

A median-based test under informative dropout: the one-sample case.

We consider a clinical trial in which the outcome can be assessed by a continuous measure and where dropouts tend to have poorer efficacy than completers. When each subject can act as his/her own control, efficacy is measured by the difference between the outcome measurements at two times. When all subjects complete the protocol, a paired t-test can be used to test for a treatment effect, i.e., whether or not the mean difference is zero. When a patient does not return for the final evaluation, a measure of efficacy cannot be computed for that subject. Often, data from dropouts are ignored and only the observed pairs are used to analyze the data. When the reason for dropping out is not random, the result may be misleading. In this paper, we assume that (1) the distribution of the measure of efficacy (i.e., the change between two outcome measurements) is Gaussian, (2) dropouts would have worse efficacy than the median if they were observed, and (3) the dropout rate is less than 50%. We propose a median-based t-like statistic using the sample median in place of the sample mean. The variance of the median is estimated using only data from the complete half-sample, i.e., the half-sample with better efficacy. Simulations under five patterns of dropouts are performed to compare the proposed statistic with the paired t-test. The results show that the median-based statistic provides a conservative bound for the test of significance of the treatment. In contrast, because the paired t-test does not preserve its level of significance, except when the dropout mechanism is uniform, the paired t-test should not be used for trials in which dropouts tend to have poorer efficacy than completers.

Bias↗
PubMed2003

A joint model for nonlinear longitudinal data with informative dropout.

Subject withdrawal from a study (also called dropout, or right censoring), is common in late phase clinical trials. A number of methods dealing with dropouts have been used in practice, the most common being "last observation carried forward" (LOCF). Many of these methods, including LOCF, can result in biased estimates of the efficacy or potency of the drug, especially in the modeling context. If the likelihood of dropout is correlated to the underlying unobserved data, the dropout is informative and should not be ignored in the modeling process. The topic of informative dropout in the context of longitudinal data has received much attention in the statistical literature, in the setting of linear and generalized linear models. We extend the approach to nonlinear models. The dropout hazard, as well as the longitudinal data, is modeled parametrically. Parameters are estimated by maximizing the approximate joint likelihood as implemented in the software NONMEM. Using data from actual clinical trials, we explore the impact of the dropout model on the ability of the joint model to predict observed longitudinal data patterns.

Clinical Trials as Topic↗
PubMed2005

Analysis of longitudinal binary data with missing data due to dropouts.

Longitudinal binary data from clinical trials with missing observations are frequently analyzed by using the Last Observation Carry Forward (LOCF) method for imputing missing values at a visit (e.g., the prospectively defined primary visit time point for analysis at the end of treatment period). Usually, to understand time trend in treatment response, analyses are also performed separately on data at intermediate time points. The objective of such analyses is to estimate the proportion of "response" at a time point and then to compare two treatment groups (e.g., drug vs. placebo) by testing for the difference in the two proportions of response. The commonly used methods are Fisher's exact test, chi-squared test, Cochran-Mantel-Haenszel test, and logistic regression. Analyses based on the Observed Cases (OC) data are usually also performed and compared with those obtained by LOCF. Another approach that is gaining popularity (after the introduction of PROC GENMOD by the SAS Institute) is to use the method of Generalized Estimating Equations (GEE) with a view to include all repeated observations in the analysis in a more comprehensive manner. It is now well recognized, however, that results obtained by these methods are susceptible to bias, depending on the "missing data mechanism." Of particular concern is the bias introduced by NMAR dropouts. Because there is no one method to satisfactorily handle dropouts in data analysis, consensus is gathering toward doing analyses by several methods (including methods to handle NMAR dropouts) to evaluate sensitivity of results to model assumptions. In this article, we demonstrate application of the following methods for handling dropouts in longitudinal binary data: Generalized Linear Mixture Models (GLMM) (for handling NMAR dropouts), Weighted GEE (for handling MAR dropouts), and GEE (MCAR dropouts). The results are also compared with those obtained by logistic regression (univariate) on both LOCF and OC data.

Clinical Trials as Topic↗
PubMed2000

The association between severity of sanction imposed for violation of tobacco policy and high school dropout rates.

