Quintile stratification based on a misspecified propensity score in longitudinal treatment effectiveness analyses of ordinal doses

Leon, Andrew C.; Hedeker, Donald

doi:10.1016/j.csda.2006.12.021

Cited by 21 publications

(14 citation statements)

References 17 publications

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“…Previous simulation studies have addressed different questions concerning the choice of the variables to be included in the PS model, such as the effect of omitting confounding variables when using quintile-stratified propensity adjustment in longitudinal studies [27], or the relative performances of PS models when including variables related to treatment allocation, variables related to outcome or all variables related to either outcome or treatment or neither [28]. However, data concerning appropriate PS models when dealing with limited sample sizes are still lacking.…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, while it is usually recommended [17-19] to include in the PS model all the potential confounders, this could lead to over parameterized PS models when the number of treated is limited. On the other hand, it has been previously reported that PS model misspecification could highly influence the estimation [25,27]. Therefore, in the context of small sample sizes, one might consider preferable to apply some variable selection procedure, but it seems crucial to adequately choose those variables to be included in the PS model.…”

Section: Discussionmentioning

confidence: 99%

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Evaluation of the Propensity score methods for estimating marginal odds ratios in case of small sample size

Pirracchio

Resche-Rigon

Chevret

2012

BMC Med Res Methodol

201

150

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BackgroundPropensity score (PS) methods are increasingly used, even when sample sizes are small or treatments are seldom used. However, the relative performance of the two mainly recommended PS methods, namely PS-matching or inverse probability of treatment weighting (IPTW), have not been studied in the context of small sample sizes.MethodsWe conducted a series of Monte Carlo simulations to evaluate the influence of sample size, prevalence of treatment exposure, and strength of the association between the variables and the outcome and/or the treatment exposure, on the performance of these two methods.ResultsDecreasing the sample size from 1,000 to 40 subjects did not substantially alter the Type I error rate, and led to relative biases below 10%. The IPTW method performed better than the PS-matching down to 60 subjects. When N was set at 40, the PS matching estimators were either similarly or even less biased than the IPTW estimators. Including variables unrelated to the exposure but related to the outcome in the PS model decreased the bias and the variance as compared to models omitting such variables. Excluding the true confounder from the PS model resulted, whatever the method used, in a significantly biased estimation of treatment effect. These results were illustrated in a real dataset.ConclusionEven in case of small study samples or low prevalence of treatment, PS-matching and IPTW can yield correct estimations of treatment effect unless the true confounders and the variables related only to the outcome are not included in the PS model.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Evaluation of the Propensity score methods for estimating marginal odds ratios in case of small sample size

Pirracchio

Resche-Rigon

Chevret

2012

BMC Med Res Methodol

201

150

View full text Add to dashboard Cite

show abstract

“…However, the propensity adjustment removes bias related only to variables included in the model and there may be residual confounding from variables related to clinical status or treatments not included, such as anticonvulsants or sedatives. 43,44 Several variables of clinical interest, such as anxiety and psychosis, could not be captured on all participants at the beginning of treatment interval. The assessments for severity of mood symptotm and treatments received were carried out annually and semiannually and have the potential for recall bias.…”

