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These models offer an alternative to standard longitudinal methods as they explicitly account for within\u2010subject temporal variability through a latent stochastic process, allowing random fluctuations within a patient to be captured. This work investigates Wiener process\u2010based degradation models with linear drift (i.e., slope) while considering a diffusion term to represent within\u2010subject temporal variability, a random\u2010effects term to capture between\u2010subject variability of the slope, and a time\u2010invariant term to account for measurement error. First\u2010difference estimators that stabilize covariance matrix inversion and remove the influence of time\u2010invariant confounders are presented and validated in clinically relevant settings. Monte Carlo simulations assessing relative error and coverage probability demonstrate that these models yield consistent and stable estimates. Profile likelihood methods, which reduce the dimensionality of the parameter space, also performed reliably, but should be used with caution when follow\u2010up times are highly clustered. As a proof of concept, we applied these models to amyotrophic lateral sclerosis (ALS) data from the Pooled Resource Open\u2010Access ALS Clinical Trials Database (PRO\u2010ACT). We observed steeper slopes of the revised ALS Functional Rating Scale (ALSFRS\u2010R) in individuals who died compared to those who survived, indicating that degradation model estimates are consistent with expected patterns of ALS decline. Our results demonstrate that these stochastic models provide accurate and efficient estimates of longitudinal deterioration. Future work aims to incorporate Wiener process degradation models into a joint modeling framework.<\/jats:p>","DOI":"10.1002\/sim.70323","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T08:42:24Z","timestamp":1763628144000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessment of Wiener Process Degradation Models With Application to Amyotrophic Lateral Sclerosis Decline"],"prefix":"10.1002","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1404-4437","authenticated-orcid":false,"given":"Matthew R.","family":"Scott","sequence":"first","affiliation":[{"name":"Department of Biostatistics Boston University School of Public Health  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1626-2588","authenticated-orcid":false,"given":"Oleksandr","family":"Sverdlov","sequence":"additional","affiliation":[{"name":"Novartis Pharmaceuticals Corporation  East Hanover New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kendra","family":"Davis\u2010Plourde","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Yale School of Public Health  New Haven Connecticut USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yorghos","family":"Tripodis","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Boston University School of Public Health  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.2013.830074"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.107504"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2018.02.033"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxu053"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6788"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomtc\/ujae011"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2020.00024"},{"key":"e_1_2_10_9_1","unstructured":"J.Pinheiro D.Bates S.DebRoy D.Sarkar andTeam RC \u201cnlme: Linear and Nonlinear Mixed Effects Models \u201d R Package Version 3.1\u2013164(2024)."},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2954717"},{"key":"e_1_2_10_11_1","volume-title":"Econometric Analysis of Cross Section and Panel Data","author":"Wooldridge J. 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