This investigation explored the association between severity of sanctions imposed on students resulting from tobacco policy violation and the event dropout rate in South Carolina public high schools. The study employed a cross-sectional design (n = 132). Surveys were mailed to school principals to assess tobacco policy and sanctions for violation. Severe sanctions were categorized as those resulting in the student being denied onsite instruction, such as out-of-school suspension or expulsion. General linear regression models adjusting for SES, ethnicity, and rural/urban status, tested for an association between event dropout rate and severity of sanction imposed. The mean dropout rate in 1998 for high schools in South Carolina was 2.58% (+1.74). Suspension at first violation and expulsion were associated with lower dropout rates. Suspension at second violation was not associated with dropout behavior while suspension at third violation was associated with higher dropout rates. Results from the study provide preliminary evidence that severe sanctions imposed for violation of tobacco policy may help reduce high school dropout rates.

Adolescent↗
PubMed1997

The effect of school dropout rates on estimates of adolescent substance use among three racial/ethnic groups.

OBJECTIVES: This study examined, across three racial/ethnic groups, how the inclusion of data on drug use of dropouts can alter estimates of adolescent drug use rates. METHODS: Self-report rates of lifetime prevalence and use in the previous 30 days were obtained from Mexican American, White non-Hispanic, and Native American student (n = 738) and dropouts (n = 774). Rates for the age cohort (students and dropouts) were estimated with a weighted correction formula. RESULTS: Rates of use reported by dropouts were 1.2 to 6.4 times higher than those reported by students. Corrected rates resulted in changes in relative rates of use by different ethnic groups. CONCLUSIONS: When only in-school data are available, errors in estimating drug use among groups with high rates of school dropout can be substantial. Correction of student-based data to include drug use of dropouts leads to important changes in estimated levels of drug use and alters estimates of the relative rates of use for racial/ethnic minority groups with high dropout rates.

Adolescent↗
PubMed1999

The causes of patient dropout from penile self-injection therapy for impotence.

PURPOSE: Penile self-injection therapy, a second line treatment for erectile dysfunction, is the most efficacious means of reestablishing functional erections when first line therapies fail and the patient wants to avoid penile prosthesis implantation. Despite high efficacy rates, injection therapy has high dropout rates. To our knowledge studies to date analyzing patient attrition have reviewed small numbers of patients followed for only short periods. We elucidate the main reasons for patient dropout in a large penile self-injection program with long-term followup. MATERIALS AND METHODS: A questionnaire was mailed to 1,424 patients who completed the office training and home use phases of a penile self-injection program. RESULTS: The overall attrition rate was 31% of the 720 men who completed the questionnaire, with a mean followup of 38 months. The main reasons for dropout were cost of therapy, patient and partner problems with the concept of penile injection, lack of partner availability and spontaneous improvement in erections. Lack of efficacy of therapy was the primary reason for only 1 of 7 dropouts. Furthermore, adverse effects of penile injections (priapism, penile nodules, pain) appeared to be only minor contributors to dropout. CONCLUSIONS: To our knowledge this study is the largest published, single center cohort of patients treated with injection and followed for an analysis of dropout rates. Based on study data a reduction in dropout rates may be achieved by keeping the cost of therapy low, and ensuring patient and partner education as well as continued support throughout treatment.

Adult↗
PubMedDate not supplied

Dropouts during treatment for leprosy (a study in the ELEP leprosy control project, Dharmapuri district, Tamilnadu during 1975-77).

Certain Social and Related Factors responsible for 231 dropout leprosy patients from treatment are discussed. Dropout rate was lowest among lepromatous patients (1.2% Vs 10.9% among nonlepromatous patients); patients with stigma/deformity were significantly less (P less than .001); among dropouts; proportion of wage-earners was high in them (p less than .001); student dropouts were few (Z-2.78, P less than .05, X2 - 10.32, d.f.-1, P less than .005); there was little association between socioeconomic status and dropout rate (P greater than .5); dropouts amongst patients who self-registered for treatment were much less than in those who were enlisted for treatment during survey (P less than .05); 38% had dropped out within the first six months of registration for treatment; lepromatous patients attended clinics for more than 25 months before becoming dropouts; fear of loss of wages, belief that it was not leprosy, social stigma attached to the disease, disinterest for treatment when lesions were small and few, dissatisfaction with treatment, and belief that patches self-healed were the main causes for discontinuance of treatment; all those who dropped out due to shyness were women of 15-44 age-group.

