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A semiparametrically efficient estimator of the time-varying effects for survival data with time-dependent treatment.

ABSTRACT: The timing of a time-dependent treatment-e.g., when to perform a kidney transplantation-is an important factor for evaluating treatment efficacy. A naïve comparison between the treated and untreated groups, while ignoring the timing of treatment, typically yields biased results that might favor the treated group because only patients who survive long enough will get treated. On the other hand, studying the effect of a time-dependent treatment is often complex, as it involves modeling treatment history and accounting for the possible time-varying nature of the treatment effect. We propose a varying-coefficient Cox model that investigates the efficacy of a time-dependent treatment by utilizing a global partial likelihood, which renders appealing statistical properties, including consistency, asymptotic normality and semiparametric efficiency. Extensive simulations verify the finite sample performance, and we apply the proposed method to study the efficacy of kidney transplantation for end-stage renal disease patients in the U.S. Scientific Registry of Transplant Recipients (SRTR).

SUBMITTER: Lin H

PROVIDER: S-EPMC5026052 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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A semiparametrically efficient estimator of the time-varying effects for survival data with time-dependent treatment.

Lin Huazhen H Fei Zhe Z Li Yi Y

Scandinavian journal of statistics, theory and applications 20151109 3

The timing of a time-dependent treatment-e.g., when to perform a kidney transplantation-is an important factor for evaluating treatment efficacy. A naïve comparison between the treated and untreated groups, while ignoring the timing of treatment, typically yields biased results that might favor the treated group because only patients who survive long enough will get treated. On the other hand, studying the effect of a time-dependent treatment is often complex, as it involves modeling treatment h ...[more]

PMID: 27647948

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Project description:PurposeIn studies of effects of time-varying drug exposures, adequate adjustment for time-varying covariates is often necessary to properly control for confounding. However, the granularity of the available covariate data may not be sufficiently fine, for example when covariates are measured for participants only when their exposure levels change.MethodsTo illustrate the impact of choices regarding the frequency of measuring time-varying covariates, we simulated data for a large target trial and for large observational studies, varying in covariate measurement design. Covariates were measured never, on a fixed-interval basis, or each time the exposure level switched. For the analysis, it was assumed that covariates remain constant in periods of no measurement. Cumulative survival probabilities for continuous exposure and non-exposure were estimated using inverse probability weighting to adjust for time-varying confounding, with special emphasis on the difference between 5-year event risks.ResultsWith monthly covariate measurements, estimates based on observational data coincided with trial-based estimates, with 5-year risk differences being zero. Without measurement of baseline or post-baseline covariates, this risk difference was estimated to be 49% based on the available observational data. With measurements on a fixed-interval basis only, 5-year risk differences deviated from the null, to 29% for 6-monthly measurements, and with magnitude increasing up to 35% as the interval length increased. Risk difference estimates diverged from the null to as low as -18% when covariates were measured depending on exposure level switching.ConclusionOur simulations highlight the need for careful consideration of time-varying covariates in designing studies on time-varying exposures. We caution against implementing designs with long intervals between measurements. The maximum length required will depend on the rates at which treatments and covariates change, with higher rates requiring shorter measurement intervals.

Project description:AimsTo identify when smoking cessation treatments affect craving, negative affect and anhedonia, and how these symptoms relate to abstinence, to help evaluate the effects of particular intervention components in multi-component treatments and accelerate treatment refinement.DesignSecondary analysis of data from a two-arm randomized controlled trial.SettingSeven primary care clinics in Wisconsin, United States.ParticipantsAdult primary care patients who smoked daily (n = 574).Intervention and comparatorIntervention was abstinence-optimized treatment (A-OT, n = 276) comprising 3 weeks of nicotine mini-lozenges pre-target quit day (TQD), 26 weeks of combination nicotine patch and mini-lozenges post-TQD and extensive psychosocial support. The comparator was recommended usual care (RUC, n = 298), comprising brief counseling and 8 weeks of nicotine patch post-TQD.MeasurementsTime-varying effect models examined dynamic effects of A-OT (versus RUC) on the primary outcomes of nightly cigarette craving, negative affect and anhedonia from 1 week pre- to 2 weeks post-TQD. Exploratory models examined within-person relations between nicotine medication use and same-day symptom ratings. Secondary logistic regression analyses examined associations between post-TQD craving, negative affect and anhedonia and 1-month post-TQD abstinence.FindingsA-OT significantly suppressed pre- and post-TQD craving (β = -0.27 to -0.46 across days) and post-TQD anhedonia (β = -0.24 to -0.38 across days), relative to RUC. Within individuals, using patches was associated with lower negative affect in RUC (β = -0.42 to -0.52), but not in A-OT. Using more mini-lozenges was associated with greater craving (β = 0.04-0.07) and negative affect (β = 0.03-0.05) early, and with lower anhedonia (β = -0.06 to -0.12) later. Greater post-TQD craving (OR = 0.68) and anhedonia (OR = 0.85) predicted lower odds of abstinence 1 month post-TQD.ConclusionTime-varying effect models showed that a multi-component treatment intervention for smoking cessation suppressed significant withdrawal symptoms more effectively than recommended usual care among daily adult smokers motivated to quit. The intervention reduced craving pre- and post-target quit day (TQD) and anhedonia post-TQD.

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A semiparametrically efficient estimator of the time-varying effects for survival data with time-dependent treatment.

Publications

A semiparametrically efficient estimator of the time-varying effects for survival data with time-dependent treatment.

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