Section: Discussionmentioning

confidence: 99%

Risk of Suicidal Behavior With Antidepressants in Bipolar and Unipolar Disorders

Leon¹,

Fiedorowicz²,

Solomon³

et al. 2014

J. Clin. Psychiatry

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Objective To examine the risk ofsuicidal behavior (suicide attempts and deaths) associated with antidepressants in participants with bipolar I, bipolar n, and unipolar major depressive disorders. Design A 27-year longitudinal (1981-2008) observational study ofmood disorders (Research Diagnostic Criteria diagnoses based on Schedule Dr Afi:ctive Disorders and Schizophrenia and review ofmedical records) was used to evaluate antidepressants and risk Dr suicidal behavior. Mixed-efi:cts logistic regression models examined propensity Dr antidepressant exposure. Mixed-efi:cts swvival models that were matched on the propensity score examined exposure status as a risk factor for time until suicidal behavior. Setting Five US academic medical centers. Results Analyses of206 participants with bipolar I disorder revealed 2,010 exposure intervals (980 exposed to antidepressants; 1,030 unexposed); 139 participants with bipolar II disorder had 1 ,407 exposure intervals (694 exposed; 713 unexposed); and 361 participants with unipolar depressive disorder had 2, 745 exposure intervals (1,328 exposed; 1,417 unexposed). Propensity score analyses confinned that more severely ill participants were more likely to initiate antidepressant treatment. In mixed-elects swvival analyses, those with bipolar I disorder had a significant reduction in risk of suicidal behavior by 54% (HR = 0.46; 95% CI, 0.31-0.69; t = -3.74; P < .001) during periods of antidepressant exposure compared to propensity-matched unexposed intervals. Similarly, the risk was reduced by 35% (HR = 0.65; 95% CI, 0.43-0.99; t = −2.01; P = .045) in bipolar II disorder. By contrast, there was no evidence of an increased or decreased risk with antidepressant exposure in unipolar disorder. Condusions Based on obsetVational data adjusted Dr propensity to receive antidepressants, antidepressants may protect patients with bipolar disorders but not unipolar depressive disorder from suicidal behavior.

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“…Yet we acknowledge that the assumption cannot be verified because unmeasured confounding variables, by their very nature, are not in our data set and that a misspecified propensity model can yield biased results. 2627 …”

Section: Discussionmentioning

confidence: 99%

Antidepressants and Risks of Suicide and Suicide Attempts

Leon¹,

Solomon²,

Li³

et al. 2011

J. Clin. Psychiatry

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OBJECTIVE The 2007 revision of the black box warning for suicidality with antidepressants states that patients of all ages who initiate antidepressants should be monitored for clinical worsening or suicidality. The objective of this study was to examine the association of antidepressants with suicide attempts and with suicide deaths. METHOD A longitudinal, observational study of mood disorders with prospective assessments for up to 27 years was conducted at 5 US academic medical centers. The study sample included 757 participants who enrolled from 1979 to 1981 during an episode of mania, depression, or schizoaffective disorder, each based on Research Diagnostic Criteria. Unlike randomized controlled clinical trials of antidepressants, the analyses included participants with psychiatric and other medical comorbidity and those receiving acute or maintenance therapy, polypharmacy, or no psychopharmacologic treatment at all. Over follow-up, these participants had 6,716 time periods that were classified as either exposed to an antidepressant or not exposed. Propensity score-adjusted mixed-effects survival analyses were used to examine risk of suicide attempt or suicide, the primary outcome. RESULTS The propensity model showed that antidepressant therapy was significantly more likely when participants' symptom severity was greater (odds ratio [OR] = 1.16; 95% CI, 1.12–1.21; z = 8.22; P < .001) or when it was worsening (OR = 1.69; 95% CI, 1.50–1.89; z = 9.02; P < .001). Quintile-stratified, propensity-adjusted safety analyses using mixed-effects grouped-time survival models indicate that the risk of suicide attempts or suicides was reduced by 20% among participants taking antidepressants (hazard ratio, 0.80; 95% CI, 0.68–0.95; z = −2.54; P = .011). CONCLUSIONS This longitudinal study of a broadly generalizable cohort found that, although those with more severe affective syndromes were more likely to initiate treatment, antidepressants were associated with a significant reduction in the risk of suicidal behavior. Nonetheless, we believe that clinicians must closely monitor patients when an antidepressant is initiated.

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Quintile stratification based on a misspecified propensity score in longitudinal treatment effectiveness analyses of ordinal doses

Cited by 21 publications

References 17 publications

Evaluation of the Propensity score methods for estimating marginal odds ratios in case of small sample size

Evaluation of the Propensity score methods for estimating marginal odds ratios in case of small sample size

Risk of Suicidal Behavior With Antidepressants in Bipolar and Unipolar Disorders

Antidepressants and Risks of Suicide and Suicide Attempts

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