Adolescent↗
PubMed1978

Multiple predictors of dropout from alcoholism treatment.

A common problem in treating alcoholics is the high dropout rate. Many studies have identified individual factors associated with dropout, eg, poor motivation and previous dropout. We believe the present study reports the first major effort to use multivariate analyses to predict dropout in a large (792), one-year follow-up study of alcoholics, and examines the possibility that medical and nonmedical treatments lead to differential dropout rates. A multiple classification analysis technique showed that treatment variables as opposed to client characteristics were the best predictors of dropout. Patients remaining in treatment were more likely to have a variety of medical interventions, eg, medication and medical assessment, than those who dropped out. Results were similar to studies using other techniques and have interesting implications for the treatment of alcoholics, raising questions about current trends toward nonmedical treatment of alcoholism.

Alcoholism↗
PubMed2005

Dropout rates in placebo-controlled and active-control clinical trials of antipsychotic drugs: a meta-analysis.

CONTEXT: Dropout rates in randomized clinical trials of antipsychotic drugs have consistently been reported to be high, and the use of a placebo-controlled design is hypothesized to be one of the reasons for this. OBJECTIVE: To investigate this hypothesis in a meta-analysis of available data from pertinent clinical trials. DATA SOURCES: Comprehensive search of PubMed- and MEDLINE-listed journals. STUDY SELECTION: Double-blind randomized controlled clinical trials of the second-generation antipsychotics risperidone, olanzapine, quetiapine, amisulpride, ziprasidone, and aripiprazole meeting the following criteria: unselected patient population with a diagnosis of schizophrenia or schizoaffective disorder, change in psychopathologic symptoms as the primary end point, and trial duration of 12 weeks or less. DATA EXTRACTION: Sample size, mean age, baseline disease severity, dropout rate, trial design, trial duration, and publication year. DATA SYNTHESIS: Thirty-one trials meeting the inclusion criteria were found, comprising 10 058 subjects. Weighted mean dropout rates in the active treatment arms were significantly higher in placebo-controlled trials (PCTs) than in active-control trials: 48.1% (PCTs) vs 28.3% (active-control trials) for second-generation antipsychotics (odds ratio, 2.34; 95% confidence interval, 1.58-3.47) and 55.4% (PCTs) vs 37.2% (active-control trials) for classical antipsychotics (odds ratio, 2.10; 95% confidence interval, 1.29-3.40). Within PCTs, attrition rates were significantly higher in the placebo arms than with second-generation antipsychotics (60.2% vs 48.1%; odds ratio, 1.63; 95% confidence interval, 1.37-1.94). Within the subset of trials in which both second-generation and classical antipsychotics were used, dropout rates were significantly higher with classical antipsychotics. CONCLUSIONS: Use of a placebo-controlled design had a major effect on the dropout rates observed. Because high dropout rates affect the generalizability of such studies, it is suggested that, in addition to the PCTs, studies with alternative designs need to be considered when evaluating an antipsychotic's clinical profile.

Antipsychotic Agents↗
PubMed1996

Infertility treatment dropout and insurance coverage.

OBJECTIVE: To assess early patient dropout rates during infertility treatment as a potential measure of wasted resources. METHODS: The study involved multifaceted population cohorts, including a prospectively observed captive health maintenance organization (HMO) population and retrospectively selected preferred provider organization (PPO) patients. One hundred twenty-eight HMO couples were followed prospectively for 6 months. The insurance carrier retroactively selected 96 couples from their PPO population who were believed to be infertility patients. They were matched by date, age, and time of hysterosalpingography to infertility patients in the carrier's HMO population. Patients were considered treatment dropouts if they either requested their provider to abandon further work-up or treatment, or if they failed to return for an appointment for 3 months. RESULTS: Forty-six of 128 (36%) HMO patients followed prospectively discontinued care within 180 days, with only eight (6.3%) providing defined reasons. Preferred provider organization patients uniformly demonstrated significantly higher dropout rates than HMO patients, a finding already apparent at 60 days (P < .002; odds ratio [OR] 3.67, 95% confidence interval [CI] 1.47-9.97) and 120 days of treatment (P = .002; OR 2.87, 95% CI 1.39-6.06). Among PPO patients, dropout rates were especially pronounced if infertility care was provided by generalists. At billing levels of at least $2000, HMO patients also demonstrated less dropout than PPO patients (P < .001; OR 6.14, 95% CI 2.72-14.79), with generalists again demonstrating a significantly larger patient loss than infertility specialists (P < .001; OR 0.18, 95% CI 0.66-0.49). CONCLUSION: Infertility patients demonstrate a surprisingly large early dropout rate, which is significantly larger if patients receive infertility care from generalists rather than specialists. Newly presenting infertility patients should be carefully evaluated, especially in indemnity situations, before expensive diagnostic and therapeutic interventions are ordered.

Adult↗
PubMed2005

Pretreatment and during treatment risk factors for dropout among patients with substance use disorders.

OBJECTIVE: The aim of this study was to use pretreatment and treatment factors to predict dropout from residential substance use disorder program and to examine how the treatment environment modifies the risk for dropout. METHOD: This study assessed 3649 male patients at entry to residential substance use disorder treatment and obtained information about their perceptions of the treatment environment. RESULTS: Baseline factors that predicted dropout included younger age, greater cognitive dysfunction, more drug use, and lower severity of alcohol dependence. Patients in treatment environments appraised as low in support or high in control also were more likely to drop out. Further, patients at high risk of dropout were especially likely to dropout when treated in a highly controlling treatment environment. CONCLUSION: Better screening of risk factors for dropout and efforts to create a less controlling treatment environment may result in increased retention in substance use disorder treatment.

Adult↗
PubMed1998

Predictors of dropout from psychosocial treatment of cocaine dependence.

The current study assessed demographic, drug and psychiatric predictors of dropout in the pilot/training phase of a large, multi-site psychotherapy outcome study for patients with cocaine dependence. The different predictors of dropout were assessed throughout the phases of the study: screening, intake, stabilization and assessment phase, and following randomization to treatment. Results showed that (1) younger patients were less likely to keep their intake appointment. (2) Of the patients who had an intake visit, those who did not complete high school and with more days of cocaine use in the previous month were less likely to complete an initial stabilization and assessment phase requiring 1 week of abstinence from all drugs. A survival analysis was used to examine time to dropout for the 286 patients randomized to individual treatment. (3) Again, younger age was associated with dropout after randomization. (4) Drug use variables did not predict time to dropout. (5) Presence of any current Axis I disorder was associated with later dropout from treatment. Minority treatment information seekers and treatment initiators were less likely to go on to complete the full treatment program.

Adult↗
PubMed1998

Structural equation socialization model of substance use among Mexican-American and white non-Hispanic school dropouts.

PURPOSE: To test a socialization model of polydrug use among Mexican-American and white non-Hispanic school dropouts. METHODS: A sample of 910 Mexican-American and white non-Hispanic school dropouts were surveyed regarding their use of alcohol, marijuana, and other drugs, and socialization characteristics that have previously been shown to be predictive of adolescent substance use. A structural equation model based on peer cluster theory was evaluated for goodness of fit and for differences in model characteristics by ethnicity and gender. RESULTS: Results partially confirmed peer cluster theory among school dropouts in that association with drug-using peers was the most powerful direct predictor of substance use. The effects of a number of other socializing influences were indirect, mediated through association with drug-using peers. Some differences were present between Mexican-American and white non-Hispanic subgroups. CONCLUSIONS: Results were similar to those obtained from previous tests of this model among youth who remain in school, suggesting that social influences on drug use are similar across students and school dropouts. Association with drug-using peers dominates the prediction of substance use among school dropouts. However, family communication of drug use sanctions helps to both limit substance use and strengthen family bonds. Prior school adjustment is likely to be an important protective factor in limiting substance use among Mexican-American dropouts.

Adolescent↗
PubMed1999

Patient dropouts before completion of optimal dose, multiple allergen immunotherapy.

BACKGROUND: Many patients don't complete the recommended 3 to 5-year course of immunotherapy. Why? OBJECTIVE: Determine the percentage of our patients receiving optimal dose, multiple-allergen immunotherapy from 1982 to 1996 who discontinued their immunotherapy prior to completion of the recommended 3 to 5-year immunotherapy protocol. Second, assess the reasons for these premature dropouts. Third, determine any differences related to the clinic location where injections are given. DESIGN AND METHODS: The medical records of patients who dropped out of our immunotherapy program before 3 years were analyzed by the author. SUMMARY OF RESULTS: Our dropout rate before 3 years was 12%. The five commonest reasons for early dropout were concurrent medical problems, noncompliance, change of residence, inconvenience, and allergic reactions. The systemic reaction rate for the 3-year dropout group was 1.00% compared with 0.9% for our overall study group. Eighty-eight percent of the systemic reactions were mild. About 1% of our immunotherapy patients quit early due to allergic reactions secondary to immunotherapy. CONCLUSIONS: The dropout rate for our optimal-dose patients is similar to/that reported previously by Tinkelman who apparently used a lower than optimal maintenance dose. (2) Many of our dropouts were predictable and avoidable. Few patients quit early due to allergic reactions secondary to our immunotherapy program.

Adolescent↗
PubMed2000

Preservation of glomerular filtration rate on dialysis when adjusted for patient dropout.

BACKGROUND: Residual renal function (RRF) plays an important role in dialysis patients. Studies in patients on maintenance dialysis suggest that RRF is better preserved in patients receiving peritoneal dialysis (PD) vis-à-vis those receiving hemodialysis (HD). We speculated that regardless of the patient's type of therapy, the estimate obtained for the rate of decline in glomerular filtration rate (GFR) may be biased because of informative censoring associated with patient dropout. Informative censoring occurs when patients who die or transfer to another modality very early have associated with them a lower starting GFR or a higher rate of decline of GFR than patients who either complete the study or who die or transfer much later. If patient dropout is indeed related to the rate of decline in GFR and if this relationship is ignored in the analysis, then the estimate obtained of the rate of decline in GFR may be biased. METHODS: In an attempt to determine if there is a relationship between patient dropout and the decline in GFR, we reanalyzed the CANUSA data by modeling GFR as a nonlinear function of time with the rate of decline being exponential. RESULTS: This article highlights the significance of "informative censoring" when studying the decline of RRF on dialysis. The results show that for the CANUSA cohort, the mean initial GFR was significantly lower, and the rate of decline was significantly higher for patients who died or transferred to HD than for patients who were randomly censored or received a transplant. It is important to emphasize that the impact of informative censoring on previous analyses of the decline of RRF between PD versus HD is presently unclear. If bias caused by informative censoring is the same regardless of what therapy a patient is on, then conclusions from previous studies comparing the decline in GFR between PD and HD would still be valid. However, if the magnitude of the bias differs according to therapy, then additional adjustments would be needed to fairly compare the decline in GFR between PD and HD. Because this analysis is restricted to patients on PD, it would be scientifically incorrect to interpret previous studies solely on the basis of the results from this analysis. CONCLUSION: In any longitudinal study designed to estimate trends in an outcome measured over time, it is important that the analysis of the data takes into account any effect patient dropout may have on the estimated trend. This analysis demonstrates that among PD patients, both the starting GFR and the rate of decline in GFR are associated with patient dropout. Consequently, future studies aimed at estimating the rate of decline in GFR among PD patients should also account for any dependencies between dropout and GFR. Similarly, data analyzing for apparent differences in the rate of decline of GFR between PD and HD should also adjust for possible informative censoring.

Glomerular Filtration Rate↗
PubMed1998

A comparison of the random-effects pattern mixture model with last-observation-carried-forward (LOCF) analysis in longitudinal clinical trials with dropouts.

The last-observation-carried-forward imputation method is commonly used for imputting data missing due to dropouts in longitudinal clinical trials. The method assumes that outcome remains constant at the last observed value after dropout, which is unlikely in many clinical trials. Recently, random-effects regression models have become popular for analysis of longitudinal clinical trial data with dropouts. However, inference obtained from random-effects regression models is valid when the missing-at-random dropout process is present. The random-effects pattern-mixture model, on the other hand, provides an approach that is valid under more general missingness mechanisms. In this article we describe the use of random-effects pattern-mixture models under different patterns for dropouts. First, subjects are divided into groups depending on their missing-data patterns, and then model parameters are estimated for each pattern. Finally, overall estimates are obtained by averaging over the missing-data patterns and corresponding standard errors are obtained using the delta method. A typical longitudinal clinical trial data set is used to illustrate and compare the above methods of data analyses in the presence of missing data due to dropouts.

Clinical Trials as Topic↗